def test_add_function(self):
code = "def foo(a, b):\n" " x, y = a, b\n" " return x, y\n" "c, d = foo(1, 2)"
block = Block(code)
info = find_functions(block.ast)
func = FunctionCall.from_ast(block.sub_blocks[-1].ast, info)
model = ExecutionModel()
model.add_function(func)
assert len(model.sorted_statements) == 1
desired = "\ndef foo(a, b): \n x, y = (a, b)\n return x, y\n\nx_, y_ = foo(1, 2)"
self.assertEqual(desired, model.code)
def __init__(self, code=None, shared_context=None, *args, **kwargs):
super(Experiment, self).__init__(*args, **kwargs)
if code is None:
self.exec_model = ExecutionModel()
else:
self.exec_model = ExecutionModel.from_code(code)
self.controller = BlockGraphController(execution_model = self.exec_model)
self.canvas = BlockCanvas(graph_controller=self.controller)
self.controller.canvas = self.canvas
self._shared_context = shared_context
self._local_context = DataContext(name = self._LOCAL_NAME_TEMPLATE())
self._update_exec_context()
def __init__(self, code=None, shared_context=None, *args, **kwargs):
super(Experiment, self).__init__(*args, **kwargs)
if code is None:
self.exec_model = ExecutionModel()
else:
self.exec_model = ExecutionModel.from_code(code)
self.controller = BlockGraphController(execution_model=self.exec_model)
self.canvas = BlockCanvas(graph_controller=self.controller)
self.controller.canvas = self.canvas
self._shared_context = shared_context
self._local_context = DataContext(name=self._LOCAL_NAME_TEMPLATE())
self._update_exec_context()
def test_imported_func_code(self):
code = "from blockcanvas.debug.my_operator import mul\n" \
"c = mul(a, b)\n" \
desired = "from blockcanvas.debug.my_operator import mul\n\nc = mul(a, b)"
model = ExecutionModel.from_code(code)
self.assertEqual(model.code, desired)
def test_code_from_local_def(self):
code = "def foo(a, b):\n" "\treturn b, a\n" "x,y = foo(1,2)\n"
desired = "\ndef foo(a, b): \n return b, a\n\nx, y = foo(1, 2)"
model = ExecutionModel.from_code(code)
self.assertEqual(desired, model.code)
def test_import_func_with_const(self):
code = "from blockcanvas.debug.my_operator import mul\n" \
"a = mul(1.0, 2.0)\n" \
desired = "from blockcanvas.debug.my_operator import mul\n\na = mul(1.0, 2.0)"
model = ExecutionModel.from_code(code)
self.assertEqual(model.code, desired)
def test_add_function(self):
code = "def foo(a, b):\n" \
" x, y = a, b\n" \
" return x, y\n" \
"c, d = foo(1, 2)"
block = Block(code)
info = find_functions(block.ast)
func = FunctionCall.from_ast(block.sub_blocks[-1].ast, info)
model = ExecutionModel()
model.add_function(func)
assert len(model.sorted_statements) == 1
desired = '\ndef foo(a, b): \n x, y = (a, b)\n return x, y\n\nx_, y_ = foo(1, 2)'
self.assertEqual(desired, model.code)
def load_code_from_file(self, filename):
# Clear out the old references
self.canvas = self.controller = self.exec_model = None
self.exec_model = ExecutionModel.from_file(filename)
self.controller = BlockGraphController(execution_model=self.exec_model)
self.canvas = BlockCanvas(graph_controller=self.controller)
self.controller.canvas = self.canvas
def load_code_from_file(self,filename):
# Clear out the old references
self.canvas = self.controller = self.exec_model = None
self.exec_model = ExecutionModel.from_file(filename)
self.controller = BlockGraphController(execution_model = self.exec_model)
self.canvas = BlockCanvas(graph_controller=self.controller)
self.controller.canvas = self.canvas
