Merge branch 'metroMap' of github.com:Marika-K/gurobi-optimods into m…

…etroMap

.github/workflows/wheel-tests.yml

@@ -38,6 +38,7 @@ jobs:
       - name: Fetch dependencies for offline install
         run: |
           pip wheel --wheel-dir=dist gurobipy pandas numpy scipy gurobipy-pandas
+          pip wheel --wheel-dir=dist "numpy<2"
       - name: Install from built wheel
         run: |
           python -m pip install --find-links dist --no-index --only-binary=:all: gurobi-optimods

Makefile

@@ -3,7 +3,7 @@
 develop:
 	python -m pip install pip setuptools wheel --upgrade
 	python -m pip install -e .[examples]
-	python -m pip install -r docs/requirements.txt
+	python -m pip install -r docs/requirements-base.txt
 	python -m pip install sphinx-autobuild pre-commit
 	pre-commit install
 

README.md

@@ -35,7 +35,7 @@ pip install gurobi-optimods
 
 ## Documentation
 
-Full documentation for `gurobi-optimods` is hosted on [readthedocs](https://gurobi-optimization-gurobi-optimods.readthedocs-hosted.com/en/stable).
+Full documentation for `gurobi-optimods` is hosted on [readthedocs](https://gurobi-optimods.readthedocs.io/en/stable).
 
 ## License
 

docs/requirements.txt

@@ -54,5 +54,5 @@ sphinxcontrib-qthelp==1.0.7
 sphinxcontrib-serializinghtml==1.1.10
 tabulate==0.9.0
 typing_extensions==4.11.0
-urllib3==2.2.1
+urllib3==2.2.2
 webencodings==0.5.1

docs/source/conf.py

@@ -93,6 +93,28 @@
 numpydoc_xref_ignore = {"optional", "or", "of"}
 
 
+# -- doctest configuration
+
+doctest_global_setup = """
+def size_limited_license():
+
+    result = False
+
+    try:
+        import gurobipy as gp
+        from gurobipy import GRB
+
+        with gp.Env(params={"OutputFlag": 0}) as env, gp.Model(env=env) as model:
+            x = model.addVars(2001)
+            model.optimize()
+    except gp.GurobiError as e:
+        if e.errno == GRB.Error.SIZE_LIMIT_EXCEEDED:
+            result = True
+
+    return result
+"""
+
+
 # -- Docstring preprocessing for autodoc
 
 autodoc_typehints = "none"

docs/source/contributing.rst

@@ -44,9 +44,9 @@ Development Environment
 
 To set up your development environment:
 
-1. Create and activate a Python 3.8 virtual environment using your preferred
-   tool. We maintain compatibility with Python 3.8 and above, so developing
-   using 3.8 locally will ensure your contributions are compatible.
+1. Create and activate a Python virtual environment using your preferred tool.
+   You can use any currently supported Python version for development (versions
+   3.8 through 3.12).
 2. Run ``make develop`` from the top level of the repository. This command will
    install (using ``pip``) all packages required to run the unit tests, run the
    doc tests, and build the sphinx documentation. It will also install

docs/source/mods/line-optimization.rst

@@ -8,14 +8,14 @@ This problem is an example of a classical network design problem.
 There are different approaches and models to solve the line optimization problem.
 A general overview on models and methods is given by Schoebel :footcite:p:`schoebel2012`.
 
-For this optimod we assume we are given a public transportation network, being
+For this optimod we assume we are given a public transportation network:
 a set of stations and the direct links between them. We are given the
-origin-destination (OD) demand, i.e., it is known how many passengers want to
-travel from one station to another in the network within a considered time horizon.
-We are also given a set of possible *lines*. A line is a path a public
-transportation network. We call a subset of these lines where each line is
-associated with a frequency a line plan. The optimod computes a line plan with
-minimum cost such that the capacity of the chosen lines sufficient to transport
+origin-destination (OD) *demand*, i.e., it is known how many passengers want to
+travel from one station to another in the network within the considered time horizon.
+We are also given a set of possible *lines*. A line is a path in a public
+transportation network. We call *line plan* a subset of these lines where each line is
+associated with a frequency. The optimod computes a line plan with
+minimum cost such that the capacity of the chosen lines is sufficient to transport
 all passengers.
 
