remove dicttools dependency

fairpyx/algorithms/almost_egalitarian.py

@@ -27,7 +27,7 @@ def almost_egalitarian_allocation(alloc: AllocationBuilder, surplus_donation:boo
 
     >>> from fairpyx.adaptors import divide
 
-    >>> from dicttools import stringify
+    >>> from fairpyx.utils.test_utils import stringify
 
     >>> instance = Instance(valuations={"avi": {"x":5, "y":4, "z":3, "w":2}, "beni": {"x":2, "y":3, "z":4, "w":5}}, agent_capacities=1, item_capacities=1)
     >>> stringify(divide(almost_egalitarian_allocation, instance=instance))

fairpyx/algorithms/iterated_maximum_matching.py

@@ -21,7 +21,7 @@ def iterated_maximum_matching(alloc:AllocationBuilder, adjust_utilities:bool=Fal
     :param alloc: an allocation builder, which tracks the allocation and the remaining capacity for items and agents. of the fair course allocation problem. 
     :param adjust_utilities: if True, the utilities of agents, who did not get their max-value item in the current iteration, will be adjusted to give them a higher chance in the next iteration.
 
-    >>> from dicttools import stringify
+    >>> from fairpyx.utils.test_utils import stringify
     >>> from fairpyx.adaptors import divide
 
     >>> instance = Instance(valuations={"avi": {"x":5, "y":4, "z":3, "w":2}, "beni": {"x":2, "y":3, "z":4, "w":5}}, agent_capacities=1, item_capacities=1)

fairpyx/algorithms/utilitarian_matching.py

@@ -18,7 +18,7 @@ def utilitarian_matching(alloc: AllocationBuilder):
     """
     Finds an allocation maximizing the sum of utilities for the given instance, using max-weight many-to-many matching.
 
-    >>> from dicttools import stringify
+    >>> from fairpyx.utils.test_utils import stringify
     >>> from fairpyx.adaptors import divide
 
     >>> instance = Instance(valuations={"avi": {"x":5, "y":4, "z":3, "w":2}, "beni": {"x":2, "y":3, "z":4, "w":5}}, agent_capacities=1, item_capacities=1)

fairpyx/utils/graph_utils.py

@@ -15,7 +15,7 @@ def many_to_many_matching(item_capacities: dict[any,int], agent_capacities:dict[
     """
     Computes a many-to-many matching of items to agents.
 
-    >>> from dicttools import stringify
+    >>> from fairpyx.utils.test_utils import stringify
 
     >>> valuations = {"a":{"x":11, "y":22}, "b":{"x":33,"y":55}}
     >>> stringify(many_to_many_matching(item_capacities={"x":2, "y":2}, agent_capacities={"a":1, "b":1}, valuations=valuations))

fairpyx/utils/test_utils.py

@@ -0,0 +1,24 @@
+"""
+Copied from dicttools library: https://github.com/trzemecki/dicttools/blob/39c19d9a5ecc965a58eed3eab18ac3e5e342fca2/dicttools/functions.py#L379C18-L379C18
+"""
+
+def stringify(d):
+    """
+    Returns a canonical string representation of the given dict,
+    by sorting its items recursively.
+    This is especially useful in doctests::
+
+    >>> stringify({"a":1,"b":2,"c":{"d":3,"e":4}})
+    '{a:1, b:2, c:{d:3, e:4}}'
+    """
+    d2 = {}
+
+    for k, v in d.items():
+        d2[k] = stringify(v) if isinstance(v, dict) else v
+
+    return "{" + ", ".join(["{}:{}".format(k, v) for k, v in sorted(d2.items())]) + "}"
+
+
+if __name__ == "__main__":
+    import doctest, sys
+    print("\n",doctest.testmod(), "\n")

fairpyx/zalternatives/yekta_day.py

@@ -24,7 +24,7 @@ def yekta_day(alloc: AllocationBuilder):
     """
     :param alloc: an allocation builder, which tracks the allocation and the remaining capacity for items and agents. of the fair course allocation problem. 
 
-    >>> from dicttools import stringify
+    >>> from fairpyx.utils.test_utils import stringify
     >>> from fairpy.courses.adaptors import divide
 
     >>> instance = Instance(valuations={"avi": {"x":5, "y":4, "z":3, "w":2}, "beni": {"x":2, "y":3, "z":4, "w":5}}, agent_capacities=1, item_capacities=1)

requirements.txt

@@ -10,5 +10,3 @@ cvxpy_base>=1.1.17
 # prtpy>=0.7.0
 # more_itertools
 # scs
-dicttools @ git+https://github.com/trzemecki/dicttools.git
-

setup.py

@@ -52,5 +52,5 @@
 # Publish to test PyPI:
 #   twine upload --repository testpypi dist/*
 
-# Publish to real PyPI:
+# Publish to real PyPI (make sure you set the environment variables TWINE_USERNAME and TWINE_PASSWORD):
 #   twine upload --repository pypi dist/*