def test_add_variable_from_series():
db = dt.GamsPandasDatabase()
t = pd.Index(range(2010, 2026), name="t")
var = pd.Series(1.4, index=t, name="var")
db.add_variable_from_series(var, add_missing_domains=True)
assert all(db["var"] == 1.4)
assert len(db["var"]) == 16
def test_add_parameter_from_dataframe():
db = dt.GamsPandasDatabase()
df = pd.DataFrame()
df["t"] = range(2025, 2036)
df["value"] = 1.3
db.add_parameter_from_dataframe("par", df, add_missing_domains=True)
assert all(db["par"] == 1.3)
assert len(db["par"]) == 11
def test_export_NAs():
db = dt.GamsPandasDatabase()
t = db.create_set("t", range(5))
p = db.create_parameter("p", t)
assert len(db["p"]) == 5
assert len(db.symbols["p"]) == 0
db.export("test_export.gdx")
db = dt.Gdx("test_export.gdx")
assert all(pd.isna(db["p"]))
assert len(db["p"]) == 0
assert len(db.symbols["p"]) == 0
def test_add_variable_from_dataframe():
db = dt.GamsPandasDatabase()
df = pd.DataFrame([
[2010, "ser", 3],
[2010, "goo", 2],
[2020, "ser", 6],
[2020, "goo", 4],
],
columns=["t", "s", "value"])
db.add_variable_from_dataframe("q", df, add_missing_domains=True)
assert all(db.t == [2010, 2020])
assert all(db.s == ["ser", "goo"])
def test_add_parameter_from_series():
db = dt.GamsPandasDatabase()
t = pd.Index(range(2025, 2036), name="t")
par = pd.Series(1.4, index=t, name="par")
db.add_parameter_from_series(par, add_missing_domains=True)
assert all(db["par"] == 1.4)
assert len(db["par"]) == 11
ss = pd.Index(["foo"], name="ss")
singleton = pd.Series(1.4, index=ss, name="singleton")
db.add_parameter_from_series(singleton, add_missing_domains=True)
assert db["singleton"]["foo"] == 1.4
assert len(db["singleton"]) == 1
scalar = pd.Series(1.4, name="scalar")
db.add_parameter_from_series(scalar)
assert all(db["scalar"] == 1.4)
assert len(db["scalar"]) == 1
def test_multiply_added():
db = dt.GamsPandasDatabase()
df = pd.DataFrame([
[2025, "ser", 3],
[2025, "goo", 2],
[2035, "ser", 6],
[2035, "goo", 4],
],
columns=["t", "s", "value"])
db.add_parameter_from_dataframe("q", df, add_missing_domains=True)
df = pd.DataFrame([
[2025, 1],
[2035, 1.2],
], columns=["t", "value"])
db.add_parameter_from_dataframe("p", df, add_missing_domains=True)
v = db["p"] * db["q"]
v.name = "v"
db.add_parameter_from_series(v)
assert db["v"][2035, "goo"] == 4.8
def test_create_set_from_index():
db = dt.GamsPandasDatabase()
t = pd.Index(range(2025, 2036), name="t")
db.create_set("t", t)
assert db["t"].name == "t"
assert all(db["t"] == t)
assert db.symbols["t"].domains_as_strings == ["*"]
assert db.t.domains == ["*"]
db.create_set("tsub", t[5:], domains=["t"])
assert db["tsub"].name == "tsub"
assert all(db["tsub"] == t[5:])
assert db.symbols["tsub"].domains_as_strings == ["t"]
assert db.tsub.domains == ["t"]
s = pd.Index(["services", "goods"], name="s")
st = pd.MultiIndex.from_product([s, t], names=["s", "t"])
db.create_set("st", st)
assert db["st"].name == "st"
assert all(db["st"] == st[:])
assert db.symbols["st"].domains_as_strings == ["s", "t"]
assert db.st.domains == ["s", "t"]
def test_import_export_empty():
# Create empty GamsPandasDatabase and alias creation methods
db = dt.GamsPandasDatabase()
Par, Var, Set = db.create_parameter, db.create_variable, db.create_set
# Create sets from scratch
t = Set("t", range(2000, 2021), "Årstal")
