chore: apply pre-commit hooks

benchmark/notebooks/plot-benchmarks.py.ipynb

@@ -7,10 +7,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import matplotlib.pyplot as plt\n",
     "import pandas as pd\n",
     "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
+    "\n",
     "sns.set_theme(\"paper\", \"white\")\n",
     "#plt.rc('text', usetex=True)\n",
     "#plt.rc('font', family='sans-serif')"
@@ -37,7 +37,7 @@
    "source": [
     "if snakemake.wildcards[\"kind\"] == 'overhead':\n",
     "    labels = ['Overhead time (s)', 'Overhead memory (MB)']\n",
-    "else: \n",
+    "else:\n",
     "    labels = ['Time (s)', 'Memory (MB)']\n",
     "\n",
     "g = sns.FacetGrid(data=df, row=\"kind\", sharey=False, height=2., aspect=2)\n",
@@ -61,7 +61,7 @@
    "source": [
     "if snakemake.wildcards[\"kind\"] == 'overhead':\n",
     "    label = 'Computational overhead [MBs]'\n",
-    "else: \n",
+    "else:\n",
     "    label = 'Computational resource [MBs]'\n",
     "\n",
     "df = data.assign(Resource = data[\"Time\"] * data[\"Memory\"])\n",

benchmark/scripts/benchmark_cvxpy.py

@@ -1,10 +1,10 @@
 import cvxpy as cp
 import numpy as np
 from common import profile
-from numpy.random import randint, seed
+from numpy.random import default_rng
 
 # Random seed for reproducibility
-seed(125)
+rng = default_rng(125)
 
 
 def basic_model(n, solver):
@@ -26,8 +26,8 @@ def basic_model(n, solver):
 
 def knapsack_model(n, solver):
     # Define the variables
-    weight = randint(1, 100, size=n)
-    value = randint(1, 100, size=n)
+    weight = rng.integers(1, 100, size=n)
+    value = rng.integers(1, 100, size=n)
 
     x = cp.Variable(n, boolean=True)
 

benchmark/scripts/benchmark_gurobipy.py

@@ -1,10 +1,10 @@
 import gurobipy as gp
 import numpy as np
 from common import profile
-from numpy.random import randint, seed
+from numpy.random import default_rng
 
 # Random seed for reproducibility
-seed(125)
+rng = default_rng(125)
 
 
 def basic_model(n, solver):
@@ -32,8 +32,8 @@ def knapsack_model(n, solver):
     # Create a new model
     m = gp.Model()
 
-    weight = randint(1, 100, size=n)
-    value = randint(1, 100, size=n)
+    weight = rng.integers(1, 100, size=n)
+    value = rng.integers(1, 100, size=n)
 
     # Create variables
     x = m.addMVar(n, vtype=gp.GRB.BINARY, name="x")

benchmark/scripts/benchmark_linopy.py

@@ -1,20 +1,18 @@
 #!/usr/bin/env python3
-# -*- coding: utf-8 -*-
 """
 Created on Fri Nov 19 17:40:33 2021.
 
 @author: fabian
 """
 
-
 from common import profile
 from numpy import arange
-from numpy.random import randint, seed
+from numpy.random import default_rng
 
 from linopy import Model
 
 # Random seed for reproducibility
-seed(125)
+rng = default_rng(125)
 
 
 def basic_model(n, solver):
@@ -33,8 +31,8 @@ def basic_model(n, solver):
 def knapsack_model(n, solver):
     m = Model()
     packages = m.add_variables(coords=[arange(n)], binary=True)
-    weight = randint(1, 100, size=n)
-    value = randint(1, 100, size=n)
+    weight = rng.integers(1, 100, size=n)
+    value = rng.integers(1, 100, size=n)
     m.add_constraints((weight * packages).sum() <= 200)
     m.add_objective(-(value * packages).sum())  # use minus because of minimization
     m.solve(solver_name=solver)

benchmark/scripts/benchmark_ortools.py

@@ -1,9 +1,9 @@
 from common import profile
-from numpy.random import randint, seed
+from numpy.random import default_rng
 from ortools.linear_solver import pywraplp
 
 # Random seed for reproducibility
-seed(125)
+rng = default_rng(125)
 
 
 def model(n, solver):
@@ -41,8 +41,8 @@ def knapsack_model(n, solver):
     # Create a new linear solver
     solver = pywraplp.Solver("LinearExample", pywraplp.Solver.GUROBI_LINEAR_PROGRAMMING)
 
