def test_facet_basic():
# wrapped facet
chart1 = (alt.Chart("data.csv").mark_point().encode(
x="x:Q",
y="y:Q",
).facet("category:N", columns=2))
dct1 = chart1.to_dict()
assert dct1["facet"] == alt.Facet("category:N").to_dict()
assert dct1["columns"] == 2
assert dct1["data"] == alt.UrlData("data.csv").to_dict()
# explicit row/col facet
chart2 = (alt.Chart("data.csv").mark_point().encode(
x="x:Q",
y="y:Q",
).facet(row="category1:Q", column="category2:Q"))
dct2 = chart2.to_dict()
assert dct2["facet"]["row"] == alt.Facet("category1:Q").to_dict()
assert dct2["facet"]["column"] == alt.Facet("category2:Q").to_dict()
assert "columns" not in dct2
assert dct2["data"] == alt.UrlData("data.csv").to_dict()
def test_facet():
# wrapped facet
chart1 = alt.Chart('data.csv').mark_point().encode(
x='x:Q',
y='y:Q',
).facet('category:N', columns=2)
dct1 = chart1.to_dict()
assert dct1['facet'] == alt.Facet('category:N').to_dict()
assert dct1['columns'] == 2
assert dct1['data'] == alt.UrlData('data.csv').to_dict()
# explicit row/col facet
chart2 = alt.Chart('data.csv').mark_point().encode(
x='x:Q',
y='y:Q',
).facet(row='category1:Q', column='category2:Q')
dct2 = chart2.to_dict()
assert dct2['facet']['row'] == alt.Facet('category1:Q').to_dict()
assert dct2['facet']['column'] == alt.Facet('category2:Q').to_dict()
assert 'columns' not in dct2
assert dct2['data'] == alt.UrlData('data.csv').to_dict()
def test_transforms():
# aggregate transform
agg1 = alt.AggregatedFieldDef(**{"as": "x1", "op": "mean", "field": "y"})
agg2 = alt.AggregatedFieldDef(**{"as": "x2", "op": "median", "field": "z"})
chart = alt.Chart().transform_aggregate([agg1], ["foo"], x2="median(z)")
kwds = dict(aggregate=[agg1, agg2], groupby=["foo"])
assert chart.transform == [alt.AggregateTransform(**kwds)]
# bin transform
chart = alt.Chart().transform_bin("binned", field="field", bin=True)
kwds = {"as": "binned", "field": "field", "bin": True}
assert chart.transform == [alt.BinTransform(**kwds)]
# calcualte transform
chart = alt.Chart().transform_calculate("calc", "datum.a * 4")
kwds = {"as": "calc", "calculate": "datum.a * 4"}
assert chart.transform == [alt.CalculateTransform(**kwds)]
# impute transform
chart = alt.Chart().transform_impute("field", "key", groupby=["x"])
kwds = {"impute": "field", "key": "key", "groupby": ["x"]}
assert chart.transform == [alt.ImputeTransform(**kwds)]
# joinaggregate transform
chart = alt.Chart().transform_joinaggregate(min="min(x)", groupby=["key"])
kwds = {
"joinaggregate":
[alt.JoinAggregateFieldDef(field="x", op="min", **{"as": "min"})],
"groupby": ["key"],
}
assert chart.transform == [alt.JoinAggregateTransform(**kwds)]
# filter transform
chart = alt.Chart().transform_filter("datum.a < 4")
assert chart.transform == [alt.FilterTransform(filter="datum.a < 4")]
# flatten transform
chart = alt.Chart().transform_flatten(["A", "B"], ["X", "Y"])
kwds = {"as": ["X", "Y"], "flatten": ["A", "B"]}
assert chart.transform == [alt.FlattenTransform(**kwds)]
# fold transform
chart = alt.Chart().transform_fold(["A", "B", "C"], as_=["key", "val"])
kwds = {"as": ["key", "val"], "fold": ["A", "B", "C"]}
assert chart.transform == [alt.FoldTransform(**kwds)]
# lookup transform
lookup_data = alt.LookupData(alt.UrlData("foo.csv"), "id", ["rate"])
chart = alt.Chart().transform_lookup(from_=lookup_data,
as_="a",
lookup="a",
default="b")
kwds = {"from": lookup_data, "as": "a", "lookup": "a", "default": "b"}
assert chart.transform == [alt.LookupTransform(**kwds)]
# sample transform
chart = alt.Chart().transform_sample()
assert chart.transform == [alt.SampleTransform(1000)]
# stack transform
chart = alt.Chart().transform_stack("stacked", "x", groupby=["y"])
assert chart.transform == [
alt.StackTransform(stack="x", groupby=["y"], **{"as": "stacked"})
]
# timeUnit transform
chart = alt.Chart().transform_timeunit("foo", field="x", timeUnit="date")
