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@@ -0,0 +1 @@ +/LambdaData-0.1.16.tar.gz diff --git a/python-lambdadata.spec b/python-lambdadata.spec new file mode 100644 index 0000000..a64a975 --- /dev/null +++ b/python-lambdadata.spec @@ -0,0 +1,2225 @@ +%global _empty_manifest_terminate_build 0 +Name: python-LambdaData +Version: 0.1.16 +Release: 1 +Summary: Data Science Iterator Algorithm Library +License: Free for non-commercial use +URL: https://pypi.org/project/LambdaData/ +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/19/29/ec71452032b99c6803a29043a15f0646d3726d5c37f70fee268500e72931/LambdaData-0.1.16.tar.gz +BuildArch: noarch + +Requires: python3-pandas +Requires: python3-MonkeyScope + +%description +## Generators +### `LambdaData.iota` +``` +Help on built-in function iota in module LambdaData: +iota(...) + iota(start, *, stop=None, step=1, stride=0) + Iota + Iterator of a given range with grouping size equal to the stride. + The stop parameter is exclusive, if none is provided the algorithm will + start at zero and use the start parameter as the exclusive stopping point. + This is the same behavior as Random.randrange() + If stride is zero - a single dimension iterator is returned. + Otherwise, stride controls the length of the inner sequences. + DocTests: + >>> list(iota(10)) + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] + >>> list(iota(start=1, stop=11)) + [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] + >>> list(iota(start=2, stop=21, step=2)) + [2, 4, 6, 8, 10, 12, 14, 16, 18, 20] + >>> list(iota(start=2, stop=21, step=2, stride=2)) + [(2, 4), (6, 8), (10, 12), (14, 16), (18, 20)] + @param start: Beginning. Required. + @param stop: Ending. Default is None. + @param step: Stepping. Default is one. + @param stride: Size of groupings. Default is zero. +``` +### `LambdaData.generate` +``` +Help on built-in function generate in module LambdaData: +generate(...) + generate(func: Callable, *args, **kwargs) + Generate + Infinite iterator of a callable with arguments. + DocTests: + >>> counter = itertools.count(1) + >>> gen = generate(next, counter) + >>> list(next(gen) for _ in range(10)) + [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] + @param func: Callable. + @param args: Positional arguments for the functor. + @param kwargs: Keyword arguments for the functor. +``` +### `LambdaData.generate_n` +``` +Help on built-in function generate_n in module LambdaData: +generate_n(...) + generate_n(n: int, func: Callable, *args, **kwargs) + Generate N + Abstract generator function. Finite. + DocTests: + >>> counter = itertools.count(1) + >>> list(generate_n(10, next, counter)) + [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] + @param n: Number of elements to generate. + @param func: Callable. + @param args: Positional arguments for the functor. + @param kwargs: Keyword arguments for the functor. +``` +## Expansions +### `LambdaData.fork` +``` +Help on built-in function fork in module LambdaData: +fork(...) + fork(array: Iterable, forks: int = 2) -> tuple + Fork + Iterator Duplicator. Same as itertools.tee but with a better name. + DocTests: + >>> it = iter(range(10)) + >>> a, b, c = fork(it, 3) + >>> list(c) + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] + >>> a == b + False + >>> list(a) == list(b) + True + @param array: Iterable to be forked. + @param forks: Optional Integer. Default is 2. Represents the number of forks. + @return: Tuple of N Iterators where N is the number of forks. +``` +### `LambdaData.inclusive_scan` +``` +Help on built-in function inclusive_scan in module LambdaData: +inclusive_scan(...) + inclusive_scan(array: Iterable, init=None) -> Iterator + Inclusive Scan -> Adjacent Pairs + DocTests: + >>> list(inclusive_scan(range(1, 10))) + [(1, 2), (2, 3), (3, 4), (4, 5), (5, 6), (6, 7), (7, 8), (8, 9)] + >>> list(inclusive_scan(range(1, 10), 0)) + [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6), (6, 7), (7, 8), (8, 9)] + @param array: Iterable to be scanned. + @param init: Optional initial value. Default is None. + @return: Iterator of Pairs. +``` +### `LambdaData.exclusive_scan` +``` +Help on built-in function exclusive_scan in module LambdaData: +exclusive_scan(...) + exclusive_scan(array: Iterable, init=None) -> Iterator + Exclusive Scan -> Adjacent Pairs + Like inclusive_scan, but ignores the last value. + DocTests: + >>> list(exclusive_scan(range(1, 10))) + [(1, 2), (2, 3), (3, 4), (4, 5), (5, 6), (6, 7), (7, 8)] + >>> list(exclusive_scan(range(1, 10), 0)) + [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6), (6, 7), (7, 8)] + @param array: Iterable to be scanned. + @param init: Initial Value. + @return: Iterator of Pairs. +``` +## Transforms +### `LambdaData.transform` +``` +Help on built-in function transform in module LambdaData: +transform(...) + transform(array: Iterable, func: Callable) -> Iterator + Transform + Similar to map but with a reversed signature. + DocTests: + >>> list(transform(range(10), add_one)) + [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] + >>> list(transform(range(10), square)) + [0, 1, 4, 9, 16, 25, 36, 49, 64, 81] + @param array: Iterable of Values. + @param func: Unary Functor. F(x) -> Value + @return: Iterator of transformed Values. +``` +### `LambdaData.adjacent_difference` +``` +Help on built-in function adjacent_difference in module LambdaData: +adjacent_difference(...) + adjacent_difference(array: Iterable) -> Iterator + Adjacent Difference + Calculates the difference between adjacent pairs. + This is the opposite of Partial Sum. + The first iteration compares with zero for proper offset. + DocTests: + >>> list(adjacent_difference(range(1, 10))) + [1, 1, 1, 1, 1, 1, 1, 1, 1] + >>> list(adjacent_difference(partial_sum(range(1, 10)))) + [1, 2, 3, 4, 5, 6, 7, 8, 9] + >>> list(adjacent_difference(partial_sum(range(-10, 11, 2)))) + [-10, -8, -6, -4, -2, 0, 2, 4, 6, 8, 10] + @param array: Iterable of Numeric Values. + @return: Iterator of adjacent differences. +``` +### `LambdaData.partial_sum` +``` +Help on built-in function partial_sum in module LambdaData: +partial_sum(...) + partial_sum(array: Iterable) -> Iterator + Partial Sum + Calculates the sum of adjacent pairs. + This is the opposite of Adjacent Difference. + DocTests: + >>> list(partial_sum(range(1, 10))) + [1, 3, 6, 10, 15, 21, 28, 36, 45] + >>> list(partial_sum([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])) + [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] + @param array: Iterable of Numeric Values. + @return: Iterator of adjacent sums. +``` +## Permutations +### `LambdaData.partition` +``` +Help on built-in function partition in module LambdaData: +partition(...) + partition(array: Iterable, predicate: Callable) -> Iterator + Stable Partition + Arranges all the elements of a group such that any that return true + when passed to the predicate will be at the front, and the rest will be + at the back. The size of the output iterator will be the same as the + size of the input iterable. + DocTests: + >>> list(partition(range(1, 10), is_even)) + [2, 4, 6, 8, 1, 3, 5, 7, 9] + >>> list(partition(range(1, 10), is_odd)) + [1, 3, 5, 7, 9, 2, 4, 6, 8] + @param array: Iterable of values to be partitioned. + @param predicate: Unary functor. F(x) -> bool + @return: Partitioned Iterator. +``` +## Reductions +### `LambdaData.reduce` +``` +Help on built-in function reduce in module LambdaData: +reduce(...) + reduce(array: Iterable, func: Callable, initial=None) + Reduce + Similar to accumulate but allows any binary functor and/or an initial value. + DocTests: + >>> reduce(range(1, 5), operator.add) + 10 + >>> reduce(range(1, 5), operator.add, 100) + 110 + >>> reduce(range(1, 5), operator.mul) + 24 + >>> reduce(range(1, 5), operator.mul, 0) + 0 + @param array: Iterable of Values to be reduced. + @param func: Binary Functor. + @param initial: Initial value. Typically 0 for add or 1 for multiply. + @return: Reduced Value. +``` +### `LambdaData.accumulate` +``` +Help on built-in function accumulate in module LambdaData: +accumulate(...) + accumulate(array: Iterable) + Accumulate + Returns the Sum of a range of elements. + Same as sum() or reduce with operator.add + DocTests: + >>> accumulate(range(5)) + 10 + >>> accumulate(range(11)) + 55 + @param array: Iterable of Values to be summed. + @return: Sum of Values. +``` +### `LambdaData.product` +``` +Help on built-in function product in module LambdaData: +product(...) + product(array: Iterable) + Product + Reduce with multiply. + For counting numbers from 1 to N: returns the factorial of N. + DocTests: + >>> product(range(1, 5)) + 24 + >>> product(range(5, 10)) + 15120 + @param array: Iterable of Values to be reduced. + @return: Product of all elements multiplied together. +``` +### `LambdaData.min_max` +``` +Help on built-in