def test_graph_graphviz(self):
fLOG(__file__,
self._testMethodName,
OutputPrint=__name__ == "__main__")
code = """
namespace hello
{
public static class world
{
public static double function(double x, doubly y)
{
return x+y ;
}
}
}
"""
clparser, cllexer = get_parser_lexer("C#")
parser = parse_code(code, clparser, cllexer)
tree = parser.parse()
st = get_tree_graph(tree, parser)
dot = st.to_dot()
# fLOG(dot)
assert len(dot) > 0
if "travis" not in sys.executable:
temp = get_temp_folder(__file__, "temp_dot_grammar")
name = os.path.join(temp, "graph.dot")
with open(name, "w") as f:
f.write(dot)
img = os.path.join(temp, "graph.png")
run_dot(name, img)
assert os.path.exists(img)
def test_simple_for_workflow(self):
fLOG(__file__,
self._testMethodName,
OutputPrint=__name__ == "__main__")
code = """
data_a = flowdata.DataCloud ;
modb = flowmodule.RandomFilter(
n= 100
) ;
connect data_a to modb.input ;
if ( positive_number(modb.average)) {
modc = flowmodule.Classify(model="LinearRegression") ;
connect ( modb.data , modc.train_data ) ;
if ( positive_number(modc.number)) {
modc2 = flowmodule.Classify(model="SVM") ;
connect ( modc.data , mod2.input ) ;
for ( loopv in range(10) ) {
modcl = flowmodule.Classify(model=loopv) ;
connect ( modc2.data , modcl.output ) ;
}
}
}
"""
clparser, cllexer = get_parser_lexer("SimpleWorkflow")
parser = parse_code(code, clparser, cllexer)
tree = parser.parse()
st = get_tree_string(tree, parser)
assert len(st) > 0
st = get_tree_graph(tree, parser)
dot = st.to_dot()
assert len(dot) > 0
if "travis" not in sys.executable:
temp = get_temp_folder(__file__, "temp_simpleworkflow_grammar_for")
name = os.path.join(temp, "graph.dot")
with open(name, "w") as f:
f.write(dot)
img = os.path.join(temp, "graph.png")
run_dot(name, img)
assert os.path.exists(img)
def test_graph_style(self):
fLOG(__file__,
self._testMethodName,
OutputPrint=__name__ == "__main__")
temp = get_temp_folder(__file__, "temp_dot")
# still some issues with scipy and fortran compiler + graphviz
# dependency
from sklearn.datasets import load_iris
from sklearn import tree
clf = tree.DecisionTreeClassifier()
iris = load_iris()
clf = clf.fit(iris.data, iris.target)
outfile = os.path.join(temp, "out_tree.dot")
tree.export_graphviz(clf, out_file=outfile)
img = os.path.join(temp, "out_tree.png")
try:
run_dot(outfile, img)
except FileNotFoundError as e:
p = os.environ.get("PATH", None)
raise Exception("PATH={0}".format(p)) from e
self.assertExists(img)