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Merged
merged 6 commits into from
Jan 20, 2025
Merged

Update code quality configuration #555

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2d45f52
Bump mypy, ruff versions
khaeru Jan 16, 2025
48989cc
Include *.ipynb in ruff format checking
khaeru Jan 16, 2025
5bf0bfc
Update copyright year 2024 → 2025
khaeru Jan 16, 2025
d0efe14
Use astral-sh/setup-uv in "pytest" GHA workflow
khaeru Jan 20, 2025
1e260eb
Work around actions/checkout#2041
khaeru Jan 20, 2025
96407b5
Simplify installation of reticulate
khaeru Jan 20, 2025
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Include *.ipynb in ruff format checking 
Format 2 files.
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@khaeru
khaeru committed Jan 20, 2025
commit 48989cc7e1d6e017ebe6f634dd69a184123eddba
4 changes: 0 additions & 4 deletions pyproject.toml
View file
Original file line number Diff line number Diff line change
@@ -112,10 +112,6 @@ markers = [
]
tmp_path_retention_policy = "none"

[tool.ruff]
# TEMPORARY Exclude tutorial files
extend-exclude = ["*.ipynb"]

[tool.ruff.lint]
select = ["C9", "E", "F", "I", "W"]
# FIXME the following exceed this limit
74 changes: 42 additions & 32 deletions tutorial/transport/py_transport.ipynb
View file
Original file line number Diff line number Diff line change
@@ -54,8 +54,9 @@
"metadata": {},
"outputs": [],
"source": [
"# load required packages \n",
"# Import required packages\n",
"import pandas as pd\n",
"\n",
"import ixmp"
]
},
@@ -65,7 +66,7 @@
"metadata": {},
"outputs": [],
"source": [
"# launch the ix modeling platform using the default back end\n",
"# Launch the ix modeling platform using the default back end\n",
"mp = ixmp.Platform()\n",
"\n",
"# The following lines have the same effect:\n",
@@ -79,14 +80,14 @@
"metadata": {},
"outputs": [],
"source": [
"# details for creating a new scenario in the ix modeling platform \n",
"# details for creating a new scenario in the ix modeling platform\n",
"model = \"transport problem\"\n",
"scenario = \"standard\"\n",
"annot = \"Dantzig's transportation problem for illustration and testing\" \n",
"annot = \"Dantzig's transportation problem for illustration and testing\"\n",
"\n",
"# initialize a new ixmp.Scenario\n",
"# the parameter version='new' indicates that this is a new scenario instamce\n",
"scen = ixmp.Scenario(mp, model, scenario, version='new', annotation=annot)"
"scen = ixmp.Scenario(mp, model, scenario, version=\"new\", annotation=annot)"
]
},
{
@@ -114,11 +115,11 @@
"metadata": {},
"outputs": [],
"source": [
"# define the sets of locations of canning plants and markets \n",
"# define the sets of locations of canning plants and markets\n",
"scen.init_set(\"i\")\n",
"scen.add_set(\"i\", [\"seattle\", \"san-diego\"])\n",
"scen.init_set(\"j\")\n",
"scen.add_set(\"j\", [\"new-york\", \"chicago\", \"topeka\"]) "
"scen.add_set(\"j\", [\"new-york\", \"chicago\", \"topeka\"])"
]
},
{
@@ -128,7 +129,7 @@
"outputs": [],
"source": [
"# display the set 'i'\n",
"scen.set('i')"
"scen.set(\"i\")"
]
},
{
@@ -161,19 +162,19 @@
"metadata": {},
"outputs": [],
"source": [
"# capacity of plant i in cases \n",
"# add parameter elements one-by-one (string and value) \n",
"# capacity of plant i in cases\n",
"# add parameter elements one-by-one (string and value)\n",
"scen.init_par(\"a\", idx_sets=\"i\")\n",
"scen.add_par(\"a\", \"seattle\", 350, \"cases\")\n",
"scen.add_par(\"a\", \"san-diego\", 600, \"cases\")\n",
"\n",
"# demand at market j in cases \n",
"# add parameter elements as dataframe (with index names) \n",
"# demand at market j in cases\n",
"# add parameter elements as dataframe (with index names)\n",
"scen.init_par(\"b\", idx_sets=\"j\")\n",
"b_data = [\n",
" {'j': \"new-york\", 'value': 325, 'unit': \"cases\"},\n",
" {'j': \"chicago\", 'value': 300, 'unit': \"cases\"},\n",
" {'j': \"topeka\", 'value': 275, 'unit': \"cases\"}\n",
" {\"j\": \"new-york\", \"value\": 325, \"unit\": \"cases\"},\n",