function min_max in module LambdaData: +min_max(...) + min_max(array: Iterable) -> tuple + Min & Max Element + DocTests: + >>> min_max(range(1, 10)) + (1, 9) + >>> min_max([100, 42, 69, 1]) + (1, 100) + @param array: Iterable of Numeric Values + @return: Tuple(Minimum, Maximum) +``` +### `LambdaData.star_sum` +``` +Help on built-in function star_sum in module LambdaData: +star_sum(...) + star_sum(*args) + Star Sum: Add All Args + Similar to accumulate, but takes an arbitrary number of arguments. + DocTests: + >>> star_sum(1) + 1 + >>> star_sum(1, 2) + 3 + >>> star_sum(1, 2, 3) + 6 + >>> star_sum(1, 2, 3, 4) + 10 + @param args: Numbers to be summed. + @return: Sum of all arguments. +``` +### `LambdaData.star_product` +``` +Help on built-in function star_product in module LambdaData: +star_product(...) + star_product(*args) + Star Product: Multiply All Args + Similar to product, but takes an arbitrary number of arguments. + DocTests: + >>> star_product(0, 42) + 0 + >>> star_product(3, 3, 3) + 27 + >>> star_product(1, 2, 3, 4) + 24 + @param args: Numbers to be multiplied. + @return: Product of all arguments. +``` +## Queries +### `LambdaData.all_of` +``` +Help on built-in function all_of in module LambdaData: +all_of(...) + all_of(array: Iterable, predicate: Callable) -> bool + All of These + DocTests: + >>> all_of([], is_even) + True + >>> all_of([2, 4, 6], is_even) + True + >>> all_of([1, 4, 6], is_even) + False + >>> all_of([1, 3, 5], is_even) + False + @param array: Iterable to inspect. + @param predicate: Callable. f(x) -> bool + @return: Boolean. +``` +### `LambdaData.any_of` +``` +Help on built-in function any_of in module LambdaData: +any_of(...) + any_of(array: Iterable, predicate: Callable) -> bool + Any of These + DocTests: + >>> any_of([], is_even) + False + >>> any_of([2, 4, 6], is_even) + True + >>> any_of([1, 4, 6], is_even) + True + >>> any_of([1, 3, 5], is_even) + False + @param array: Iterable to inspect. + @param predicate: Callable. f(x) -> bool + @return: Boolean. +``` +### `LambdaData.none_of` +``` +Help on built-in function none_of in module LambdaData: +none_of(...) + none_of(array: Iterable, predicate: Callable) -> bool + None Of These + DocTests: + >>> none_of([], is_even) + True + >>> none_of([2, 4, 6], is_even) + False + >>> none_of([1, 4, 6], is_even) + False + >>> none_of([1, 3, 5], is_even) + True + @param array: Iterable to inspect. + @param predicate: Callable. f(x) -> bool + @return: Boolean. +``` +## Transform & Reduce +### `LambdaData.transform_reduce` +``` +Help on built-in function transform_reduce in module LambdaData: +transform_reduce(...) + transform_reduce(lhs: Iterable, rhs: Iterable, transformer: Callable, reducer: Callable) + Transform Reduce + Pairwise transform and then reduction across all results. + DocTests: + >>> transform_reduce(range(1, 6), range(1, 6), operator.mul, sum) + 55 + >>> transform_reduce(range(1, 6), range(1, 6), operator.add, product) + 3840 + @param lhs: Left Iterator + @param rhs: Right Iterator + @param transformer: Binary Functor F(x, y) -> Value + @param reducer: Reduction Functor F(Iterable) -> Value + @return: Reduced Value +``` +### `LambdaData.inner_product` +``` +Help on built-in function inner_product in module LambdaData: +inner_product(...) + inner_product(lhs: Iterable, rhs: Iterable) + Inner Product + Preforms pairwise multiplication across the iterables, + then returns the sum of the products. + DocTests: + >>> inner_product(range(1, 6), range(1, 6)) + 55 + >>> inner_product(range(11), range(11)) + 385 + @param lhs: Left Iterator + @param rhs: Right Iterator + @return: Sum of the products. +``` +### `LambdaData.matrix_multiply` +``` +Help on built-in function matrix_multiply in module LambdaData: +matrix_multiply(...) + matrix_multiply(left, right) + Matrix Product + Row by Column inner product. + DocTests + >>> list(matrix_multiply([[1,2], [3,4]], [[1], [2]])) + [(5,), (11,)] + >>> list(matrix_multiply([[10,20], [30,40]], [[10], [20]])) + [(500,), (1100,)] + @param left: M x N matrix + @param right: N x P matrix + @return: M x P matrix +``` +## Multidimensional Reductions +### `LambdaData.zip_transform` +``` +Help on built-in function zip_transform in module LambdaData: +zip_transform(...) + zip_transform(transducer: Callable, *args: Iterable) -> Iterator + Zip Transform + The transducer should take the same number of arguments as the number of + iterators passed. Each iteration will call the transducer with the ith element + of each iterable. F(a[i], b[i], c[i]...) ... + DocTests: + >>> l1 = (0, 1, 2, 3) + >>> l2 = (8, 7, 6, 5) + >>> l3 = (1, 1, 1, 1) + >>> list(zip_transform(star_sum, [])) + [] + >>> list(zip_transform(star_sum, l1)) + [0, 1, 2, 3] + >>> list(zip_transform(star_sum, l1, l2)) + [8, 8, 8, 8] + >>> list(zip_transform(star_sum, l1, l2, l3)) + [9, 9, 9, 9] + @param transducer: Callable + @param args: Any number of iterables. + @return: Iterator of values from the transducer. +``` +### `LambdaData.transposed_sums` +``` +Help on built-in function transposed_sums in module LambdaData: +transposed_sums(...) + transposed_sums(*args: Iterable) -> Iterator + Transposed Sums - Column Sums + The size of the output iterator will be the same as + the smallest input iterator. + DocTests: + >>> l1 = (0, 1, 2, 3) + >>> l2 = (8, 7, 6, 5) + >>> l3 = (1, 1, 1, 1) + >>> list(transposed_sums(l1, l2, l3)) + [9, 9, 9, 9] + @param args: Arbitrary number of Iterators of numeric values. + @return: Iterator of transposed sums aka column sums. +``` +## Multi-Set Operations +### `LambdaData.union` +``` +Help on built-in function union in module LambdaData: +union(...) + union(*args: set) -> set + Multiple Set Union + Includes all elements of every set passed in. + DocTests: + >>> s1 = {0, 2, 4, 6, 8} + >>> s2 = {1, 2, 3, 4, 5} + >>> s3 = {2, 8, 9, 1, 7} + >>> union(s1, s2, s3) + {0, 1, 2, 3, 4, 5, 6, 7, 8, 9} + @param args: Arbitrary number of sets. + @return: Unified set +``` +### `LambdaData.intersection` +``` +Help on built-in function intersection in module LambdaData: +intersection(...) + intersection(*args: set) -> set + Multiple Set Intersection + Includes all elements that are common to every set passed in. + If there is no intersection, it will return the empty set. + If all sets are the same, it will return the union of all sets. + Opposite of symmetric_difference. + DocTests: + >>> s1 = {0, 2, 4, 6, 8} + >>> s2 = {1, 2, 3, 4, 5} + >>> s3 = {2, 8, 9, 1, 7} + >>> intersection(s1, s2, s3) + {2} + @param args: Arbitrary number of sets. + @return: Set of common elements +``` +### `LambdaData.difference` +``` +Help on built-in function difference in module LambdaData: +difference(...) + difference(*args: set) -> set + Multiple Set Difference + Includes every element in the first set that isn't in one of the others. + If there is no difference, it will return the empty set. + DocTests: + >>> s1 = {0, 2, 4, 6, 8} + >>> s2 = {1, 2, 3, 4, 5} + >>> s3 = {2, 8, 9, 1, 7} + >>> difference(s1, s2, s3) + {0, 6} + @param args: Arbitrary number of sets. + @return: Difference between the first set and the rest. +``` +### `LambdaData.symmetric_difference` +``` +Help on built-in function symmetric_difference in module LambdaData: +symmetric_difference(...) + symmetric_difference(*args: set) -> set + Multiple Set Symmetric Difference + Includes all elements that are not common to every set passed in. + If there is no intersection, it will return the union of all sets. + If all sets are the same, it will return the empty set. + Opposite of intersection. + DocTests: + >>> s1 = {0, 2, 4, 6, 8} + >>> s2 = {1, 2, 3, 4, 5} + >>> s3 = {2, 8, 9, 1, 7} + >>> symmetric_difference(s1, s2, s3) + {0, 1, 3, 4, 5, 6, 7, 8, 9} + @param args: Arbitrary number of sets. + @return: Symmetric difference considering all sets. +``` +## Pandas Helpers +### `LambdaData.value_span` +``` +Help on built-in function value_span in module LambdaData: +value_span(...) + value_span(data: pd.Series, start, stop) + Value Span + Returns a conditional matching values in the inclusive range [start, stop] + @param data: Series + @param start: Sortable Value, lower bound + @param stop: Sortable Value, upper bound + @return: Conditional +``` +### `LambdaData.star_cat_row` +``` +Help on built-in function star_cat_row in module LambdaData: +star_cat_row(...) + star_cat_row(*args: pd.DataFrame, crop=False, reset_index=True) + Star Cat Rows + Concatenates two or more pandas data frames in the order you provide them. + @param args: Two or more DataFrames + @param crop: Boolean to indicate join type, default is False + True: inner join, this will crop columns that are not in all data frames + False: outer join, this will fill missing columns with NaN + @param reset_index: Boolean to indicate resetting the index, default is True + @return: DataFrame +``` +### `LambdaData.star_cat_col` +``` +Help on built-in function star_cat_col in module LambdaData: +star_cat_col(...) + star_cat_col(*args: pd.DataFrame, crop=False, reset_index=False) + Star Cat Cols + Concatenates two or more pandas data frames in the order you provide them. + @param args: Two or more DataFrames + @param crop: Boolean to indicate join type, default is False + True: inner join, this will crop columns that are not in all data frames + False: outer join, this will fill missing columns with NaN + @param reset_index: Boolean to indicate resetting the index, default is True + @return: DataFrame +``` +### `LambdaData.StateLookup` +``` +Help on class StateLookup in module LambdaData: +class StateLookup(builtins.object) + | Class methods defined here: + | + | abbrev_lookup(state_name: 'unicode') -> 'unicode' from builtins.type + | StateLookup.abbrev_lookup(cls, unicode state_name: str) -> str + | State Abbreviation Lookup Utility + | + | DocTests: + | >>> StateLookup.abbrev_lookup('Texas') + | 'TX' + | + | @param state_name: the state name + | @return string: abbreviation + | + | name_lookup(state_abbrev: 'unicode') -> 'unicode' from builtins.type + | StateLookup.name_lookup(cls, unicode state_abbrev: str) -> str + | State Name Lookup Utility + | + | DocTests: + | >>> StateLookup.name_lookup('CA') + | 'California' + | + | @param state_abbrev: state abbreviation + | @return string: state name + | + | ---------------------------------------------------------------------- + | Data descriptors defined here: + | + | __dict__ + | dictionary for instance variables (if defined) + | + | __weakref__ + | list of weak references to the object (if defined) +``` +## Randomizers +### `LambdaData.random_range` +``` +Help on built-in function random_range in module LambdaData: +random_range(...) + random_range(start: int, stop: int = 0, step: int = 1) -> int + Random Range: Flat uniform distribution. + The order of the inputs `start` and `stop` are interchangeable. + Conceptually: A = min(start, stop), B = max(start, stop), C = step + The sign of the step parameter controls the phase of the output. + Negative stepping will flip the inclusively of the distribution. + In other words: a negative step means to count down, not up. + DocTests: + >>> all(random_range(10) in range(10) for _ in range(100)) + True + >>> all(random_range(1, 10) in range(1, 10) for _ in range(100)) + True + >>> all(random_range(1, 10, 2) in range(1, 10, 2) for _ in range(100)) + True + >>> random_range(0) + 0 + @param start: Typically the lower bound. Inclusive. + @param stop: Typically the upper limit. Exclusive. + @param step: Size of the increments within the distribution. + @return: Random Integer in range [A, B) by C, or (A, B] by |C| for -C +``` +### `LambdaData.shuffle` +``` +Help on built-in function shuffle in module LambdaData: +shuffle(...) + shuffle(array: List[Any]) + Shuffle: Knuth B Algorithm + Destructive, in-place shuffle. + Reverse Order Random Swap Back. + @param array: List of values to be shuffled. +``` +### `LambdaData.TruffleShuffle` +``` +Help on class TruffleShuffle in module LambdaData: +class TruffleShuffle(builtins.object) + | TruffleShuffle(collection: Iterable[Any]) + | + | Truffle Shuffle + | Produces random values from a collection with a Wide Uniform Distribution. + | + | @param collection :: Collection of Values. Any list-like object, a Set is + | recommended but not required. + | @return :: Callable Object. `Callable() -> Value` + | @return :: Random Value, wide distribution + | + | Wide Uniform Distribution: "Wide" refers to the average distance between + | consecutive occurrences of the same value. The average width of the output + | distribution will naturally scale up with the size of the collection. + | The goal of this type of distribution is to keep the output sequence free + | of clumps or streaks of the same value, while maintaining randomness and + | uniform probability. This is not the same as a flat uniform distribution. + | The two distributions over time will be statistically similar for any + | given set, but the repetitiveness of the output sequence will be + | very different. + | + | Methods defined here: + | + | __call__(self, *args, **kwargs) -> Any + | TruffleShuffle.__call__(self, *args, **kwargs) -> Any + | + | __init__(self, collection: Iterable[Any]) + | TruffleShuffle.__init__(self, collection: Iterable[Any]) + | + | ---------------------------------------------------------------------- + | Data descriptors defined here: + | + | data + | + | rotate_size +``` +## Tests +``` +$ python LambdaDataTests/LDTests.py +``` +``` +8 items had no tests: + LambdaData + LambdaData.TruffleShuffle + LambdaData.TruffleShuffle.__call__ + LambdaData.TruffleShuffle.__init__ + LambdaData.shuffle + LambdaData.star_cat_col + LambdaData.star_cat_row + LambdaData.value_span +68 items passed all tests: + 2 tests in LambdaData.accumulate + 2 tests in LambdaData.add_one + 3 tests in LambdaData.adjacent_difference + 4 tests in LambdaData.all_of + 4 tests in LambdaData.any_of + 4 tests in LambdaData.difference + 2 tests in LambdaData.exclusive_scan + 5 tests in LambdaData.fork + 3 tests in LambdaData.generate + 2 tests in LambdaData.generate_n + 2 tests in LambdaData.inclusive_scan + 2 tests in LambdaData.inner_product + 4 tests in LambdaData.intersection + 4 tests in LambdaData.iota + 5 tests in LambdaData.is_even + 5 tests in LambdaData.is_odd + 2 tests in LambdaData.matrix_multiply + 2 tests in LambdaData.min_max + 4 tests in LambdaData.none_of + 2 tests in LambdaData.partial_sum + 2 tests in LambdaData.partition + 2 tests in LambdaData.product + 4 tests in LambdaData.random_range + 4 tests in LambdaData.reduce + 3 tests in LambdaData.square + 3 tests in LambdaData.star_product + 4 tests in LambdaData.star_sum + 2 tests in LambdaData.state_name_lookup + 4 tests in LambdaData.symmetric_difference + 2 tests in LambdaData.transform + 2 tests in LambdaData.transform_reduce + 4 tests in LambdaData.transposed_sums + 4 tests in LambdaData.union + 7 tests in LambdaData.zip_transform +220 tests in 76 items. +220 passed and 0 failed. +Test passed. +``` + +%package -n python3-LambdaData +Summary: Data Science Iterator Algorithm Library +Provides: python-LambdaData +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-LambdaData +## Generators +### `LambdaData.iota` +``` +Help on built-in function iota in module LambdaData: +iota(...) + iota(start, *, stop=None, step=1, stride=0) + Iota + Iterator of a given range with grouping size equal to the stride. + The stop parameter is exclusive, if none is provided the algorithm will + start at zero and use the start parameter as the exclusive stopping point. + This is the same behavior as Random.randrange() + If stride is zero - a single dimension iterator is returned. + Otherwise, stride controls the length of the inner sequences. + DocTests: + >>> list(iota(10)) + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] + >>> list(iota(start=1, stop=11)) + [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] + >>> list(iota(start=2, stop=21, step=2)) + [2, 4, 6, 8, 10, 12, 14, 16, 18, 20] + >>> list(iota(start=2, stop=21, step=2, stride=2)) + [(2, 4), (6, 8), (10, 12), (14, 16), (18, 20)] + @param start: Beginning. Required. + @param stop: Ending. Default is None. + @param step: Stepping. Default is one. + @param stride: Size of groupings. Default is zero. +``` +### `LambdaData.generate` +``` +Help on built-in function generate in module LambdaData: +generate(...) + generate(func: Callable, *args, **kwargs) + Generate + Infinite iterator of a callable with arguments. + DocTests: + >>> counter = itertools.count(1) + >>> gen = generate(next, counter) + >>> list(next(gen) for _ in range(10)) + [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] + @param func: Callable. + @param args: Positional arguments for the functor. + @param kwargs: Keyword arguments for the functor. +``` +### `LambdaData.generate_n` +``` +Help on built-in function generate_n in module LambdaData: +generate_n(...) + generate_n(n: int, func: Callable, *args, **kwargs) + Generate N + Abstract generator function. Finite. + DocTests: + >>> counter = itertools.count(1) + >>> list(generate_n(10, next, counter)) + [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] + @param n: Number of elements to generate. + @param func: Callable. + @param args: Positional arguments for the functor. + @param kwargs: Keyword arguments for the functor. +``` +## Expansions +### `LambdaData.fork` +``` +Help on built-in function fork in module LambdaData: +fork(...) + fork(array: Iterable, forks: int = 2) -> tuple + Fork + Iterator Duplicator. Same as itertools.tee but with a better name. + DocTests: + >>> it = iter(range(10)) + >>> a, b, c = fork(it, 