def define_ctx():
with macro_mode_on():
ctx = context(toplevel=True)
ctx.cell1 = cell("json").set(1)
ctx.cell2 = cell("json").set(2)
ctx.result = cell("json")
ctx.tf = transformer({"a": "input", "b": "input", "c": "output"})
ctx.cell1_link = link(ctx.cell1)
ctx.cell1_link.connect(ctx.tf.a)
ctx.cell2.connect(ctx.tf.b)
ctx.code = pytransformercell().set("c = a + b")
ctx.code.connect(ctx.tf.code)
ctx.result_link = link(ctx.result)
ctx.tf.c.connect(ctx.result_link)
return ctx
def define_ctx():
with macro_mode_on():
ctx = context(toplevel=True)
ctx.cell1 = cell().set(1)
ctx.cell2 = cell().set(2)
ctx.result = cell()
ctx.tf = transformer({
"a": "input",
"b": "input",
"c": "output"
})
ctx.cell1_link = link(ctx.cell1)
ctx.cell1_link.connect(ctx.tf.a)
ctx.cell2.connect(ctx.tf.b)
ctx.code = pytransformercell().set("c = a + b")
ctx.code.connect(ctx.tf.code)
ctx.result_link = link(ctx.result)
ctx.tf.c.connect(ctx.result_link)
return ctx
def build(ctx, param, run):
print("BUILD")
tf = transformer({
"a": "input",
"b": "input",
"c": "output",
})
if param.startswith("PARAM"):
ctx.tf_alt1 = tf
else:
ctx.tf_alt2 = tf
ctx.tf = link(tf)
ctx.run = pytransformercell().set(run)
ctx.run.connect(tf.code)
ctx.a = link(tf.a).export()
ctx.b = link(tf.b).export()
ctx.c = link(tf.c).export()
ctx.param = cell("json").set(param)
def build(ctx, param, run):
print("BUILD")
tf = transformer(
{
"a": "input",
"b": "input",
"c": "output",
})
if param.startswith("PARAM"):
ctx.tf_alt1 = tf
else:
ctx.tf_alt2 = tf
ctx.tf = link(tf)
ctx.run = pytransformercell().set(run)
ctx.run.connect(tf.code)
ctx.a = cell()
ctx.a.connect(tf.a)
ctx.b = cell()
ctx.b.connect(tf.b)
ctx.c = cell()
tf.c.connect(ctx.c)
ctx.param = cell().set(param)
import seamless
from seamless.core import macro_mode_on
from seamless.core import context, cell, transformer, pytransformercell
with macro_mode_on():
ctx = context(toplevel=True)
ctx.cell1 = cell().set(1)
ctx.cell2 = cell().set(2)
ctx.result = cell()
ctx.tf = transformer({"a": "input", "b": "input", "c": "output"})
ctx.cell1.connect(ctx.tf.a)
ctx.cell2.connect(ctx.tf.b)
ctx.code = pytransformercell().set("c = a + b")
ctx.code.connect(ctx.tf.code)
ctx.tf.c.connect(ctx.result)
shell = ctx.tf.shell()
"a": "input",
"b": "input",
"c": "output"
})
ctx.a = StructuredCell(
"a",
ctx.cell1,
schema = None,
buffer = None,
plain = True,
inchannels = None,
outchannels = [()]
)
ctx.a.outchannels[()].connect(ctx.tf.a)
ctx.cell2.connect(ctx.tf.b)
ctx.code = pytransformercell().set("c = a + b")
ctx.code.connect(ctx.tf.code)
ctx.tf.c.connect(ctx.result)
ctx.mount("/tmp/mount-test")
ctx.equilibrate()
print(ctx.result.value)
ctx.a.set(1)
ctx.equilibrate()
print(ctx.result.value)
#shell = ctx.tf.shell()
#redis_cache = seamless.RedisCache()
with macro_mode_on():
ctx = context(toplevel=True)
ctx.cell1 = cell().set(2)
ctx.cell2 = cell().set(3)
ctx.result = cell()
ctx.tf = transformer({
"a": "input",
"b": "input",
"c": "output"
})
ctx.cell1.connect(ctx.tf.a)
ctx.cell2.connect(ctx.tf.b)
ctx.code = pytransformercell()
ctx.code.connect(ctx.tf.code)
ctx.tf.c.connect(ctx.result)
import asyncio
done = asyncio.sleep(1)
ctx.equilibrate()
asyncio.get_event_loop().run_until_complete(done)
ctx.code.from_label("Secret source code")
print("Secret source code", ctx.code.checksum)
ctx.equilibrate()
print(ctx.status)
print(ctx.result.checksum)
print(ctx.result.value)
import seamless
from seamless.core import macro_mode_on
from seamless.core import context, textcell, transformer, pytransformercell
with macro_mode_on():
ctx = context(toplevel=True)
ctx.cell1 = textcell().set(1)
ctx.cell2 = textcell().set(2)
ctx.result = textcell()
ctx.tf = transformer({"a": "input", "b": "input", "c": "output"})
ctx.cell1.connect(ctx.tf.a)
ctx.cell2.connect(ctx.tf.b)
ctx.code = pytransformercell().set("c = float(a) + float(b)")
ctx.code.connect(ctx.tf.code)
ctx.tf.c.connect(ctx.result)
ctx.result.mount("/tmp/mount-test/myresult", persistent=True)
ctx.mount("/tmp/mount-test")
ctx.sub = context(toplevel=False, context=ctx, name="sub")