" {\"j\": \"chicago\", \"value\": 300, \"unit\": \"cases\"},\n",
" {\"j\": \"topeka\", \"value\": 275, \"unit\": \"cases\"},\n",
"]\n",
"b = pd.DataFrame(b_data)\n",
"scen.add_par(\"b\", b)"
@@ -185,7 +186,7 @@
"metadata": {},
"outputs": [],
"source": [
"scen.par('b')"
"scen.par(\"b\")"
]
},
{
@@ -204,14 +205,14 @@
"metadata": {},
"outputs": [],
"source": [
"# distance in thousands of miles \n",
"# distance in thousands of miles\n",
"scen.init_par(\"d\", idx_sets=[\"i\", \"j\"])\n",
"# add more parameter elements as dataframe by index names \n",
"# add more parameter elements as dataframe by index names\n",
"d_data = [\n",
" {'i': \"seattle\", 'j': \"new-york\", 'value': 2.5, 'unit': \"km\"},\n",
" {'i': \"seattle\", 'j': \"chicago\", 'value': 1.7, 'unit': \"km\"},\n",
" {'i': \"seattle\", 'j': \"topeka\", 'value': 1.8, 'unit': \"km\"},\n",
" {'i': \"san-diego\", 'j': \"new-york\", 'value': 2.5, 'unit': \"km\"},\n",
" {\"i\": \"seattle\", \"j\": \"new-york\", \"value\": 2.5, \"unit\": \"km\"},\n",
" {\"i\": \"seattle\", \"j\": \"chicago\", \"value\": 1.7, \"unit\": \"km\"},\n",
" {\"i\": \"seattle\", \"j\": \"topeka\", \"value\": 1.8, \"unit\": \"km\"},\n",
" {\"i\": \"san-diego\", \"j\": \"new-york\", \"value\": 2.5, \"unit\": \"km\"},\n",
"]\n",
"d = pd.DataFrame(d_data)\n",
"scen.add_par(\"d\", d)\n",
@@ -223,9 +224,13 @@
},
{
"cell_type": "raw",
"metadata": {},
"metadata": {
"vscode": {
"languageId": "raw"
}
},
"source": [
"Scalar f freight in dollars per case per thousand miles /90/ ; "
"Scalar f freight in dollars per case per thousand miles /90/ ;"
]
},
{
@@ -234,8 +239,8 @@
"metadata": {},
"outputs": [],
"source": [
"# cost per case per 1000 miles \n",
"# initialize scalar with a value and a unit (and optionally a comment) \n",
"# cost per case per 1000 miles\n",
"# initialize scalar with a value and a unit (and optionally a comment)\n",
"scen.init_scalar(\"f\", 90.0, \"USD/km\")"
]
},
@@ -255,8 +260,8 @@
"# commit new scenario to the database\n",
"# no changes can then be made to the scenario data until a check-out is performed\n",
"comment = \"importing Dantzig's transport problem for illustration\"\n",
"comment += \" and testing of the Python interface using a generic datastructure\" \n",
"scen.commit(comment) \n",
"comment += \" and testing of the Python interface using a generic datastructure\"\n",
"scen.commit(comment)\n",
"\n",
"# set this new scenario as the default version for the model/scenario name\n",
"scen.set_as_default()"
@@ -273,12 +278,16 @@
},
{
"cell_type": "raw",
"metadata": {},
"metadata": {
"vscode": {
"languageId": "raw"
}
},
"source": [
"Variables\n",
" x(i,j) shipment quantities in cases\n",
" z total transportation costs in thousands of dollars ;\n",
" \n",
"\n",
"Equations\n",
" cost define objective function\n",
" supply(i) observe supply limit at plant i\n",
@@ -321,7 +330,7 @@
"metadata": {},
"outputs": [],
"source": [
"scen.solve(model='dantzig')"
"scen.solve(model=\"dantzig\")"
]
},
{
@@ -357,7 +366,8 @@
"metadata": {},
"outputs": [],
"source": [
"# display the quantities and marginals (=shadow prices) of the demand balance constraints\n",
"# Display the quantities and marginals (=shadow prices) of the demand balance\n",
"# constraints\n",
"scen.equ(\"demand\")"
]
},
64 changes: 37 additions & 27 deletions tutorial/transport/py_transport_scenario.ipynb
View file
Original file line number Diff line number Diff line change
@@ -38,8 +38,7 @@
"metadata": {},
"outputs": [],
"source": [
"# load required packages\n",
"import pandas as pd\n",
"# Import required packages\n",
"import ixmp"
]
},
@@ -60,8 +59,8 @@
"outputs": [],
"source": [
"# Model and scenario name for Dantzig's transport problem\n",
"model = 'canning problem'\n",
"scenario = 'standard'"
"model = \"canning problem\"\n",
"scenario = \"standard\""
]
},
{
@@ -106,7 +105,8 @@
"outputs": [],
"source": [
"from ixmp.testing import make_dantzig\n",
"scen = make_dantzig(mp, solve='.')"