3) + >>> list(c) + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] + >>> a == b + False + >>> list(a) == list(b) + True + @param array: Iterable to be forked. + @param forks: Optional Integer. Default is 2. Represents the number of forks. + @return: Tuple of N Iterators where N is the number of forks. +``` +### `LambdaData.inclusive_scan` +``` +Help on built-in function inclusive_scan in module LambdaData: +inclusive_scan(...) + inclusive_scan(array: Iterable, init=None) -> Iterator + Inclusive Scan -> Adjacent Pairs + DocTests: + >>> list(inclusive_scan(range(1, 10))) + [(1, 2), (2, 3), (3, 4), (4, 5), (5, 6), (6, 7), (7, 8), (8, 9)] + >>> list(inclusive_scan(range(1, 10), 0)) + [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6), (6, 7), (7, 8), (8, 9)] + @param array: Iterable to be scanned. + @param init: Optional initial value. Default is None. + @return: Iterator of Pairs. +``` +### `LambdaData.exclusive_scan` +``` +Help on built-in function exclusive_scan in module LambdaData: +exclusive_scan(...) + exclusive_scan(array: Iterable, init=None) -> Iterator + Exclusive Scan -> Adjacent Pairs + Like inclusive_scan, but ignores the last value. + DocTests: + >>> list(exclusive_scan(range(1, 10))) + [(1, 2), (2, 3), (3, 4), (4, 5), (5, 6), (6, 7), (7, 8)] + >>> list(exclusive_scan(range(1, 10), 0)) + [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6), (6, 7), (7, 8)] + @param array: Iterable to be scanned. + @param init: Initial Value. + @return: Iterator of Pairs. +``` +## Transforms +### `LambdaData.transform` +``` +Help on built-in function transform in module LambdaData: +transform(...) + transform(array: Iterable, func: Callable) -> Iterator + Transform + Similar to map but with a reversed signature. + DocTests: + >>> list(transform(range(10), add_one)) + [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] + >>> list(transform(range(10), square)) + [0, 1, 4, 9, 16, 25, 36, 49, 64, 81] + @param array: Iterable of Values. + @param func: Unary Functor. F(x) -> Value + @return: Iterator of transformed Values. +``` +### `LambdaData.adjacent_difference` +``` +Help on built-in function adjacent_difference in module LambdaData: +adjacent_difference(...) + adjacent_difference(array: Iterable) -> Iterator + Adjacent Difference + Calculates the difference between adjacent pairs. + This is the opposite of Partial Sum. + The first iteration compares with zero for proper offset. + DocTests: + >>> list(adjacent_difference(range(1, 10))) + [1, 1, 1, 1, 1, 1, 1, 1, 1] + >>> list(adjacent_difference(partial_sum(range(1, 10)))) + [1, 2, 3, 4, 5, 6, 7, 8, 9] + >>> list(adjacent_difference(partial_sum(range(-10, 11, 2)))) + [-10, -8, -6, -4, -2, 0, 2, 4, 6, 8, 10] + @param array: Iterable of Numeric Values. + @return: Iterator of adjacent differences. +``` +### `LambdaData.partial_sum` +``` +Help on built-in function partial_sum in module LambdaData: +partial_sum(...) + partial_sum(array: Iterable) -> Iterator + Partial Sum + Calculates the sum of adjacent pairs. + This is the opposite of Adjacent Difference. + DocTests: + >>> list(partial_sum(range(1, 10))) + [1, 3, 6, 10, 15, 21, 28, 36, 45] + >>> list(partial_sum([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])) + [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] + @param array: Iterable of Numeric Values. + @return: Iterator of adjacent sums. +``` +## Permutations +### `LambdaData.partition` +``` +Help on built-in function partition in module LambdaData: +partition(...) + partition(array: Iterable, predicate: Callable) -> Iterator + Stable Partition + Arranges all the elements of a group such that any that return true + when passed to the predicate will be at the front, and the rest will be + at the back. The size of the output iterator will be the same as the + size of the input iterable. + DocTests: + >>> list(partition(range(1, 10), is_even)) + [2, 4, 6, 8, 1, 3, 5, 7, 9] + >>> list(partition(range(1, 10), is_odd)) + [1, 3, 5, 7, 9, 2, 4, 6, 8] + @param array: Iterable of values to be partitioned. + @param predicate: Unary functor. F(x) -> bool + @return: Partitioned Iterator. +``` +## Reductions +### `LambdaData.reduce` +``` +Help on built-in function reduce in module LambdaData: +reduce(...) + reduce(array: Iterable, func: Callable, initial=None) + Reduce + Similar to accumulate but allows any binary functor and/or an initial value. + DocTests: + >>> reduce(range(1, 5), operator.add) + 10 + >>> reduce(range(1, 5), operator.add, 100) + 110 + >>> reduce(range(1, 5), operator.mul) + 24 + >>> reduce(range(1, 5), operator.mul, 0) + 0 + @param array: Iterable of Values to be reduced. + @param func: Binary Functor. + @param initial: Initial value. Typically 0 for add or 1 for multiply. + @return: Reduced Value. +``` +### `LambdaData.accumulate` +``` +Help on built-in function accumulate in module LambdaData: +accumulate(...) + accumulate(array: Iterable) + Accumulate + Returns the Sum of a range of elements. + Same as sum() or reduce with operator.add + DocTests: + >>> accumulate(range(5)) + 10 + >>> accumulate(range(11)) + 55 + @param array: Iterable of Values to be summed. + @return: Sum of Values. +``` +### `LambdaData.product` +``` +Help on built-in function product in module LambdaData: +product(...) + product(array: Iterable) + Product + Reduce with multiply. + For counting numbers from 1 to N: returns the factorial of N. + DocTests: + >>> product(range(1, 5)) + 24 + >>> product(range(5, 10)) + 15120 + @param array: Iterable of Values to be reduced. + @return: Product of all elements multiplied together. +``` +### `LambdaData.min_max` +``` +Help on built-in function min_max in module LambdaData: +min_max(...) + min_max(array: Iterable) -> tuple + Min & Max Element + DocTests: + >>> min_max(range(1, 10)) + (1, 9) + >>> min_max([100, 42, 69, 1]) + (1, 100) + @param array: Iterable of Numeric Values + @return: Tuple(Minimum, Maximum) +``` +### `LambdaData.star_sum` +``` +Help on built-in function star_sum in module LambdaData: +star_sum(...) + star_sum(*args) + Star Sum: Add All Args + Similar to accumulate, but takes an arbitrary number of arguments. + DocTests: + >>> star_sum(1) + 1 + >>> star_sum(1, 2) + 3 + >>> star_sum(1, 2, 3) + 6 + >>> star_sum(1, 2, 3, 4) + 10 + @param args: Numbers to be summed. + @return: Sum of all arguments. +``` +### `LambdaData.star_product` +``` +Help on built-in function star_product in module LambdaData: +star_product(...) + star_product(*args) + Star Product: Multiply All Args + Similar to product, but takes an arbitrary number of arguments. + DocTests: + >>> star_product(0, 42) + 0 + >>> star_product(3, 3, 3) + 27 + >>> star_product(1, 2, 3, 4) + 24 + @param args: Numbers to be multiplied. + @return: Product of all arguments. +``` +## Queries +### `LambdaData.all_of` +``` +Help on built-in function all_of in module LambdaData: +all_of(...) + all_of(array: Iterable, predicate: Callable) -> bool + All of These + DocTests: + >>> all_of([], is_even) + True + >>> all_of([2, 4, 6], is_even) + True + >>> all_of([1, 4, 6], is_even) + False + >>> all_of([1, 3, 5], is_even) + False + @param array: Iterable to inspect. + @param predicate: Callable. f(x) -> bool + @return: Boolean. +``` +### `LambdaData.any_of` +``` +Help on built-in function any_of in module LambdaData: +any_of(...) + any_of(array: Iterable, predicate: Callable) -> bool + Any of These + DocTests: + >>> any_of([], is_even) + False + >>> any_of([2, 4, 6], is_even) + True + >>> any_of([1, 4, 6], is_even) + True + >>> any_of([1, 3, 5], is_even) + False + @param array: Iterable to inspect. + @param predicate: Callable. f(x) -> bool + @return: Boolean. +``` +### `LambdaData.none_of` +``` +Help on built-in function none_of in module LambdaData: +none_of(...) + none_of(array: Iterable, predicate: Callable) -> bool + None Of These + DocTests: + >>> none_of([], is_even) + True + >>> none_of([2, 4, 6], is_even) + False + >>> none_of([1, 4, 6], is_even) + False + >>> none_of([1, 3, 5], is_even) + True + @param array: Iterable to inspect. + @param predicate: Callable. f(x) -> bool + @return: Boolean. +``` +## Transform & Reduce +### `LambdaData.transform_reduce` +``` +Help on built-in function transform_reduce in module LambdaData: +transform_reduce(...) + transform_reduce(lhs: Iterable, rhs: Iterable, transformer: Callable, reducer: Callable) + Transform Reduce + Pairwise transform and then reduction across all results. + DocTests: + >>> transform_reduce(range(1, 6), range(1, 6), operator.mul, sum) + 55 + >>> transform_reduce(range(1, 6), range(1, 6), operator.add, product) + 3840 + @param lhs: Left Iterator + @param rhs: Right Iterator + @param transformer: Binary Functor F(x, y) -> Value + @param reducer: Reduction Functor F(Iterable) -> Value + @return: Reduced Value +``` +### `LambdaData.inner_product` +``` +Help on built-in function