"""
if not hasattr(self, "kw"):
self.kw = {}
n = 0
while 1:
n += 1
c = "cell%d" % n
if c not in self.kw:
break
self.kw[c] = function.path
"""
# 2: obtain graph and zip
ctx0.constructor_code = Cell("code").set(constructor)
ctx0.constructor_params = parameters
ctx0.api_schema = api_schema
ctx0.compute()
graph = ctx0.get_graph()
zip = ctx0.get_zip()
# 5: Save graph and zip
import os, json
currdir = os.path.dirname(os.path.abspath(__file__))
graph_filename = os.path.join(currdir, "../channel.seamless")
json.dump(graph, open(graph_filename, "w"), sort_keys=True, indent=2)
zip_filename = os.path.join(currdir, "../channel.zip")
"io": "edit"
},
"merged": {
"type": "cell",
"celltype": "text",
"io": "output"
},
"state": {
"type": "cell",
"celltype": "str",
"io": "output"
}
}
ctx.constructor_params = constructor_params
ctx.macro_code = Cell("code").set(macro_code)
ctx.code_start = set_resource("cell-merge-START.py")
ctx.code_update = set_resource("cell-merge-UPDATE.py")
ctx.compute()
# 2: obtain graph and zip
graph = ctx.get_graph()
zip = ctx.get_zip()
# 3: Package the contexts in a library
from seamless.highlevel.library import LibraryContainer
mylib = LibraryContainer("mylib")
subinstance = getattr(subinstance, subpath)
if not isinstance(subinstance, Cell):
raise TypeError("Invalid path {} is {} instead of Cell" %
(path, type(subinstance)))
export_cell.connect_from(subinstance, target_path=name)
ctx.constructor_code = Cell("code").set(constructor)
ctx.constructor_params = {
"template": "context",
"pattern": "value",
"ncopies": "value",
"imports": {
"type": "celldict",
"io": "input"
},
"exports": {
"type": "celldict",
"io": "output"
},
"entries": "value",
"exits": "value",
}
ctx.compute()
# 2: obtain graph and zip
graph = ctx.get_graph()
zip = ctx.get_zip()
result.connect_from(ctx.result)
ctx.macro_code = Cell("code").set(macro_code)
ctx.constructor_code = Cell("code").set(constructor)
ctx.constructor_params = {
"language": {
"type": "value",
"io": "input",
"default": "python"
},
"code": "value",
"result": {
"type": "cell",
"io": "output"
},
"scatter": "value",
"celltypes": {
"type": "value",
"io": "input",
"default": {}
},
"inputpins": {
"type": "kwargs",
"io": "input"
},
}
ctx.compute()
# 2: obtain graph and zip
setattr(ctx.join_macro, input_name, input_cell)
inputs[input_name].connect(input_cell)
ctx.output = ctx.join_macro.output
ctx_switch.switch_code = Cell("code")
ctx_switch.switch_code = switch_func
ctx_switch.constructor_code = Cell("code")
ctx_switch.constructor_code = constructor_switch
ctx_switch.constructor_params = {
"celltype": "value",
"input": {
"type": "cell",
"io": "input"
},
"selected": {
"type": "cell",
"io": "input"
},
"outputs": {
"type": "celldict",
"io": "output"
},
}
ctx_switch.compute()
ctx_join.join_code = Cell("code")
ctx_join.join_code = join_func
ctx_join.constructor_code = Cell("code")
ctx_join.constructor_code = constructor_join
ctx_join.constructor_params = {
"celltype": "value",
ctx.subtract.pins.b.celltype = celltype
ctx.c = ctx.subtract
ctx.subtract_code = Cell("code")
ctx.subtract_code = subtract_func
ctx.constructor_code = Cell("code")
ctx.constructor_code = constructor
ctx.constructor_params = {
"celltype": "value",
"a": {
"type": "cell",
"io": "input"
},
"b": {
"type": "cell",
"io": "input"
},
"c": {
"type": "cell",
"io": "output"
},
}
ctx.compute()
# 2: obtain graph and zip
graph = ctx.get_graph()
zip = ctx.get_zip()
# 3: Package the context in a library
