diff --git a/.basedpyright/baseline.json b/.basedpyright/baseline.json index 8a15fab4..f56903c8 100644 --- a/.basedpyright/baseline.json +++ b/.basedpyright/baseline.json @@ -8453,23 +8453,7 @@ "code": "reportAssignmentType", "range": { "startColumn": 13, - "endColumn": 21, - "lineCount": 1 - } - }, - { - "code": "reportIndexIssue", - "range": { - "startColumn": 8, - "endColumn": 10, - "lineCount": 1 - } - }, - { - "code": "reportIndexIssue", - "range": { - "startColumn": 33, - "endColumn": 35, + "endColumn": 26, "lineCount": 1 } }, @@ -8510,80 +8494,24 @@ { "code": "reportMissingTypeStubs", "range": { - "startColumn": 7, - "endColumn": 12, - "lineCount": 1 - } - }, - { - "code": "reportMissingTypeStubs", - "range": { - "startColumn": 7, - "endColumn": 31, - "lineCount": 1 - } - }, - { - "code": "reportUnknownArgumentType", - "range": { - "startColumn": 23, - "endColumn": 57, - "lineCount": 1 - } - }, - { - "code": "reportUnknownArgumentType", - "range": { - "startColumn": 36, - "endColumn": 56, - "lineCount": 1 - } - }, - { - "code": "reportUnannotatedClassAttribute", - "range": { - "startColumn": 4, - "endColumn": 12, - "lineCount": 1 - } - }, - { - "code": "reportUnannotatedClassAttribute", - "range": { - "startColumn": 4, - "endColumn": 12, - "lineCount": 1 - } - }, - { - "code": "reportImplicitOverride", - "range": { - "startColumn": 8, - "endColumn": 16, + "startColumn": 9, + "endColumn": 33, "lineCount": 1 } }, { "code": "reportArgumentType", "range": { - "startColumn": 17, - "endColumn": 47, + "startColumn": 33, + "endColumn": 67, "lineCount": 1 } }, { "code": "reportArgumentType", "range": { - "startColumn": 18, - "endColumn": 66, - "lineCount": 1 - } - }, - { - "code": "reportUnknownArgumentType", - "range": { - "startColumn": 34, - "endColumn": 35, + "startColumn": 17, + "endColumn": 69, "lineCount": 1 } }, @@ -8598,16 +8526,8 @@ { "code": "reportUnknownArgumentType", "range": { - "startColumn": 41, - "endColumn": 45, - "lineCount": 1 - } - }, - { - "code": "reportUnknownMemberType", - "range": { - "startColumn": 12, - "endColumn": 22, + "startColumn": 23, + "endColumn": 29, "lineCount": 1 } }, @@ -8622,24 +8542,24 @@ { "code": "reportUnknownMemberType", "range": { - "startColumn": 26, - "endColumn": 36, - "lineCount": 2 + "startColumn": 9, + "endColumn": 67, + "lineCount": 1 } }, { "code": "reportUnknownMemberType", "range": { - "startColumn": 37, - "endColumn": 49, + "startColumn": 17, + "endColumn": 29, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 37, - "endColumn": 51, + "startColumn": 17, + "endColumn": 31, "lineCount": 1 } }, @@ -8659,34 +8579,10 @@ "lineCount": 1 } }, - { - "code": "reportUnknownMemberType", - "range": { - "startColumn": 12, - "endColumn": 25, - "lineCount": 1 - } - }, - { - "code": "reportUnknownMemberType", - "range": { - "startColumn": 13, - "endColumn": 26, - "lineCount": 1 - } - }, { "code": "reportUnknownMemberType", "range": { "startColumn": 21, - "endColumn": 32, - "lineCount": 1 - } - }, - { - "code": "reportAny", - "range": { - "startColumn": 16, "endColumn": 29, "lineCount": 1 } @@ -8695,63 +8591,55 @@ "code": "reportUnknownMemberType", "range": { "startColumn": 11, - "endColumn": 33, - "lineCount": 1 - } - }, - { - "code": "reportAny", - "range": { - "startColumn": 35, - "endColumn": 39, + "endColumn": 30, "lineCount": 1 } }, { "code": "reportUnknownMemberType", "range": { - "startColumn": 21, - "endColumn": 37, + "startColumn": 8, + "endColumn": 24, "lineCount": 1 } }, { "code": "reportUnknownMemberType", "range": { - "startColumn": 21, - "endColumn": 50, + "startColumn": 8, + "endColumn": 37, "lineCount": 1 } }, { "code": "reportUnknownMemberType", "range": { - "startColumn": 52, - "endColumn": 79, + "startColumn": 39, + "endColumn": 66, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 52, - "endColumn": 81, + "startColumn": 39, + "endColumn": 68, "lineCount": 1 } }, { "code": "reportUnknownMemberType", "range": { - "startColumn": 27, - "endColumn": 47, + "startColumn": 23, + "endColumn": 43, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 27, - "endColumn": 50, + "startColumn": 23, + "endColumn": 46, "lineCount": 1 } }, @@ -8782,24 +8670,24 @@ { "code": "reportUnknownArgumentType", "range": { - "startColumn": 41, - "endColumn": 62, + "startColumn": 19, + "endColumn": 40, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 56, - "endColumn": 30, - "lineCount": 2 + "startColumn": 19, + "endColumn": 50, + "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 8, - "endColumn": 29, + "startColumn": 28, + "endColumn": 49, "lineCount": 1 } }, @@ -8814,16 +8702,8 @@ { "code": "reportUnknownArgumentType", "range": { - "startColumn": 39, - "endColumn": 42, - "lineCount": 1 - } - }, - { - "code": "reportAny", - "range": { - "startColumn": 45, - "endColumn": 49, + "startColumn": 36, + "endColumn": 39, "lineCount": 1 } }, @@ -8870,40 +8750,32 @@ { "code": "reportUnknownMemberType", "range": { - "startColumn": 31, - "endColumn": 48, + "startColumn": 35, + "endColumn": 52, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 31, - "endColumn": 50, + "startColumn": 35, + "endColumn": 54, "lineCount": 1 } }, { "code": "reportUnknownMemberType", "range": { - "startColumn": 52, - "endColumn": 69, + "startColumn": 35, + "endColumn": 52, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 52, - "endColumn": 71, - "lineCount": 1 - } - }, - { - "code": "reportUnknownMemberType", - "range": { - "startColumn": 8, - "endColumn": 22, + "startColumn": 35, + "endColumn": 54, "lineCount": 1 } }, @@ -8924,122 +8796,126 @@ } }, { - "code": "reportUnknownMemberType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 12, - "endColumn": 23, + "startColumn": 39, + "endColumn": 44, "lineCount": 1 } - }, + } + ], + "./sumpy/expansion/level_to_order.py": [ { - "code": "reportUnknownArgumentType", + "code": "reportMissingTypeStubs", "range": { - "startColumn": 11, - "endColumn": 18, + "startColumn": 13, + "endColumn": 21, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportAttributeAccessIssue", "range": { - "startColumn": 34, - "endColumn": 47, + "startColumn": 29, + "endColumn": 37, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportAttributeAccessIssue", "range": { "startColumn": 39, - "endColumn": 46, + "endColumn": 47, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportAttributeAccessIssue", "range": { - "startColumn": 39, - "endColumn": 44, + "startColumn": 49, + "endColumn": 57, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportAttributeAccessIssue", "range": { - "startColumn": 19, - "endColumn": 36, + "startColumn": 59, + "endColumn": 67, + "lineCount": 1 + } + } + ], + "./sumpy/expansion/local.py": [ + { + "code": "reportUnknownMemberType", + "range": { + "startColumn": 49, + "endColumn": 65, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 32, - "endColumn": 35, + "startColumn": 49, + "endColumn": 73, "lineCount": 1 } }, { "code": "reportUnknownMemberType", "range": { - "startColumn": 8, - "endColumn": 18, + "startColumn": 33, + "endColumn": 43, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportUnknownMemberType", "range": { - "startColumn": 40, - "endColumn": 50, + "startColumn": 42, + "endColumn": 52, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportArgumentType", "range": { "startColumn": 46, - "endColumn": 49, + "endColumn": 50, "lineCount": 1 } }, { "code": "reportUnknownMemberType", "range": { - "startColumn": 8, - "endColumn": 18, + "startColumn": 21, + "endColumn": 44, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportOperatorIssue", "range": { - "startColumn": 43, - "endColumn": 57, + "startColumn": 44, + "endColumn": 52, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 37, - "endColumn": 51, - "lineCount": 1 - } - }, - { - "code": "reportUnknownMemberType", - "range": { - "startColumn": 8, - "endColumn": 18, + "startColumn": 44, + "endColumn": 52, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportOperatorIssue", "range": { - "startColumn": 48, - "endColumn": 58, + "startColumn": 54, + "endColumn": 62, "lineCount": 1 } }, @@ -9047,5039 +8923,299 @@ "code": "reportUnknownArgumentType", "range": { "startColumn": 54, - "endColumn": 57, + "endColumn": 62, "lineCount": 1 } } ], - "./sumpy/expansion/level_to_order.py": [ + "./sumpy/expansion/loopy.py": [ { - "code": "reportUnknownParameterType", + "code": "reportArgumentType", "range": { - "startColumn": 23, - "endColumn": 26, + "startColumn": 36, + "endColumn": 47, "lineCount": 1 } }, { - "code": "reportMissingParameterType", + "code": "reportUnknownMemberType", "range": { - "startColumn": 23, - "endColumn": 26, + "startColumn": 16, + "endColumn": 28, "lineCount": 1 } }, { - "code": "reportMissingParameterType", + "code": "reportUnknownMemberType", "range": { - "startColumn": 28, - "endColumn": 39, + "startColumn": 16, + "endColumn": 28, "lineCount": 1 } }, { - "code": "reportUnannotatedClassAttribute", + "code": "reportUnknownMemberType", "range": { - "startColumn": 13, - "endColumn": 16, + "startColumn": 16, + "endColumn": 28, "lineCount": 1 } }, { - "code": "reportUnannotatedClassAttribute", + "code": "reportUnknownMemberType", "range": { - "startColumn": 13, - "endColumn": 24, + "startColumn": 16, + "endColumn": 28, "lineCount": 1 } }, { - "code": "reportUnknownParameterType", + "code": "reportArgumentType", "range": { - "startColumn": 8, - "endColumn": 16, + "startColumn": 28, + "endColumn": 37, "lineCount": 1 } }, { - "code": "reportUnknownParameterType", + "code": "reportUnknownMemberType", "range": { - "startColumn": 23, - "endColumn": 29, + "startColumn": 16, + "endColumn": 28, "lineCount": 1 } }, { - "code": "reportMissingParameterType", + "code": "reportUnknownMemberType", "range": { - "startColumn": 23, - "endColumn": 29, + "startColumn": 16, + "endColumn": 28, "lineCount": 1 } }, { - 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"startColumn": 42, - "endColumn": 59, + "startColumn": 16, + "endColumn": 28, "lineCount": 1 } }, @@ -14092,10 +9228,10 @@ } }, { - "code": "reportUnknownArgumentType", + "code": "reportUnknownMemberType", "range": { - "startColumn": 26, - "endColumn": 37, + "startColumn": 12, + "endColumn": 24, "lineCount": 1 } } @@ -15279,14 +10415,6 @@ "lineCount": 1 } }, - { - "code": "reportUnknownMemberType", - "range": { - "startColumn": 18, - "endColumn": 72, - "lineCount": 1 - } - }, { "code": "reportUnknownParameterType", "range": { @@ -29140,265 +24268,41 @@ { "code": "reportArgumentType", "range": { - "startColumn": 46, - "endColumn": 56, - "lineCount": 1 - } - }, - { - "code": "reportUnknownArgumentType", - "range": { - "startColumn": 48, - "endColumn": 53, - "lineCount": 1 - } - }, - { - "code": "reportUnknownArgumentType", - "range": { - "startColumn": 63, - "endColumn": 68, - "lineCount": 1 - } - }, - { - "code": "reportUnknownMemberType", - "range": { - "startColumn": 18, - "endColumn": 64, - "lineCount": 1 - } - }, - { - "code": "reportUnknownMemberType", - "range": { - "startColumn": 19, - "endColumn": 53, - "lineCount": 2 - } - }, - { - "code": "reportUnknownMemberType", - "range": { - "startColumn": 27, - "endColumn": 39, - "lineCount": 1 - } - }, - { - "code": "reportUnknownArgumentType", - "range": { - "startColumn": 27, - "endColumn": 39, - "lineCount": 1 - } - }, - { - "code": "reportAttributeAccessIssue", - "range": { - "startColumn": 32, - "endColumn": 39, - "lineCount": 1 - } - }, - { - "code": "reportUnknownArgumentType", - "range": { - "startColumn": 41, - "endColumn": 45, - "lineCount": 1 - } - }, - { - "code": "reportUnknownMemberType", - "range": { - "startColumn": 26, - "endColumn": 36, - "lineCount": 1 - } - }, - { - "code": "reportUnknownArgumentType", - "range": { - "startColumn": 26, - "endColumn": 36, - "lineCount": 1 - } - }, - { - "code": "reportAttributeAccessIssue", - "range": { - "startColumn": 31, - "endColumn": 36, - "lineCount": 1 - } - }, - { - "code": "reportUnknownArgumentType", - "range": { - "startColumn": 14, - "endColumn": 26, - "lineCount": 1 - } - }, - { - "code": "reportUnknownArgumentType", - "range": { - "startColumn": 34, - "endColumn": 40, - "lineCount": 1 - } - }, - { - "code": "reportUnknownMemberType", - "range": { - "startColumn": 31, - "endColumn": 43, - "lineCount": 1 - } - }, - { - "code": "reportUnknownArgumentType", - "range": { - "startColumn": 31, - "endColumn": 43, - "lineCount": 1 - } - }, - { - "code": "reportAttributeAccessIssue", - "range": { - "startColumn": 36, - "endColumn": 43, - "lineCount": 1 - } - }, - { - "code": "reportUnknownArgumentType", - "range": { - "startColumn": 45, - "endColumn": 49, - "lineCount": 1 - } - }, - { - "code": "reportUnknownMemberType", - "range": { - "startColumn": 30, - "endColumn": 40, - "lineCount": 1 - } - }, - { - "code": "reportUnknownArgumentType", - "range": { - "startColumn": 30, - "endColumn": 40, - "lineCount": 1 - } - }, - { - "code": "reportAttributeAccessIssue", - "range": { - "startColumn": 35, - "endColumn": 40, - "lineCount": 1 - } - }, - { - "code": "reportUnknownArgumentType", - "range": { - "startColumn": 14, - "endColumn": 33, - "lineCount": 1 - } - }, - { - "code": "reportUnknownArgumentType", - "range": { - "startColumn": 31, - "endColumn": 37, - "lineCount": 1 - } - }, - { - "code": "reportUnknownParameterType", - "range": { - "startColumn": 12, - "endColumn": 20, - "lineCount": 1 - } - }, - { - "code": "reportMissingParameterType", - "range": { - "startColumn": 12, - "endColumn": 20, - "lineCount": 1 - } - }, - { - "code": "reportUnknownParameterType", - "range": { - "startColumn": 12, - "endColumn": 22, - "lineCount": 1 - } - }, - { - "code": "reportMissingParameterType", - "range": { - "startColumn": 12, - "endColumn": 22, - "lineCount": 1 - } - }, - { - "code": "reportUnknownParameterType", - "range": { - "startColumn": 12, - "endColumn": 17, - "lineCount": 1 - } - }, - { - "code": "reportMissingParameterType", - "range": { - "startColumn": 12, - "endColumn": 17, + "startColumn": 46, + "endColumn": 56, "lineCount": 1 } }, { - "code": "reportUnknownParameterType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 12, - "endColumn": 34, + "startColumn": 48, + "endColumn": 53, "lineCount": 1 } }, { - "code": "reportMissingParameterType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 12, - "endColumn": 34, + "startColumn": 63, + "endColumn": 68, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportUnknownMemberType", "range": { - "startColumn": 42, - "endColumn": 50, + "startColumn": 18, + "endColumn": 64, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportUnknownMemberType", "range": { - "startColumn": 36, - "endColumn": 39, - "lineCount": 1 + "startColumn": 19, + "endColumn": 53, + "lineCount": 2 } }, { @@ -29436,79 +24340,79 @@ { "code": "reportUnknownMemberType", "range": { - "startColumn": 27, - "endColumn": 39, + "startColumn": 26, + "endColumn": 36, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 27, - "endColumn": 39, + "startColumn": 26, + "endColumn": 36, "lineCount": 1 } }, { "code": "reportAttributeAccessIssue", "range": { - "startColumn": 32, - "endColumn": 39, + "startColumn": 31, + "endColumn": 36, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 41, - "endColumn": 45, + "startColumn": 14, + "endColumn": 26, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 55, - "endColumn": 69, + "startColumn": 34, + "endColumn": 40, "lineCount": 1 } }, { "code": "reportUnknownMemberType", "range": { - "startColumn": 14, - "endColumn": 26, + "startColumn": 31, + "endColumn": 43, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 14, - "endColumn": 26, + "startColumn": 31, + "endColumn": 43, "lineCount": 1 } }, { "code": "reportAttributeAccessIssue", "range": { - "startColumn": 19, - "endColumn": 26, + "startColumn": 36, + "endColumn": 43, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 28, - "endColumn": 42, + "startColumn": 45, + "endColumn": 49, "lineCount": 1 } }, { "code": "reportUnknownMemberType", "range": { - "startColumn": 33, + "startColumn": 30, "endColumn": 40, "lineCount": 1 } @@ -29516,215 +24420,191 @@ { "code": "reportUnknownArgumentType", "range": { - "startColumn": 33, - "endColumn": 52, - "lineCount": 1 - } - }, - { - "code": "reportUnknownMemberType", - "range": { - "startColumn": 34, - "endColumn": 41, - "lineCount": 1 - } - }, - { - "code": "reportCallIssue", - "range": { - "startColumn": 16, - "endColumn": 61, + "startColumn": 30, + "endColumn": 40, "lineCount": 1 } }, { - "code": "reportArgumentType", + "code": "reportAttributeAccessIssue", "range": { - "startColumn": 50, - "endColumn": 60, + "startColumn": 35, + "endColumn": 40, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 36, - "endColumn": 41, + "startColumn": 14, + "endColumn": 33, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 51, - "endColumn": 56, + "startColumn": 31, + "endColumn": 37, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportUnknownParameterType", "range": { - "startColumn": 51, - "endColumn": 58, + "startColumn": 12, + "endColumn": 20, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportMissingParameterType", "range": { - "startColumn": 22, - "endColumn": 68, + "startColumn": 12, + "endColumn": 20, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportUnknownParameterType", "range": { - "startColumn": 69, - "endColumn": 76, + "startColumn": 12, + "endColumn": 22, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportMissingParameterType", "range": { - "startColumn": 69, - "endColumn": 76, + "startColumn": 12, + "endColumn": 22, "lineCount": 1 } }, { - "code": "reportArgumentType", + "code": "reportUnknownParameterType", "range": { - "startColumn": 49, - "endColumn": 50, + "startColumn": 12, + "endColumn": 17, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportMissingParameterType", "range": { - "startColumn": 52, - "endColumn": 59, + "startColumn": 12, + "endColumn": 17, "lineCount": 1 } }, { - "code": "reportArgumentType", + "code": "reportUnknownParameterType", "range": { - "startColumn": 