inner_product in module LambdaData: +inner_product(...) + inner_product(lhs: Iterable, rhs: Iterable) + Inner Product + Preforms pairwise multiplication across the iterables, + then returns the sum of the products. + DocTests: + >>> inner_product(range(1, 6), range(1, 6)) + 55 + >>> inner_product(range(11), range(11)) + 385 + @param lhs: Left Iterator + @param rhs: Right Iterator + @return: Sum of the products. +``` +### `LambdaData.matrix_multiply` +``` +Help on built-in function matrix_multiply in module LambdaData: +matrix_multiply(...) + matrix_multiply(left, right) + Matrix Product + Row by Column inner product. + DocTests + >>> list(matrix_multiply([[1,2], [3,4]], [[1], [2]])) + [(5,), (11,)] + >>> list(matrix_multiply([[10,20], [30,40]], [[10], [20]])) + [(500,), (1100,)] + @param left: M x N matrix + @param right: N x P matrix + @return: M x P matrix +``` +## Multidimensional Reductions +### `LambdaData.zip_transform` +``` +Help on built-in function zip_transform in module LambdaData: +zip_transform(...) + zip_transform(transducer: Callable, *args: Iterable) -> Iterator + Zip Transform + The transducer should take the same number of arguments as the number of + iterators passed. Each iteration will call the transducer with the ith element + of each iterable. F(a[i], b[i], c[i]...) ... + DocTests: + >>> l1 = (0, 1, 2, 3) + >>> l2 = (8, 7, 6, 5) + >>> l3 = (1, 1, 1, 1) + >>> list(zip_transform(star_sum, [])) + [] + >>> list(zip_transform(star_sum, l1)) + [0, 1, 2, 3] + >>> list(zip_transform(star_sum, l1, l2)) + [8, 8, 8, 8] + >>> list(zip_transform(star_sum, l1, l2, l3)) + [9, 9, 9, 9] + @param transducer: Callable + @param args: Any number of iterables. + @return: Iterator of values from the transducer. +``` +### `LambdaData.transposed_sums` +``` +Help on built-in function transposed_sums in module LambdaData: +transposed_sums(...) + transposed_sums(*args: Iterable) -> Iterator + Transposed Sums - Column Sums + The size of the output iterator will be the same as + the smallest input iterator. + DocTests: + >>> l1 = (0, 1, 2, 3) + >>> l2 = (8, 7, 6, 5) + >>> l3 = (1, 1, 1, 1) + >>> list(transposed_sums(l1, l2, l3)) + [9, 9, 9, 9] + @param args: Arbitrary number of Iterators of numeric values. + @return: Iterator of transposed sums aka column sums. +``` +## Multi-Set Operations +### `LambdaData.union` +``` +Help on built-in function union in module LambdaData: +union(...) + union(*args: set) -> set + Multiple Set Union + Includes all elements of every set passed in. + DocTests: + >>> s1 = {0, 2, 4, 6, 8} + >>> s2 = {1, 2, 3, 4, 5} + >>> s3 = {2, 8, 9, 1, 7} + >>> union(s1, s2, s3) + {0, 1, 2, 3, 4, 5, 6, 7, 8, 9} + @param args: Arbitrary number of sets. + @return: Unified set +``` +### `LambdaData.intersection` +``` +Help on built-in function intersection in module LambdaData: +intersection(...) + intersection(*args: set) -> set + Multiple Set Intersection + Includes all elements that are common to every set passed in. + If there is no intersection, it will return the empty set. + If all sets are the same, it will return the union of all sets. + Opposite of symmetric_difference. + DocTests: + >>> s1 = {0, 2, 4, 6, 8} + >>> s2 = {1, 2, 3, 4, 5} + >>> s3 = {2, 8, 9, 1, 7} + >>> intersection(s1, s2, s3) + {2} + @param args: Arbitrary number of sets. + @return: Set of common elements +``` +### `LambdaData.difference` +``` +Help on built-in function difference in module LambdaData: +difference(...) + difference(*args: set) -> set + Multiple Set Difference + Includes every element in the first set that isn't in one of the others. + If there is no difference, it will return the empty set. + DocTests: + >>> s1 = {0, 2, 4, 6, 8} + >>> s2 = {1, 2, 3, 4, 5} + >>> s3 = {2, 8, 9, 1, 7} + >>> difference(s1, s2, s3) + {0, 6} + @param args: Arbitrary number of sets. + @return: Difference between the first set and the rest. +``` +### `LambdaData.symmetric_difference` +``` +Help on built-in function symmetric_difference in module LambdaData: +symmetric_difference(...) + symmetric_difference(*args: set) -> set + Multiple Set Symmetric Difference + Includes all elements that are not common to every set passed in. + If there is no intersection, it will return the union of all sets. + If all sets are the same, it will return the empty set. + Opposite of intersection. + DocTests: + >>> s1 = {0, 2, 4, 6, 8} + >>> s2 = {1, 2, 3, 4, 5} + >>> s3 = {2, 8, 9, 1, 7} + >>> symmetric_difference(s1, s2, s3) + {0, 1, 3, 4, 5, 6, 7, 8, 9} + @param args: Arbitrary number of sets. + @return: Symmetric difference considering all sets. +``` +## Pandas Helpers +### `LambdaData.value_span` +``` +Help on built-in function value_span in module LambdaData: +value_span(...) + value_span(data: pd.Series, start, stop) + Value Span + Returns a conditional matching values in the inclusive range [start, stop] + @param data: Series + @param start: Sortable Value, lower bound + @param stop: Sortable Value, upper bound + @return: Conditional +``` +### `LambdaData.star_cat_row` +``` +Help on built-in function star_cat_row in module LambdaData: +star_cat_row(...) + star_cat_row(*args: pd.DataFrame, crop=False, reset_index=True) + Star Cat Rows + Concatenates two or more pandas data frames in the order you provide them. + @param args: Two or more DataFrames + @param crop: Boolean to indicate join type, default is False + True: inner join, this will crop columns that are not in all data frames + False: outer join, this will fill missing columns with NaN + @param reset_index: Boolean to indicate resetting the index, default is True + @return: DataFrame +``` +### `LambdaData.star_cat_col` +``` +Help on built-in function star_cat_col in module LambdaData: +star_cat_col(...) + star_cat_col(*args: pd.DataFrame, crop=False, reset_index=False) + Star Cat Cols + Concatenates two or more pandas data frames in the order you provide them. + @param args: Two or more DataFrames + @param crop: Boolean to indicate join type, default is False + True: inner join, this will crop columns that are not in all data frames + False: outer join, this will fill missing columns with NaN + @param reset_index: Boolean to indicate resetting the index, default is True + @return: DataFrame +``` +### `LambdaData.StateLookup` +``` +Help on class StateLookup in module LambdaData: +class StateLookup(builtins.object) + | Class methods defined here: + | + | abbrev_lookup(state_name: 'unicode') -> 'unicode' from builtins.type + | StateLookup.abbrev_lookup(cls, unicode state_name: str) -> str + | State Abbreviation Lookup Utility + | + | DocTests: + | >>> StateLookup.abbrev_lookup('Texas') + | 'TX' + | + | @param state_name: the state name + | @return string: abbreviation + | + | name_lookup(state_abbrev: 'unicode') -> 'unicode' from builtins.type + | StateLookup.name_lookup(cls, unicode state_abbrev: str) -> str + | State Name Lookup Utility + | + | DocTests: + | >>> StateLookup.name_lookup('CA') + | 'California' + | + | @param state_abbrev: state abbreviation + | @return string: state name + | + | ---------------------------------------------------------------------- + | Data descriptors defined here: + | + | __dict__ + | dictionary for instance variables (if defined) + | + | __weakref__ + | list of weak references to the object (if defined) +``` +## Randomizers +### `LambdaData.random_range` +``` +Help on built-in function random_range in module LambdaData: +random_range(...) + random_range(start: int, stop: int = 0, step: int = 1) -> int + Random Range: Flat uniform distribution. + The order of the inputs `start` and `stop` are interchangeable. + Conceptually: A = min(start, stop), B = max(start, stop), C = step + The sign of the step parameter controls the phase of the output. + Negative stepping will flip the inclusively of the distribution. + In other words: a negative step means to count down, not up. + DocTests: + >>> all(random_range(10) in range(10) for _ in range(100)) + True + >>> all(random_range(1, 10) in range(1, 10) for _ in range(100)) + True + >>> all(random_range(1, 10, 2) in range(1, 10, 2) for _ in range(100)) + True + >>> random_range(0) + 0 + @param start: Typically the lower bound. Inclusive. + @param stop: Typically the upper limit. Exclusive. + @param step: Size of the increments within the distribution. + @return: Random Integer in range [A, B) by C, or (A, B] by |C| for -C +``` +### `LambdaData.shuffle` +``` +Help on built-in function shuffle in module LambdaData: +shuffle(...) + shuffle(array: List[Any]) + Shuffle: Knuth B Algorithm + Destructive, in-place shuffle. + Reverse Order Random Swap Back. + @param array: List of values to be shuffled. +``` +### `LambdaData.TruffleShuffle` +``` +Help on class TruffleShuffle in module LambdaData: +class TruffleShuffle(builtins.object) + | TruffleShuffle(collection: Iterable[Any]) + | + | Truffle Shuffle + | Produces random values from a collection with a Wide Uniform Distribution. + | + | @param collection :: Collection of Values. Any list-like object, a Set is + | recommended but not required. + | @return :: Callable Object. `Callable() -> Value` + | @return :: Random Value, wide distribution + | + | Wide Uniform Distribution: "Wide" refers to the average distance between + | consecutive occurrences of the same value. The average width of the output + | distribution will naturally scale up with the size of the collection. + | The goal of this type of distribution is to keep the output sequence free + | of clumps or streaks of the same value, while maintaining randomness and + | uniform probability. This is not the same as a flat uniform distribution. + | The two distributions over time will be statistically similar for any + | given set, but the repetitiveness of the output sequence will be + | very different. + | + | Methods defined here: + | + | __call__(self, *args, **kwargs) -> Any + | TruffleShuffle.