s = Set("s", ["tjenester", "fremstilling"], "Brancher",
["Tjenester", "Fremstilling"])
p = Par("p", [s, t])
v = Var("v", [s, t])
db.p = p.loc[[], []]
db.v = p.loc[[], []]
db.export("test_export.gdx")
db = dt.Gdx("test_export.gdx")
db.p.loc[t, s] = 1
db.v.loc[t, s] = 1
db.export("test_export.gdx")
db = dt.Gdx("test_export.gdx")
assert all(db.p == 1)
assert all(db.v == 1)
def test_create_methods():
# Create empty GamsPandasDatabase and alias creation methods
db = dt.GamsPandasDatabase()
Par, Var, Set = db.create_parameter, db.create_variable, db.create_set
# Create sets from scratch
t = Set("t", range(2000, 2021), "Årstal")
s = Set("s", ["tjenester", "fremstilling"], "Brancher",
["Tjenester", "Fremstilling"])
st = Set("st", [s, t], "Branche x år dummy")
sub = Set("sub", ["tjenester"], "Subset af brancher", domains=["s"])
one2one = Set("one2one", [(2010, 2015), (2011, 2016)],
"1 til 1 mapping",
domains=["t", "t"])
one2many = Set(
"one2many",
[("tot", "tjenester"), ("tot", "fremstilling")],
"1 til mange mapping",
domains=["*", "s"],
)
assert one2many.name == "one2many"
assert one2many.names == ["*", "s"]
assert one2many.domains == ["*", "s"]
# Create parameters and variables based on zero ore more sets
gq = Par("gq", None, "Produktivitets-vækst", 0.01)
fq = Par("fp", t, "Vækstkorrektionsfaktor", (1 + 0.01)**(t - 2010))
d = Par("d", st, "Dummy")
y = Var("y", [s, t], "Produktion")
assert gq == 0.01
assert all(fq.loc[2010:2011] == [1, 1.01])
assert pd.isna(d["tjenester", 2010])
assert pd.isna(y["tjenester", 2010])
def test_create_parameter():
db = dt.GamsPandasDatabase()
db.create_parameter("scalar", data=3.2)
assert db.scalar == 3.2
db.create_parameter("vector",
data=[1, 2],
index=pd.Index(["a", "b"], name="ab"),
add_missing_domains=True)
assert all(db.vector == [1, 2])
db.create_parameter("dataframe",
data=pd.DataFrame([
[2025, "ser", 3],
[2025, "goo", 2],
[2035, "ser", 6],
[2035, "goo", 4],
],
columns=["t", "s", "value"]),
add_missing_domains=True)
db.export("test_export.gdx")
assert dt.Gdx("test_export.gdx")["scalar"] == 3.2
assert all(dt.Gdx("test_export.gdx")["vector"] == [1, 2])
assert all(db.s == ["ser", "goo"])
assert all(db.t == [2025, 2035])
# Check at der eksisterer en 'images' mappe og opret den hvis den ikke gør
import os
if not os.path.exists("images"):
os.mkdir("images")
# Plot som statisk billed, fx som svg, pdf eller png. Bemærk at mange billed-formater kan åbnes direkte i PyCharm
qBNP.plot(file="images/plot.svg")
# Dette kræver at man har Orca installeret - spørg evt. Lucas eller undertegnede for hjælp
# Skift om der renderes til browser, pdf, png etc. som standard
import plotly.io as pio
pio.renderers.default = "browser"
# Lav ny GamsPandasDatabase og alias af metoder til at lave nye symboler
db = dt.GamsPandasDatabase()
Par, Var, Set = db.create_parameter, db.create_variable, db.create_set
# Definer sets
t = Set("t", range(2000, 2020), "Årstal")
s = Set("s", ["tjenester", "fremstilling"], "Brancher")
st = Set("st", [s, t], "Branche x år dummy")
sub = Set("sub", ["tjenester"], "Subset af brancher", domains=["s"])
one2one = Set("one2one", [(2010, 2015), (2011, 2016)],
"1 til 1 mapping",
domains=["t", "t"])
one2many = Set(
"one2many",
[("tot", "tjenester"), ("tot", "fremstilling")],