50, - "endColumn": 53, + "startColumn": 12, + "endColumn": 34, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportMissingParameterType", "range": { - "startColumn": 23, - "endColumn": 57, - "lineCount": 2 + "startColumn": 12, + "endColumn": 34, + "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 49, - "endColumn": 56, + "startColumn": 42, + "endColumn": 50, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 58, - "endColumn": 65, + "startColumn": 36, + "endColumn": 39, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportUnknownMemberType", "range": { - "startColumn": 58, - "endColumn": 65, + "startColumn": 27, + "endColumn": 39, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 34, - "endColumn": 61, + "startColumn": 27, + "endColumn": 39, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportAttributeAccessIssue", "range": { - "startColumn": 51, - "endColumn": 60, + "startColumn": 32, + "endColumn": 39, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 51, - "endColumn": 60, + "startColumn": 41, + "endColumn": 45, "lineCount": 1 } }, { "code": "reportUnknownMemberType", "range": { - "startColumn": 30, - "endColumn": 40, + "startColumn": 27, + "endColumn": 39, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 30, - "endColumn": 40, + "startColumn": 27, + "endColumn": 39, "lineCount": 1 } }, { "code": "reportAttributeAccessIssue", "range": { - "startColumn": 35, - "endColumn": 40, + "startColumn": 32, + "endColumn": 39, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 18, - "endColumn": 37, - "lineCount": 1 - } - }, - { - "code": "reportUnknownMemberType", - "range": { - "startColumn": 19, - "endColumn": 28, + "startColumn": 41, + "endColumn": 45, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 19, - "endColumn": 34, + "startColumn": 55, + "endColumn": 69, "lineCount": 1 } }, { "code": "reportUnknownMemberType", "range": { - "startColumn": 16, + "startColumn": 14, "endColumn": 26, "lineCount": 1 } @@ -29732,7 +24612,7 @@ { "code": "reportUnknownArgumentType", "range": { - "startColumn": 16, + "startColumn": 14, "endColumn": 26, "lineCount": 1 } @@ -29740,7 +24620,7 @@ { "code": "reportAttributeAccessIssue", "range": { - "startColumn": 21, + "startColumn": 19, "endColumn": 26, "lineCount": 1 } @@ -29748,391 +24628,367 @@ { "code": "reportUnknownArgumentType", "range": { - "startColumn": 31, - "endColumn": 37, - "lineCount": 1 - } - }, - { - "code": "reportUnknownArgumentType", - "range": { - "startColumn": 18, - "endColumn": 30, + "startColumn": 28, + "endColumn": 42, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportUnknownMemberType", "range": { - "startColumn": 28, - "endColumn": 31, + "startColumn": 33, + "endColumn": 40, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 31, - "endColumn": 37, + "startColumn": 33, + "endColumn": 52, "lineCount": 1 } }, { "code": "reportUnknownMemberType", "range": { - "startColumn": 16, - "endColumn": 26, + "startColumn": 34, + "endColumn": 41, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportCallIssue", "range": { "startColumn": 16, - "endColumn": 26, + "endColumn": 61, "lineCount": 1 } }, { - "code": "reportAttributeAccessIssue", + "code": "reportArgumentType", "range": { - "startColumn": 21, - "endColumn": 26, + "startColumn": 50, + "endColumn": 60, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 16, - "endColumn": 23, + "startColumn": 36, + "endColumn": 41, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 12, - "endColumn": 22, + "startColumn": 51, + "endColumn": 56, "lineCount": 1 } }, { - "code": "reportAny", + "code": "reportUnknownMemberType", "range": { - "startColumn": 12, - "endColumn": 14, + "startColumn": 51, + "endColumn": 58, "lineCount": 1 } }, { "code": "reportUnknownMemberType", "range": { - "startColumn": 17, - "endColumn": 45, + "startColumn": 22, + "endColumn": 68, "lineCount": 1 } }, { "code": "reportUnknownMemberType", "range": { - "startColumn": 46, - "endColumn": 54, + "startColumn": 69, + "endColumn": 76, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 46, - "endColumn": 54, + "startColumn": 69, + "endColumn": 76, "lineCount": 1 } }, { - "code": "reportAttributeAccessIssue", + "code": "reportArgumentType", "range": { - "startColumn": 50, - "endColumn": 54, + "startColumn": 49, + "endColumn": 50, "lineCount": 1 } }, { - "code": "reportAny", + "code": "reportUnknownMemberType", "range": { - "startColumn": 12, - "endColumn": 23, + "startColumn": 52, + "endColumn": 59, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportArgumentType", "range": { - "startColumn": 12, - "endColumn": 22, + "startColumn": 50, + "endColumn": 53, "lineCount": 1 } }, { - "code": "reportAny", + "code": "reportUnknownMemberType", "range": { - "startColumn": 12, - "endColumn": 14, - "lineCount": 1 + "startColumn": 23, + "endColumn": 57, + "lineCount": 2 } }, { "code": "reportUnknownMemberType", "range": { - "startColumn": 17, - "endColumn": 45, + "startColumn": 49, + "endColumn": 56, "lineCount": 1 } }, { "code": "reportUnknownMemberType", "range": { - "startColumn": 46, - "endColumn": 61, + "startColumn": 58, + "endColumn": 65, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 46, - "endColumn": 61, + "startColumn": 58, + "endColumn": 65, "lineCount": 1 } }, { - "code": "reportAttributeAccessIssue", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 57, + "startColumn": 34, "endColumn": 61, "lineCount": 1 } }, { - "code": "reportAny", + "code": "reportUnknownMemberType", "range": { - "startColumn": 12, - "endColumn": 23, + "startColumn": 51, + "endColumn": 60, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 12, - "endColumn": 23, + "startColumn": 51, + "endColumn": 60, "lineCount": 1 } }, { "code": "reportUnknownMemberType", "range": { - "startColumn": 12, - "endColumn": 22, + "startColumn": 30, + "endColumn": 40, "lineCount": 1 } }, { - "code": "reportAny", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 12, - "endColumn": 14, + "startColumn": 30, + "endColumn": 40, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportAttributeAccessIssue", "range": { - "startColumn": 17, - "endColumn": 45, + "startColumn": 35, + "endColumn": 40, "lineCount": 1 } }, { - "code": "reportAny", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 12, - "endColumn": 23, + "startColumn": 18, + "endColumn": 37, "lineCount": 1 } }, { "code": "reportUnknownMemberType", "range": { - "startColumn": 12, - "endColumn": 23, + "startColumn": 19, + "endColumn": 28, "lineCount": 1 } }, { "code": "reportUnknownMemberType", "range": { - "startColumn": 12, - "endColumn": 19, - "lineCount": 1 - } - }, - { - "code": "reportArgumentType", - "range": { - "startColumn": 38, - "endColumn": 45, + "startColumn": 19, + "endColumn": 34, "lineCount": 1 } }, { - "code": "reportArgumentType", + "code": "reportUnknownMemberType", "range": { - "startColumn": 41, - "endColumn": 51, + "startColumn": 16, + "endColumn": 26, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 47, - "endColumn": 57, + "startColumn": 16, + "endColumn": 26, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportAttributeAccessIssue", "range": { - "startColumn": 59, - "endColumn": 62, + "startColumn": 21, + "endColumn": 26, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 64, - "endColumn": 69, + "startColumn": 31, + "endColumn": 37, "lineCount": 1 } }, { - "code": "reportUnknownParameterType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 12, - "endColumn": 15, + "startColumn": 18, + "endColumn": 30, "lineCount": 1 } }, { - "code": "reportMissingParameterType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 12, - "endColumn": 15, + "startColumn": 28, + "endColumn": 31, "lineCount": 1 } }, { - "code": "reportUnknownParameterType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 12, - "endColumn": 28, + "startColumn": 31, + "endColumn": 37, "lineCount": 1 } }, { - "code": "reportMissingParameterType", + "code": "reportUnknownMemberType", "range": { - "startColumn": 12, - "endColumn": 28, + "startColumn": 16, + "endColumn": 26, "lineCount": 1 } }, { - "code": "reportUnknownParameterType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 12, - "endColumn": 28, + "startColumn": 16, + "endColumn": 26, "lineCount": 1 } }, { - "code": "reportMissingParameterType", + "code": "reportAttributeAccessIssue", "range": { - "startColumn": 12, - "endColumn": 28, + "startColumn": 21, + "endColumn": 26, "lineCount": 1 } }, { - "code": "reportUnknownParameterType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 12, - "endColumn": 19, + "startColumn": 16, + "endColumn": 23, "lineCount": 1 } }, { - "code": "reportMissingParameterType", + "code": "reportUnknownMemberType", "range": { "startColumn": 12, - "endColumn": 19, + "endColumn": 22, "lineCount": 1 } }, { - "code": "reportMissingParameterType", + "code": "reportAny", "range": { "startColumn": 12, - "endColumn": 21, + "endColumn": 14, "lineCount": 1 } }, { "code": "reportUnknownMemberType", "range": { - "startColumn": 33, - "endColumn": 40, - "lineCount": 1 - } - }, - { - "code": "reportUnknownArgumentType", - "range": { - "startColumn": 33, - "endColumn": 52, + "startColumn": 17, + "endColumn": 45, "lineCount": 1 } }, { "code": "reportUnknownMemberType", "range": { - "startColumn": 34, - "endColumn": 41, + "startColumn": 46, + "endColumn": 54, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 42, - "endColumn": 49, + "startColumn": 46, + "endColumn": 54, "lineCount": 1 } }, { - "code": "reportArgumentType", + "code": "reportAttributeAccessIssue", "range": { - "startColumn": 51, + "startColumn": 50, "endColumn": 54, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportAny", "range": { - "startColumn": 14, + "startColumn": 12, "endColumn": 23, "lineCount": 1 } @@ -30140,64 +24996,64 @@ { "code": "reportUnknownMemberType", "range": { - "startColumn": 14, - "endColumn": 41, - "lineCount": 15 + "startColumn": 12, + "endColumn": 22, + "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportAny", "range": { - "startColumn": 14, - "endColumn": 46, - "lineCount": 15 + "startColumn": 12, + "endColumn": 14, + "lineCount": 1 } }, { "code": "reportUnknownMemberType", "range": { - "startColumn": 27, - "endColumn": 34, + "startColumn": 17, + "endColumn": 45, "lineCount": 1 } }, { "code": "reportUnknownMemberType", "range": { - "startColumn": 41, - "endColumn": 48, + "startColumn": 46, + "endColumn": 61, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 55, - "endColumn": 62, + "startColumn": 46, + "endColumn": 61, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportAttributeAccessIssue", "range": { - "startColumn": 7, - "endColumn": 14, + "startColumn": 57, + "endColumn": 61, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportAny", "range": { - "startColumn": 60, - "endColumn": 63, + "startColumn": 12, + "endColumn": 23, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportUnknownMemberType", "range": { - "startColumn": 65, - "endColumn": 81, + "startColumn": 12, + "endColumn": 23, "lineCount": 1 } }, @@ -30205,398 +25061,406 @@ "code": "reportUnknownMemberType", "range": { "startColumn": 12, - "endColumn": 24, + "endColumn": 22, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportAny", "range": { "startColumn": 12, - "endColumn": 24, + "endColumn": 14, "lineCount": 1 } }, { - "code": "reportAttributeAccessIssue", + "code": "reportUnknownMemberType", "range": { "startColumn": 17, - "endColumn": 24, + "endColumn": 45, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportAny", "range": { - "startColumn": 19, - "endColumn": 22, + "startColumn": 12, + "endColumn": 23, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportUnknownMemberType", "range": { - "startColumn": 29, - "endColumn": 57, - "lineCount": 2 + "startColumn": 12, + "endColumn": 23, + "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportUnknownMemberType", "range": { - "startColumn": 29, - "endColumn": 45, + "startColumn": 12, + "endColumn": 19, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportArgumentType", "range": { - 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"code": "reportUnknownArgumentType", + "code": "reportMissingParameterType", "range": { - "startColumn": 25, - "endColumn": 32, + "startColumn": 12, + "endColumn": 15, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportUnknownParameterType", "range": { - "startColumn": 8, - "endColumn": 43, + "startColumn": 12, + "endColumn": 28, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportMissingParameterType", "range": { - "startColumn": 51, - "endColumn": 54, + "startColumn": 12, + "endColumn": 28, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportUnknownParameterType", "range": { - "startColumn": 16, - "endColumn": 34, + "startColumn": 12, + "endColumn": 28, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportMissingParameterType", "range": { - "startColumn": 40, - "endColumn": 53, + "startColumn": 12, + "endColumn": 28, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportUnknownParameterType", "range": { - 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"startColumn": 42, - "endColumn": 50, + "startColumn": 32, + "endColumn": 44, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 36, - "endColumn": 43, + "startColumn": 17, + "endColumn": 24, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportUnknownMemberType", "range": { - "startColumn": 36, - "endColumn": 43, + "startColumn": 14, + "endColumn": 33, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 70, - "endColumn": 80, + "startColumn": 14, + "endColumn": 33, "lineCount": 1 } }, { "code": "reportUnknownMemberType", "range": { - "startColumn": 17, - "endColumn": 42, + "startColumn": 14, + "endColumn": 32, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 17, - "endColumn": 42, + "startColumn": 36, + "endColumn": 49, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 28, - "endColumn": 38, + "startColumn": 25, + "endColumn": 32, + "lineCount": 1 + } + }, + { + "code": "reportUnknownMemberType", + "range": { + "startColumn": 8, + "endColumn": 43, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 40, - "endColumn": 50, + "startColumn": 51, + "endColumn": 54, "lineCount": 1 } }, { "code": "reportUnknownMemberType", "range": { - "startColumn": 21, - "endColumn": 51, + "startColumn": 16, + "endColumn": 34, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 21, + "startColumn": 40, "endColumn": 53, "lineCount": 1 } @@ -30604,32 +25468,32 @@ { "code": "reportUnknownArgumentType", "range": { - "startColumn": 30, - "endColumn": 43, + "startColumn": 29, + "endColumn": 36, "lineCount": 1 } }, { "code": "reportUnknownMemberType", "range": { - "startColumn": 17, - "endColumn": 42, + "startColumn": 8, + "endColumn": 45, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 17, - "endColumn": 42, + "startColumn": 53, + "endColumn": 56, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportUnknownMemberType", "range": { - "startColumn": 28, - "endColumn": 38, + "startColumn": 18, + "endColumn": 32, "lineCount": 1 } }, @@ -30637,254 +25501,238 @@ "code": "reportUnknownArgumentType", "range": { "startColumn": 40, - "endColumn": 50, + "endColumn": 53, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 21, - "endColumn": 51, + "startColumn": 33, + "endColumn": 40, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportUnknownMemberType", "range": { - "startColumn": 21, - "endColumn": 53, + "startColumn": 8, + "endColumn": 47, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 30, - "endColumn": 43, + "startColumn": 55, + "endColumn": 58, "lineCount": 1 } }, { - "code": "reportUnknownParameterType", + "code": "reportUnknownMemberType", "range": { - 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} - }, - { - "code": "reportArgumentType", - "range": { - "startColumn": 26, - "endColumn": 36, + "startColumn": 21, + "endColumn": 53, "lineCount": 1 } }, { - "code": "reportArgumentType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 28, + "startColumn": 30, "endColumn": 43, "lineCount": 1 } @@ -30892,841 +25740,833 @@ { "code": "reportUnknownMemberType", "range": { - "startColumn": 16, - "endColumn": 30, + "startColumn": 17, + "endColumn": 42, "lineCount": 1 } }, { "code": "reportUnknownMemberType", "range": { - "startColumn": 16, - "endColumn": 30, + "startColumn": 17, + "endColumn": 42, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 19, - "endColumn": 32, + "startColumn": 28, + "endColumn": 38, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 38, - "endColumn": 43, - "lineCount": 1 - } - }, - { - "code": "reportUnknownParameterType", - "range": { - "startColumn": 75, - "endColumn": 78, + "startColumn": 40, + "endColumn": 50, "lineCount": 1 } }, { - "code": "reportMissingParameterType", + "code": "reportUnknownMemberType", "range": { - "startColumn": 75, - "endColumn": 78, + "startColumn": 21, + "endColumn": 51, "lineCount": 1 } }, { - "code": "reportUnknownParameterType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 80, - "endColumn": 85, + "startColumn": 21, + "endColumn": 53, "lineCount": 1 } }, { - "code": "reportMissingParameterType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 80, - "endColumn": 85, + "startColumn": 30, + "endColumn": 43, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportUnknownParameterType", "range": { - "startColumn": 26, - "endColumn": 29, + "startColumn": 4, + "endColumn": 25, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportUnknownParameterType", "range": { - "startColumn": 18, - "endColumn": 57, + "startColumn": 26, + "endColumn": 39, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportMissingParameterType", "range": { - 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"code": "reportAny", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 14, - "endColumn": 35, + "startColumn": 70, + "endColumn": 73, "lineCount": 1 } - } - ], - "./sumpy/test/test_matrixgen.py": [ + }, { - "code": "reportUnusedImport", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 67, - "endColumn": 71, + "startColumn": 27, + "endColumn": 30, "lineCount": 1 } }, { - "code": "reportUnknownParameterType", + "code": "reportUnknownMemberType", "range": { - "startColumn": 4, - "endColumn": 19, + "startColumn": 19, + "endColumn": 69, "lineCount": 1 } }, { - "code": "reportUnknownParameterType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 20, - "endColumn": 24, + "startColumn": 70, + "endColumn": 73, "lineCount": 1 } }, { - "code": "reportMissingParameterType", + "code": "reportAny", "range": { - "startColumn": 20, - "endColumn": 24, + "startColumn": 8, + "endColumn": 9, "lineCount": 1 } }, { - "code": "reportUnknownParameterType", + "code": "reportAny", "range": { - 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"startColumn": 12, - "endColumn": 27, + "startColumn": 20, + "endColumn": 41, "lineCount": 1 } }, { - "code": "reportUnknownParameterType", + "code": "reportAny", "range": { - "startColumn": 4, - "endColumn": 25, + "startColumn": 23, + "endColumn": 44, "lineCount": 1 } }, { - "code": "reportUnknownParameterType", + "code": "reportAny", "range": { - "startColumn": 26, - "endColumn": 30, + "startColumn": 23, + "endColumn": 49, "lineCount": 1 } }, { - "code": "reportMissingParameterType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 26, - "endColumn": 30, + "startColumn": 22, + "endColumn": 27, "lineCount": 1 } }, { - "code": "reportUnknownParameterType", + "code": "reportAny", "range": { - "startColumn": 32, - "endColumn": 40, + "startColumn": 23, + "endColumn": 24, "lineCount": 1 } }, { - "code": "reportMissingParameterType", + "code": "reportCallIssue", "range": { - "startColumn": 32, - "endColumn": 40, + "startColumn": 12, + "endColumn": 23, "lineCount": 1 } }, { - "code": "reportUnknownParameterType", + "code": "reportArgumentType", "range": { - "startColumn": 42, - "endColumn": 50, + "startColumn": 12, + "endColumn": 23, "lineCount": 1 } }, { - "code": "reportMissingParameterType", + "code": "reportAny", "range": { - "startColumn": 42, - "endColumn": 50, + "startColumn": 31, + "endColumn": 46, "lineCount": 1 } }, { - "code": "reportUnknownParameterType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 52, - "endColumn": 58, + "startColumn": 48, + "endColumn": 51, "lineCount": 1 } }, { - "code": "reportMissingParameterType", + "code": "reportAny", "range": { - "startColumn": 52, - "endColumn": 58, + "startColumn": 4, + "endColumn": 10, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportAny", "range": { - "startColumn": 30, - "endColumn": 38, + "startColumn": 12, + "endColumn": 19, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 30, - "endColumn": 38, + "startColumn": 36, + "endColumn": 37, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportAny", "range": { - "startColumn": 11, - "endColumn": 23, + "startColumn": 4, + "endColumn": 7, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportAny", "range": { - "startColumn": 32, - "endColumn": 42, + "startColumn": 14, + "endColumn": 35, "lineCount": 1 } - }, + } + ], + "./sumpy/test/test_matrixgen.py": [ { - "code": "reportUnknownArgumentType", + "code": "reportUnusedImport", "range": { - "startColumn": 25, - "endColumn": 42, + "startColumn": 67, + "endColumn": 71, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportUnknownParameterType", "range": { - "startColumn": 32, - "endColumn": 42, + "startColumn": 4, + "endColumn": 19, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportUnknownParameterType", "range": { - "startColumn": 25, - "endColumn": 42, + "startColumn": 20, + "endColumn": 24, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportMissingParameterType", "range": { "startColumn": 20, - "endColumn": 30, + "endColumn": 24, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportUnknownParameterType", "range": { - "startColumn": 20, - "endColumn": 30, + "startColumn": 26, + "endColumn": 34, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportMissingParameterType", "range": { - "startColumn": 41, - "endColumn": 51, + "startColumn": 26, + "endColumn": 34, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportUnknownParameterType", "range": { - "startColumn": 53, - "endColumn": 63, + "startColumn": 36, + "endColumn": 44, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportMissingParameterType", "range": { - "startColumn": 12, - "endColumn": 23, + "startColumn": 36, + "endColumn": 44, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportUnknownParameterType", "range": { - "startColumn": 24, - "endColumn": 39, + "startColumn": 46, + "endColumn": 50, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportMissingParameterType", "range": { - "startColumn": 12, - "endColumn": 23, + "startColumn": 46, + "endColumn": 50, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportMissingParameterType", "range": { - "startColumn": 24, - "endColumn": 39, + "startColumn": 52, + "endColumn": 65, "lineCount": 1 } }, { - "code": "reportUnknownParameterType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 12, - "endColumn": 18, + "startColumn": 38, + "endColumn": 46, "lineCount": 1 } }, { - "code": "reportMissingParameterType", + "code": "reportAny", "range": { - "startColumn": 12, - "endColumn": 18, + "startColumn": 4, + "endColumn": 9, "lineCount": 1 } }, { - "code": "reportUnknownParameterType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 12, - "endColumn": 19, + "startColumn": 19, + "endColumn": 27, "lineCount": 1 } }, { - "code": "reportMissingParameterType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 12, - "endColumn": 19, + "startColumn": 38, + "endColumn": 46, "lineCount": 1 } }, { - "code": "reportMissingParameterType", + "code": "reportUnknownMemberType", "range": { - "startColumn": 12, - "endColumn": 21, + "startColumn": 22, + "endColumn": 29, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 26, + "startColumn": 30, "endColumn": 38, "lineCount": 1 } @@ -31734,135 +26574,135 @@ { "code": "reportUnknownArgumentType", "range": { - "startColumn": 26, - "endColumn": 38, + "startColumn": 40, + "endColumn": 46, "lineCount": 1 } }, { - "code": "reportAttributeAccessIssue", + "code": "reportUnknownMemberType", "range": { - "startColumn": 31, - "endColumn": 38, + "startColumn": 12, + "endColumn": 27, "lineCount": 1 } }, { "code": "reportUnknownMemberType", "range": { - "startColumn": 44, - "endColumn": 56, + "startColumn": 12, + "endColumn": 27, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportUnknownMemberType", "range": { - "startColumn": 44, - "endColumn": 56, + "startColumn": 12, + "endColumn": 27, "lineCount": 1 } }, { - "code": "reportAttributeAccessIssue", + "code": "reportUnknownMemberType", "range": { - "startColumn": 49, - "endColumn": 56, + "startColumn": 12, + "endColumn": 27, "lineCount": 1 } }, { "code": "reportUnknownMemberType", "range": { - "startColumn": 50, - "endColumn": 62, + "startColumn": 12, + "endColumn": 27, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportUnknownParameterType", "range": { - "startColumn": 50, - "endColumn": 62, + "startColumn": 4, + "endColumn": 25, "lineCount": 1 } }, { - "code": "reportAttributeAccessIssue", + "code": "reportUnknownParameterType", "range": { - "startColumn": 55, - "endColumn": 62, + "startColumn": 26, + "endColumn": 30, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportMissingParameterType", "range": { - "startColumn": 47, - "endColumn": 53, + "startColumn": 26, + "endColumn": 30, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportUnknownParameterType", "range": { - "startColumn": 36, - "endColumn": 72, + "startColumn": 32, + "endColumn": 40, "lineCount": 1 } }, { - "code": "reportArgumentType", + "code": "reportMissingParameterType", "range": { - "startColumn": 53, - "endColumn": 71, + "startColumn": 32, + "endColumn": 40, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportUnknownParameterType", "range": { - "startColumn": 33, - "endColumn": 43, + "startColumn": 42, + "endColumn": 50, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportMissingParameterType", "range": { - "startColumn": 33, - "endColumn": 43, + "startColumn": 42, + "endColumn": 50, "lineCount": 1 } }, { - "code": "reportAttributeAccessIssue", + "code": "reportUnknownParameterType", "range": { - "startColumn": 38, - "endColumn": 43, + "startColumn": 52, + "endColumn": 58, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportMissingParameterType", "range": { - "startColumn": 39, - "endColumn": 51, + "startColumn": 52, + "endColumn": 58, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 36, - "endColumn": 47, + "startColumn": 30, + "endColumn": 38, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 28, + "startColumn": 30, "endColumn": 38, "lineCount": 1 } @@ -31870,56 +26710,56 @@ { "code": "reportUnknownArgumentType", "range": { - "startColumn": 28, - "endColumn": 38, + "startColumn": 11, + "endColumn": 23, "lineCount": 1 } }, { - "code": "reportAttributeAccessIssue", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 33, - "endColumn": 38, + "startColumn": 32, + "endColumn": 42, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 51, - "endColumn": 63, + "startColumn": 25, + "endColumn": 42, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 28, - "endColumn": 31, + "startColumn": 32, + "endColumn": 42, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 28, - "endColumn": 38, + "startColumn": 25, + "endColumn": 42, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 28, - "endColumn": 38, + "startColumn": 20, + "endColumn": 30, "lineCount": 1 } }, { - "code": "reportAttributeAccessIssue", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 33, - "endColumn": 38, + "startColumn": 20, + "endColumn": 30, "lineCount": 1 } }, @@ -31927,31 +26767,47 @@ "code": "reportUnknownArgumentType", "range": { "startColumn": 41, - "endColumn": 53, + "endColumn": 51, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 28, - "endColumn": 31, + "startColumn": 53, + "endColumn": 63, "lineCount": 1 } }, { - "code": "reportUnknownParameterType", + "code": "reportUnknownMemberType", "range": { "startColumn": 12, - "endColumn": 24, + "endColumn": 23, "lineCount": 1 } }, { - "code": "reportMissingParameterType", + "code": "reportUnknownMemberType", + "range": { + "startColumn": 24, + "endColumn": 39, + "lineCount": 1 + } + }, + { + "code": "reportUnknownMemberType", "range": { "startColumn": 12, - "endColumn": 24, + "endColumn": 23, + "lineCount": 1 + } + }, + { + "code": "reportUnknownMemberType", + "range": { + "startColumn": 24, + "endColumn": 39, "lineCount": 1 } }, @@ -31998,96 +26854,72 @@ { "code": "reportUnknownMemberType", "range": { - "startColumn": 15, - "endColumn": 27, + "startColumn": 26, + "endColumn": 38, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 15, - "endColumn": 27, + "startColumn": 26, + "endColumn": 38, "lineCount": 1 } }, { "code": "reportAttributeAccessIssue", "range": { - "startColumn": 20, - "endColumn": 27, - "lineCount": 1 - } - }, - { - "code": "reportUnknownArgumentType", - "range": { - "startColumn": 50, - "endColumn": 62, + "startColumn": 31, + "endColumn": 38, "lineCount": 1 } }, { "code": "reportUnknownMemberType", "range": { - "startColumn": 33, - "endColumn": 45, + "startColumn": 44, + "endColumn": 56, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 33, - "endColumn": 45, + "startColumn": 44, + "endColumn": 56, "lineCount": 1 } }, { "code": "reportAttributeAccessIssue", "range": { - "startColumn": 38, - "endColumn": 45, - "lineCount": 1 - } - }, - { - "code": "reportUnknownArgumentType", - "range": { - "startColumn": 68, - "endColumn": 80, + "startColumn": 49, + "endColumn": 56, "lineCount": 1 } }, { "code": "reportUnknownMemberType", "range": { - "startColumn": 8, - "endColumn": 20, + "startColumn": 50, + "endColumn": 62, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 8, - "endColumn": 20, - "lineCount": 1 - } - }, - { - "code": "reportAttributeAccessIssue", - "range": { - "startColumn": 13, - "endColumn": 20, + "startColumn": 50, + "endColumn": 62, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportAttributeAccessIssue", "range": { - "startColumn": 43, - "endColumn": 55, + "startColumn": 55, + "endColumn": 62, "lineCount": 1 } }, @@ -32139,27 +26971,19 @@ "lineCount": 1 } }, - { - "code": "reportUnknownMemberType", - "range": { - "startColumn": 28, - "endColumn": 38, - "lineCount": 1 - } - }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 28, - "endColumn": 38, + "startColumn": 39, + "endColumn": 51, "lineCount": 1 } }, { - "code": "reportAttributeAccessIssue", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 33, - "endColumn": 38, + "startColumn": 36, + "endColumn": 47, "lineCount": 1 } }, @@ -32186,1390 +27010,1326 @@ "endColumn": 38, "lineCount": 1 } - } - ], - "./sumpy/test/test_misc.py": [ - { - "code": "reportUnusedImport", - "range": { - "startColumn": 67, - "endColumn": 71, - "lineCount": 1 - } - }, - { - "code": "reportUnknownParameterType", - "range": { - "startColumn": 23, - "endColumn": 29, - "lineCount": 1 - } - }, - { - "code": "reportMissingParameterType", - "range": { - "startColumn": 23, - "endColumn": 29, - "lineCount": 1 - } - }, - { - "code": "reportUnknownParameterType", - "range": { - "startColumn": 33, - "endColumn": 39, - "lineCount": 1 - } }, { - "code": "reportMissingParameterType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 33, - "endColumn": 39, + "startColumn": 51, + "endColumn": 63, "lineCount": 1 } }, { - "code": "reportUnannotatedClassAttribute", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 13, - "endColumn": 19, + "startColumn": 28, + "endColumn": 31, "lineCount": 1 } }, { "code": "reportUnknownMemberType", "range": { - "startColumn": 8, - "endColumn": 25, + "startColumn": 28, + "endColumn": 38, "lineCount": 1 } }, { - "code": "reportUnannotatedClassAttribute", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 13, - "endColumn": 25, + "startColumn": 28, + "endColumn": 38, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportAttributeAccessIssue", "range": { - "startColumn": 18, - "endColumn": 48, + "startColumn": 33, + "endColumn": 38, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 19, - "endColumn": 30, + "startColumn": 41, + "endColumn": 53, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 19, - "endColumn": 30, + "startColumn": 28, + "endColumn": 31, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportUnknownParameterType", "range": { - "startColumn": 13, + "startColumn": 12, "endColumn": 24, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", - "range": { - "startColumn": 8, - "endColumn": 15, - "lineCount": 1 - } - }, - { - "code": "reportUnannotatedClassAttribute", - "range": { - "startColumn": 13, - "endColumn": 15, - "lineCount": 1 - } - }, - { - "code": "reportUnknownParameterType", + "code": "reportMissingParameterType", "range": { - "startColumn": 8, - "endColumn": 16, + "startColumn": 12, + "endColumn": 24, "lineCount": 1 } }, { "code": "reportUnknownParameterType", "range": { - "startColumn": 23, - "endColumn": 25, + "startColumn": 12, + "endColumn": 18, "lineCount": 1 } }, { "code": "reportMissingParameterType", "range": { - "startColumn": 23, - "endColumn": 25, + "startColumn": 12, + "endColumn": 18, "lineCount": 1 } }, { "code": "reportUnknownParameterType", "range": { - "startColumn": 27, - "endColumn": 30, + "startColumn": 12, + "endColumn": 19, "lineCount": 1 } }, { "code": "reportMissingParameterType", "range": { - "startColumn": 27, - "endColumn": 30, + "startColumn": 12, + "endColumn": 19, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportMissingParameterType", "range": { - "startColumn": 50, - "endColumn": 67, + "startColumn": 12, + "endColumn": 21, "lineCount": 1 } }, { "code": "reportUnknownMemberType", "range": { - "startColumn": 28, - "endColumn": 35, + "startColumn": 15, + "endColumn": 27, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 28, - 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"startColumn": 21, - "endColumn": 29, + "startColumn": 16, + "endColumn": 23, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 31, - "endColumn": 35, + "startColumn": 25, + "endColumn": 32, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 19, - "endColumn": 24, + "startColumn": 34, + "endColumn": 41, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 26, - "endColumn": 31, + "startColumn": 32, + "endColumn": 47, "lineCount": 1 } }, { "code": "reportUnknownMemberType", "range": { - "startColumn": 24, - "endColumn": 33, + "startColumn": 16, + "endColumn": 26, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 24, - "endColumn": 35, + "startColumn": 16, + "endColumn": 26, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 19, + "startColumn": 16, "endColumn": 23, "lineCount": 1 } @@ -34512,7 +29282,7 @@ { "code": "reportUnknownArgumentType", "range": { - "startColumn": 28, + "startColumn": 25, "endColumn": 32, "lineCount": 1 } @@ -34520,509 +29290,507 @@ { "code": "reportUnknownArgumentType", "range": { - "startColumn": 40, - "endColumn": 45, + "startColumn": 34, + "endColumn": 41, "lineCount": 1 } }, { - "code": "reportUnknownParameterType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 4, - "endColumn": 20, + "startColumn": 32, + "endColumn": 47, "lineCount": 1 } }, { - "code": "reportUnknownParameterType", + "code": "reportUnknownMemberType", "range": { - "startColumn": 21, - "endColumn": 26, + "startColumn": 24, + "endColumn": 37, "lineCount": 1 } }, { - "code": "reportMissingParameterType", + "code": "reportUnknownMemberType", "range": { - "startColumn": 21, - "endColumn": 26, + "startColumn": 24, + "endColumn": 37, "lineCount": 1 } }, { - "code": "reportUnknownParameterType", + "code": "reportAny", "range": { - "startColumn": 28, - "endColumn": 31, + "startColumn": 33, + "endColumn": 72, "lineCount": 