def test_remove_function(self):
code = "def foo(a, b):\n" " x, y = a, b\n" " return x, y\n" "c, d = foo(1, 2)\n" "e, f = foo(3, 4)"
block = Block(code)
info = find_functions(block.ast)
foo1_func = FunctionCall.from_ast(block.sub_blocks[-1].ast, info)
foo2_func = FunctionCall.from_ast(block.sub_blocks[-2].ast, info)
assert foo1_func != foo2_func
model = ExecutionModel()
model.add_function(foo1_func)
model.add_function(foo2_func)
model.remove_function(foo2_func)
assert not (foo2_func in model.statements)
assert foo1_func in model.statements
def test_code_from_local_def(self):
code = "def foo(a, b):\n" \
"\treturn b, a\n" \
"x,y = foo(1,2)\n"
desired = '\ndef foo(a, b): \n return b, a\n\nx, y = foo(1, 2)'
model = ExecutionModel.from_code(code)
self.assertEqual(desired, model.code)
def test_merge_statements(self):
code = "from blockcanvas.debug.my_operator import add, mul\n" \
"a = mul(1.0,2.0)\n" \
"b = add(1.0,3.0)\n" \
model = ExecutionModel.from_code(code)
model.merge_statements(
ids=[model.statements[0].uuid, model.statements[1].uuid])
def setUp(self):
unittest.TestCase.setUp(self)
self.code = 'from blockcanvas.debug.my_operator import mul, add\n' \
'a = mul(1,2)\n' \
'b = mul(5,6)\n' \
'c = add(a,b)'
self.model = ExecutionModel.from_code(self.code)
self.controller = BlockGraphController(execution_model=self.model)
self.file_path = 'temp.pickle'
def setUp(self):
unittest.TestCase.setUp(self)
self.code = 'from blockcanvas.debug.my_operator import mul, add\n' \
'a = mul(1,2)\n' \
'b = mul(5,6)\n' \
'c = add(a,b)'
self.model = ExecutionModel.from_code(self.code)
self.controller = BlockGraphController(execution_model=self.model)
self.file_path = 'temp.pickle'
def setUp(self):
self.exec_code = (
"from blockcanvas.debug.my_operator import add, mul\n" "c = mul(a,b)\n" "d = add(c,b)\n" "f = mul(d,e)\n"
)
self.exec_model = ExecutionModel.from_code(self.exec_code)
self.inputs = {}
for stmt in self.exec_model.statements:
for iv in stmt.inputs:
self.inputs[iv.binding] = iv
self.simple_context = dict(a=2, b=4, e=5)
def test_restricted(self):
code = "from blockcanvas.debug.my_operator import add, mul\n" "c = mul(a,b)\n" "d = add(c,b)\n" "f = mul(d,e)\n"
model = ExecutionModel.from_code(code)
not_really_restricted = model.restricted()
self.assertEqual(not_really_restricted.sorted_statements, model.sorted_statements)
cstmt, dstmt, fstmt = model.statements
self.assertEqual(model.restricted(inputs=["e"]).sorted_statements, [fstmt])
self.assertEqual(model.restricted(outputs=["d"]).sorted_statements, [cstmt, dstmt])
self.assertEqual(model.restricted(inputs=["b"], outputs=["c"]).sorted_statements, [cstmt])
def test_local_def_code(self):
code = "from blockcanvas.debug.my_operator import mul\n" \
"def foo(a):\n" \
"\tb = a\n" \
"\treturn b\n" \
"a = mul(1.0, 2.0)\n" \
"b = foo(a)\n"
desired = 'def foo(a): \n b = a\n return b\n'
model = ExecutionModel.from_code(code)
assert model.code.find(desired) > 0
def setUp(self):
self.exec_code = (
"from blockcanvas.debug.my_operator import add, mul\n"
"c = mul(a,b)\n"
"d = add(c,b)\n"
"f = mul(d,e)\n")
self.exec_model = ExecutionModel.from_code(self.exec_code)
self.inputs = {}
for stmt in self.exec_model.statements:
for iv in stmt.inputs:
self.inputs[iv.binding] = iv
self.simple_context = dict(a=2, b=4, e=5)
def test_change_binding(self):
""" Does a change in the binding update the code from the ExecutionModel
correctly?