-    weight = randint(1, 100, size=n)
-    value = randint(1, 100, size=n)
+    weight = rng.integers(1, 100, size=n)
+    value = rng.integers(1, 100, size=n)
 
     x = {i: solver.BoolVar("x_%d" % i) for i in range(n)}
     # Create constraints

benchmark/scripts/benchmark_pulp.py

@@ -1,10 +1,9 @@
 import pulp
 from common import profile
-from numpy import arange
-from numpy.random import randint, seed
+from numpy.random import default_rng
 
 # Random seed for reproducibility
-seed(125)
+rng = default_rng(125)
 
 
 def basic_model(n, solver):
@@ -34,8 +33,8 @@ def knapsack_model(n, solver):
     m.i = list(range(n))
 
     # Define the variables
-    weight = randint(1, 100, size=n)
-    value = randint(1, 100, size=n)
+    weight = rng.integers(1, 100, size=n)
+    value = rng.integers(1, 100, size=n)
 
     x = pulp.LpVariable.dicts("x", (m.i,), lowBound=0, upBound=1, cat=pulp.LpInteger)
 

benchmark/scripts/benchmark_pyomo.py

@@ -1,15 +1,13 @@
 #!/usr/bin/env python3
-# -*- coding: utf-8 -*-
 """
 Created on Fri Nov 19 17:40:33 2021.
 
 @author: fabian
 """
 
-
 from common import profile
 from numpy import arange
-from numpy.random import randint, seed
+from numpy.random import default_rng
 from pyomo.environ import (
     Binary,
     ConcreteModel,
@@ -18,12 +16,11 @@
     Set,
     Var,
     maximize,
-    value,
 )
 from pyomo.opt import SolverFactory
 
 # Random seed for reproducibility
-seed(125)
+rng = default_rng(125)
 
 
 def basic_model(n, solver):
@@ -57,8 +54,8 @@ def knapsack_model(n, solver):
     m.i = Set(initialize=arange(n))
 
     m.x = Var(m.i, domain=Binary)
-    m.weight = randint(1, 100, size=n)
-    m.value = randint(1, 100, size=n)
+    m.weight = rng.integers(1, 100, size=n)
+    m.value = rng.integers(1, 100, size=n)
 
     def bound1(m):
         return sum(m.x[i] * m.weight[i] for i in m.i) <= 200

benchmark/scripts/benchmarks-pypsa-eur/benchmark-linopy.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python3
-# -*- coding: utf-8 -*-
 """
 Created on Tue Feb 15 16:20:51 2022.
 

benchmark/scripts/benchmarks-pypsa-eur/benchmark-pyomo.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python3
-# -*- coding: utf-8 -*-
 """
 Created on Tue Feb 15 16:20:51 2022.
 
@@ -18,7 +17,7 @@ def solve():
     n.generators.p_nom_max.fillna(np.inf, inplace=True)
     n.snapshots = n.snapshots[:NSNAPSHOTS]
 
-    m = n.lopf(
+    m = n.lopf(  # noqa: F841
         solver_options=SOLVER_PARAMS, formulation="kirchhoff", solver_name=SOLVER
     )
 

benchmark/scripts/benchmarks-pypsa-eur/benchmark-pypsa-linopf.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python3
-# -*- coding: utf-8 -*-
 """
 Created on Tue Feb 15 16:20:51 2022.
 
@@ -18,7 +17,7 @@ def solve():
     n.generators.p_nom_max.fillna(np.inf, inplace=True)
     n.snapshots = n.snapshots[:NSNAPSHOTS]
 
-    m = n.lopf(solver_options=SOLVER_PARAMS, pyomo=False, solver_name=SOLVER)
+    m = n.lopf(solver_options=SOLVER_PARAMS, pyomo=False, solver_name=SOLVER)  # noqa: F841
 
 
 solve()

benchmark/scripts/benchmarks-pypsa-eur/plot-benchmarks.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python3
-# -*- coding: utf-8 -*-
 """
 Created on Tue Feb 15 17:11:01 2022.
 

benchmark/scripts/concat-benchmarks.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python3
-# -*- coding: utf-8 -*-
 """
 Created on Wed Feb  9 09:34:00 2022.
 

benchmark/scripts/leftovers/benchmark-linopy.py

@@ -1,15 +1,12 @@
 #!/usr/bin/env python3
-# -*- coding: utf-8 -*-
 """
 Created on Fri Nov 19 17:40:33 2021.
 