1 } }, { - "code": "reportMissingParameterType", + "code": "reportAny", "range": { - "startColumn": 28, - "endColumn": 31, + "startColumn": 40, + "endColumn": 71, "lineCount": 1 } }, { - "code": "reportUnknownParameterType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 15, - "endColumn": 18, + "startColumn": 47, + "endColumn": 70, "lineCount": 1 } - }, + } + ], + "./sumpy/test/test_target_deriv.py": [ { - "code": "reportMissingParameterType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 15, - "endColumn": 18, + "startColumn": 35, + "endColumn": 45, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 25, - "endColumn": 30, + "startColumn": 35, + "endColumn": 45, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 32, - "endColumn": 35, + "startColumn": 36, + "endColumn": 47, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 31, - "endColumn": 37, + "startColumn": 36, + "endColumn": 47, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportAny", "range": { - "startColumn": 39, - "endColumn": 42, + "startColumn": 8, + "endColumn": 15, "lineCount": 1 } }, { - "code": "reportUnknownParameterType", + "code": "reportAny", "range": { - "startColumn": 4, - "endColumn": 22, + "startColumn": 8, + "endColumn": 16, "lineCount": 1 } }, { - "code": "reportUnknownParameterType", + "code": "reportAny", "range": { - "startColumn": 23, - "endColumn": 28, + "startColumn": 8, + "endColumn": 18, "lineCount": 1 } }, { - "code": "reportMissingParameterType", + "code": "reportAny", "range": { - "startColumn": 23, - "endColumn": 28, + "startColumn": 28, + "endColumn": 50, "lineCount": 1 } }, { - "code": "reportUnknownParameterType", + "code": "reportAny", "range": { - "startColumn": 30, - "endColumn": 33, + "startColumn": 37, + "endColumn": 55, "lineCount": 1 } }, { - "code": "reportMissingParameterType", + "code": "reportAny", "range": { - "startColumn": 30, - "endColumn": 33, + "startColumn": 44, + "endColumn": 54, + "lineCount": 1 + } + } + ], + "./sumpy/test/test_tools.py": [ + { + "code": "reportUnusedImport", + "range": { + "startColumn": 67, + "endColumn": 71, "lineCount": 1 } }, { - "code": "reportUnknownParameterType", + "code": "reportArgumentType", "range": { - "startColumn": 8, - "endColumn": 16, + "startColumn": 40, + "endColumn": 41, "lineCount": 1 } }, { - "code": "reportUnknownParameterType", + "code": "reportArgumentType", "range": { - "startColumn": 17, - "endColumn": 20, + "startColumn": 43, + "endColumn": 44, "lineCount": 1 } }, { - "code": "reportMissingParameterType", + "code": "reportArgumentType", "range": { - "startColumn": 17, - "endColumn": 20, + "startColumn": 46, + "endColumn": 47, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportArgumentType", "range": { - "startColumn": 15, - "endColumn": 22, + "startColumn": 49, + "endColumn": 50, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportArgumentType", "range": { - "startColumn": 31, - "endColumn": 39, + "startColumn": 40, + "endColumn": 41, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportArgumentType", "range": { - "startColumn": 41, + "startColumn": 43, "endColumn": 44, "lineCount": 1 } }, { - "code": "reportUnannotatedClassAttribute", + "code": "reportArgumentType", "range": { - "startColumn": 13, + "startColumn": 14, "endColumn": 17, "lineCount": 1 } }, { - "code": "reportMissingTypeArgument", + "code": "reportUnknownMemberType", "range": { - "startColumn": 17, - "endColumn": 27, + "startColumn": 32, + "endColumn": 42, "lineCount": 1 } }, { - "code": "reportUnknownParameterType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 23, - "endColumn": 31, + "startColumn": 32, + "endColumn": 42, "lineCount": 1 } }, { - "code": "reportMissingParameterType", + "code": "reportAttributeAccessIssue", "range": { - "startColumn": 23, - "endColumn": 31, + "startColumn": 37, + "endColumn": 42, "lineCount": 1 } }, { - "code": "reportUnannotatedClassAttribute", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 13, - "endColumn": 16, + "startColumn": 37, + "endColumn": 44, "lineCount": 1 } }, { - "code": "reportUnannotatedClassAttribute", + "code": "reportArgumentType", "range": { - "startColumn": 13, - "endColumn": 16, + "startColumn": 47, + "endColumn": 50, "lineCount": 1 } - }, + } + ], + "./sumpy/tools.py": [ { - "code": "reportImplicitOverride", + "code": "reportUnknownParameterType", "range": { - "startColumn": 8, - "endColumn": 15, + "startColumn": 17, + "endColumn": 19, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportMissingParameterType", "range": { - "startColumn": 19, - "endColumn": 27, + "startColumn": 17, + "endColumn": 19, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportUnknownParameterType", "range": { - "startColumn": 19, - "endColumn": 27, + "startColumn": 21, + "endColumn": 26, "lineCount": 1 } }, { - "code": "reportImplicitOverride", + "code": "reportMissingParameterType", "range": { - "startColumn": 8, - "endColumn": 20, + "startColumn": 21, + "endColumn": 26, "lineCount": 1 } }, { "code": "reportUnknownParameterType", "range": { - 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"startColumn": 8, - "endColumn": 16, - "lineCount": 1 - } - }, - { - "code": "reportImplicitOverride", - "range": { - "startColumn": 8, - "endColumn": 16, + "startColumn": 15, + "endColumn": 18, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportMissingParameterType", "range": { - "startColumn": 14, - "endColumn": 22, + "startColumn": 15, + "endColumn": 18, "lineCount": 1 } }, { - "code": "reportUnknownParameterType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 8, - "endColumn": 20, + "startColumn": 25, + "endColumn": 30, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 14, - "endColumn": 22, + "startColumn": 32, + "endColumn": 35, "lineCount": 1 } }, { - "code": "reportUnknownParameterType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 8, - "endColumn": 11, + "startColumn": 31, + "endColumn": 37, "lineCount": 1 } }, { - "code": "reportImplicitOverride", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 8, - "endColumn": 11, + "startColumn": 39, + "endColumn": 42, "lineCount": 1 } }, { - "code": "reportMissingParameterType", + "code": "reportUnknownParameterType", "range": { - "startColumn": 18, + "startColumn": 4, "endColumn": 22, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportUnknownParameterType", "range": { - "startColumn": 14, - "endColumn": 22, + "startColumn": 23, + "endColumn": 28, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportMissingParameterType", "range": { - "startColumn": 42, - "endColumn": 50, + "startColumn": 23, + "endColumn": 28, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportUnknownParameterType", "range": { - "startColumn": 8, - "endColumn": 20, + "startColumn": 30, + "endColumn": 33, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportMissingParameterType", "range": { - "startColumn": 21, - "endColumn": 24, + "startColumn": 30, + "endColumn": 33, "lineCount": 1 } }, { - "code": "reportImplicitOverride", + "code": "reportUnknownParameterType", "range": { "startColumn": 8, "endColumn": 16, @@ -35030,450 +29798,458 @@ } }, { - "code": "reportImplicitOverride", + "code": "reportUnknownParameterType", "range": { - "startColumn": 8, - "endColumn": 14, + "startColumn": 17, + "endColumn": 20, "lineCount": 1 } }, { "code": "reportMissingParameterType", "range": { - "startColumn": 21, - "endColumn": 26, + "startColumn": 17, + "endColumn": 20, "lineCount": 1 } }, { - "code": "reportArgumentType", + "code": "reportUnknownMemberType", "range": { - "startColumn": 32, - "endColumn": 37, + "startColumn": 15, + "endColumn": 22, "lineCount": 1 } }, { - "code": "reportUnknownParameterType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 43, - "endColumn": 49, + "startColumn": 31, + "endColumn": 39, "lineCount": 1 } }, { - "code": "reportMissingParameterType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 43, - "endColumn": 49, + "startColumn": 41, + "endColumn": 44, "lineCount": 1 } }, { - 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"startColumn": 44, - "endColumn": 53, + "startColumn": 13, + "endColumn": 16, + "lineCount": 1 + } + }, + { + "code": "reportImplicitOverride", + "range": { + "startColumn": 8, + "endColumn": 15, "lineCount": 1 } }, { "code": "reportUnknownMemberType", "range": { - "startColumn": 15, + "startColumn": 19, "endColumn": 27, "lineCount": 1 } }, { - "code": "reportUnknownParameterType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 22, - "endColumn": 25, + "startColumn": 19, + "endColumn": 27, "lineCount": 1 } }, { - "code": "reportMissingParameterType", + "code": "reportImplicitOverride", "range": { - "startColumn": 22, - "endColumn": 25, + "startColumn": 8, + "endColumn": 20, "lineCount": 1 } }, { "code": "reportUnknownParameterType", "range": { - "startColumn": 4, - "endColumn": 28, + "startColumn": 27, + "endColumn": 30, "lineCount": 1 } }, { - "code": "reportAny", + "code": "reportMissingParameterType", "range": { - "startColumn": 4, - "endColumn": 9, + "startColumn": 27, + "endColumn": 30, "lineCount": 1 } }, { - 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"startColumn": 38, - "endColumn": 39, + "startColumn": 35, + "endColumn": 49, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 26, - "endColumn": 29, + "startColumn": 73, + "endColumn": 82, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportUnknownMemberType", "range": { - "startColumn": 31, - "endColumn": 35, + "startColumn": 23, + "endColumn": 38, "lineCount": 1 } }, { - "code": "reportUnknownMemberType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 15, - "endColumn": 51, - "lineCount": 2 + "startColumn": 27, + "endColumn": 36, + "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 30, - "endColumn": 33, + "startColumn": 44, + "endColumn": 53, "lineCount": 1 } }, @@ -35481,263 +30257,263 @@ "code": "reportUnknownMemberType", "range": { "startColumn": 15, - "endColumn": 51, - "lineCount": 2 + "endColumn": 27, + "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportUnknownParameterType", "range": { - "startColumn": 29, - "endColumn": 32, + "startColumn": 22, + "endColumn": 25, "lineCount": 1 } }, { - "code": "reportUnknownParameterType", + "code": "reportMissingParameterType", "range": { - "startColumn": 4, - "endColumn": 33, + "startColumn": 22, + "endColumn": 25, "lineCount": 1 } }, { "code": "reportUnknownParameterType", "range": { - "startColumn": 34, - "endColumn": 43, + "startColumn": 4, + "endColumn": 28, "lineCount": 1 } }, { - "code": "reportMissingParameterType", + "code": "reportAny", "range": { - "startColumn": 34, - "endColumn": 43, + "startColumn": 4, + "endColumn": 9, "lineCount": 1 } }, { - "code": "reportUnknownParameterType", + "code": "reportAny", "range": { - "startColumn": 45, - "endColumn": 46, + "startColumn": 4, + "endColumn": 9, "lineCount": 1 } }, { - "code": "reportMissingParameterType", + "code": "reportAny", "range": { - "startColumn": 45, - "endColumn": 46, + "startColumn": 19, + "endColumn": 24, "lineCount": 1 } }, { - "code": "reportUnknownParameterType", + "code": "reportAny", "range": { - "startColumn": 48, - "endColumn": 51, + "startColumn": 8, + "endColumn": 13, "lineCount": 1 } }, { - "code": "reportMissingParameterType", + "code": "reportAny", "range": { - "startColumn": 48, - "endColumn": 51, + "startColumn": 30, + "endColumn": 35, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportAny", "range": { - "startColumn": 15, - "endColumn": 24, + "startColumn": 64, + "endColumn": 83, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportAny", "range": { - "startColumn": 33, - "endColumn": 34, + "startColumn": 32, + "endColumn": 41, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportAny", "range": { - "startColumn": 12, - "endColumn": 21, + "startColumn": 36, + "endColumn": 49, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportUnknownMemberType", "range": { - "startColumn": 13, - "endColumn": 22, + "startColumn": 8, + "endColumn": 25, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportAny", "range": { - "startColumn": 13, - "endColumn": 24, + "startColumn": 8, + "endColumn": 13, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportAny", "range": { - "startColumn": 22, - "endColumn": 23, + "startColumn": 23, + "endColumn": 35, "lineCount": 1 } }, { - "code": "reportArgumentType", + "code": "reportAny", "range": { - "startColumn": 19, - "endColumn": 22, + "startColumn": 12, + "endColumn": 16, "lineCount": 1 } }, { - "code": "reportArgumentType", + "code": "reportAny", "range": { - "startColumn": 19, - "endColumn": 22, + "startColumn": 23, + "endColumn": 28, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportAny", "range": { - "startColumn": 26, - "endColumn": 29, + "startColumn": 12, + "endColumn": 17, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportUnknownParameterType", "range": { - "startColumn": 31, - "endColumn": 36, + "startColumn": 4, + "endColumn": 13, "lineCount": 1 } }, { "code": "reportUnknownArgumentType", "range": { - "startColumn": 54, - "endColumn": 59, + "startColumn": 22, + "endColumn": 32, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportAny", "range": { - "startColumn": 61, - "endColumn": 66, + "startColumn": 4, + "endColumn": 8, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportAny", "range": { - "startColumn": 16, - "endColumn": 33, + "startColumn": 34, + "endColumn": 38, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportAny", "range": { - "startColumn": 25, - "endColumn": 32, + "startColumn": 8, + "endColumn": 11, "lineCount": 1 } }, { - "code": "reportUnknownParameterType", + "code": "reportUnknownArgumentType", "range": { - "startColumn": 37, - "endColumn": 46, + "startColumn": 42, + "endColumn": 52, "lineCount": 1 } }, { - "code": "reportMissingParameterType", + "code": "reportAny", "range": { - "startColumn": 37, - "endColumn": 46, + "startColumn": 40, + "endColumn": 57, "lineCount": 1 } }, { - "code": "reportUnknownParameterType", + "code": "reportUnknownMemberType", "range": { - "startColumn": 48, - "endColumn": 54, + "startColumn": 8, + "endColumn": 16, "lineCount": 1 } }, { - "code": "reportMissingParameterType", + "code": "reportAny", "range": { - "startColumn": 48, - "endColumn": 54, + "startColumn": 17, + "endColumn": 20, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportUnknownMemberType", "range": { - "startColumn": 12, - "endColumn": 21, + "startColumn": 11, + "endColumn": 38, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportAny", "range": { - "startColumn": 15, - "endColumn": 21, + "startColumn": 31, + "endColumn": 32, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportAny", "range": { - "startColumn": 22, - "endColumn": 77, + "startColumn": 38, + "endColumn": 39, "lineCount": 1 } }, { - "code": "reportCallIssue", + "code": "reportUnknownMemberType", "range": { - "startColumn": 8, - "endColumn": 19, + "startColumn": 19, + "endColumn": 54, "lineCount": 1 } }, { - "code": "reportArgumentType", + "code": "reportReturnType", "range": { - "startColumn": 8, - "endColumn": 19, + "startColumn": 19, + "endColumn": 66, "lineCount": 1 } }, { - "code": "reportUnknownArgumentType", + "code": "reportUnknownMemberType", "range": { - "startColumn": 31, - "endColumn": 36, + "startColumn": 11, + "endColumn": 24, "lineCount": 1 } }, @@ -36311,14 +31087,6 @@ "lineCount": 1 } }, - { - "code": "reportUnknownMemberType", - "range": { - "startColumn": 15, - "endColumn": 65, - "lineCount": 1 - } - }, { "code": "reportUnknownParameterType", "range": { diff --git a/sumpy/expansion/__init__.py b/sumpy/expansion/__init__.py index da5dba7d..47087acb 100644 --- a/sumpy/expansion/__init__.py +++ b/sumpy/expansion/__init__.py @@ -25,7 +25,7 @@ import logging from abc import ABC, abstractmethod from dataclasses import dataclass, field, replace -from typing import TYPE_CHECKING, Any, ClassVar, Protocol, TypeAlias +from typing import TYPE_CHECKING, Any, ClassVar, Protocol, TypeAlias, overload from warnings import warn from typing_extensions import Self, override @@ -388,9 +388,24 @@ def _split_coeffs_into_hyperplanes( class FullExpansionTermsWrangler(ExpansionTermsWrangler): - def get_storage_index(self, mi: MultiIndex, order: int | None = None) -> int: + @overload + def get_storage_index(self, + mi: MultiIndex, + order: int | None = None) -> int: ... + + @overload + def get_storage_index(self, + mi: tuple[prim.ExpressionNode, ...], + order: int | prim.ExpressionNode | None = None, + ) -> prim.ExpressionNode: ... + + def get_storage_index(self, + mi: MultiIndex | tuple[prim.ExpressionNode, ...], + order: int | prim.ExpressionNode | None = None, + ) -> int | prim.ExpressionNode: if not order: order = sum(mi) + if self.dim == 3: return (order*(order + 1)*(order + 2))//6 + \ (order + 2)*mi[2] - (mi[2]*(mi[2] + 1))//2 + mi[1] @@ -670,8 +685,22 @@ def get_full_coefficient_identifiers(self) -> Sequence[MultiIndex]: key, _ = self._get_mi_ordering_key_and_axis_permutation() return sorted(identifiers, key=key) - def get_storage_index(self, mi: MultiIndex, order: int | None = None): - if not order: + @overload + def get_storage_index(self, + mi: MultiIndex, + order: int | None = None) -> int: ... + + @overload + def get_storage_index(self, + mi: tuple[prim.ExpressionNode, ...], + order: int | prim.ExpressionNode | None = None, + ) -> prim.ExpressionNode: ... + + def get_storage_index(self, + mi: MultiIndex | tuple[prim.ExpressionNode, ...], + order: int | prim.ExpressionNode | None = None, + ) -> int | prim.ExpressionNode: + if order is None: order = sum(mi) ordering_key, axis_permutation = \ @@ -684,23 +713,32 @@ def get_storage_index(self, mi: MultiIndex, order: int | None = None): c = max_mi[axis_permutation[0]] - mi = list(mi) - mi[axis_permutation[0]], mi[0] = mi[0], mi[axis_permutation[0]] + new_mi = list(mi) + new_mi[axis_permutation[0]], new_mi[0] = mi[0], mi[axis_permutation[0]] + mi = tuple(new_mi) if self.dim == 3: if all(isinstance(axis, int) for axis in mi): if order < c - 1: - return (order*(order + 1)*(order + 2))//6 + \ - (order + 2)*mi[0] - (mi[0]*(mi[0] + 1))//2 + mi[1] + return ( + (order*(order + 1)*(order + 2))//6 + + (order + 2)*mi[0] + - (mi[0]*(mi[0] + 1))//2 + + mi[1]) else: - return (c*(c-1)*(c-2))//6 + (c * order * (2 + order - c) - + mi[0]*(3 - mi[0]+2*order))//2 + mi[1] + return ( + (c*(c-1)*(c-2))//6 + + (c * order * (2 + order - c) + mi[0]*(3 - mi[0]+2*order))//2 + + mi[1]) else: return prim.If(prim.Comparison(order, "<", c - 1), (order*(order + 1)*(order + 2))//6 - + (order + 2)*mi[0] - (mi[0]*(mi[0] + 1))//2 + mi[1], - (c*(c-1)*(c-2))//6 + (c * order * (2 + order - c) - + mi[0]*(3 - mi[0]+2*order))//2 + mi[1] + + (order + 2)*mi[0] + - (mi[0]*(mi[0] + 1))//2 + + mi[1], + (c*(c-1)*(c-2))//6 + + (c * order * (2 + order - c) + mi[0]*(3 - mi[0]+2*order))//2 + + mi[1] ) elif self.dim == 2: if all(isinstance(axis, int) for axis in mi): diff --git a/sumpy/expansion/diff_op.py b/sumpy/expansion/diff_op.py index fc67fb48..b71d88b5 100644 --- a/sumpy/expansion/diff_op.py +++ b/sumpy/expansion/diff_op.py @@ -1,5 +1,3 @@ -# mypy: disallow-untyped-defs - from __future__ import annotations @@ -31,19 +29,21 @@ from typing import TYPE_CHECKING, TypeAlias import numpy as np -import sympy as sp -import sympy.polys.agca.modules as sp_modules from constantdict import constantdict +from typing_extensions import override from pytools import memoize import sumpy.symbolic as sym -from sumpy.tools import add_mi if TYPE_CHECKING: from collections.abc import Mapping, Sequence + from sympy.polys.agca.modules import FreeModuleElement, SubModulePolyRing + + from pymbolic.typing import Number + logger = logging.getLogger(__name__) @@ -88,61 +88,69 @@ class DerivativeIdentifier: """ -Number_ish = int | float | complex | np.number - - @dataclass(frozen=True, eq=True) class LinearPDESystemOperator: r""" Represents a constant-coefficient linear differential operator of a - vector-valued function with `dim` spatial variables. It is represented by a - tuple of immutable dictionaries. The dictionary maps a - :class:`DerivativeIdentifier` to the coefficient. This object is immutable. - Optionally supports a time variable as the last variable in the multi-index - of the :class:`DerivativeIdentifier`. + vector-valued function with `dim` spatial variables. + + It is represented by a tuple of immutable dictionaries. The dictionary maps a + :class:`DerivativeIdentifier` to the coefficient. Optionally supports a + time variable as the last variable in the multi-index of the + :class:`DerivativeIdentifier`. .. autoattribute:: dim .. autoattribute:: eqs - .. autoattribute:: order - .. autoattribute:: total_dims + .. autoproperty:: order + .. autoproperty:: total_dims .. automethod:: to_sym """ dim: int - eqs: tuple[Mapping[DerivativeIdentifier, sp.Expr], ...] + eqs: tuple[Mapping[DerivativeIdentifier, sym.Expr], ...] if __debug__: + def __post_init__(self) -> None: - hash(self) + # NOTE: this will raise a TypeError if it's not hashable + _ = hash(self) @property def order(self) -> int: deg = 0 for eq in self.eqs: deg = max(deg, max(sum(ident.mi) for ident in eq)) + return deg - def __mul__(self, param: Number_ish | sym.Expr) -> LinearPDESystemOperator: - eqs: list[Mapping[DerivativeIdentifier, sp.Expr]] = [] + def __mul__(self, other: Number | sym.Expr) -> LinearPDESystemOperator: + import numbers + if not isinstance(other, (numbers.Number, np.number, sym.Expr)): + return NotImplemented + + eqs: list[Mapping[DerivativeIdentifier, sym.Expr]] = [] for eq in self.eqs: - deriv_ident_to_coeff = {} + deriv_ident_to_coeff: Mapping[DerivativeIdentifier, sym.Expr] = {} for k, v in eq.items(): - deriv_ident_to_coeff[k] = v * param + deriv_ident_to_coeff[k] = v * other + eqs.append(constantdict(deriv_ident_to_coeff)) return LinearPDESystemOperator(self.dim, tuple(eqs)) - __rmul__ = __mul__ + def __rmul__(self, param: Number | sym.Expr) -> LinearPDESystemOperator: + return self.__mul__(param) - def __add__( - self, other_diff_op: LinearPDESystemOperator - ) -> LinearPDESystemOperator: - assert self.dim == other_diff_op.dim - assert len(self.eqs) == len(other_diff_op.eqs) + def __add__(self, other: LinearPDESystemOperator) -> LinearPDESystemOperator: + if not isinstance(other, LinearPDESystemOperator): + return NotImplemented - eqs: list[Mapping[DerivativeIdentifier, sp.Expr]] = [] - for eq, other_eq in zip(self.eqs, other_diff_op.eqs, strict=True): + assert self.dim == other.dim + assert len(self.eqs) == len(other.eqs) + + eqs: list[Mapping[DerivativeIdentifier, sym.Expr]] = [] + for eq, other_eq in zip(self.eqs, other.eqs, strict=True): res = dict(eq) for k, v in other_eq.items(): if k in res: @@ -153,13 +161,16 @@ def __add__( return LinearPDESystemOperator(self.dim, tuple(eqs)) - __radd__ = __add__ + def __radd__(self, other: LinearPDESystemOperator) -> LinearPDESystemOperator: + return self.__add__(other) + + def __sub__(self, other: LinearPDESystemOperator) -> LinearPDESystemOperator: + return self + (-1)*other - def __sub__( - self, other_diff_op: LinearPDESystemOperator - ) -> LinearPDESystemOperator: - return self + (-1)*other_diff_op + def __neg__(self) -> LinearPDESystemOperator: + return (-1) * self + @override def __repr__(self) -> str: return f"LinearPDESystemOperator({self.dim}, {self.eqs!r})" @@ -176,10 +187,9 @@ def total_dims(self) -> int: did = next(iter(self.eqs[0].keys())) return len(did.mi) - def to_sym(self, fnames: Sequence[str] | None = None) -> list[sp.Expr]: - from sumpy.symbolic import Function, make_sym_vector - x = list(make_sym_vector("x", self.dim)) - x += list(make_sym_vector("t", self.total_dims - self.dim)) + def to_sym(self, fnames: Sequence[str] | None = None) -> list[sym.Expr]: + x: list[sym.Expr] = list(sym.make_sym_vector("x", self.dim)) + x.extend(sym.make_sym_vector("t", self.total_dims - self.dim)) if fnames is None: noutputs = 0 @@ -188,45 +198,52 @@ def to_sym(self, fnames: Sequence[str] | None = None) -> list[sp.Expr]: noutputs = max(noutputs, deriv_ident.vec_idx) fnames = [f"f{i}" for i in range(noutputs+1)] - funcs = [Function(fname)(*x) for fname in fnames] + funcs = [sym.Function(fname)(*x) for fname in fnames] - res = [] + res: list[sym.Expr] = [] for eq in self.eqs: - sym_eq: sp.Expr = sp.sympify(0) + sym_eq: sym.Expr = sym.sympify(0) for deriv_ident, coeff in eq.items(): expr = funcs[deriv_ident.vec_idx] for i, val in enumerate(deriv_ident.mi): for _ in range(val): expr = expr.diff(x[i]) + sym_eq += expr * coeff + res.append(sym_eq) + return res def convert_module_to_matrix( - module: Sequence[sp_modules.FreeModuleElement], - generators: Sequence[sp.Expr] - ) -> sp.Matrix: - import sympy + module: Sequence[FreeModuleElement], + generators: Sequence[sym.Expr] + ) -> sym.Matrix: + import sympy as sp + # poly is a sympy DMP (dense multi-variate polynomial) # type and we convert it to a sympy expression because # sympy matrices with polynomial entries are not supported. # see https://github.com/sympy/sympy/issues/21497 - return sympy.Matrix([[sympy.Poly(poly.to_dict(), *generators, - domain=sympy.EX).as_expr() for poly in ideal.data] for ideal in module]) + return sp.Matrix([ + [sp.Poly(poly.to_dict(), *generators, domain=sp.EX).as_expr() + for poly in ideal.data] + for ideal in module]) @memoize def _get_all_scalar_pdes(pde: LinearPDESystemOperator) -> list[LinearPDESystemOperator]: - import sympy + import sympy as sp from sympy.polys.orderings import grevlex - gens = [sympy.symbols(f"_x{i}") for i in range(pde.dim)] - gens += [sympy.symbols(f"_t{i}") for i in range(pde.total_dims - pde.dim)] + + gens = [sp.Symbol(f"_x{i}") for i in range(pde.dim)] + gens += [sp.Symbol(f"_t{i}") for i in range(pde.total_dims - pde.dim)] max_vec_idx = max(deriv_ident.vec_idx for eq in pde.eqs for deriv_ident in eq) - pde_system_mat = sympy.zeros(len(pde.eqs), max_vec_idx + 1) + pde_system_mat = sp.zeros(len(pde.eqs), max_vec_idx + 1) for row, eq in enumerate(pde.eqs): for deriv_ident, coeff in eq.items(): deriv_as_poly = 1 @@ -234,12 +251,12 @@ def _get_all_scalar_pdes(pde: LinearPDESystemOperator) -> list[LinearPDESystemOp deriv_as_poly *= gens[i]**val pde_system_mat[row, deriv_ident.vec_idx] += coeff * deriv_as_poly - ring = sympy.EX.old_poly_ring(*gens, order=grevlex) - column_ideals = [ring.free_module(1).submodule(*pde_system_mat[:, i].tolist(), - order=grevlex) - for i in range(pde_system_mat.shape[1])] + ring = sp.EX.old_poly_ring(*gens, order=grevlex) + column_ideals = [ + ring.free_module(1).submodule(*pde_system_mat[:, i].tolist(), order=grevlex) + for i in range(pde_system_mat.shape[1]) + ] column_syzygy_modules = [ideal.syzygy_module() for ideal in column_ideals] - ncols = len(column_syzygy_modules) # For each column i, we need to get the intersection of all the syzygy modules @@ -250,15 +267,13 @@ def _get_all_scalar_pdes(pde: LinearPDESystemOperator) -> list[LinearPDESystemOp # for each column we calculate the intersection of the left modules and the # right modules. This requires only $3*(n-2)$ work. - def intersect( - a: sp_modules.SubModulePolyRing, - b: sp_modules.SubModulePolyRing, - ) -> sp_modules.SubModulePolyRing: + def intersect(a: SubModulePolyRing, b: SubModulePolyRing) -> SubModulePolyRing: return a.intersect(b) - left_intersections = list(accumulate(column_syzygy_modules, func=intersect)) - right_intersections = list(reversed(list(accumulate(reversed( - column_syzygy_modules), func=intersect)))) + left_intersections = list( + accumulate(column_syzygy_modules, func=intersect)) + right_intersections = list(reversed(list( + accumulate(reversed(column_syzygy_modules), func=intersect)))) # At the end, calculate the intersection of the left modules and right modules # and calculate a groebner basis for it. @@ -272,27 +287,30 @@ def intersect( # column. scalar_pdes_vec = [ (convert_module_to_matrix(module_intersections[i]._groebner_vec(), - gens) * pde_system_mat)[:, i] + gens) * pde_system_mat)[:, i] for i in range(ncols) ] - results = [] + results: list[LinearPDESystemOperator] = [] for col in range(ncols): - scalar_pde_polys = [sympy.Poly(pde, *gens, domain=sympy.EX) for - pde in scalar_pdes_vec[col]] + scalar_pde_polys = [sp.Poly(pde, *gens, domain=sp.EX) + for pde in scalar_pdes_vec[col]] scalar_pdes = [pde for pde in scalar_pde_polys if pde.degree() > 0] scalar_pde = min(scalar_pdes, key=lambda x: x.degree()).monic() + pde_dict = { - DerivativeIdentifier(mi, 0): sym.sympify(coeff.as_expr().simplify()) for - (mi, coeff) in zip(scalar_pde.monoms(), scalar_pde.coeffs(), strict=True) + DerivativeIdentifier(mi, 0): sym.sympify(coeff.as_expr().simplify()) + for (mi, coeff) in zip(scalar_pde.monoms(), + scalar_pde.coeffs(), strict=True) } results.append(LinearPDESystemOperator(pde.dim, (constantdict(pde_dict),))) return results -def as_scalar_pde(pde: LinearPDESystemOperator, comp_idx: int) \ - -> LinearPDESystemOperator: +def as_scalar_pde( + pde: LinearPDESystemOperator, comp_idx: int, + ) -> LinearPDESystemOperator: r""" Returns a scalar PDE that is satisfied by the *comp_idx* component of *pde*. @@ -305,14 +323,16 @@ def as_scalar_pde(pde: LinearPDESystemOperator, comp_idx: int) \ eg: .. math:: - \frac{\partial^2 u}{\partial x^2} + \ + + \frac{\partial^2 u}{\partial x^2} + 2 \frac{\partial^2 v}{\partial x y} = 0 \\ - 3 \frac{\partial^2 u}{\partial y^2} + \ + 3 \frac{\partial^2 u}{\partial y^2} + \frac{\partial^2 v}{\partial x^2} = 0 - is converted into, + is converted into .. math:: + \begin{bmatrix} x^2 & 2xy \\ 2y^2 & x^2 @@ -350,7 +370,7 @@ def as_scalar_pde(pde: LinearPDESystemOperator, comp_idx: int) \ :arg comp_idx: the index of the component of the PDE solution vector for which a scalar PDE is requested. """ - indices = set() + indices: set[int] = set() for eq in pde.eqs: for deriv_ident in eq: indices.add(deriv_ident.vec_idx) @@ -364,8 +384,9 @@ def as_scalar_pde(pde: LinearPDESystemOperator, comp_idx: int) \ def laplacian(diff_op: LinearPDESystemOperator) -> LinearPDESystemOperator: dim = diff_op.dim - empty: tuple[Mapping[DerivativeIdentifier, sp.Expr], ...] = ( + empty: tuple[Mapping[DerivativeIdentifier, sym.Expr], ...] = ( (constantdict(),) * len(diff_op.eqs)) + res = LinearPDESystemOperator(dim, empty) for j in range(dim): mi = [0]*diff_op.total_dims @@ -377,19 +398,25 @@ def laplacian(diff_op: LinearPDESystemOperator) -> LinearPDESystemOperator: def diff( diff_op: LinearPDESystemOperator, mi: tuple[int, ...] ) -> LinearPDESystemOperator: - eqs: list[Mapping[DerivativeIdentifier, sp.Expr]] = [] + from sumpy.tools import add_mi + + eqs: list[Mapping[DerivativeIdentifier, sym.Expr]] = [] for eq in diff_op.eqs: - res = {} + res: Mapping[DerivativeIdentifier, sym.Expr] = {} for deriv_ident, v in eq.items(): new_mi = add_mi(deriv_ident.mi, mi) res[DerivativeIdentifier(new_mi, deriv_ident.vec_idx)] = v + eqs.append(constantdict(res)) return LinearPDESystemOperator(diff_op.dim, tuple(eqs)) def divergence(diff_op: LinearPDESystemOperator) -> LinearPDESystemOperator: - assert len(diff_op.eqs) == diff_op.dim + if len(diff_op.eqs) != diff_op.dim: + raise ValueError( + "number of equations does not match system dimension: " + f"got {len(diff_op.eqs)} equations for {diff_op.dim}d system") res = LinearPDESystemOperator(diff_op.dim, (constantdict(),)) for i in range(diff_op.dim): @@ -401,61 +428,80 @@ def divergence(diff_op: LinearPDESystemOperator) -> LinearPDESystemOperator: def gradient(diff_op: LinearPDESystemOperator) -> LinearPDESystemOperator: - assert len(diff_op.eqs) == 1 - eqs = [] + if len(diff_op.eqs) != 1: + raise ValueError( + f"can only take gradient of scalar system: got {len(diff_op.eqs)}d") + + eqs: list[Mapping[DerivativeIdentifier, sym.Expr]] = [] dim = diff_op.dim for i in range(dim): mi = [0]*diff_op.total_dims mi[i] = 1 eqs.append(diff(diff_op, tuple(mi)).eqs[0]) + return LinearPDESystemOperator(dim, tuple(eqs)) def curl(diff_op: LinearPDESystemOperator) -> LinearPDESystemOperator: - assert len(diff_op.eqs) == 3 - assert diff_op.dim == 3 - eqs = [] - mis = [] + if len(diff_op.eqs) != diff_op.dim: + raise ValueError( + "number of equations does not match system dimension: " + f"got {len(diff_op.eqs)} equations for {diff_op.dim}d system") + + if diff_op.dim != 3: + raise ValueError(f"can only take curl of 3d system: got {diff_op.dim}d") + + eqs: list[Mapping[DerivativeIdentifier, sym.Expr]] = [] + mis: list[MultiIndex] = [] for i in range(3): mi = [0]*diff_op.total_dims mi[i] = 1 mis.append(tuple(mi)) for i in range(3): - new_pde = diff(diff_op[(i+2) % 3], mis[(i+1) % 3]) - \ - diff(diff_op[(i+1) % 3], mis[(i+2) % 3]) + new_pde = ( + diff(diff_op[(i+2) % 3], mis[(i+1) % 3]) + - diff(diff_op[(i+1) % 3], mis[(i+2) % 3])) eqs.append(new_pde.eqs[0]) return LinearPDESystemOperator(diff_op.dim, tuple(eqs)) def concat(*ops: LinearPDESystemOperator) -> LinearPDESystemOperator: - assert len(ops) >= 1 + if not ops: + raise TypeError("concat() takes at least 1 positional argument (0 given)") + + if len(ops) == 1: + return ops[0] + dim = ops[0].dim - for op in ops: - assert op.dim == dim + if not all(op.dim == dim for op in ops): + raise ValueError(f"operators must have the same dimension (expected {dim}d)") + eqs = list(ops[0].eqs) for op in ops[1:]: eqs.extend(list(op.eqs)) + return LinearPDESystemOperator(dim, tuple(eqs)) def make_identity_diff_op( - ninput: int, noutput: int = 1, time_dependent: bool = False + ninput: int, noutput: int = 1, *, + time_dependent: bool = False ) -> LinearPDESystemOperator: """ - Returns the identity as a linear PDE system operator. - if *include_time* is true, then the last dimension of the - multi-index is time. - - :arg ninput: number of spatial variables of the function - :arg noutput: number of output values of function - :arg time_dependent: include time as a dimension + :arg ninput: number of spatial variables of the function. + :arg noutput: number of output values of function. + :arg time_dependent: include time as a dimension. + :returns: the identity as a linear PDE system operator. If *time_dependent* + is *True*, then the last dimension of the multi-index is time. """ + if time_dependent: # noqa: SIM108 mi = tuple([0]*(ninput + 1)) else: mi = tuple([0]*ninput) + return LinearPDESystemOperator(ninput, tuple( - constantdict({DerivativeIdentifier(mi, i): sp.sympify(1)}) + constantdict({DerivativeIdentifier(mi, i): sym.sympify(1)}) for i in range(noutput))) diff --git a/sumpy/expansion/level_to_order.py b/sumpy/expansion/level_to_order.py index 76a3a05f..0c975da6 100644 --- a/sumpy/expansion/level_to_order.py +++ b/sumpy/expansion/level_to_order.py @@ -24,39 +24,58 @@ """ __doc__ = """ +.. autoclass:: TreeLike + :members: + :undoc-members: + .. autoclass:: FMMLibExpansionOrderFinder .. autoclass:: SimpleExpansionOrderFinder """ import math +from dataclasses import dataclass +from typing import TYPE_CHECKING, Protocol, cast import numpy as np +if TYPE_CHECKING: + from collections.abc import Sequence + + import sumpy.symbolic as sym + from sumpy.kernel import Kernel + + +class TreeLike(Protocol): + dimensions: int + root_extent: float + stick_out_factor: float + + +@dataclass(frozen=True) class FMMLibExpansionOrderFinder: r"""Return expansion orders that meet the tolerance for a given level using routines wrapped from ``pyfmmlib``. - .. automethod:: __init__ + .. autoattribute:: tol + .. autoattribute:: extra_order + .. automethod:: __call__ """ - def __init__(self, tol, extra_order=0): - """ - :arg tol: error tolerance - :arg extra_order: order increase to accommodate, say, the taking of - derivatives of the FMM expansions. - """ - self.tol = tol - self.extra_order = extra_order - - def __call__(self, kernel, kernel_args, tree, level): - from pyfmmlib import ( - h2dterms, - h3dterms, - l2dterms, - l3dterms, - ) + tol: float + """Error tolerance.""" + extra_order: int = 0 + """Order increase to accommodate, say, the taking of derivatives of the FMM + expansion. + """ + + def __call__(self, + kernel: Kernel, + kernel_args: dict[str, sym.Expr] | Sequence[tuple[str, sym.Expr]], + tree: TreeLike, + level: int) -> int: + from pyfmmlib import h2dterms, h3dterms, l2dterms, l3dterms from sumpy.kernel import HelmholtzKernel, LaplaceKernel @@ -76,7 +95,7 @@ def __call__(self, kernel, kernel_args, tree, level): elif isinstance(kernel, HelmholtzKernel): helmholtz_k = dict(kernel_args)[kernel.helmholtz_k_name] - size = tree.root_extent / 2 ** level + size = cast("int", tree.root_extent / 2 ** level) if tree.dimensions == 2: nterms, ier = h2dterms(size, helmholtz_k, self.tol) @@ -97,6 +116,7 @@ def __call__(self, kernel, kernel_args, tree, level): return nterms + self.extra_order +@dataclass(frozen=True) class SimpleExpansionOrderFinder: r""" This models the Laplace truncation error as: @@ -109,42 +129,53 @@ class SimpleExpansionOrderFinder: .. math:: - C_{\text{helm}} \frac 1{p!