__call__(self, *args, **kwargs) -> Any + | + | __init__(self, collection: Iterable[Any]) + | TruffleShuffle.__init__(self, collection: Iterable[Any]) + | + | ---------------------------------------------------------------------- + | Data descriptors defined here: + | + | data + | + | rotate_size +``` +## Tests +``` +$ python LambdaDataTests/LDTests.py +``` +``` +8 items had no tests: + LambdaData + LambdaData.TruffleShuffle + LambdaData.TruffleShuffle.__call__ + LambdaData.TruffleShuffle.__init__ + LambdaData.shuffle + LambdaData.star_cat_col + LambdaData.star_cat_row + LambdaData.value_span +68 items passed all tests: + 2 tests in LambdaData.accumulate + 2 tests in LambdaData.add_one + 3 tests in LambdaData.adjacent_difference + 4 tests in LambdaData.all_of + 4 tests in LambdaData.any_of + 4 tests in LambdaData.difference + 2 tests in LambdaData.exclusive_scan + 5 tests in LambdaData.fork + 3 tests in LambdaData.generate + 2 tests in LambdaData.generate_n + 2 tests in LambdaData.inclusive_scan + 2 tests in LambdaData.inner_product + 4 tests in LambdaData.intersection + 4 tests in LambdaData.iota + 5 tests in LambdaData.is_even + 5 tests in LambdaData.is_odd + 2 tests in LambdaData.matrix_multiply + 2 tests in LambdaData.min_max + 4 tests in LambdaData.none_of + 2 tests in LambdaData.partial_sum + 2 tests in LambdaData.partition + 2 tests in LambdaData.product + 4 tests in LambdaData.random_range + 4 tests in LambdaData.reduce + 3 tests in LambdaData.square + 3 tests in LambdaData.star_product + 4 tests in LambdaData.star_sum + 2 tests in LambdaData.state_name_lookup + 4 tests in LambdaData.symmetric_difference + 2 tests in LambdaData.transform + 2 tests in LambdaData.transform_reduce + 4 tests in LambdaData.transposed_sums + 4 tests in LambdaData.union + 7 tests in LambdaData.zip_transform +220 tests in 76 items. +220 passed and 0 failed. +Test passed. +``` + +%package help +Summary: Development documents and examples for LambdaData +Provides: python3-LambdaData-doc +%description help +## Generators +### `LambdaData.iota` +``` +Help on built-in function iota in module LambdaData: +iota(...) + iota(start, *, stop=None, step=1, stride=0) + Iota + Iterator of a given range with grouping size equal to the stride. + The stop parameter is exclusive, if none is provided the algorithm will + start at zero and use the start parameter as the exclusive stopping point. + This is the same behavior as Random.randrange() + If stride is zero - a single dimension iterator is returned. + Otherwise, stride controls the length of the inner sequences. + DocTests: + >>> list(iota(10)) + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] + >>> list(iota(start=1, stop=11)) + [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] + >>> list(iota(start=2, stop=21, step=2)) + [2, 4, 6, 8, 10, 12, 14, 16, 18, 20] + >>> list(iota(start=2, stop=21, step=2, stride=2)) + [(2, 4), (6, 8), (10, 12), (14, 16), (18, 20)] + @param start: Beginning. Required. + @param stop: Ending. Default is None. + @param step: Stepping. Default is one. + @param stride: Size of groupings. Default is zero. +``` +### `LambdaData.generate` +``` +Help on built-in function generate in module LambdaData: +generate(...) + generate(func: Callable, *args, **kwargs) + Generate + Infinite iterator of a callable with arguments. + DocTests: + >>> counter = itertools.count(1) + >>> gen = generate(next, counter) + >>> list(next(gen) for _ in range(10)) + [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] + @param func: Callable. + @param args: Positional arguments for the functor. + @param kwargs: Keyword arguments for the functor. +``` +### `LambdaData.generate_n` +``` +Help on built-in function generate_n in module LambdaData: +generate_n(...) + generate_n(n: int, func: Callable, *args, **kwargs) + Generate N + Abstract generator function. Finite. + DocTests: + >>> counter = itertools.count(1) + >>> list(generate_n(10, next, counter)) + [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] + @param n: Number of elements to generate. + @param func: Callable. + @param args: Positional arguments for the functor. + @param kwargs: Keyword arguments for the functor. +``` +## Expansions +### `LambdaData.fork` +``` +Help on built-in function fork in module LambdaData: +fork(...) + fork(array: Iterable, forks: int = 2) -> tuple + Fork + Iterator Duplicator. Same as itertools.tee but with a better name. + DocTests: + >>> it = iter(range(10)) + >>> a, b, c = fork(it, 3) + >>> list(c) + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] + >>> a == b + False + >>> list(a) == list(b) + True + @param array: Iterable to be forked. + @param forks: Optional Integer. Default is 2. Represents the number of forks. + @return: Tuple of N Iterators where N is the number of forks. +``` +### `LambdaData.inclusive_scan` +``` +Help on built-in function inclusive_scan in module LambdaData: +inclusive_scan(...) + inclusive_scan(array: Iterable, init=None) -> Iterator + Inclusive Scan -> Adjacent Pairs + DocTests: + >>> list(inclusive_scan(range(1, 10))) + [(1, 2), (2, 3), (3, 4), (4, 5), (5, 6), (6, 7), (7, 8), (8, 9)] + >>> list(inclusive_scan(range(1, 10), 0)) + [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6), (6, 7), (7, 8), (8, 9)] + @param array: Iterable to be scanned. + @param init: Optional initial value. Default is None. + @return: Iterator of Pairs. +``` +### `LambdaData.exclusive_scan` +``` +Help on built-in function exclusive_scan in module LambdaData: +exclusive_scan(...) + exclusive_scan(array: Iterable, init=None) -> Iterator + Exclusive Scan -> Adjacent Pairs + Like inclusive_scan, but ignores the last value. + DocTests: + >>> list(exclusive_scan(range(1, 10))) + [(1, 2), (2, 3), (3, 4), (4, 5), (5, 6), (6, 7), (7, 8)] + >>> list(exclusive_scan(range(1, 10), 0)) + [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6), (6, 7), (7, 8)] + @param array: Iterable to be scanned. + @param init: Initial Value. + @return: Iterator of Pairs. +``` +## Transforms +### `LambdaData.transform` +``` +Help on built-in function transform in module LambdaData: +transform(...) + transform(array: Iterable, func: Callable) -> Iterator + Transform + Similar to map but with a reversed signature. + DocTests: + >>> list(transform(range(10), add_one)) + [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] + >>> list(transform(range(10), square)) + [0, 1, 4, 9, 16, 25, 36, 49, 64, 81] + @param array: Iterable of Values. + @param func: Unary Functor. F(x) -> Value + @return: Iterator of transformed Values. +``` +### `LambdaData.adjacent_difference` +``` +Help on built-in function adjacent_difference in module LambdaData: +adjacent_difference(...) + adjacent_difference(array: Iterable) -> Iterator + Adjacent Difference + Calculates the difference between adjacent pairs. + This is the opposite of Partial Sum. + The first iteration compares with zero for proper offset. + DocTests: + >>> list(adjacent_difference(range(1, 10))) + [1, 1, 1, 1, 1, 1, 1, 1, 1] + >>> list(adjacent_difference(partial_sum(range(1, 10)))) + [1, 2, 3, 4, 5, 6, 7, 8, 9] + >>> list(adjacent_difference(partial_sum(range(-10, 11, 2)))) + [-10, -8, -6, -4, -2, 0, 2, 4, 6, 8, 10] + @param array: Iterable of Numeric Values. + @return: Iterator of adjacent differences. +``` +### `LambdaData.partial_sum` +``` +Help on built-in function partial_sum in module LambdaData: +partial_sum(...) + partial_sum(array: Iterable) -> Iterator + Partial Sum + Calculates the sum of adjacent pairs. + This is the opposite of Adjacent Difference. + DocTests: + >>> list(partial_sum(range(1, 10))) + [1, 3, 6, 10, 15, 21, 28, 36, 45] + >>> list(partial_sum([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])) + [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] + @param