"\n",
"scen = make_dantzig(mp, solve=\".\")"
]
},
{
@@ -135,8 +135,9 @@
"metadata": {},
"outputs": [],
"source": [
"# show only the distances for connections from Seattle by filtering the pandas.DataFrame returned above\n",
"d[d['i'] == \"seattle\"]"
"# Show only the distances for connections from Seattle by filtering the pandas.DataFrame\n",
"# returned above\n",
"d[d[\"i\"] == \"seattle\"]"
]
},
{
@@ -148,8 +149,8 @@
"# for faster access or more complex filtering,\n",
"# it may be easier to only load specific parameter elements using a dictionary\n",
"ele_filter = {}\n",
"ele_filter['i'] = ['seattle']\n",
"ele_filter['j'] = ['chicago', 'topeka']\n",
"ele_filter[\"i\"] = [\"seattle\"]\n",
"ele_filter[\"j\"] = [\"chicago\", \"topeka\"]\n",
"\n",
"d_filtered = scen.par(\"d\", ele_filter)\n",
"d_filtered"
@@ -172,7 +173,12 @@
"outputs": [],
"source": [
"# create a new scenario by cloning the scenario (without keeping the solution)\n",
"scen_detroit = scen.clone(model=model, scenario='detroit', annotation='extend the Transport problem by a new city', keep_solution=False)"
"scen_detroit = scen.clone(\n",
" model=model,\n",
" scenario=\"detroit\",\n",
" annotation=\"extend the Transport problem by a new city\",\n",
" keep_solution=False,\n",
")"
]
},
{
@@ -192,13 +198,13 @@
"outputs": [],
"source": [
"# reduce demand in chicago\n",
"scen_detroit.add_par('b', 'chicago', 200, 'cases')\n",
"scen_detroit.add_par(\"b\", \"chicago\", 200, \"cases\")\n",
"\n",
"# add a new city with demand and distances\n",
"scen_detroit.add_set('j', 'detroit')\n",
"scen_detroit.add_par('b', 'detroit', 150, 'cases')\n",
"scen_detroit.add_par('d', ['seattle', 'detroit'], 1.7, 'cases')\n",
"scen_detroit.add_par('d', ['san-diego', 'detroit'], 1.9, 'cases')"
"scen_detroit.add_set(\"j\", \"detroit\")\n",
"scen_detroit.add_par(\"b\", \"detroit\", 150, \"cases\")\n",
"scen_detroit.add_par(\"d\", [\"seattle\", \"detroit\"], 1.7, \"cases\")\n",
"scen_detroit.add_par(\"d\", [\"san-diego\", \"detroit\"], 1.9, \"cases\")"
]
},
{
@@ -226,7 +232,7 @@
"metadata": {},
"outputs": [],
"source": [
"scen_detroit.solve(model='dantzig')"
"scen_detroit.solve(model=\"dantzig\")"
]
},
{
@@ -249,21 +255,21 @@
"outputs": [],
"source": [
"# display the objective value of the solution in the baseline scenario\n",
"scen.var('z')"
"scen.var(\"z\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"jupyter": {
"name": "scen-detroit-z"
}
"jupyter": {
"name": "scen-detroit-z"
}
},
"outputs": [],
"source": [
"# display the objective value of the solution in the \"detroit\" scenario\n",
"scen_detroit.var('z')"
"scen_detroit.var(\"z\")"
]
},
{
@@ -272,8 +278,9 @@
"metadata": {},
"outputs": [],
"source": [
"# display the quantities transported from canning plants to demand locations in the baseline scenario\n",
"scen.var('x')"
"# Display the quantities transported from canning plants to demand locations in the\n",
"# baseline scenario\n",
"scen.var(\"x\")"
]
},
{
@@ -282,8 +289,9 @@
"metadata": {},
"outputs": [],
"source": [
"# display the quantities transported from canning plants to demand locations in the \"detroit\" scenario\n",
"scen_detroit.var('x')"
"# Display the quantities transported from canning plants to demand locations in the\n",
"# \"detroit\" scenario\n",
"scen_detroit.var(\"x\")"
]
},
{
@@ -292,7 +300,8 @@
"metadata": {},
"outputs": [],
"source": [
"# display the quantities and marginals (=shadow prices) of the demand balance constraints in the baseline scenario\n",
"# Display the quantities and marginals (=shadow prices) of the demand balance\n",
"# constraints in the baseline scenario\n",
"scen.equ(\"demand\")"
]
},
@@ -302,7 +311,8 @@
"metadata": {},
"outputs": [],
"source": [
"# display the quantities and marginals (=shadow prices) of the demand balance constraints in the \"detroit\" scenario\n",
"# Display the quantities and marginals (=shadow prices) of the demand balance\n",
"# constraints in the \"detroit\" scenario\n",
"scen_detroit.equ(\"demand\")"
]
},