def test_multi_input_summary(self):
class Str(str):
pass
@summarize.register(Str)
def _(s):
return PartialSummary(str(s), None)
class TestWidget(OWBaseWidget):
class Inputs:
input_a = MultiInput("A", Str)
@Inputs.input_a
def set_a(self, index, a):
pass
@Inputs.input_a.insert
def insert_a(self, index, a):
pass
@Inputs.input_a.remove
def remove_a(self, index):
pass
w = TestWidget()
w.insert_a(0, Str("00"))
w.insert_a(1, Str("11"))
self.assertSequenceEqual(
list(w.input_summaries["A"].values()),
[PartialSummary("00", None),
PartialSummary("11", None)])
w.set_a(0, None)
self.assertSequenceEqual(list(w.input_summaries["A"].values()),
[PartialSummary("11", None)])
w.set_a(0, Str("00"))
self.assertSequenceEqual(
list(w.input_summaries["A"].values()),
[PartialSummary("00", None),
PartialSummary("11", None)])
w.insert_a(1, Str("05"))
self.assertSequenceEqual(list(w.input_summaries["A"].values()), [
PartialSummary("00", None),
PartialSummary("05", None),
PartialSummary("11", None)
])
w.set_a(1, None)
w.remove_a(1)
self.assertSequenceEqual(
list(w.input_summaries["A"].values()),
[PartialSummary("00", None),
PartialSummary("11", None)])
def summarize_(preprocessor: Preprocess):
if isinstance(preprocessor, PreprocessorList):
if preprocessor.preprocessors:
details = "<br/>".join(map(_name_of, preprocessor.preprocessors))
else:
details = _nobr(f"{_name_of(preprocessor)} (empty)")
else:
details = _name_of(preprocessor)
return PartialSummary("🄿", details)
def summarize_(attributes): # pylint: disable=function-redefined
n = len(attributes)
if n == 0:
details = "empty list"
elif n <= 3:
details = _nobr(", ".join(var.name for var in attributes))
else:
details = _nobr(", ".join(var.name for var in attributes[:2]) +
f" and {n - 2} others")
return PartialSummary(n, details)
def summarize_(corpus: Corpus) -> PartialSummary:
"""
Provides automated input and output summaries for Corpus
"""
table_summary = summarize.dispatch(Table)(corpus)
extras = ((f"<br/><nobr>Tokens: {sum(map(len, corpus.tokens))}, "
f"Types: {len(corpus.dictionary)}</nobr>")
if corpus.has_tokens() else
"<br/><nobr>Corpus is not preprocessed</nobr>")
return PartialSummary(table_summary.summary,
table_summary.details + extras)
def summarize_(net: Network):
n = net.number_of_nodes()
if len(net.edges) == 1:
nettype = ['Network', 'Directed network'][net.edges[0].directed]
details = f"<nobr>{nettype} with {n} nodes " \
f"and {net.number_of_edges()} edges</nobr>"
else:
details = f"<nobr>Network with {n} nodes"
if net.edges:
details += " and {len(net.edges)} edge types:</nobr><ul>" + "".join(
f"<li>{len(edges)} edges, "
f"{['undirected', 'directed'][edges.directed]}</li>"
for edges in net.edges)
return PartialSummary(n, details)
def summarize(_: SignalTypeA):
return PartialSummary("foo", "bar")
def summarize_(model: type_):
return PartialSummary(symbol, _name_of(model))
def summarize_(results: Results): # pylint: disable=function-redefined
nmethods, ninstances = results.predicted.shape
summary = f"{nmethods}×{ninstances}"
details = f"{nmethods} method{_plural(nmethods)} " \
f"on {ninstances} test instance{_plural(ninstances)}"
return PartialSummary(summary, _nobr(details))
def summarize_(matrix: DistMatrix): # pylint: disable=function-redefined
n, m = matrix.shape
return PartialSummary(f"{n}×{m}", _nobr(f"{n}×{m} distance matrix"))
def summarize_(data: Table):
return PartialSummary(data.approx_len(),
format_summary_details(data, format=Qt.RichText))
def _(s):
return PartialSummary(str(s), None)