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Adapt tests to current pandas-implementation
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@glatterf42
glatterf42 committed Aug 30, 2024
1 parent a56cf4e commit 1d5d5e2
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Showing 2 changed files with 59 additions and 57 deletions.
57 changes: 29 additions & 28 deletions tests/core/test_optimization_variable.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,7 @@
import ixmp4
from ixmp4.core import IndexSet, OptimizationVariable

from ..utils import create_indexsets_for_run
from ..utils import assert_unordered_equality, create_indexsets_for_run


def df_from_list(variables: list):
Expand Down Expand Up @@ -58,6 +58,7 @@ def test_create_variable(self, platform: ixmp4.Platform):
assert variable_2.run_id == run.id
assert variable_2.name == "Variable 2"
assert variable_2.data == {} # JsonDict type currently requires dict, not None
assert variable_2.columns is not None
assert variable_2.columns[0].name == indexset.name
assert variable_2.constrained_to_indexsets == [indexset.name]
assert variable_2.levels == []
Expand All @@ -83,6 +84,7 @@ def test_create_variable(self, platform: ixmp4.Platform):
constrained_to_indexsets=[indexset.name],
column_names=["Column 1"],
)
assert variable_3.columns is not None
assert variable_3.columns[0].name == "Column 1"

# Test duplicate column_names raise
Expand All @@ -100,6 +102,7 @@ def test_create_variable(self, platform: ixmp4.Platform):
constrained_to_indexsets=[indexset.name, indexset_2.name],
)
# If indexset doesn't have elements, a generic dtype is registered
assert variable_4.columns is not None
assert variable_4.columns[0].dtype == "object"
assert variable_4.columns[1].dtype == "int64"

Expand All @@ -118,6 +121,7 @@ def test_get_variable(self, platform: ixmp4.Platform):
assert variable.data == {}
assert variable.levels == []
assert variable.marginals == []
assert variable.columns is not None
assert variable.columns[0].name == indexset.name
assert variable.constrained_to_indexsets == [indexset.name]

Expand Down Expand Up @@ -217,37 +221,34 @@ def test_variable_add_data(self, platform: ixmp4.Platform):
assert variable_2.levels == test_data_2["levels"]
assert variable_2.marginals == test_data_2["marginals"]

# Test order is conserved with varying types and upon later addition of data
variable_3 = run.optimization.variables.create(
name="Variable 3",
# Test updating of existing keys
variable_4 = run.optimization.variables.create(
name="Variable 4",
constrained_to_indexsets=[indexset.name, indexset_2.name],
column_names=["Column 1", "Column 2"],
)

test_data_3 = {
"Column 1": ["bar", "foo", ""],
"Column 2": [2, 3, 1],
"levels": [3, 2.0, 1],
"marginals": [100000, 1, 0.00001],
test_data_6 = {
indexset.name: ["foo", "foo", "bar", "bar"],
indexset_2.name: [1, 3, 1, 2],
"levels": [0.00001, "2", 2.3, 400000],
"marginals": [6, 7.8, 9, 0],
}
variable_3.add(data=test_data_3)
assert variable_3.data == test_data_3
assert variable_3.levels == test_data_3["levels"]
assert variable_3.marginals == test_data_3["marginals"]

test_data_4 = {
"Column 1": ["foo", "", "bar"],
"Column 2": [2, 3, 1],
"levels": [3.14, 2, 1.0],
"marginals": [1, 0.00001, 100000],
variable_4.add(data=test_data_6)
test_data_7 = {
indexset.name: ["foo", "foo", "bar", "bar", "bar"],
indexset_2.name: [1, 2, 3, 2, 1],
"levels": [0.00001, 2.3, 3, "400000", "5"],
"marginals": [6, 7.8, 9, "0", 3],
}
variable_3.add(data=test_data_4)
test_data_5 = test_data_3.copy()
for key, value in test_data_4.items():
test_data_5[key].extend(value) # type: ignore
assert variable_3.data == test_data_5
assert variable_3.levels == test_data_5["levels"]
assert variable_3.marginals == test_data_5["marginals"]
variable_4.add(data=test_data_7)
expected = (
pd.DataFrame(test_data_7)
.set_index([indexset.name, indexset_2.name])
.combine_first(
pd.DataFrame(test_data_6).set_index([indexset.name, indexset_2.name])
)
.reset_index()
)
assert_unordered_equality(expected, pd.DataFrame(variable_4.data))