"""
code = "from blockcanvas.debug.my_operator import add\n" "a = add(1, 2)\n"
model = ExecutionModel.from_code(code)
func_call = model.statements[0]
func_call.inputs[0].binding = "2"
desired = "from blockcanvas.debug.my_operator import add\n\n" "a = add(2, 2)"
self.assertEqual(desired, model.code)
def test_local_def_code(self):
code = (
"from blockcanvas.debug.my_operator import mul\n"
"def foo(a):\n"
"\tb = a\n"
"\treturn b\n"
"a = mul(1.0, 2.0)\n"
"b = foo(a)\n"
)
desired = "def foo(a): \n b = a\n return b\n"
model = ExecutionModel.from_code(code)
assert model.code.find(desired) > 0
def load_from_config(self, config, dirname, project=None):
""" Loads the experiment. The existing state of the experiment is
completely modified.
Parameters
----------
config: a dict-like object
The keys should correspond to the configspec for experiments as
defined in project_config_spec.
dirname: a string
the absolute path to the subdirectory of the project that
holds the experiment's saved data
project: Project instance
If provided, the project is used to hook up references to
the shared context.
"""
join = os.path.join
self.name = config.get("name", "")
if "code_file" in config:
# Clear out the old references
self.canvas = self.controller = self.exec_model = None
self.exec_model = ExecutionModel.from_file(
join(dirname, config["code_file"]))
self.controller = BlockGraphController(
execution_model=self.exec_model)
self.canvas = BlockCanvas(graph_controller=self.controller)
self.controller.canvas = self.canvas
if "layout_file" in config:
self.canvas.load_layout(join(dirname, config["layout_file"]))
if "local_context" in config:
self._local_context = DataContext.load(
join(dirname, config["local_context"]))
shared_context = None
if project is not None:
name = config.get("shared_context")
if name != "":
shared_context = project.find_context(name)
self._shared_context = shared_context
self._update_exec_context()
def test_change_binding(self):
""" Does a change in the binding update the code from the ExecutionModel
correctly?
"""
code = "from blockcanvas.debug.my_operator import add\n" \
"a = add(1, 2)\n"
model = ExecutionModel.from_code(code)
func_call = model.statements[0]
func_call.inputs[0].binding = '2'
desired = "from blockcanvas.debug.my_operator import add\n\n" \
"a = add(2, 2)"
self.assertEqual(desired, model.code)
def test_imported_func_multi_line(self):
""" Right now, we can't ensure the order of the import statements nor
can we consolidate imports from the same locations. """
code = "from blockcanvas.debug.my_operator import add, mul\n" \
"a = add(1.0,2.0)\n" \
"b = add(a,3.0)\n" \
"c = mul(b,b)\n" \
desired = ["a = add(1.0, 2.0)", "b = add(a, 3.0)", "c = mul(b, b)"]
model = ExecutionModel.from_code(code)
codelines = model.code.split('\n')
for line in desired:
assert line in codelines
import_line = codelines[0]
assert import_line.find('add') and import_line.find('mul')
def load_from_config(self, config, dirname, project=None):
""" Loads the experiment. The existing state of the experiment is
completely modified.
Parameters
----------
config: a dict-like object
The keys should correspond to the configspec for experiments as
defined in project_config_spec.
dirname: a string
the absolute path to the subdirectory of the project that
holds the experiment's saved data
project: Project instance
If provided, the project is used to hook up references to
the shared context.