array: Iterable of Numeric Values. + @return: Iterator of adjacent sums. +``` +## Permutations +### `LambdaData.partition` +``` +Help on built-in function partition in module LambdaData: +partition(...) + partition(array: Iterable, predicate: Callable) -> Iterator + Stable Partition + Arranges all the elements of a group such that any that return true + when passed to the predicate will be at the front, and the rest will be + at the back. The size of the output iterator will be the same as the + size of the input iterable. + DocTests: + >>> list(partition(range(1, 10), is_even)) + [2, 4, 6, 8, 1, 3, 5, 7, 9] + >>> list(partition(range(1, 10), is_odd)) + [1, 3, 5, 7, 9, 2, 4, 6, 8] + @param array: Iterable of values to be partitioned. + @param predicate: Unary functor. F(x) -> bool + @return: Partitioned Iterator. +``` +## Reductions +### `LambdaData.reduce` +``` +Help on built-in function reduce in module LambdaData: +reduce(...) + reduce(array: Iterable, func: Callable, initial=None) + Reduce + Similar to accumulate but allows any binary functor and/or an initial value. + DocTests: + >>> reduce(range(1, 5), operator.add) + 10 + >>> reduce(range(1, 5), operator.add, 100) + 110 + >>> reduce(range(1, 5), operator.mul) + 24 + >>> reduce(range(1, 5), operator.mul, 0) + 0 + @param array: Iterable of Values to be reduced. + @param func: Binary Functor. + @param initial: Initial value. Typically 0 for add or 1 for multiply. + @return: Reduced Value. +``` +### `LambdaData.accumulate` +``` +Help on built-in function accumulate in module LambdaData: +accumulate(...) + accumulate(array: Iterable) + Accumulate + Returns the Sum of a range of elements. + Same as sum() or reduce with operator.add + DocTests: + >>> accumulate(range(5)) + 10 + >>> accumulate(range(11)) + 55 + @param array: Iterable of Values to be summed. + @return: Sum of Values. +``` +### `LambdaData.product` +``` +Help on built-in function product in module LambdaData: +product(...) + product(array: Iterable) + Product + Reduce with multiply. + For counting numbers from 1 to N: returns the factorial of N. + DocTests: + >>> product(range(1, 5)) + 24 + >>> product(range(5, 10)) + 15120 + @param array: Iterable of Values to be reduced. + @return: Product of all elements multiplied together. +``` +### `LambdaData.min_max` +``` +Help on built-in function min_max in module LambdaData: +min_max(...) + min_max(array: Iterable) -> tuple + Min & Max Element + DocTests: + >>> min_max(range(1, 10)) + (1, 9) + >>> min_max([100, 42, 69, 1]) + (1, 100) + @param array: Iterable of Numeric Values + @return: Tuple(Minimum, Maximum) +``` +### `LambdaData.star_sum` +``` +Help on built-in function star_sum in module LambdaData: +star_sum(...) + star_sum(*args) + Star Sum: Add All Args + Similar to accumulate, but takes an arbitrary number of arguments. + DocTests: + >>> star_sum(1) + 1 + >>> star_sum(1, 2) + 3 + >>> star_sum(1, 2, 3) + 6 + >>> star_sum(1, 2, 3, 4) + 10 + @param args: Numbers to be summed. + @return: Sum of all arguments. +``` +### `LambdaData.star_product` +``` +Help on built-in function star_product in module LambdaData: +star_product(...) + star_product(*args) + Star Product: Multiply All Args + Similar to product, but takes an arbitrary number of arguments. + DocTests: + >>> star_product(0, 42) + 0 + >>> star_product(3, 3, 3) + 27 + >>> star_product(1, 2, 3, 4) + 24 + @param args: Numbers to be multiplied. + @return: Product of all arguments. +``` +## Queries +### `LambdaData.all_of` +``` +Help on built-in function all_of in module LambdaData: +all_of(...) + all_of(array: Iterable, predicate: Callable) -> bool + All of These + DocTests: + >>> all_of([], is_even) + True + >>> all_of([2, 4, 6], is_even) + True + >>> all_of([1, 4, 6], is_even) + False + >>> all_of([1, 3, 5], is_even) + False + @param array: Iterable to inspect. + @param predicate: Callable. f(x) -> bool + @return: Boolean. +``` +### `LambdaData.any_of` +``` +Help on built-in function any_of in module LambdaData: +any_of(...) + any_of(array: Iterable, predicate: Callable) -> bool + Any of These + DocTests: + >>> any_of([], is_even) + False + >>> any_of([2, 4, 6], is_even) + True + >>> any_of([1, 4, 6], is_even) + True + >>> any_of([1, 3, 5], is_even) + False + @param array: Iterable to inspect. + @param predicate: Callable. f(x) -> bool + @return: Boolean. +``` +### `LambdaData.none_of` +``` +Help on built-in function none_of in module LambdaData: +none_of(...) + none_of(array: Iterable, predicate: Callable) -> bool + None Of These + DocTests: + >>> none_of([], is_even) + True + >>> none_of([2, 4, 6], is_even) + False + >>> none_of([1, 4, 6], is_even) + False + >>> none_of([1, 3, 5], is_even) + True + @param array: Iterable to inspect. + @param predicate: Callable. f(x) -> bool + @return: Boolean. +``` +## Transform & Reduce +### `LambdaData.transform_reduce` +``` +Help on built-in function transform_reduce in module LambdaData: +transform_reduce(...) + transform_reduce(lhs: Iterable, rhs: Iterable, transformer: Callable, reducer: Callable) + Transform Reduce + Pairwise transform and then reduction across all results. + DocTests: + >>> transform_reduce(range(1, 6), range(1, 6), operator.mul, sum) + 55 + >>> transform_reduce(range(1, 6), range(1, 6), operator.add, product) + 3840 + @param lhs: Left Iterator + @param rhs: Right Iterator + @param transformer: Binary Functor F(x, y) -> Value + @param reducer: Reduction Functor F(Iterable) -> Value + @return: Reduced Value +``` +### `LambdaData.inner_product` +``` +Help on built-in function inner_product in module LambdaData: +inner_product(...) + inner_product(lhs: Iterable, rhs: Iterable) + Inner Product + Preforms pairwise multiplication across the iterables, + then returns the sum of the products. + DocTests: + >>> inner_product(range(1, 6), range(1, 6)) + 55 + >>> inner_product(range(11), range(11)) + 385 + @param lhs: Left Iterator + @param rhs: Right Iterator + @return: Sum of the products. +``` +### `LambdaData.matrix_multiply` +``` +Help on built-in function matrix_multiply in module LambdaData: +matrix_multiply(...) + matrix_multiply(left, right) + Matrix Product + Row by Column inner product. + DocTests + >>> list(matrix_multiply([[1,2], [3,4]], [[1], [2]])) + [(5,), (11,)] + >>> list(matrix_multiply([[10,20], [30,40]], [[10], [20]])) + [(500,), (1100,)] + @param left: M x N matrix + @param right: N x P matrix + @return: M x P matrix +``` +## Multidimensional Reductions +### `LambdaData.zip_transform` +``` +Help on built-in function zip_transform in module LambdaData: +zip_transform(...) + zip_transform(transducer: Callable, *args: Iterable) -> Iterator + Zip Transform + The transducer should take the same number of arguments as the number of + iterators passed. Each iteration will call the transducer with the ith element + of each iterable. F(a[i], b[i], c[i]...) ... + DocTests: + >>> l1 = (0, 1, 2, 3) + >>> l2 = (8, 7, 6, 5) + >>> l3 = (1, 1, 1, 1) + >>> list(zip_transform(star_sum, [])) + [] + >>> list(zip_transform(star_sum, l1)) + [0, 1, 2, 3] + >>> list(zip_transform(star_sum, l1, l2)) + [8, 8, 8, 8] + >>> list(zip_transform(star_sum, l1, l2, l3)) + [9, 9, 9, 9] + @param transducer: Callable + @param args: Any number of iterables. + @return: Iterator of values from the transducer. +``` +### `LambdaData.transposed_sums` +``` +Help on built-in function transposed_sums in module LambdaData: +transposed_sums(...) + transposed_sums(*args: Iterable) -> Iterator + Transposed Sums - Column Sums + The size of the output iterator will be the same as + the smallest input iterator. + DocTests: + >>> l1 = (0, 1, 2, 3) + >>> l2 = (8, 7, 6, 5) + >>> l3 = (1, 1, 1, 1) + >>> list(transposed_sums(l1, l2, l3)) + [9, 9, 9, 9] + @param args: Arbitrary number of Iterators of numeric values. + @return: Iterator of transposed sums aka column sums. +``` +## Multi-Set Operations +### `LambdaData.union` +``` +Help on built-in function union in module LambdaData: +union(...) + union(*args: set) -> set + Multiple Set Union + Includes all elements of every set passed in. + DocTests: + >>> s1 = {0, 2, 4, 6, 8} + >>> s2 = {1, 2, 3, 4, 5} + >>> s3 = {2, 8, 9, 1, 7} + >>> union(s1, s2, s3) + {0, 1, 2, 3, 4, 5, 6, 7, 8, 9} + @param args: Arbitrary number of sets. + @return: Unified set +``` +### `LambdaData.intersection` +``` +Help on built-in function intersection in module LambdaData: +intersection(...) + intersection(*args: set) -> set + Multiple Set Intersection + Includes all elements that are common to every set passed in. + If there is no intersection, it will return the empty set. + If all sets are the same, it will return the union of all sets. + Opposite of symmetric_difference. + DocTests: + >>> s1 = {0, 2, 4, 6, 8} + >>> s2 = {1, 2, 3, 4, 5} + >>> s3 = {2, 8, 9, 1, 7} + >>> intersection(s1, s2, s3) + {2} + @param