ctx.sub.mycell = textcell().set("This is my cell\nend")
ctx.equilibrate()
print(ctx.result.value)
ctx.cell1.set(10)
ctx.equilibrate()
print(ctx.result.value)
print(ctx.result.value)
ctx.code.set("c = float(a) + float(b) + 1000")
ctx.equilibrate()
print(ctx.result.value)
print(ctx.status())
import seamless
from seamless.core import macro_mode_on
from seamless.core import context, cell, transformer, pytransformercell, link
with macro_mode_on():
ctx = context(toplevel=True)
ctx.cell1 = cell().set(1)
ctx.cell2 = cell().set(2)
ctx.result = cell()
ctx.tf = transformer({"a": "input", "b": "input", "c": "output"})
ctx.cell1_link = link(ctx.cell1)
ctx.cell1_link.connect(ctx.tf.a)
ctx.cell2.connect(ctx.tf.b)
ctx.code = pytransformercell().set("""
import time
time.sleep(3)
c = a + b
""")
ctx.code.connect(ctx.tf.code)
ctx.result_link = link(ctx.result)
ctx.tf.c.connect(ctx.result_link)
def callback():
print("Equilibration complete")
ctx._get_manager().on_equilibrate(callback)
ctx.cell1.set(10)
for n in range(5):
ctx.equilibrate(1)
ctx.cell1 = cell().set(1)
ctx.cell2 = cell().set(2)
ctx.result = cell()
ctx.tf = transformer({
"a": "input",
"b": "input",
"testmodule": ("input", "ref", "module", "python"),
"c": "output",
})
ctx.cell1.connect(ctx.tf.a)
ctx.cell2.connect(ctx.tf.b)
ctx.code = pytransformercell().set("""
print("macro execute")
print(testmodule)
print(testmodule.q)
from .testmodule import q
print(q)
import sys
print([m for m in sys.modules if m.find("testmodule") > -1])
c = a + b
print("/macro execute")""")
ctx.code.connect(ctx.tf.code)
ctx.tf.c.connect(ctx.result)
ctx.testmodule = pythoncell().set("q = 10")
ctx.testmodule.connect(ctx.tf.testmodule)
ctx.equilibrate()
print(ctx.result.value)
ctx.cell1.set(10)
ctx.equilibrate()
print(ctx.result.value)
cell1.mount("/tmp/mixedcell.mixed")
ctx.cell1 = cell1
print(ctx.cell1.storage)
print(ctx.cell1.form)
print(ctx.cell1.value)
ctx.cell2 = cell("mixed").set(80)
ctx.result = cell("mixed")
ctx.tf = transformer({
"a": "input",
"b": "input",
"c": "output"
})
ctx.cell1_link = link(ctx.cell1)
ctx.cell1_link.connect(ctx.tf.a)
ctx.cell2.connect(ctx.tf.b)
ctx.code = pytransformercell().set("c = a['x'] * a['y'] + b")
ctx.code.connect(ctx.tf.code)
ctx.result_link = link(ctx.result)
ctx.tf.c.connect(ctx.result_link)
ctx.result_copy = cell("mixed")
ctx.result.connect(ctx.result_copy)
print(ctx.cell1.value)
print(ctx.code.value)
ctx.equilibrate()
print(ctx.result.value, ctx.status)
ctx.cell2.set(10)
ctx.equilibrate()
print(ctx.result.value, ctx.status)
ctx.run.connect(tf.code)
ctx.a = cell()
ctx.a.connect(tf.a)
ctx.b = cell()
ctx.b.connect(tf.b)
ctx.c = cell()
tf.c.connect(ctx.c)
ctx.param = cell().set(param)
with macro_mode_on():
ctx = context(toplevel=True)
ctx.macro = macro({
"param":"copy",
"run": ("copy", "text"),
})
ctx.macro_code = pytransformercell().set(build)
ctx.macro_code.connect(ctx.macro.code)
ctx.run = pytransformercell().set(run)
ctx.run.connect(ctx.macro.run)
ctx.param = cell().set("PARAM")
ctx.param.connect(ctx.macro.param)
ctx.a = cell().set(1)
ctx.b = cell().set(2)
ctx.c = cell()
ctx.a.connect(ctx.macro.ctx.a)
ctx.b.connect(ctx.macro.ctx.b)
ctx.macro.ctx.c.connect(ctx.c)
ctx.equilibrate(0.5)
print(ctx.c.value)
ctx.run = pytransformercell().set(run)
ctx.run.connect(tf.code)
ctx.a = link(tf.a).export()
ctx.b = link(tf.b).export()
ctx.c = link(tf.c).export()
ctx.param = cell("json").set(param)
with macro_mode_on():
ctx = context(toplevel=True)
ctx.macro = macro({
"param": "copy",
"run": ("copy", "text"),
})
ctx.macro_code = pytransformercell().set(build)
ctx.macro_code.connect(ctx.macro.code)
ctx.run = pytransformercell().set(run)
ctx.run.connect(ctx.macro.run)
ctx.param = cell("json").set("PARAM")
ctx.param.connect(ctx.macro.param)
ctx.a = cell("json").set(1)
ctx.b = cell("json").set(2)
ctx.c = cell()
ctx.a.connect(ctx.macro.ctx.a)
ctx.b.connect(ctx.macro.ctx.b)
ctx.macro.ctx.c.connect(ctx.c)
ctx.equilibrate(0.5)
print(ctx.c.value)
print(ctx.macro.ctx.tf.status())