def test_map_list(mylib):
from seamless.highlevel import Context, Cell
ctx = Context()
ctx.include(mylib.map_list)
ctx.add = Context()
ctx.add.inp = Cell("mixed")
def add(a, b):
print("ADD", a, b)
return a + b
ctx.add.tf = add
ctx.add.tf.debug = True
ctx.add.tf.a = ctx.add.inp
ctx.add.tf.b = 1000
ctx.add.result = ctx.add.tf
ctx.add.result.celltype = "int"
ctx.compute()
ctx.inp = [10, 20, 30, 40]
ctx.inp.hash_pattern = {"!": "#"}
ctx.result = Cell()
ctx.mapping = ctx.lib.map_list(context_graph=ctx.add,
inp=ctx.inp,
result=ctx.result,
elision=True,
elision_chunksize=2)
ctx.compute()
print(ctx.mapping.ctx.m.exception)
print(ctx.result.value)
ctx.inp += [80, 12, 1, 1, 10, 20, 30, 40]
ctx.compute()
print(ctx.result.value)
def test_map_dict(mylib):
from seamless.highlevel import Context, Cell
ctx = Context()
ctx.include(mylib.map_dict)
ctx.add = Context()
ctx.add.inp = Cell("mixed")
def add(a, b):
print("ADD", a, b)
return a + b
ctx.add.tf = add
ctx.add.tf.debug = True
ctx.add.tf.a = ctx.add.inp
ctx.add.tf.b = 1000
ctx.add.result = ctx.add.tf
ctx.add.result.celltype = "int"
ctx.compute()
ctx.inp = {"key1": 10, "key2": 220, "key3": 30, "key4": 40}
ctx.inp.hash_pattern = {"*": "#"}
ctx.result = Cell()
ctx.keyorder = Cell("plain")
ctx.mapping = ctx.lib.map_dict(context_graph=ctx.add,
inp=ctx.inp,
keyorder0=[],
keyorder=ctx.keyorder,
result=ctx.result,
elision=True,
elision_chunksize=2)
ctx.compute()
print(ctx.mapping.ctx.status)
print(ctx.mapping.ctx.m.ctx.top.exception)
print(ctx.result.value)
ctx.mapping.keyorder0 = ctx.keyorder.value
ctx.compute()
print(ctx.result.value)
inp = ctx.inp.value
inp.update({
"a": 80,
"b": 30,
"c": 999,
"d": -1,
})
ctx.inp = inp
ctx.compute()
print(ctx.result.value)
print(ctx.keyorder.value)
def test_map_list_N_uniform(mylib):
from seamless.highlevel import Context, Cell
ctx = Context()
ctx.include(mylib.map_list_N)
ctx.add = Context()
ctx.add.uniform = Cell("mixed")
ctx.add.inp = Context()
ctx.add.inp.a = Cell("mixed")
ctx.add.inp.b = Cell("mixed")
def add(a, b, c):
print("ADD", a, b, c)
return a + b + c
ctx.add.tf = add
ctx.add.tf.debug = True
ctx.add.tf.a = ctx.add.inp.a
ctx.add.tf.b = ctx.add.inp.b
ctx.add.tf.c = ctx.add.uniform
ctx.add.result = ctx.add.tf
ctx.add.result.celltype = "int"
ctx.compute()
ctx.a = [110, 120, 130, 140]
ctx.a.hash_pattern = {"!": "#"}
ctx.b = [2, 4, 8, 12]
ctx.b.hash_pattern = {"!": "#"}
ctx.c = 7000
ctx.result = Cell()
ctx.mapping = ctx.lib.map_list_N(context_graph=ctx.add,
inp={
"a": ctx.a,
"b": ctx.b,
},
uniform=ctx.c,
result=ctx.result,
elision=True,
elision_chunksize=2)
ctx.compute()
print(ctx.result.value)
ctx.c = 8000
ctx.compute()
print(ctx.result.value)
from seamless.highlevel import Context, Cell, Macro
ctx = Context()
ctx.a = Cell("int")
ctx.b = Cell("int")
def add(a,b):
return a+b
ctx.add = add
ctx.add.a = ctx.a
ctx.add.b = ctx.b
ctx.result = ctx.add
ctx.result.celltype = "int"
ctx.compute()
graph = ctx.get_graph(runtime=True)
ctx = Context()
ctx.graph = Cell("plain").set(graph)
m = ctx.m = Macro()
ctx.par_static = 100
ctx.par_dynamic = 20
m.par_static = ctx.par_static
m.graph = ctx.graph
m.pins.par_dynamic = {"io": "input", "celltype": "int"}
m.pins.graph_result = {"io": "output", "celltype": "int"}
def run_macro(ctx, par_static, graph):
print("RUN MACRO", par_static)
ctx.subctx = HighLevelContext(graph)
ctx.subctx.a.set(par_static)
ctx.par_dynamic = cell("int")
ctx.par_dynamic.connect(ctx.subctx.b)
from seamless.highlevel import Context, Transformer, Cell
ctx = Context()
ctx.a = Cell()
ctx.a.set(2)
ctx.b = Cell()
ctx.b.set(3)
ctx.add = Transformer()
ctx.add.a = ctx.a
ctx.add.b = ctx.b
ctx.add.code = "a + b"
ctx.result = Cell()
ctx.result = ctx.add
ctx.compute()
ctx.save_graph("intro.seamless")
ctx.save_zip("intro.zip")