} - \left(C_{\text{helmscale}} \cdot \frac{hk}{2\pi}\right)^{p+1}, + C_{\text{helm}} \frac{1}{p!} + \left(C_{\text{helmscale}} \cdot \frac{hk}{2\pi}\right)^{p+1}, where :math:`d` is the number of dimensions, :math:`p` is the expansion order, :math:`h` is the box size, and :math:`k` is the wave number. - .. automethod:: __init__ + .. autoattribute:: tol + .. autoattribute:: extra_order + .. autoattribute:: err_const_laplace + .. autoattribute:: err_const_helmholtz + .. autoattribute:: scaling_const_helmholtz + .. automethod:: __call__ """ - def __init__(self, tol, err_const_laplace=0.01, err_const_helmholtz=100, - scaling_const_helmholtz=4, - extra_order=1): - """ - :arg extra_order: order increase to accommodate, say, the taking of - derivatives of the FMM expansions. - """ - self.tol = tol - - self.err_const_laplace = err_const_laplace - self.err_const_helmholtz = err_const_helmholtz - self.scaling_const_helmholtz = scaling_const_helmholtz + tol: float + """Error tolerance.""" + + err_const_laplace: float = 0.01 + """Constant :math:`C_{\text{lap}}` used in the Laplace truncation error.""" + err_const_helmholtz: float = 100.0 + """Constant :math:`C_{\text{helm}}` used in the Helmholtz truncation error.""" + scaling_const_helmholtz: float = 4.0 + """Constant :math:`C_{\text{helmscale}}` used in the Helmholtz truncation error. + This can be used to tweak the scaling with respect to the box size or wave + number. + """ - self.extra_order = extra_order + extra_order: int = 1 + """Order increase to accommodate, say, the taking of derivatives of the FMM + expansion. + """ - def __call__(self, kernel, kernel_args, tree, level): + def __call__(self, + kernel: Kernel, + kernel_args: dict[str, sym.Expr] | Sequence[tuple[str, sym.Expr]], + tree: TreeLike, + level: int) -> int: from sumpy.kernel import HelmholtzKernel, LaplaceKernel assert isinstance(kernel, LaplaceKernel | HelmholtzKernel) - laplace_order = int(np.ceil( - (np.log(self.tol) - np.log(self.err_const_laplace)) + laplace_order = math.ceil( + (math.log(self.tol) - math.log(self.err_const_laplace)) / - np.log( - np.sqrt(tree.dimensions)/3 - ) - 1)) + math.log(math.sqrt(tree.dimensions) / 3.0) + - 1) if isinstance(kernel, HelmholtzKernel): helmholtz_k = dict(kernel_args)[kernel.helmholtz_k_name] diff --git a/sumpy/expansion/loopy.py b/sumpy/expansion/loopy.py index 79b28bee..b662808c 100644 --- a/sumpy/expansion/loopy.py +++ b/sumpy/expansion/loopy.py @@ -29,7 +29,7 @@ import numpy as np import loopy as lp -import pymbolic +import pymbolic.primitives as prim import sumpy.symbolic as sym from sumpy.assignment_collection import SymbolicAssignmentCollection @@ -39,6 +39,8 @@ if TYPE_CHECKING: from collections.abc import Sequence + from pymbolic.typing import ArithmeticExpression + from sumpy.expansion import ExpansionBase from sumpy.kernel import Kernel @@ -49,27 +51,30 @@ def make_e2p_loopy_kernel( expansion: ExpansionBase, kernels: Sequence[Kernel]) -> lp.TranslationUnit: """ - This is a helper function to create a loopy kernel for multipole/local - evaluation. This function uses symbolic expressions given by the expansion class, - converts them to pymbolic expressions and generates a loopy - kernel. Note that the loopy kernel returned has lots of expressions in it and - takes a long time. Therefore, this function should be used only as a fallback - when there is no "loop-y" kernel to evaluate the expansion. + A helper function that creates a :mod:`loopy` kernel for multipole/local evaluation. + + This function uses symbolic expressions given by the expansion class, + converts them to :mod:`pymbolic` expressions and generates a :mod:`loopy` + kernel. Note that the :mod:`loopy` kernel returned has lots of expressions + in it and (likely) takes a long time. Therefore, this function should be + used only as a fallback when there is no "loop-y" kernel to evaluate the + expansion. """ - dim = expansion.dim - bvec = sym.make_sym_vector("b", dim) + sac = SymbolicAssignmentCollection() + + dim = expansion.dim ncoeffs = len(expansion.get_coefficient_identifiers()) + bvec = sym.make_sym_vector("b", dim) rscale = sym.Symbol("rscale") - sac = SymbolicAssignmentCollection() - domains = [ "{[idim]: 0<=idim lp.TranslationUnit: + expansion: ExpansionBase, + kernels: Sequence[Kernel], + strength_usage: Sequence[int], + nstrengths: int) -> lp.TranslationUnit: """ - This is a helper function to create a loopy kernel for multipole/local - expression. This function uses symbolic expressions given by the expansion - class, converts them to pymbolic expressions and generates a loopy - kernel. Note that the loopy kernel returned has lots of expressions in it and - takes a long time. Therefore, this function should be used only as a fallback - when there is no "loop-y" kernel to evaluate the expansion. + A helper function that creates a :mod:`loopy` kernel for multipole/local evaluation. + + This function uses symbolic expressions given by the expansion class, + converts them to :mod:`pymbolic` expressions and generates a :mod:`loopy` + kernel. Note that the :mod:`loopy` kernel returned has lots of expressions + in it and (likely) takes a long time. Therefore, this function should be + used only as a fallback when there is no "loop-y" kernel to evaluate the + expansion. """ - dim = expansion.dim + sac = SymbolicAssignmentCollection() - avec = sym.make_sym_vector("a", dim) + dim = expansion.dim ncoeffs = len(expansion.get_coefficient_identifiers()) + avec = sym.make_sym_vector("a", dim) rscale = sym.Symbol("rscale") - sac = SymbolicAssignmentCollection() - domains = [ "{[idim]: 0<=idim None: # These are intended to be stateless. - raise AttributeError(f"{type(self)} is stateless and does not permit " - "attribute modification.") + raise AttributeError( + f"{type(self)} is stateless and does not permit attribute modification") - def __eq__(self, other): + @override + def __eq__(self, other: object) -> bool: return type(self) is type(other) @abstractmethod @@ -260,46 +264,64 @@ def translation_classes_dependent_ndata(self, """ return 0 - def loopy_translation_classes_dependent_data(self, tgt_expansion, - src_expansion, result_dtype): - """Return a :mod:`loopy` kernel that calculates the data described by - :func:`~sumpy.expansion.m2l.M2LTranslationBase.translation_classes_dependent_data`. - :arg result_dtype: The :mod:`numpy` type of the result. + def loopy_translation_classes_dependent_data(self, + tgt_expansion: LocalExpansionBase, + src_expansion: MultipoleExpansionBase, + result_dtype: DTypeLike) -> lp.TranslationUnit: + """ + :arg result_dtype: the :mod:`numpy` type of the result. + :returns: a :mod:`loopy` kernel that calculates the data described by + :func:`~sumpy.expansion.m2l.M2LTranslationBase.translation_classes_dependent_data`. """ - return loopy_translation_classes_dependent_data(tgt_expansion, - src_expansion, result_dtype) + return loopy_translation_classes_dependent_data( + tgt_expansion, src_expansion, result_dtype + ) @abstractmethod - def preprocess_multipole_exprs(self, tgt_expansion, src_expansion, - src_coeff_exprs, sac, src_rscale): - """Return the preprocessed multipole expansion for an optimized M2L. + def preprocess_multipole_exprs(self, + tgt_expansion: LocalExpansionBase, + src_expansion: MultipoleExpansionBase, + src_coeff_exprs: Sequence[sym.Expr], + sac: SymbolicAssignmentCollection | None, + src_rscale: sym.Expr, + ) -> Sequence[sym.Expr]: + """Preprocess the multipole expansion for an optimized M2L. + Preprocessing happens once per source box before M2L translation is done. - These expressions are used in a separate :mod:`loopy` kernel - to avoid having to process for each target and source box pair. - When FFT is turned on, the output expressions are assumed to be - transformed into Fourier space at the end by the caller. - When FFT is turned off, the output expressions are equal to the multipole - expansion coefficients with zeros added to make the M2L computation a - circulant matvec. + These expressions are used in a separate :mod:`loopy` kernel to avoid + having to process for each target and source box pair. When FFT is + turned on, the output expressions are assumed to be transformed into + Fourier space at the end by the caller. When FFT is turned off, the + output expressions are equal to the multipole expansion coefficients + with zeros added to make the M2L computation a circulant matrix. """ - def preprocess_multipole_nexprs(self, tgt_expansion, src_expansion): + def preprocess_multipole_nexprs(self, + tgt_expansion: LocalExpansionBase, + src_expansion: MultipoleExpansionBase, + ) -> int: """Return the number of expressions returned by :func:`~sumpy.expansion.m2l.M2LTranslationBase.preprocess_multipole_exprs`. + This method exists because calculating the number of expressions using the above method might be costly and it cannot be memoized due to it having side effects through the argument *sac*. """ # For all use-cases we have right now, this is equal to the number of # translation classes dependent exprs. Use that as a default. - return self.translation_classes_dependent_ndata(tgt_expansion, - src_expansion) + return self.translation_classes_dependent_ndata(tgt_expansion, src_expansion) @abstractmethod - def postprocess_local_exprs(self, tgt_expansion, src_expansion, m2l_result, - src_rscale, tgt_rscale, sac): - """Return postprocessed local expansion for an optimized M2L. + def postprocess_local_exprs(self, + tgt_expansion: LocalExpansionBase, + src_expansion: MultipoleExpansionBase, + m2l_result: Sequence[sym.Expr], + src_rscale: sym.Expr, + tgt_rscale: sym.Expr, + sac: SymbolicAssignmentCollection | None = None) -> Sequence[sym.Expr]: + """Postprocess the local expansion for an optimized M2L. + Postprocessing happens once per target box just after the M2L translation is done and before storing the expansion coefficients for the local expansion. @@ -308,22 +330,29 @@ def postprocess_local_exprs(self, tgt_expansion, src_expansion, m2l_result, transformed from Fourier space back to the original space by the caller. """ - def postprocess_local_nexprs(self, tgt_expansion, src_expansion): + def postprocess_local_nexprs(self, + tgt_expansion: LocalExpansionBase, + src_expansion: MultipoleExpansionBase + ) -> int: """Return the number of expressions given as input to :func:`~sumpy.expansion.m2l.M2LTranslationBase.postprocess_local_exprs`. + This method exists because calculating the number of expressions using the above method might be costly and it cannot be memoized due to it having side effects through the argument *sac*. """ # For all use-cases we have right now, this is equal to the number of # translation classes dependent exprs. Use that as a default. - return self.translation_classes_dependent_ndata(tgt_expansion, - src_expansion) + return self.translation_classes_dependent_ndata(tgt_expansion, src_expansion) - def update_persistent_hash(self, key_hash, key_builder): + def update_persistent_hash(self, key_hash: Hash, key_builder: KeyBuilder) -> None: key_hash.update(type(self).__name__.encode("utf8")) - def optimize_loopy_kernel(self, knl, tgt_expansion, src_expansion): + def optimize_loopy_kernel(self, + knl: lp.TranslationUnit, + tgt_expansion: LocalExpansionBase, + src_expansion: MultipoleExpansionBase, + ) -> lp.TranslationUnit: return lp.tag_inames(knl, {"itgt_box": "g.0"}) @@ -332,9 +361,17 @@ def optimize_loopy_kernel(self, knl, tgt_expansion, src_expansion): # {{{ VolumeTaylorM2LTranslation class VolumeTaylorM2LTranslation(M2LTranslationBase): - def translate(self, tgt_expansion, src_expansion, src_coeff_exprs, src_rscale, - dvec, tgt_rscale, sac=None, translation_classes_dependent_data=None): - + @override + def translate(self, + tgt_expansion: LocalExpansionBase, + src_expansion: MultipoleExpansionBase, + src_coeff_exprs: Sequence[sym.Expr], + src_rscale: sym.Expr, + dvec: sym.Matrix, + tgt_rscale: sym.Expr, + sac: SymbolicAssignmentCollection | None = None, + translation_classes_dependent_data: ( + TranslationClassesDepData | None) = None) -> Sequence[sym.Expr]: if translation_classes_dependent_data: derivatives = translation_classes_dependent_data else: @@ -347,25 +384,28 @@ def translate(self, tgt_expansion, src_expansion, src_coeff_exprs, src_rscale, # Returns a big symbolic sum of matrix entries # (FIXME? Though this is just the correctness-checking # fallback for the FFT anyhow) - result = matvec_toeplitz_upper_triangular(src_coeff_exprs, - derivatives) - + result = matvec_toeplitz_upper_triangular(src_coeff_exprs, derivatives) result = self.postprocess_local_exprs(tgt_expansion, src_expansion, result, src_rscale, tgt_rscale, sac) return result - def translation_classes_dependent_ndata(self, tgt_expansion, src_expansion): + @override + def translation_classes_dependent_ndata(self, + tgt_expansion: LocalExpansionBase, + src_expansion: MultipoleExpansionBase, + ) -> int: """Returns number of expressions in M2L global precomputation step. """ - mis_with_dummy_rows, _, _ = \ - self._translation_classes_dependent_data_mis(tgt_expansion, - src_expansion) + mis_with_dummy_rows, _, _ = self._translation_classes_dependent_data_mis( + tgt_expansion, src_expansion) return len(mis_with_dummy_rows) - def _translation_classes_dependent_data_mis(self, tgt_expansion, - src_expansion): + def _translation_classes_dependent_data_mis(self, + tgt_expansion: LocalExpansionBase, + src_expansion: MultipoleExpansionBase, + ) -> tuple[Sequence[MultiIndex], Sequence[MultiIndex], MultiIndex]: """We would like to compute the M2L by way of a circulant matrix below. To get the matrix representing the M2L into circulant form, a certain numbering of rows and columns (as identified by multi-indices) is @@ -393,10 +433,8 @@ def _translation_classes_dependent_data_mis(self, tgt_expansion, # element-wise maximum of target multi-indices. max_mi = [0]*dim for i in range(dim): - max_mi[i] = max(mi[i] for mi in - src_expansion.get_coefficient_identifiers()) - max_mi[i] += max(mi[i] for mi in - tgt_expansion.get_coefficient_identifiers()) + max_mi[i] = max(mi[i] for mi in src_expansion.get_coefficient_identifiers()) + max_mi[i] += max(mi[i] for mi in tgt_expansion.get_coefficient_identifiers()) # noqa: E501 # These are the multi-indices representing the rows # in the circulant matrix. Note that to get the circulant @@ -410,7 +448,8 @@ def _translation_classes_dependent_data_mis(self, tgt_expansion, # These are the multi-indices representing the rows # in the M2L translation matrix without the additional # multi-indices in the circulant matrix - needed_vector_terms = set() + needed_vector_terms: set[MultiIndex] = set() + # For eg: 2D full Taylor Laplace, we only need kernel derivatives # (n1+n2, m1+m2), n1+m1<=p, n2+m2<=p for tgt_deriv in tgt_expansion.get_coefficient_identifiers(): @@ -419,11 +458,16 @@ def _translation_classes_dependent_data_mis(self, tgt_expansion, if needed not in needed_vector_terms: needed_vector_terms.add(needed) - return circulant_matrix_mis, tuple(needed_vector_terms), max_mi - - def translation_classes_dependent_data(self, tgt_expansion, src_expansion, - src_rscale, dvec, sac): + return circulant_matrix_mis, tuple(needed_vector_terms), tuple(max_mi) + @override + def translation_classes_dependent_data(self, + tgt_expansion: LocalExpansionBase, + src_expansion: MultipoleExpansionBase, + src_rscale: sym.Expr, + dvec: sym.Matrix, + sac: SymbolicAssignmentCollection | None = None, + ) -> TranslationClassesDepData: assert isinstance(tgt_expansion, VolumeTaylorLocalExpansionBase) assert isinstance(src_expansion, VolumeTaylorMultipoleExpansionBase) @@ -445,106 +489,125 @@ def translation_classes_dependent_data(self, tgt_expansion, src_expansion, if not tgt_expansion.use_rscale: src_rscale = sym.sympify(1) - circulant_matrix_mis, needed_vector_terms, max_mi = \ - self._translation_classes_dependent_data_mis(tgt_expansion, - src_expansion) + circulant_matrix_mis, needed_vector_terms, max_mi = ( + self._translation_classes_dependent_data_mis(tgt_expansion, + src_expansion)) - circulant_matrix_ident_to_index = {ident: i for i, ident in - enumerate(circulant_matrix_mis)} + circulant_matrix_ident_to_index = { + ident: i for i, ident in enumerate(circulant_matrix_mis)} # Create a expansion terms wrangler for derivatives up to order # (tgt order)+(src order) including a corresponding reduction matrix # For eg: 2D full Taylor Laplace, this is (n, m), # n+m<=2*p, n<=2*p, m<=2*p - srcplusderiv_terms_wrangler = \ - src_expansion.expansion_terms_wrangler.copy( + srcplusderiv_terms_wrangler = ( + src_expansion.expansion_terms_wrangler.copy( order=tgt_expansion.order + src_expansion.order, - max_mi=tuple(max_mi)) - srcplusderiv_full_coeff_ids = \ - srcplusderiv_terms_wrangler.get_full_coefficient_identifiers() - srcplusderiv_ident_to_index = {ident: i for i, ident in - enumerate(srcplusderiv_full_coeff_ids)} + max_mi=tuple(max_mi))) + srcplusderiv_full_coeff_ids = ( + srcplusderiv_terms_wrangler.get_full_coefficient_identifiers()) + srcplusderiv_ident_to_index = { + ident: i for i, ident in enumerate(srcplusderiv_full_coeff_ids)} # The vector has the kernel derivatives and depends only on the distance # between the two centers taker = src_expansion.kernel.get_derivative_taker(dvec, src_rscale, sac) - vector_stored = [] + vector_stored: list[sym.Expr] = [] + # Calculate the kernel derivatives for the compressed set - for term in \ - srcplusderiv_terms_wrangler.get_coefficient_identifiers(): + for term in srcplusderiv_terms_wrangler.get_coefficient_identifiers(): kernel_deriv = taker.diff(term) vector_stored.append(kernel_deriv) + # Calculate the kernel derivatives for the full set - vector_full = \ + vector_full = ( srcplusderiv_terms_wrangler.get_full_kernel_derivatives_from_stored( - vector_stored, src_rscale) + vector_stored, src_rscale)) for term in srcplusderiv_full_coeff_ids: assert term in needed_vector_terms - vector = [0]*len(needed_vector_terms) + vector: list[sym.Expr] = [sym.sympify(0)] * len(needed_vector_terms) for i, term in enumerate(needed_vector_terms): - vector[i] = add_to_sac(sac, - vector_full[srcplusderiv_ident_to_index[term]]) + vector[i] = add_to_sac(sac, vector_full[srcplusderiv_ident_to_index[term]]) # Add zero values needed to make the translation matrix circulant - derivatives_full = [0]*len(circulant_matrix_mis) + derivatives_full: list[sym.Expr] = [sym.sympify(0)] * len(circulant_matrix_mis) for expr, mi in zip(vector, needed_vector_terms, strict=True): derivatives_full[circulant_matrix_ident_to_index[mi]] = expr - return derivatives_full + return tuple(derivatives_full) - def preprocess_multipole_exprs(self, tgt_expansion, src_expansion, - src_coeff_exprs, sac, src_rscale): - circulant_matrix_mis, _, _ = \ - self._translation_classes_dependent_data_mis(tgt_expansion, - src_expansion) - circulant_matrix_ident_to_index = {ident: i for i, ident in - enumerate(circulant_matrix_mis)} + @override + def preprocess_multipole_exprs(self, + tgt_expansion: LocalExpansionBase, + src_expansion: MultipoleExpansionBase, + src_coeff_exprs: Sequence[sym.Expr], + sac: SymbolicAssignmentCollection | None, + src_rscale: sym.Expr) -> Sequence[sym.Expr]: + circulant_matrix_mis, _, _ = self._translation_classes_dependent_data_mis( + tgt_expansion, src_expansion) + circulant_matrix_ident_to_index = { + ident: i for i, ident in enumerate(circulant_matrix_mis)} # Calculate the input vector for the circulant matrix - input_vector = [0] * len(circulant_matrix_mis) + input_vector: list[sym.Expr] = [sym.sympify(0)] * len(circulant_matrix_mis) for coeff, term in zip( src_coeff_exprs, src_expansion.get_coefficient_identifiers(), strict=True): - input_vector[circulant_matrix_ident_to_index[term]] = \ - add_to_sac(sac, coeff) + input_vector[circulant_matrix_ident_to_index[term]] = add_to_sac(sac, coeff) return input_vector - def preprocess_multipole_nexprs(self, tgt_expansion, src_expansion): - circulant_matrix_mis, _, _ = \ - self._translation_classes_dependent_data_mis(tgt_expansion, - src_expansion) + @override + def preprocess_multipole_nexprs(self, + tgt_expansion: LocalExpansionBase, + src_expansion: MultipoleExpansionBase) -> int: + circulant_matrix_mis, _, _ = self._translation_classes_dependent_data_mis( + tgt_expansion, src_expansion) + return len(circulant_matrix_mis) - def loopy_preprocess_multipole(self, tgt_expansion, src_expansion, - result_dtype): + def loopy_preprocess_multipole(self, + tgt_expansion: LocalExpansionBase, + src_expansion: MultipoleExpansionBase, + result_dtype: DTypeLike, + ) -> tuple[lp.TranslationUnit, Sequence[OptimizationCallable]]: + assert isinstance(tgt_expansion, VolumeTaylorLocalExpansionBase) + assert isinstance(src_expansion, VolumeTaylorMultipoleExpansionBase) - _circulant_matrix_mis, _, max_mi = \ - self._translation_classes_dependent_data_mis(tgt_expansion, - src_expansion) + _, _, max_mi = self._translation_classes_dependent_data_mis( + tgt_expansion, src_expansion) ncoeff_src = len(src_expansion.get_coefficient_identifiers()) - ncoeff_preprocessed = self.preprocess_multipole_nexprs(tgt_expansion, - src_expansion) + ncoeff_preprocessed = self.preprocess_multipole_nexprs( + tgt_expansion, src_expansion) order = src_expansion.order - output_coeffs = pymbolic.var("output_coeffs") - input_coeffs = pymbolic.var("input_coeffs") - output_icoeff = pymbolic.var("output_icoeff") - input_icoeff = pymbolic.var("input_icoeff") - input_coeffs_copy = pymbolic.var("input_coeffs_copy") + output_coeffs = p.Variable("output_coeffs") + input_coeffs = p.Variable("input_coeffs") + output_icoeff = p.Variable("output_icoeff") + input_icoeff = p.Variable("input_icoeff") + input_coeffs_copy = p.Variable("input_coeffs_copy") dim = tgt_expansion.dim - v = [pymbolic.var(f"x{i}") for i in range(dim)] + v = tuple(p.Variable(f"x{i}") for i in range(dim)) + + from sumpy.expansion import ( + FullExpansionTermsWrangler, + LinearPDEBasedExpansionTermsWrangler, + ) wrangler = src_expansion.expansion_terms_wrangler + assert isinstance(wrangler, (FullExpansionTermsWrangler, + LinearPDEBasedExpansionTermsWrangler)) + _, axis_permutation = wrangler._get_mi_ordering_key_and_axis_permutation() slowest_idx = axis_permutation[0] + # max_mi[slowest_idx] = 2*(c - 1) c = max_mi[slowest_idx] // 2 + 1 - noutput_coeffs = c * (2*order + 1) ** (dim - 1) + noutput_coeffs = cast("int", c * (2*order + 1) ** (dim - 1)) domains = [ "{[output_icoeff]: 0<=output_icoeff Sequence[sym.Expr]: + circulant_matrix_mis, _, _ = self._translation_classes_dependent_data_mis( + tgt_expansion, src_expansion) + circulant_matrix_ident_to_index = { + ident: i for i, ident in enumerate(circulant_matrix_mis)} # Filter out the dummy rows and scale them for target rscale_ratio = add_to_sac(sac, tgt_rscale/src_rscale) @@ -631,21 +701,26 @@ def postprocess_local_exprs(self, tgt_expansion, src_expansion, m2l_result, return result - def postprocess_local_nexprs(self, tgt_expansion, src_expansion): - return self.translation_classes_dependent_ndata( - tgt_expansion, src_expansion) + @override + def postprocess_local_nexprs(self, + tgt_expansion: LocalExpansionBase, + src_expansion: MultipoleExpansionBase, + ) -> int: + return self.translation_classes_dependent_ndata(tgt_expansion, src_expansion) - def loopy_postprocess_local(self, tgt_expansion, src_expansion, - result_dtype): - circulant_matrix_mis, _needed_vector_terms, _ = \ - self._translation_classes_dependent_data_mis(tgt_expansion, - src_expansion) - circulant_matrix_ident_to_index = {ident: i for i, ident in - enumerate(circulant_matrix_mis)} + def loopy_postprocess_local(self, + tgt_expansion: LocalExpansionBase, + src_expansion: MultipoleExpansionBase, + result_dtype: DTypeLike, + ) -> tuple[lp.TranslationUnit, Sequence[OptimizationCallable]]: + circulant_matrix_mis, _, _ = self._translation_classes_dependent_data_mis( + tgt_expansion, src_expansion) + circulant_matrix_ident_to_index = { + ident: i for i, ident in enumerate(circulant_matrix_mis)} ncoeff_tgt = len(tgt_expansion.get_coefficient_identifiers()) - ncoeff_before_postprocessed = self.postprocess_local_nexprs(tgt_expansion, - src_expansion) + ncoeff_before_postprocessed = self.postprocess_local_nexprs( + tgt_expansion, src_expansion) order = tgt_expansion.order fixed_parameters = { @@ -660,9 +735,9 @@ def loopy_postprocess_local(self, tgt_expansion, src_expansion, insns = ["<> rscale_ratio = tgt_rscale / src_rscale {id=rscale_ratio}"] - rscale_arr = pymbolic.var("rscale_arr") - rscale_ratio = pymbolic.var("rscale_ratio") - iorder = pymbolic.var("iorder") + rscale_arr = p.Variable("rscale_arr") + rscale_ratio = p.Variable("rscale_ratio") + iorder = p.Variable("iorder") insns += [ lp.Assignment( @@ -679,18 +754,17 @@ def loopy_postprocess_local(self, tgt_expansion, src_expansion, ), ] - if self.use_fft and result_dtype in \ - (np.float64, np.float32): - result_func = pymbolic.var("real") + if self.use_fft and result_dtype in (np.float64, np.float32): + result_func = p.Variable("real") else: - def result_func(x): + def result_func(x: ArithmeticExpression) -> ArithmeticExpression: return x - output_coeffs = pymbolic.var("output_coeffs") - input_coeffs = pymbolic.var("input_coeffs") - src_idx_sym = pymbolic.var("src_idx") - rscale_idx_arr_sym = pymbolic.var("rscale_idx_arr") - output_icoeff_sym = pymbolic.var("output_icoeff") + output_coeffs = p.Variable("output_coeffs") + input_coeffs = p.Variable("input_coeffs") + src_idx_sym = p.Variable("src_idx") + rscale_idx_arr_sym = p.Variable("rscale_idx_arr") + output_icoeff_sym = p.Variable("output_icoeff") src_idx = np.full(ncoeff_tgt, -1, dtype=np.int32) for output_icoeff, term in enumerate( @@ -712,7 +786,8 @@ def result_func(x): insns += [ lp.Assignment( assignee=output_coeffs[output_icoeff_sym], - expression=(result_func(input_coeffs[src_idx_sym[output_icoeff_sym]]) + expression=( + result_func(input_coeffs[src_idx_sym[output_icoeff_sym]]) * rscale_arr[rscale_idx_arr_sym[output_icoeff_sym]]), id="coeff_insn", happens_after=frozenset(["rscale_arr"]), @@ -723,16 +798,13 @@ def result_func(x): "{[output_icoeff]: 0<=output_icoeff Sequence[sym.Expr]: assert translation_classes_dependent_data + derivatives = translation_classes_dependent_data # Returns a big symbolic sum of matrix entries # (FIXME? Though this is just the correctness-checking # fallback for the FFT anyhow) - result = matvec_toeplitz_upper_triangular(src_coeff_exprs, - derivatives) + result = matvec_toeplitz_upper_triangular(src_coeff_exprs, derivatives) + return result - def loopy_translate(self, tgt_expansion, src_expansion): - ncoeff_src = self.preprocess_multipole_nexprs(tgt_expansion, - src_expansion) + @override + def loopy_translate(self, + tgt_expansion: LocalExpansionBase, + src_expansion: MultipoleExpansionBase) -> lp.TranslationUnit: + ncoeff_src = self.preprocess_multipole_nexprs(tgt_expansion, src_expansion) ncoeff_tgt = self.postprocess_local_nexprs(tgt_expansion, src_expansion) - icoeff_src = pymbolic.var("icoeff_src") - icoeff_tgt = pymbolic.var("icoeff_tgt") + + icoeff_src = p.Variable("icoeff_src") + icoeff_tgt = p.Variable("icoeff_tgt") domains = [f"{{[icoeff_tgt]: 0<=icoeff_tgt<{ncoeff_tgt} }}"] - tgt_coeffs = pymbolic.var("tgt_coeffs") - src_coeffs = pymbolic.var("src_coeffs") - translation_classes_dependent_data = pymbolic.var("data") + tgt_coeffs = p.Variable("tgt_coeffs") + src_coeffs = p.Variable("src_coeffs") + translation_classes_dependent_data = p.Variable("data") if self.use_fft: - expr = src_coeffs[icoeff_tgt] \ - * translation_classes_dependent_data[icoeff_tgt] + expr = src_coeffs[icoeff_tgt]*translation_classes_dependent_data[icoeff_tgt] else: toeplitz_first_row = src_coeffs[icoeff_src-icoeff_tgt] vector = translation_classes_dependent_data[icoeff_src] expr = toeplitz_first_row * vector - domains.append( - f"{{[icoeff_src]: icoeff_tgt<=icoeff_src<{ncoeff_src} }}") - expr = src_coeffs[icoeff_tgt] \ - * translation_classes_dependent_data[icoeff_tgt] + domains.append(f"{{[icoeff_src]: icoeff_tgt<=icoeff_src<{ncoeff_src} }}") + + expr = src_coeffs[icoeff_tgt] * translation_classes_dependent_data[icoeff_tgt] insns = [ lp.Assignment( @@ -804,17 +895,23 @@ def loopy_translate(self, tgt_expansion, src_expansion): expression=tgt_coeffs[icoeff_tgt] + expr ), ] - return lp.make_function(domains, insns, - kernel_data=[ - lp.GlobalArg("tgt_coeffs", shape=lp.auto, is_input=True, - is_output=True), - lp.GlobalArg("src_coeffs, data", - shape=lp.auto, is_input=True, is_output=False), - lp.ValueArg("src_rscale, tgt_rscale", is_input=True), - ...], - name="e2e", - lang_version=lp.MOST_RECENT_LANGUAGE_VERSION, - ) + + knl = lp.make_function( + domains, + insns, + kernel_data=[ + lp.GlobalArg("tgt_coeffs", + shape=lp.auto, is_input=True, is_output=True), + lp.GlobalArg("src_coeffs, data", + shape=lp.auto, is_input=True, is_output=False), + lp.ValueArg("src_rscale, tgt_rscale", is_input=True), + ...], + name="e2e", + lang_version=lp.MOST_RECENT_LANGUAGE_VERSION, + ) + + return knl + # }}} VolumeTaylorM2LWithPreprocessedMultipoles @@ -824,24 +921,34 @@ def loopy_translate(self, tgt_expansion, src_expansion): class VolumeTaylorM2LWithFFT(VolumeTaylorM2LWithPreprocessedMultipoles): use_fft: ClassVar[bool] = True + @override def translate(self, - tgt_expansion: VolumeTaylorLocalExpansion, - src_expansion: VolumeTaylorMultipoleExpansion, + tgt_expansion: LocalExpansionBase, + src_expansion: MultipoleExpansionBase, src_coeff_exprs: Sequence[sym.Expr], src_rscale: sym.Expr, dvec: sym.Matrix, tgt_rscale: sym.Expr, sac: SymbolicAssignmentCollection | None = None, - translation_classes_dependent_data=None): - + translation_classes_dependent_data: ( + TranslationClassesDepData | None) = None, + ) -> Sequence[sym.Expr]: assert translation_classes_dependent_data + derivatives = translation_classes_dependent_data assert len(src_coeff_exprs) == len(derivatives) + result = [a*b for a, b in zip(derivatives, src_coeff_exprs, strict=True)] return result - def translation_classes_dependent_data(self, tgt_expansion, src_expansion, - src_rscale, dvec, sac): + @override + def translation_classes_dependent_data(self, + tgt_expansion: LocalExpansionBase, + src_expansion: MultipoleExpansionBase, + src_rscale: sym.Expr, + dvec: sym.Matrix, + sac: SymbolicAssignmentCollection | None = None, + ) -> TranslationClassesDepData: """Return an iterable of expressions that needs to be precomputed for multipole-to-local translations that depend only on the distance between the multipole center and the local center which @@ -851,19 +958,27 @@ def translation_classes_dependent_data(self, tgt_expansion, src_expansion, """ derivatives_full = super().translation_classes_dependent_data( tgt_expansion, src_expansion, src_rscale, dvec, sac) + # Note that the matrix we have now is a mirror image of a # circulant matrix. We reverse the first column to get the # first column for the circulant matrix and then finally # use the FFT for convolution represented by the circulant # matrix. - return list(reversed(derivatives_full)) + return tuple(reversed(derivatives_full)) - def postprocess_local_exprs(self, tgt_expansion, src_expansion, m2l_result, - src_rscale, tgt_rscale, sac): - circulant_matrix_mis, _, _ = \ - self._translation_classes_dependent_data_mis(tgt_expansion, - src_expansion) + @override + def postprocess_local_exprs(self, + tgt_expansion: LocalExpansionBase, + src_expansion: MultipoleExpansionBase, + m2l_result: Sequence[sym.Expr], + src_rscale: sym.Expr, + tgt_rscale: sym.Expr, + sac: SymbolicAssignmentCollection | None = None, + ) -> Sequence[sym.Expr]: + circulant_matrix_mis, _, _ = self._translation_classes_dependent_data_mis( + tgt_expansion, src_expansion) n = len(circulant_matrix_mis) + # since we reversed the M2L matrix, we reverse the result # to get the correct result m2l_result = list(reversed(m2l_result[:n])) @@ -871,7 +986,12 @@ def postprocess_local_exprs(self, tgt_expansion, src_expansion, m2l_result, return super().postprocess_local_exprs(tgt_expansion, src_expansion, m2l_result, src_rscale, tgt_rscale, sac) - def optimize_loopy_kernel(self, knl, tgt_expansion, src_expansion): + @override + def optimize_loopy_kernel(self, + knl: lp.TranslationUnit, + tgt_expansion: LocalExpansionBase, + src_expansion: MultipoleExpansionBase, + ) -> lp.TranslationUnit: # Transform the kernel so that icoeff_tgt and its duplicates # become the outermost iname inames = knl.default_entrypoint.all_inames() @@ -881,12 +1001,14 @@ def optimize_loopy_kernel(self, knl, tgt_expansion, src_expansion): knl = lp.add_inames_to_insn(knl, "icoeff_tgt", None) # unprivatize icoeff_tgt because it is the outermost iname - knl = lp.unprivatize_temporaries_with_inames(knl, - {"icoeff_tgt"}, {"tgt_expansion"}) + knl = lp.unprivatize_temporaries_with_inames( + knl, + frozenset({"icoeff_tgt"}), frozenset({"tgt_expansion"})) knl = lp.split_iname(knl, "icoeff_tgt", 64, inner_iname="inner", - inner_tag="l.0", outer_tag="g.1") + inner_tag="l.0", outer_tag="g.1") knl = lp.tag_inames(knl, {"itgt_box": "g.0"}) + return knl @@ -895,9 +1017,17 @@ def optimize_loopy_kernel(self, knl, tgt_expansion, src_expansion): # {{{ FourierBesselM2LTranslation class FourierBesselM2LTranslation(M2LTranslationBase): - def translate(self, tgt_expansion, src_expansion, src_coeff_exprs, src_rscale, - dvec, tgt_rscale, sac=None, translation_classes_dependent_data=None): - + @override + def translate(self, + tgt_expansion: LocalExpansionBase, + src_expansion: MultipoleExpansionBase, + src_coeff_exprs: Sequence[sym.Expr], + src_rscale: sym.Expr, + dvec: sym.Matrix, + tgt_rscale: sym.Expr, + sac: SymbolicAssignmentCollection | None = None, + translation_classes_dependent_data: ( + TranslationClassesDepData | None) = None) -> Sequence[sym.Expr]: if translation_classes_dependent_data is None: derivatives = self.translation_classes_dependent_data(tgt_expansion, src_expansion, src_rscale, dvec, sac=sac) @@ -908,9 +1038,10 @@ def translate(self, tgt_expansion, src_expansion, src_coeff_exprs, src_rscale, src_expansion, src_coeff_exprs, sac, src_rscale) translated_coeffs = [ - sum(derivatives[m + j + tgt_expansion.order + src_expansion.order] - * src_coeff_exprs[src_expansion.get_storage_index((m,))] - for m, in src_expansion.get_coefficient_identifiers()) + sum((derivatives[m + j + tgt_expansion.order + src_expansion.order] + * src_coeff_exprs[src_expansion.get_storage_index((m,))] + for m, in src_expansion.get_coefficient_identifiers()), + sym.sympify(0)) for j, in tgt_expansion.get_coefficient_identifiers()] translated_coeffs = self.postprocess_local_exprs(tgt_expansion, @@ -919,7 +1050,11 @@ def translate(self, tgt_expansion, src_expansion, src_coeff_exprs, src_rscale, return translated_coeffs - def translation_classes_dependent_ndata(self, tgt_expansion, src_expansion): + @override + def translation_classes_dependent_ndata(self, + tgt_expansion: LocalExpansionBase, + src_expansion: MultipoleExpansionBase, + ) -> int: nexpr = 2 * tgt_expansion.order + 2 * src_expansion.order + 1 return nexpr @@ -929,19 +1064,18 @@ def translation_classes_dependent_data(self, src_expansion: MultipoleExpansionBase, src_rscale: sym.Expr, dvec: sym.Matrix, - sac: SymbolicAssignmentCollection | None = None): - + sac: SymbolicAssignmentCollection | None = None, + ) -> TranslationClassesDepData: assert isinstance(tgt_expansion, FourierBesselLocalExpansionMixin) assert isinstance(src_expansion, HankelBased2DMultipoleExpansion) - from sumpy.symbolic import Hankel1, sym_real_norm_2 - - dvec_len = sym_real_norm_2(dvec) + dvec_len = sym.sym_real_norm_2(dvec) new_center_angle_rel_old_center = sym.atan2(dvec[1], dvec[0]) arg_scale = tgt_expansion.get_bessel_arg_scaling() + # [-(src_order+tgt_order), ..., 0, ..., (src_order + tgt_order)] - translation_classes_dependent_data: list[sym.Expr] = \ - [sym.sympify(0)] * (2*tgt_expansion.order + 2 * src_expansion.order + 1) + translation_classes_dependent_data: list[sym.Expr] = ( + [sym.sympify(0)] * (2*tgt_expansion.order + 2 * src_expansion.order + 1)) # The M2L is a mirror image of a Toeplitz matvec with Hankel function # evaluations. https://dlmf.nist.gov/10.23.F1 @@ -952,34 +1086,51 @@ def translation_classes_dependent_data(self, for m, in src_expansion.get_coefficient_identifiers(): idx_m = src_expansion.get_storage_index((m,)) translation_classes_dependent_data[idx_j + idx_m] = ( - Hankel1(m + j, arg_scale * dvec_len, 0) + sym.Hankel1(m + j, arg_scale * dvec_len, 0) * sym.exp(sym.I * (m + j) * new_center_angle_rel_old_center)) return tuple(translation_classes_dependent_data) - def preprocess_multipole_exprs(self, tgt_expansion, src_expansion, - src_coeff_exprs, sac, src_rscale): - + @override + def preprocess_multipole_exprs(self, + tgt_expansion: LocalExpansionBase, + src_expansion: MultipoleExpansionBase, + src_coeff_exprs: Sequence[sym.Expr], + sac: SymbolicAssignmentCollection | None, + src_rscale: sym.Expr) -> Sequence[sym.Expr]: src_coeff_exprs = list(src_coeff_exprs) for m, in src_expansion.get_coefficient_identifiers(): src_coeff_exprs[src_expansion.get_storage_index((m,))] *= src_rscale**abs(m) + return src_coeff_exprs - def preprocess_multipole_nexprs(self, tgt_expansion, src_expansion): + @override + def preprocess_multipole_nexprs(self, + tgt_expansion: LocalExpansionBase, + src_expansion: MultipoleExpansionBase) -> int: return 2*src_expansion.order + 1 - def postprocess_local_exprs(self, tgt_expansion, src_expansion, - m2l_result, src_rscale, tgt_rscale, sac): - + @override + def postprocess_local_exprs(self, + tgt_expansion: LocalExpansionBase, + src_expansion: MultipoleExpansionBase, + m2l_result: Sequence[sym.Expr], + src_rscale: sym.Expr, + tgt_rscale: sym.Expr, + sac: SymbolicAssignmentCollection | None = None) -> Sequence[sym.Expr]: # Filter out the dummy rows and scale them for target - result = [] + result: list[sym.Expr] = [] for j, in tgt_expansion.get_coefficient_identifiers(): - result.append(m2l_result[tgt_expansion.get_storage_index((j,))] + result.append( + m2l_result[tgt_expansion.get_storage_index((j,))] * tgt_rscale**(abs(j)) * sym.Integer(-1)**j) return result - def postprocess_local_nexprs(self, tgt_expansion, src_expansion): + @override + def postprocess_local_nexprs(self, + tgt_expansion: LocalExpansionBase, + src_expansion: MultipoleExpansionBase) -> int: return 2*tgt_expansion.order + 1 # }}} FourierBesselM2LTranslation @@ -990,58 +1141,72 @@ def postprocess_local_nexprs(self, tgt_expansion, src_expansion): class FourierBesselM2LWithPreprocessedMultipoles(FourierBesselM2LTranslation): use_preprocessing: ClassVar[bool] = True - def translate(self, tgt_expansion, src_expansion, src_coeff_exprs, src_rscale, - dvec, tgt_rscale, sac=None, translation_classes_dependent_data=None): - + @override + def translate(self, + tgt_expansion: LocalExpansionBase, + src_expansion: MultipoleExpansionBase, + src_coeff_exprs: Sequence[sym.Expr], + src_rscale: sym.Expr, + dvec: sym.Matrix, + tgt_rscale: sym.Expr, + sac: SymbolicAssignmentCollection | None = None, + translation_classes_dependent_data: ( + TranslationClassesDepData | None) = None) -> Sequence[sym.Expr]: assert translation_classes_dependent_data derivatives = translation_classes_dependent_data translated_coeffs = [ - sum(derivatives[m + j + tgt_expansion.order + src_expansion.order] - * src_coeff_exprs[src_expansion.get_storage_index(m)] - for m in src_expansion.get_coefficient_identifiers()) - for j in tgt_expansion.get_coefficient_identifiers()] + sum((derivatives[m + j + tgt_expansion.order + src_expansion.order] + * src_coeff_exprs[src_expansion.get_storage_index((m,))] + for m, in src_expansion.get_coefficient_identifiers()), + sym.sympify(0)) + for j, in tgt_expansion.get_coefficient_identifiers() + ] return translated_coeffs - def loopy_translate(self, tgt_expansion, src_expansion): + @override + def loopy_translate(self, + tgt_expansion: LocalExpansionBase, + src_expansion: MultipoleExpansionBase) -> lp.TranslationUnit: ncoeff_src = self.preprocess_multipole_nexprs(tgt_expansion, src_expansion) ncoeff_tgt = self.postprocess_local_nexprs(tgt_expansion, src_expansion) - icoeff_src = pymbolic.var("icoeff_src") - icoeff_tgt = pymbolic.var("icoeff_tgt") + icoeff_src = p.Variable("icoeff_src") + icoeff_tgt = p.Variable("icoeff_tgt") domains = [f"{{[icoeff_tgt]: 0<=icoeff_tgt<{ncoeff_tgt} }}"] - tgt_coeffs = pymbolic.var("tgt_coeffs") - src_coeffs = pymbolic.var("src_coeffs") - translation_classes_dependent_data = pymbolic.var("data") + tgt_coeffs = p.Variable("tgt_coeffs") + src_coeffs = p.Variable("src_coeffs") + translation_classes_dependent_data = p.Variable("data") if self.use_fft: - expr = src_coeffs[icoeff_tgt] \ - * translation_classes_dependent_data[icoeff_tgt] + expr = (src_coeffs[icoeff_tgt] + * translation_classes_dependent_data[icoeff_tgt]) else: - expr = src_coeffs[icoeff_src] \ - * translation_classes_dependent_data[ - icoeff_tgt + icoeff_src] - domains.append( - f"{{[icoeff_src]: 0<=icoeff_src<{ncoeff_src} }}") + expr = (src_coeffs[icoeff_src] + * translation_classes_dependent_data[icoeff_tgt + icoeff_src]) + domains.append(f"{{[icoeff_src]: 0<=icoeff_src<{ncoeff_src} }}") insns = [ lp.Assignment( assignee=tgt_coeffs[icoeff_tgt], expression=tgt_coeffs[icoeff_tgt] + expr), ] - return lp.make_function(domains, insns, - kernel_data=[ - lp.GlobalArg("tgt_coeffs", shape=lp.auto, is_input=True, - is_output=True), - lp.GlobalArg("src_coeffs, data", - shape=lp.auto, is_input=True, is_output=False), - lp.ValueArg("src_rscale, tgt_rscale", is_input=True), - ...], - name="e2e", - lang_version=lp.MOST_RECENT_LANGUAGE_VERSION, - ) + + knl = lp.make_function(domains, insns, + kernel_data=[ + lp.GlobalArg("tgt_coeffs", shape=lp.auto, is_input=True, + is_output=True), + lp.GlobalArg("src_coeffs, data", + shape=lp.auto, is_input=True, is_output=False), + lp.ValueArg("src_rscale, tgt_rscale", is_input=True), + ...], + name="e2e", + lang_version=lp.MOST_RECENT_LANGUAGE_VERSION, + ) + + return knl # }}} FourierBesselM2LWithPreprocessedMultipoles @@ -1051,7 +1216,7 @@ def loopy_translate(self, tgt_expansion, src_expansion): class FourierBesselM2LWithFFT(FourierBesselM2LWithPreprocessedMultipoles): use_fft: ClassVar[bool] = True - def __init__(self): + def __init__(self) -> None: # FIXME: expansion with FFT is correct symbolically and can be verified # with sympy. However there are numerical issues that we have to deal # with. Greengard and Rokhlin 1988 attributes this to numerical @@ -1060,24 +1225,43 @@ def __init__(self): # might be the reason for this numerical issue. raise ValueError("Bessel based expansions with FFT are not supported yet.") - def translate(self, tgt_expansion, src_expansion, src_coeff_exprs, src_rscale, - dvec, tgt_rscale, sac=None, translation_classes_dependent_data=None): - + @override + def translate(self, + tgt_expansion: LocalExpansionBase, + src_expansion: MultipoleExpansionBase, + src_coeff_exprs: Sequence[sym.Expr], + src_rscale: sym.Expr, + dvec: sym.Matrix, + tgt_rscale: sym.Expr, + sac: SymbolicAssignmentCollection | None = None, + translation_classes_dependent_data: ( + TranslationClassesDepData | None) = None) -> Sequence[sym.Expr]: assert translation_classes_dependent_data is not None + derivatives = translation_classes_dependent_data assert len(derivatives) == len(src_coeff_exprs) + return [a * b for a, b in zip(derivatives, src_coeff_exprs, strict=True)] - def loopy_translate(self, tgt_expansion, src_expansion): + @override + def loopy_translate(self, + tgt_expansion: LocalExpansionBase, + src_expansion: MultipoleExpansionBase) -> lp.TranslationUnit: raise NotImplementedError - def translation_classes_dependent_data(self, tgt_expansion, src_expansion, - src_rscale, dvec, sac=None): - - translation_classes_dependent_data = \ - super().translation_classes_dependent_data(tgt_expansion, - src_expansion, src_rscale, dvec, sac) + @override + def translation_classes_dependent_data(self, + tgt_expansion: LocalExpansionBase, + src_expansion: MultipoleExpansionBase, + src_rscale: sym.Expr, + dvec: sym.Matrix, + sac: SymbolicAssignmentCollection | None = None, + ) -> TranslationClassesDepData: + translation_classes_dependent_data = ( + super().translation_classes_dependent_data( + tgt_expansion, src_expansion, src_rscale, dvec, sac)) order = src_expansion.order + # For this expansion, we have a mirror image of a Toeplitz matrix. # First, we have to take the mirror image of the M2L matrix. # @@ -1087,74 +1271,91 @@ def translation_classes_dependent_data(self, tgt_expansion, src_expansion, # the last column of the M2L matrix. The second part is the # reverse of the first row of the Toeplitz matrix which # is the reverse of the first row of the M2L matrix. - first_row_m2l, last_column_m2l = \ - translation_classes_dependent_data[:2*order], \ - translation_classes_dependent_data[2*order:] + first_row_m2l, last_column_m2l = ( + translation_classes_dependent_data[:2*order], + translation_classes_dependent_data[2*order:]) + first_column_toeplitz = last_column_m2l first_row_toeplitz = list(reversed(first_row_m2l)) - first_column_circulant = list(first_column_toeplitz) + \ - list(reversed(first_row_toeplitz)) - return tuple(first_column_circulant) + first_column_circulant = ( + list(first_column_toeplitz) + + list(reversed(first_row_toeplitz))) - def preprocess_multipole_exprs(self, tgt_expansion, src_expansion, - src_coeff_exprs, sac, src_rscale): + return tuple(first_column_circulant) - result = super().preprocess_multipole_exprs(tgt_expansion, - src_expansion, src_coeff_exprs, sac, src_rscale) + @override + def preprocess_multipole_exprs(self, + tgt_expansion: LocalExpansionBase, + src_expansion: MultipoleExpansionBase, + src_coeff_exprs: Sequence[sym.Expr], + sac: SymbolicAssignmentCollection | None, + src_rscale: sym.Expr) -> Sequence[sym.Expr]: + result = super().preprocess_multipole_exprs( + tgt_expansion, src_expansion, src_coeff_exprs, sac, src_rscale) result = list(reversed(result)) - result += [0] * (len(result) - 1) - return result + result += [sym.sympify(0)] * (len(result) - 1) - def postprocess_local_exprs(self, tgt_expansion, src_expansion, - m2l_result, src_rscale, tgt_rscale, sac): + return result - m2l_result = m2l_result[:2*tgt_expansion.order+1] - return super().postprocess_local_exprs(tgt_expansion, - src_expansion, m2l_result, src_rscale, tgt_rscale, sac) + @override + def postprocess_local_exprs(self, + tgt_expansion: LocalExpansionBase, + src_expansion: MultipoleExpansionBase, + m2l_result: Sequence[sym.Expr], + src_rscale: sym.Expr, + tgt_rscale: sym.Expr, + sac: SymbolicAssignmentCollection | None = None) -> Sequence[sym.Expr]: + m2l_result = m2l_result[:2*tgt_expansion.order + 1] + return super().postprocess_local_exprs( + tgt_expansion, src_expansion, m2l_result, src_rscale, tgt_rscale, sac) # }}} FourierBesselM2LWithFFT # {{{ loopy_translation_classes_dependent_data -def loopy_translation_classes_dependent_data(tgt_expansion, src_expansion, - result_dtype): +def loopy_translation_classes_dependent_data( + tgt_expansion: LocalExpansionBase, + src_expansion: MultipoleExpansionBase, + result_dtype: DTypeLike) -> lp.TranslationUnit: """ This is a helper function to create a loopy kernel to generate translation classes dependent data. This function uses symbolic expressions given by the - M2L translation, converts them to pymbolic expressions and generates a loopy + M2L translation, converts them to pymboltc expressions and generates a loopy kernel. Note that the loopy kernel returned has lots of expressions in it and takes a long time. Therefore, this function should be used only as a fallback when there is no "loop-y" kernel to calculate the data. """ src_rscale = sym.Symbol("src_rscale") dvec = sym.make_sym_vector("d", tgt_expansion.dim) + from sumpy.assignment_collection import SymbolicAssignmentCollection + sac = SymbolicAssignmentCollection() derivatives = tgt_expansion.m2l_translation.translation_classes_dependent_data( tgt_expansion, src_expansion, src_rscale, dvec, sac) vec_name = "m2l_translation_classes_dependent_data" - tgt_coeff_names = [ - sac.assign_unique(f"m2l_translation_classes_dependent_data{i}", - coeff_i) + sac.assign_unique(f"m2l_translation_classes_dependent_data{i}", coeff_i) for i, coeff_i in enumerate(derivatives)] sac.run_global_cse() from sumpy.codegen import to_loopy_insns from sumpy.tools import to_complex_dtype + insns = to_loopy_insns( sac.assignments.items(), - vector_names=frozenset({"d"}), + vector_names=frozenset(["d"]), pymbolic_expr_maps=[tgt_expansion.get_code_transformer()], - retain_names=tgt_coeff_names, + retain_names=frozenset(tgt_coeff_names), complex_dtype=to_complex_dtype(result_dtype), ) + insns = list(insns) - data = pymbolic.var("m2l_translation_classes_dependent_data") + data = p.Variable("m2l_translation_classes_dependent_data") happens_after = None for i in range(len(insns)): insn = insns[i] diff --git a/sumpy/p2p.py b/sumpy/p2p.py index 683c30ac..a5d2bb89 100644 --- a/sumpy/p2p.py +++ b/sumpy/p2p.py @@ -763,7 +763,7 @@ def get_optimized_kernel(self, max_nsources_in_one_box, # be as large as before. Need to simplify before unprivatizing knl = lp.simplify_indices(knl) knl = lp.unprivatize_temporaries_with_inames(knl, - "iprefetch", only_var_names=local_arrays) + "iprefetch", only_var_names=frozenset(local_arrays)) knl = lp.add_inames_to_insn(knl, "inner", "id:init_* or id:*_scaling or id:src_box_insn_*") diff --git a/sumpy/test/test_misc.py b/sumpy/test/test_misc.py index 76ab911c..6cede60a 100644 --- a/sumpy/test/test_misc.py +++ b/sumpy/test/test_misc.py @@ -60,6 +60,7 @@ ElasticityKernel, ExpressionKernel, HelmholtzKernel, + Kernel, LaplaceKernel, LineOfCompressionKernel, StokesletKernel, @@ -199,7 +200,7 @@ class FakeTree: @pytest.mark.parametrize("knl", [ LaplaceKernel(2), HelmholtzKernel(2), LaplaceKernel(3), HelmholtzKernel(3)]) -def test_order_finder(knl): +def test_order_finder(knl: Kernel) -> None: from sumpy.expansion.level_to_order import SimpleExpansionOrderFinder ofind = SimpleExpansionOrderFinder(1e-5) diff --git a/sumpy/tools.py b/sumpy/tools.py index f644a304..036b745c 100644 --- a/sumpy/tools.py +++ b/sumpy/tools.py @@ -635,7 +635,13 @@ def nullspace(m: Array2D[Any], atol: float = 0): # {{{ FFT -def fft(seq, inverse=False, sac=None): +# FIXME(pyright): this function can take `ndarrays` and sequences of other types +# as well. The current types are just for uses in `sumpy.expansion.m2l` (same +# for `fft_toeplitz_upper_triangular` and `matvec_toeplitz_upper_triangular`) + +def fft(seq: Sequence[sym.Expr], + inverse: bool = False, + sac: SymbolicAssignmentCollection | None = None) -> Sequence[sym.Expr]: """ Return the discrete fourier transform of the sequence seq. seq should be a python iterable with tuples of length 2 @@ -644,21 +650,31 @@ def fft(seq, inverse=False, sac=None): from pymbolic.algorithm import fft as _fft, ifft as _ifft - def wrap(level, expr): + def wrap(level: int, expr: sym.Expr) -> sym.Expr: if isinstance(expr, np.ndarray): res = [wrap(level, a) for a in expr] return np.array(res, dtype=object).reshape(expr.shape) - return add_to_sac(sac, expr) + else: + return add_to_sac(sac, expr) if inverse: - return _ifft(np.array(seq), wrap_intermediate_with_level=wrap, - complex_dtype=np.complex128).tolist() + result = _ifft( + np.array(seq), + wrap_intermediate_with_level=wrap, + complex_dtype=np.complex128) else: - return _fft(np.array(seq), wrap_intermediate_with_level=wrap, - complex_dtype=np.complex128).tolist() + result = _fft( + np.array(seq), + wrap_intermediate_with_level=wrap, + complex_dtype=np.complex128) + return result.tolist() -def fft_toeplitz_upper_triangular(first_row, x, sac=None): + +def fft_toeplitz_upper_triangular( + first_row: Sequence[sym.Expr], + x: Sequence[sym.Expr], + sac: SymbolicAssignmentCollection | None = None) -> Sequence[sym.Expr]: """ Returns the matvec of the Toeplitz matrix given by the first row and the vector x using a Fourier transform @@ -668,23 +684,28 @@ def fft_toeplitz_upper_triangular(first_row, x, sac=None): v = list(first_row) v += [0]*(n-1) - x = list(reversed(x)) - x += [0]*(n-1) + y = list(reversed(x)) + y += [0]*(n-1) - v_fft = fft(v, sac) - x_fft = fft(x, sac) + v_fft = fft(v, sac=sac) # pyright: ignore[reportArgumentType] + x_fft = fft(y, sac=sac) # pyright: ignore[reportArgumentType] res_fft = [add_to_sac(sac, a * b) for a, b in zip(v_fft, x_fft, strict=True)] res = fft(res_fft, inverse=True, sac=sac) return list(reversed(res[:n])) -def matvec_toeplitz_upper_triangular(first_row, vector): +def matvec_toeplitz_upper_triangular( + first_row: Sequence[sym.Expr], + vector: Sequence[sym.Expr], + ) -> Sequence[sym.Expr]: n = len(first_row) assert len(vector) == n - output = [0]*n + + output: list[sym.Expr] = [sym.sympify(0)] * n for row in range(n): terms = tuple(first_row[col-row]*vector[col] for col in range(row, n)) output[row] = sym.Add(*terms) + return output