 We provide two different strategies to find a line plan with minimum cost:
@@ -39,9 +39,9 @@ vertices :math:`V` represent the stations and the set of edges
 :math:`E` represent all possibilities to travel from one station to another without
 an intermediate station.
 A directed edge :math:`(u,v)\in E` has the attribute time :math:`\tau_{uv}\geq 0` that
-represents the amount of time needed traveling from :math:`u` to :math:`v`.
-For each pair of nodes :math:`u,v\in V` a demand :math:`d_{uv}\geq 0` can be defined.
-The demand represents the number of passengers that want to travel from :math:`u`
+represents the amount of time needed to travel from :math:`u` to :math:`v`.
+For each pair of nodes :math:`u,v\in V` a demand :math:`d_{uv}\geq 0` is given.
+The demand represents the number of passengers who want to travel from :math:`u`
 to :math:`v` in the considered time horizon. Let :math:`D` be the set of all node pairs with
 positive demand. This set is also called OD pairs.
 Further given is a set of lines :math:`L`. A line :math:`l\in L` contains the stations it traverses
@@ -53,7 +53,7 @@ A line has the following additional attributes:
 - operating cost: :math:`c_{l}\geq 0` for operating the line once in the given time horizon
 - capacity: :math:`\kappa_{l}\geq 0` when operating the line :math:`l` once in the given time horizon
 
-Additionally, we have a given list of frequencies. The frequencies define the possible
+Additionally, we are given a list of frequencies. The frequencies define the possible
 number of operations for the lines in the given time horizon.
 If a line :math:`l` is operated with frequency :math:`f` the overall cost for the line is
 :math:`C_{lf}=C_l + c_{lf}\cdot f` and the total capacity provided by the line is

docs/source/mods/metromap.rst

@@ -388,6 +388,7 @@ The OptiMod can be run as follows:
 
 .. doctest:: solve
     :options: +NORMALIZE_WHITESPACE
+    :skipif: size_limited_license()
 
     >>> from gurobi_optimods import datasets
     >>> from gurobi_optimods.metromap import metromap
@@ -520,6 +521,7 @@ this OptiMod. Here is an example of how this could be done
 
 
 .. testcode:: combine
+  :skipif: size_limited_license()
 
   # import all requirements
   import networkx as nx

pyproject.toml

@@ -26,7 +26,7 @@ classifiers = [
 dependencies = [
     "gurobipy[matrixapi]>=10.0.3",
     "gurobipy-pandas>=1.0.0",
-    "numpy",
+    "numpy<2",
     "pandas",
     "scipy>=1.8.0",
 ]

src/gurobi_optimods/__init__.py

@@ -1,3 +1,3 @@
 __doc__ = "Gurobi OptiMods"
 
-__version__ = "2.0.1dev0"
+__version__ = "2.0.1"

tests/opf/test_graphics.py

@@ -6,8 +6,6 @@
 import pathlib
 import unittest
 
-import gurobipy as gp
-
 from gurobi_optimods.datasets import load_opf_example, load_opf_extra
 from gurobi_optimods.opf import (
     compute_violations,
@@ -16,23 +14,15 @@
     violation_plot,
 )
 
+from ..utils import size_limited_license
+
 # If plotly is not installed, tests will be skipped
 try:
     import plotly
 except ImportError:
     plotly = None
 
 
-def size_limited_license():
-    with gp.Env(params={"OutputFlag": 0}) as env, gp.Model(env=env) as model:
-        model.addVars(2001)
-        try:
-            model.optimize()
-            return False
-        except gp.GurobiError:
-            return True
-
-
 @unittest.skipIf(plotly is None, "plotly is not installed")
 class TestGraphicsCase9(unittest.TestCase):
     def setUp(self):

tests/opf/test_solver.py

@@ -4,20 +4,10 @@
 import random
 import unittest
 
-import gurobipy as gp
-
 from gurobi_optimods.datasets import load_opf_example
 from gurobi_optimods.opf import solve_opf
 
-
-def size_limited_license():
-    with gp.Env(params={"OutputFlag": 0}) as env, gp.Model(env=env) as model:
-        model.addVars(2001)
-        try:
-            model.optimize()
-            return False
-        except gp.GurobiError:
-            return True
+from ..utils import size_limited_license
 
 
 class TestInvalidData(unittest.TestCase):

tests/utils.py

@@ -27,3 +27,20 @@ def skip_wrapper(*args, **kwargs):
             raise
 
     return skip_wrapper
+
+
+def size_limited_license():
+    result = False
+
+    try:
+        import gurobipy as gp
+        from gurobipy import GRB
+
+        with gp.Env(params={"OutputFlag": 0}) as env, gp.Model(env=env) as model:
+            x = model.addVars(2001)
+            model.optimize()
+    except gp.GurobiError as e:
+        if e.errno == GRB.Error.SIZE_LIMIT_EXCEEDED:
+            result = True
+
+    return result