 @author: fabian
 """
 
-
-import pandas as pd
 from common import profile
-from numpy import arange, cos, sin
+from numpy import arange
 
 from linopy import Model
 

benchmark/scripts/leftovers/benchmarks-pypsa-eur/benchmark-linopy.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python3
-# -*- coding: utf-8 -*-
 """
 Created on Tue Feb 15 16:20:51 2022.
 

benchmark/scripts/leftovers/benchmarks-pypsa-eur/benchmark-pyomo.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python3
-# -*- coding: utf-8 -*-
 """
 Created on Tue Feb 15 16:20:51 2022.
 
@@ -18,7 +17,7 @@ def solve():
     n.generators.p_nom_max.fillna(np.inf, inplace=True)
     n.snapshots = n.snapshots[:NSNAPSHOTS]
 
-    m = n.lopf(
+    m = n.lopf(  # noqa: F841
         solver_options=SOLVER_PARAMS, formulation="kirchhoff", solver_name=SOLVER
     )
 

benchmark/scripts/leftovers/benchmarks-pypsa-eur/benchmark-pypsa-linopf.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python3
-# -*- coding: utf-8 -*-
 """
 Created on Tue Feb 15 16:20:51 2022.
 
@@ -18,7 +17,7 @@ def solve():
     n.generators.p_nom_max.fillna(np.inf, inplace=True)
     n.snapshots = n.snapshots[:NSNAPSHOTS]
 
-    m = n.lopf(solver_options=SOLVER_PARAMS, pyomo=False, solver_name=SOLVER)
+    m = n.lopf(solver_options=SOLVER_PARAMS, pyomo=False, solver_name=SOLVER)  # noqa: F841
 
 
 solve()

benchmark/scripts/leftovers/benchmarks-pypsa-eur/plot-benchmarks.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python3
-# -*- coding: utf-8 -*-
 """
 Created on Tue Feb 15 17:11:01 2022.
 

benchmark/scripts/merge-benchmarks.py

@@ -1,14 +1,10 @@
 #!/usr/bin/env python3
-# -*- coding: utf-8 -*-
 """
 Created on Mon Feb 14 18:00:44 2022.
 
 @author: fabian
 """
 
-
-from pathlib import Path
-
 import pandas as pd
 
 df = [pd.read_csv(fn, index_col=0) for fn in snakemake.input.benchmarks]

benchmark/scripts/plot-benchmarks.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python3
-# -*- coding: utf-8 -*-
 """
 Created on Wed Jan 26 23:37:38 2022.
 

benchmark/scripts/run-linopy.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python3
-# -*- coding: utf-8 -*-
 """
 Created on Fri Nov 19 17:40:33 2021.
 

benchmark/scripts/run-pyomo.py

@@ -1,12 +1,10 @@
 #!/usr/bin/env python3
-# -*- coding: utf-8 -*-
 """
 Created on Fri Nov 19 17:40:33 2021.
 
 @author: fabian
 """
 
-
 from benchmark_pyomo import basic_model, knapsack_model
 
 if snakemake.config["benchmark"] == "basic":

benchmark/scripts/write-lp-file.py

@@ -1,11 +1,10 @@
-import pandas as pd
 from numpy import arange
-from numpy.random import randint, seed
+from numpy.random import default_rng
 
 from linopy import Model
 
 # Random seed for reproducibility
-seed(125)
+rng = default_rng(125)
 
 
 if snakemake.config["benchmark"] == "basic":
@@ -25,8 +24,8 @@ def create_model(n):
     def create_model(n):
         m = Model()
         packages = m.add_variables(coords=[arange(n)], binary=True)
-        weight = randint(1, 100, size=n)
-        value = randint(1, 100, size=n)
+        weight = rng.integers(1, 100, size=n)
+        value = rng.integers(1, 100, size=n)
         m.add_constraints((weight * packages).sum() <= 200)
         m.add_objective(-(value * packages).sum())  # use minus because of minimization
         return m

doc/logo.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python3
-# -*- coding: utf-8 -*-
 """
 Created on Sun Oct 17 12:08:47 2021.
 

examples/create-a-model-with-coordinates.ipynb

@@ -70,6 +70,7 @@
    "outputs": [],
    "source": [
     "import pandas as pd\n",
+    "\n",
     "time = pd.Index(range(10), name='time')\n",
     "\n",
     "x = m.add_variables(lower=0, coords=[time], name='x', )\n",

examples/create-a-model.ipynb

@@ -55,6 +55,7 @@
    "outputs": [],
    "source": [
     "from linopy import Model\n",
+    "\n",
     "m = Model()"
    ]
   },