"""
join = os.path.join
self.name = config.get("name", "")
if "code_file" in config:
# Clear out the old references
self.canvas = self.controller = self.exec_model = None
self.exec_model = ExecutionModel.from_file(join(dirname, config["code_file"]))
self.controller = BlockGraphController(execution_model = self.exec_model)
self.canvas = BlockCanvas(graph_controller=self.controller)
self.controller.canvas = self.canvas
if "layout_file" in config:
self.canvas.load_layout(join(dirname, config["layout_file"]))
if "local_context" in config:
self._local_context = DataContext.load(join(dirname, config["local_context"]))
shared_context = None
if project is not None:
name = config.get("shared_context")
if name != "":
shared_context = project.find_context(name)
self._shared_context = shared_context
self._update_exec_context()
def test_imported_func_multi_line(self):
""" Right now, we can't ensure the order of the import statements nor
can we consolidate imports from the same locations. """
code = (
"from blockcanvas.debug.my_operator import add, mul\n"
"a = add(1.0,2.0)\n"
"b = add(a,3.0)\n"
"c = mul(b,b)\n"
)
desired = ["a = add(1.0, 2.0)", "b = add(a, 3.0)", "c = mul(b, b)"]
model = ExecutionModel.from_code(code)
codelines = model.code.split("\n")
for line in desired:
assert line in codelines
import_line = codelines[0]
assert import_line.find("add") and import_line.find("mul")
def test_restricted(self):
code = "from blockcanvas.debug.my_operator import add, mul\n" \
"c = mul(a,b)\n" \
"d = add(c,b)\n" \
"f = mul(d,e)\n"
model = ExecutionModel.from_code(code)
not_really_restricted = model.restricted()
self.assertEqual(not_really_restricted.sorted_statements,
model.sorted_statements)
cstmt, dstmt, fstmt = model.statements
self.assertEqual(
model.restricted(inputs=['e']).sorted_statements, [fstmt])
self.assertEqual(
model.restricted(outputs=['d']).sorted_statements, [cstmt, dstmt])
self.assertEqual(
model.restricted(inputs=['b'], outputs=['c']).sorted_statements,
[cstmt])
def test_remove_function(self):
code = "def foo(a, b):\n" \
" x, y = a, b\n" \
" return x, y\n" \
"c, d = foo(1, 2)\n" \
"e, f = foo(3, 4)"
block = Block(code)
info = find_functions(block.ast)
foo1_func = FunctionCall.from_ast(block.sub_blocks[-1].ast, info)
foo2_func = FunctionCall.from_ast(block.sub_blocks[-2].ast, info)
assert foo1_func != foo2_func
model = ExecutionModel()
model.add_function(foo1_func)
model.add_function(foo2_func)
model.remove_function(foo2_func)
assert not (foo2_func in model.statements)
assert foo1_func in model.statements
def test_unmerge_statements(self):
code = "from blockcanvas.debug.my_operator import add, mul\n" "a = mul(1.0,2.0)\n" "b = add(1.0,3.0)\n"
model = ExecutionModel.from_code(code)
model.merge_statements(ids=[model.statements[0].uuid, model.statements[1].uuid])
model.unmerge_statements(model.statements[0].uuid)
def test_import_func_with_const(self):
code = "from blockcanvas.debug.my_operator import mul\n" "a = mul(1.0, 2.0)\n"
desired = "from blockcanvas.debug.my_operator import mul\n\na = mul(1.0, 2.0)"
model = ExecutionModel.from_code(code)
self.assertEqual(model.code, desired)
class Experiment(HasTraits):
"""
An Experiment represents a particular workflow and its associated data.
It is associated with a particular context, but any data it produces
is placed into a shadowing "local" context.
It encapsulates an execution model, its associated canvas, and the
local context.
"""
# The name of the experiment.
name = String()
# The Execution Model object
exec_model = Instance(ExecutionModel)
# The Block Canvas
canvas = Instance(BlockCanvas)
# The controller between the canvas and execution model
controller = Instance(BlockGraphController)
# The execution context
context = Instance(ExecutingContext)
# A reference to a context that might be shared with other experiments.
# This is usually a context from the project's list of contexts. If None,
# then all of the data in the experiment is kept "locally".
#
# Note: For most purposes, you should use the **context** attribute
# and not this one, unless you really know what you are doing.
shared_context = Property(Instance(IListenableContext))
#---------------------------------------------------------------------
# Private Traits
#---------------------------------------------------------------------
# A shadow trait for **shared_context** property
_shared_context = Instance(IListenableContext, adapt='yes',
rich_compare=False)
# The context in which all of our execution model's generated values are
# stored. This context is exposed as part of self.context. This remains
# constant even as the _shared_context gets changed.
_local_context = Instance(IListenableContext, rich_compare=False)