kwds = {"as": "foo", "field": "x", "timeUnit": "date"}
assert chart.transform == [alt.TimeUnitTransform(**kwds)]
# window transform
chart = alt.Chart().transform_window(xsum="sum(x)",
ymin="min(y)",
frame=[None, 0])
window = [
alt.WindowFieldDef(**{
"as": "xsum",
"field": "x",
"op": "sum"
}),
alt.WindowFieldDef(**{
"as": "ymin",
"field": "y",
"op": "min"
}),
]
# kwargs don't maintain order in Python < 3.6, so window list can
# be reversed
assert chart.transform == [
alt.WindowTransform(frame=[None, 0], window=window)
] or chart.transform == [
alt.WindowTransform(frame=[None, 0], window=window[::-1])
]
def test_transforms():
# aggregate transform
agg1 = alt.AggregatedFieldDef(**{'as': 'x1', 'op': 'mean', 'field': 'y'})
agg2 = alt.AggregatedFieldDef(**{'as': 'x2', 'op': 'median', 'field': 'z'})
chart = alt.Chart().transform_aggregate([agg1], ['foo'], x2='median(z)')
kwds = dict(aggregate=[agg1, agg2], groupby=['foo'])
assert chart.transform == [alt.AggregateTransform(**kwds)]
# bin transform
chart = alt.Chart().transform_bin("binned", field="field", bin=True)
kwds = {'as': 'binned', 'field': 'field', 'bin': True}
assert chart.transform == [alt.BinTransform(**kwds)]
# calcualte transform
chart = alt.Chart().transform_calculate("calc", "datum.a * 4")
kwds = {'as': 'calc', 'calculate': 'datum.a * 4'}
assert chart.transform == [alt.CalculateTransform(**kwds)]
# impute transform
chart = alt.Chart().transform_impute("field", "key", groupby=["x"])
kwds = {"impute": "field", "key": "key", "groupby": ["x"]}
assert chart.transform == [alt.ImputeTransform(**kwds)]
# joinaggregate transform
chart = alt.Chart().transform_joinaggregate(min='min(x)', groupby=['key'])
kwds = {
'joinaggregate': [
alt.JoinAggregateFieldDef(field='x',
op=alt.AggregateOp('min'),
**{'as': 'min'})
],
'groupby': ['key']
}
assert chart.transform == [
alt.JoinAggregateTransform(joinaggregate=[
alt.JoinAggregateFieldDef(field='x',
op=alt.AggregateOp('min'),
**{'as': 'min'})
],
groupby=['key'])
]
# filter transform
chart = alt.Chart().transform_filter("datum.a < 4")
assert chart.transform == [alt.FilterTransform(filter="datum.a < 4")]
# flatten transform
chart = alt.Chart().transform_flatten(['A', 'B'], ['X', 'Y'])
kwds = {'as': ['X', 'Y'], 'flatten': ['A', 'B']}
assert chart.transform == [alt.FlattenTransform(**kwds)]
# fold transform
chart = alt.Chart().transform_fold(['A', 'B', 'C'], as_=['key', 'val'])
kwds = {'as': ['key', 'val'], 'fold': ['A', 'B', 'C']}
assert chart.transform == [alt.FoldTransform(**kwds)]
# lookup transform
lookup_data = alt.LookupData(alt.UrlData('foo.csv'), 'id', ['rate'])
chart = alt.Chart().transform_lookup(from_=lookup_data,
as_='a',
lookup='a',
default='b')
kwds = {'from': lookup_data, 'as': 'a', 'lookup': 'a', 'default': 'b'}
assert chart.transform == [alt.LookupTransform(**kwds)]
# sample transform
chart = alt.Chart().transform_sample()
assert chart.transform == [alt.SampleTransform(1000)]
# stack transform
chart = alt.Chart().transform_stack('stacked', 'x', groupby=['y'])
assert chart.transform == [
alt.StackTransform(stack='x', groupby=['y'], **{'as': 'stacked'})
]
# timeUnit transform
chart = alt.Chart().transform_timeunit("foo", field="x", timeUnit="date")
kwds = {'as': 'foo', 'field': 'x', 'timeUnit': 'date'}
assert chart.transform == [alt.TimeUnitTransform(**kwds)]
# window transform
chart = alt.Chart().transform_window(xsum='sum(x)',
ymin='min(y)',
frame=[None, 0])
window = [
alt.WindowFieldDef(**{
'as': 'xsum',
'field': 'x',
'op': 'sum'
}),
alt.WindowFieldDef(**{
'as': 'ymin',
'field': 'y',
'op': 'min'
})
]
# kwargs don't maintain order in Python < 3.6, so window list can
# be reversed
assert (chart.transform
== [alt.WindowTransform(frame=[None, 0], window=window)]
or chart.transform
== [alt.WindowTransform(frame=[None, 0], window=window[::-1])])