args: Arbitrary number of sets. + @return: Set of common elements +``` +### `LambdaData.difference` +``` +Help on built-in function difference in module LambdaData: +difference(...) + difference(*args: set) -> set + Multiple Set Difference + Includes every element in the first set that isn't in one of the others. + If there is no difference, it will return the empty set. + DocTests: + >>> s1 = {0, 2, 4, 6, 8} + >>> s2 = {1, 2, 3, 4, 5} + >>> s3 = {2, 8, 9, 1, 7} + >>> difference(s1, s2, s3) + {0, 6} + @param args: Arbitrary number of sets. + @return: Difference between the first set and the rest. +``` +### `LambdaData.symmetric_difference` +``` +Help on built-in function symmetric_difference in module LambdaData: +symmetric_difference(...) + symmetric_difference(*args: set) -> set + Multiple Set Symmetric Difference + Includes all elements that are not common to every set passed in. + If there is no intersection, it will return the union of all sets. + If all sets are the same, it will return the empty set. + Opposite of intersection. + DocTests: + >>> s1 = {0, 2, 4, 6, 8} + >>> s2 = {1, 2, 3, 4, 5} + >>> s3 = {2, 8, 9, 1, 7} + >>> symmetric_difference(s1, s2, s3) + {0, 1, 3, 4, 5, 6, 7, 8, 9} + @param args: Arbitrary number of sets. + @return: Symmetric difference considering all sets. +``` +## Pandas Helpers +### `LambdaData.value_span` +``` +Help on built-in function value_span in module LambdaData: +value_span(...) + value_span(data: pd.Series, start, stop) + Value Span + Returns a conditional matching values in the inclusive range [start, stop] + @param data: Series + @param start: Sortable Value, lower bound + @param stop: Sortable Value, upper bound + @return: Conditional +``` +### `LambdaData.star_cat_row` +``` +Help on built-in function star_cat_row in module LambdaData: +star_cat_row(...) + star_cat_row(*args: pd.DataFrame, crop=False, reset_index=True) + Star Cat Rows + Concatenates two or more pandas data frames in the order you provide them. + @param args: Two or more DataFrames + @param crop: Boolean to indicate join type, default is False + True: inner join, this will crop columns that are not in all data frames + False: outer join, this will fill missing columns with NaN + @param reset_index: Boolean to indicate resetting the index, default is True + @return: DataFrame +``` +### `LambdaData.star_cat_col` +``` +Help on built-in function star_cat_col in module LambdaData: +star_cat_col(...) + star_cat_col(*args: pd.DataFrame, crop=False, reset_index=False) + Star Cat Cols + Concatenates two or more pandas data frames in the order you provide them. + @param args: Two or more DataFrames + @param crop: Boolean to indicate join type, default is False + True: inner join, this will crop columns that are not in all data frames + False: outer join, this will fill missing columns with NaN + @param reset_index: Boolean to indicate resetting the index, default is True + @return: DataFrame +``` +### `LambdaData.StateLookup` +``` +Help on class StateLookup in module LambdaData: +class StateLookup(builtins.object) + | Class methods defined here: + | + | abbrev_lookup(state_name: 'unicode') -> 'unicode' from builtins.type + | StateLookup.abbrev_lookup(cls, unicode state_name: str) -> str + | State Abbreviation Lookup Utility + | + | DocTests: + | >>> StateLookup.abbrev_lookup('Texas') + | 'TX' + | + | @param state_name: the state name + | @return string: abbreviation + | + | name_lookup(state_abbrev: 'unicode') -> 'unicode' from builtins.type + | StateLookup.name_lookup(cls, unicode state_abbrev: str) -> str + | State Name Lookup Utility + | + | DocTests: + | >>> StateLookup.name_lookup('CA') + | 'California' + | + | @param state_abbrev: state abbreviation + | @return string: state name + | + | ---------------------------------------------------------------------- + | Data descriptors defined here: + | + | __dict__ + | dictionary for instance variables (if defined) + | + | __weakref__ + | list of weak references to the object (if defined) +``` +## Randomizers +### `LambdaData.random_range` +``` +Help on built-in function random_range in module LambdaData: +random_range(...) + random_range(start: int, stop: int = 0, step: int = 1) -> int + Random Range: Flat uniform distribution. + The order of the inputs `start` and `stop` are interchangeable. + Conceptually: A = min(start, stop), B = max(start, stop), C = step + The sign of the step parameter controls the phase of the output. + Negative stepping will flip the inclusively of the distribution. + In other words: a negative step means to count down, not up. + DocTests: + >>> all(random_range(10) in range(10) for _ in range(100)) + True + >>> all(random_range(1, 10) in range(1, 10) for _ in range(100)) + True + >>> all(random_range(1, 10, 2) in range(1, 10, 2) for _ in range(100)) + True + >>> random_range(0) + 0 + @param start: Typically the lower bound. Inclusive. + @param stop: Typically the upper limit. Exclusive. + @param step: Size of the increments within the distribution. + @return: Random Integer in range [A, B) by C, or (A, B] by |C| for -C +``` +### `LambdaData.shuffle` +``` +Help on built-in function shuffle in module LambdaData: +shuffle(...) + shuffle(array: List[Any]) + Shuffle: Knuth B Algorithm + Destructive, in-place shuffle. + Reverse Order Random Swap Back. + @param array: List of values to be shuffled. +``` +### `LambdaData.TruffleShuffle` +``` +Help on class TruffleShuffle in module LambdaData: +class TruffleShuffle(builtins.object) + | TruffleShuffle(collection: Iterable[Any]) + | + | Truffle Shuffle + | Produces random values from a collection with a Wide Uniform Distribution. + | + | @param collection :: Collection of Values. Any list-like object, a Set is + | recommended but not required. + | @return :: Callable Object. `Callable() -> Value` + | @return :: Random Value, wide distribution + | + | Wide Uniform Distribution: "Wide" refers to the average distance between + | consecutive occurrences of the same value. The average width of the output + | distribution will naturally scale up with the size of the collection. + | The goal of this type of distribution is to keep the output sequence free + | of clumps or streaks of the same value, while maintaining randomness and + | uniform probability. This is not the same as a flat uniform distribution. + | The two distributions over time will be statistically similar for any + | given set, but the repetitiveness of the output sequence will be + | very different. + | + | Methods defined here: + | + | __call__(self, *args, **kwargs) -> Any + | TruffleShuffle.__call__(self, *args, **kwargs) -> Any + | + | __init__(self, collection: Iterable[Any]) + | TruffleShuffle.__init__(self, collection: Iterable[Any]) + | + | ---------------------------------------------------------------------- + | Data descriptors defined here: + | + | data + | + | rotate_size +``` +## Tests +``` +$ python LambdaDataTests/LDTests.py +``` +``` +8 items had no tests: + LambdaData + LambdaData.TruffleShuffle + LambdaData.TruffleShuffle.__call__ + LambdaData.TruffleShuffle.__init__ + LambdaData.shuffle + LambdaData.star_cat_col + LambdaData.star_cat_row + LambdaData.value_span +68 items passed all tests: + 2 tests in LambdaData.accumulate + 2 tests in LambdaData.add_one + 3 tests in LambdaData.adjacent_difference + 4 tests in LambdaData.all_of + 4 tests in LambdaData.any_of + 4 tests in LambdaData.difference + 2 tests in LambdaData.exclusive_scan + 5 tests in LambdaData.fork + 3 tests in LambdaData.generate + 2 tests in LambdaData.generate_n + 2 tests in LambdaData.inclusive_scan + 2 tests in LambdaData.inner_product + 4 tests in LambdaData.intersection + 4 tests in LambdaData.iota + 5 tests in LambdaData.is_even + 5 tests in LambdaData.is_odd + 2 tests in LambdaData.matrix_multiply + 2 tests in LambdaData.min_max + 4 tests in LambdaData.none_of + 2 tests in LambdaData.partial_sum + 2 tests in LambdaData.partition + 2 tests in LambdaData.product + 4 tests in LambdaData.random_range + 4 tests in LambdaData.reduce + 3 tests in LambdaData.square + 3 tests in LambdaData.star_product + 4 tests in LambdaData.star_sum + 2 tests in LambdaData.state_name_lookup + 4 tests in LambdaData.symmetric_difference + 2 tests in LambdaData.transform + 2 tests in LambdaData.transform_reduce + 4 tests in LambdaData.transposed_sums + 4 tests in LambdaData.union + 7 tests in LambdaData.zip_transform +220 tests in 76 items. +220 passed and 0 failed. +Test passed. +``` + +%prep +%autosetup -n LambdaData-0.1.16 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-LambdaData -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Mon May 29 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.16-1 +- Package Spec generated @@ -0,0 +1 @@ +59bd3b20648ad121e317905cf69c35b7 LambdaData-0.1.16.tar.gz |