# Class-level generator for the name of the local context; takes
# **self** as an argument.
_LOCAL_NAME_TEMPLATE = lambda x: "%s_local_%d" % (x.name, id(x))
#---------------------------------------------------------------------
# Public methods
#---------------------------------------------------------------------
def __init__(self, code=None, shared_context=None, *args, **kwargs):
super(Experiment, self).__init__(*args, **kwargs)
if code is None:
self.exec_model = ExecutionModel()
else:
self.exec_model = ExecutionModel.from_code(code)
self.controller = BlockGraphController(execution_model = self.exec_model)
self.canvas = BlockCanvas(graph_controller=self.controller)
self.controller.canvas = self.canvas
self._shared_context = shared_context
self._local_context = DataContext(name = self._LOCAL_NAME_TEMPLATE())
self._update_exec_context()
#---------------------------------------------------------------------
# Persistence
#---------------------------------------------------------------------
def load_code_from_file(self,filename):
# Clear out the old references
self.canvas = self.controller = self.exec_model = None
self.exec_model = ExecutionModel.from_file(filename)
self.controller = BlockGraphController(execution_model = self.exec_model)
self.canvas = BlockCanvas(graph_controller=self.controller)
self.controller.canvas = self.canvas
def load_from_config(self, config, dirname, project=None):
""" Loads the experiment. The existing state of the experiment is
completely modified.
Parameters
----------
config: a dict-like object
The keys should correspond to the configspec for experiments as
defined in project_config_spec.
dirname: a string
the absolute path to the subdirectory of the project that
holds the experiment's saved data
project: Project instance
If provided, the project is used to hook up references to
the shared context.
"""
join = os.path.join
self.name = config.get("name", "")
if "code_file" in config:
# Clear out the old references
self.canvas = self.controller = self.exec_model = None
self.exec_model = ExecutionModel.from_file(join(dirname, config["code_file"]))
self.controller = BlockGraphController(execution_model = self.exec_model)
self.canvas = BlockCanvas(graph_controller=self.controller)
self.controller.canvas = self.canvas
if "layout_file" in config:
self.canvas.load_layout(join(dirname, config["layout_file"]))
if "local_context" in config:
self._local_context = DataContext.load(join(dirname, config["local_context"]))
shared_context = None
if project is not None:
name = config.get("shared_context")
if name != "":
shared_context = project.find_context(name)
self._shared_context = shared_context
self._update_exec_context()
def save(self, basename, dirname):
""" Saves the experiment into a directory.
Parameters
----------
basename: string
the name of the project directory
dirname: string
the name of the experiment's subdirectory
Returns
-------
A dict representing the saved state for this object. All path
references in the dict will be relative to the given root directory.
"""
# Make the directory using the absolute path.
fullpath = join(basename, dirname)
if not os.path.isdir(fullpath):
os.mkdir(fullpath)
config = {}
config["name"] = self.name
config["save_dir"] = dirname
# Save out the canvas
config["layout_file"] = "layout.dat"
self.canvas.save_layout(join(fullpath, "layout.dat"))
# Save the execution model's code
config["code_file"] = "code.txt"
f = file(join(fullpath, "code.txt"), "w")
try:
f.write(self.exec_model.code)
finally:
f.close()
# Clean context
self.exec_model._clean_old_results_from_context(self.context.subcontext)
# Save the local context
config["local_context"] = "local_context.pickle"
self._local_context.save(join(fullpath, "local_context.pickle"))
# Store the name of the shared context
if self._shared_context is not None:
config["shared_context"] = self._shared_context.name
return config
def save_script(self,filename):
""" Save the execution model code as script. """
f = file(filename, "w")
try:
f.write(self.exec_model.code)
finally:
f.close()
def export_as_function(self,filename,func_name, mode="w"):
""" Export this experiment as a function for batch application.
It puts together the model (i.e. code) and the parameters
(i.e. context) to create a self-contained function to be used
in batch application.
"""
imports_and_locals = self.exec_model.imports_and_locals
sorted_statements = self.exec_model.sorted_statements
context = self.context
# Clean context
self.exec_model._clean_old_results_from_context(context.subcontext)
export_as_function(filename, func_name, \
imports_and_locals, sorted_statements, context, \
reserved_inputs=[], \
reserved_outputs=[], \
mode=mode)
def export_as_script(self,filename, mode="w"):
""" Export this experiment as a script for batch application.