def test_variable_remove_data(self, platform: ixmp4.Platform):
run = platform.runs.create("Model", "Scenario")
Expand Down
59 changes: 30 additions & 29 deletions tests/data/test_optimization_variable.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,7 @@
import ixmp4
from ixmp4.data.abstract import OptimizationVariable

from ..utils import create_indexsets_for_run
from ..utils import assert_unordered_equality, create_indexsets_for_run


def df_from_list(variables: list):
Expand Down Expand Up @@ -57,6 +57,7 @@ def test_create_variable(self, platform: ixmp4.Platform):
assert variable_2.run__id == run.id
assert variable_2.name == "Variable 2"
assert variable_2.data == {} # JsonDict type currently requires dict, not None
assert variable_2.columns is not None
assert variable_2.columns[0].name == indexset.name
assert variable_2.columns[0].constrained_to_indexset == indexset.id

Expand Down Expand Up @@ -94,6 +95,7 @@ def test_create_variable(self, platform: ixmp4.Platform):
constrained_to_indexsets=[indexset.name],
column_names=["Column 1"],
)
assert variable_3.columns is not None
assert variable_3.columns[0].name == "Column 1"

# Test duplicate column_names raise
Expand All @@ -118,6 +120,7 @@ def test_create_variable(self, platform: ixmp4.Platform):
constrained_to_indexsets=[indexset.name, indexset_2.name],
)
# If indexset doesn't have elements, a generic dtype is registered
assert variable_4.columns is not None
assert variable_4.columns[0].dtype == "object"
assert variable_4.columns[1].dtype == "int64"

Expand Down Expand Up @@ -247,44 +250,42 @@ def test_variable_add_data(self, platform: ixmp4.Platform):
)
assert variable_2.data == test_data_2

# Test order is conserved with varying types and upon later addition of data
variable_3 = platform.backend.optimization.variables.create(
# Test updating of existing keys
variable_4 = platform.backend.optimization.variables.create(
run_id=run.id,
name="Variable 3",
name="Variable 4",
constrained_to_indexsets=[indexset.name, indexset_2.name],
column_names=["Column 1", "Column 2"],
)

test_data_3 = {
"Column 1": ["bar", "foo", ""],
"Column 2": [2, 3, 1],
"levels": [3, 2.0, -1],
"marginals": [100000, 1, 0.00001],
test_data_6 = {
indexset.name: ["foo", "foo", "bar", "bar"],
indexset_2.name: [1, 3, 1, 2],
"levels": [0.00001, "2", 2.3, 400000],
"marginals": [6, 7.8, 9, 0],
}
platform.backend.optimization.variables.add_data(
variable_id=variable_3.id, data=test_data_3
variable_id=variable_4.id, data=test_data_6
)
variable_3 = platform.backend.optimization.variables.get(
run_id=run.id, name="Variable 3"
)
assert variable_3.data == test_data_3

test_data_4 = {
"Column 1": ["foo", "", "bar"],
"Column 2": [2, 3, 1],
"levels": [3.14, 2, -1],
"marginals": [1, 0.00001, 100000],
test_data_7 = {
indexset.name: ["foo", "foo", "bar", "bar", "bar"],
indexset_2.name: [1, 2, 3, 2, 1],
"levels": [0.00001, 2.3, 3, "400000", "5"],
"marginals": [6, 7.8, 9, "0", 3],
}
platform.backend.optimization.variables.add_data(
variable_id=variable_3.id, data=test_data_4
variable_id=variable_4.id, data=test_data_7
)
variable_3 = platform.backend.optimization.variables.get(
run_id=run.id, name="Variable 3"
variable_4 = platform.backend.optimization.variables.get(
run_id=run.id, name="Variable 4"
)
expected = (
pd.DataFrame(test_data_7)
.set_index([indexset.name, indexset_2.name])
.combine_first(
pd.DataFrame(test_data_6).set_index([indexset.name, indexset_2.name])
)
.reset_index()
)
test_data_5 = test_data_3.copy()
for key, value in test_data_4.items():
test_data_5[key].extend(value) # type: ignore
assert variable_3.data == test_data_5
assert_unordered_equality(expected, pd.DataFrame(variable_4.data))

def test_variable_remove_data(self, platform: ixmp4.Platform):
run = platform.backend.runs.create("Model", "Scenario")
Expand Down

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