It puts together the model (i.e. code) and the parameters
(i.e. context) to create a self-contained script to be used
in batch application.
"""
imports_and_locals = self.exec_model.imports_and_locals
sorted_statements = self.exec_model.sorted_statements
context = self.context
# Clean context
self.exec_model._clean_old_results_from_context(context.subcontext)
export_as_script(filename, \
imports_and_locals, sorted_statements, context, \
reserved_inputs=[], \
reserved_outputs=[], \
mode=mode)
#---------------------------------------------------------------------
# Trait Event Handlers
#---------------------------------------------------------------------
@on_trait_change('exec_model')
def _exec_model_changed(self, name, old, new):
""" Propagate the change to the objects under this one.
"""
if self.controller is not None:
self.controller.execution_model = new
if self.context is not None:
self.context.executable = new
#---------------------------------------------------------------------
# Property getters/setters
#---------------------------------------------------------------------
def _get_shared_context(self):
return self._shared_context
def _set_shared_context(self, newcontext):
subcontexts = self.context.subcontext.subcontexts
if self._shared_context is not None:
assert(self._shared_context in subcontexts)
if newcontext is None:
subcontexts.remove(self._shared_context)
self._shared_context = None
else:
# Swap out the new context for the old, in-place
ndx = subcontexts.index(self._shared_context)
subcontexts[ndx] = newcontext
self._shared_context = newcontext
elif newcontext is not None:
self._shared_context = newcontext
subcontexts.append(newcontext)
return
def _update_exec_context(self):
mc = MultiContext(
# Put the function filter in front so we don't dirty up the data
# context with function objects.
FunctionFilterContext(name='functions'),
self._local_context,
name='multi',
)
if self._shared_context is not None:
mc.subcontexts.append(self._shared_context)
self.context = ExecutingContext(
executable=self.exec_model,
subcontext=mc,
name='exec',
)
def test_imported_func_code(self):
code = "from blockcanvas.debug.my_operator import mul\n" "c = mul(a, b)\n"
desired = "from blockcanvas.debug.my_operator import mul\n\nc = mul(a, b)"
model = ExecutionModel.from_code(code)
self.assertEqual(model.code, desired)
class Experiment(HasTraits):
"""
An Experiment represents a particular workflow and its associated data.
It is associated with a particular context, but any data it produces
is placed into a shadowing "local" context.
It encapsulates an execution model, its associated canvas, and the
local context.
"""
# The name of the experiment.
name = String()
# The Execution Model object
exec_model = Instance(ExecutionModel)
# The Block Canvas
canvas = Instance(BlockCanvas)
# The controller between the canvas and execution model
controller = Instance(BlockGraphController)
# The execution context
context = Instance(ExecutingContext)
# A reference to a context that might be shared with other experiments.
# This is usually a context from the project's list of contexts. If None,
# then all of the data in the experiment is kept "locally".
#
# Note: For most purposes, you should use the **context** attribute
# and not this one, unless you really know what you are doing.
shared_context = Property(Instance(IListenableContext))
#---------------------------------------------------------------------
# Private Traits
#---------------------------------------------------------------------
# A shadow trait for **shared_context** property
_shared_context = Instance(IListenableContext,
adapt='yes',
rich_compare=False)
# The context in which all of our execution model's generated values are
# stored. This context is exposed as part of self.context. This remains
# constant even as the _shared_context gets changed.
_local_context = Instance(IListenableContext, rich_compare=False)
# Class-level generator for the name of the local context; takes
# **self** as an argument.
_LOCAL_NAME_TEMPLATE = lambda x: "%s_local_%d" % (x.name, id(x))
#---------------------------------------------------------------------
# Public methods
#---------------------------------------------------------------------
def __init__(self, code=None, shared_context=None, *args, **kwargs):
super(Experiment, self).__init__(*args, **kwargs)
if code is None:
self.exec_model = ExecutionModel()
else:
self.exec_model = ExecutionModel.from_code(code)
self.controller = BlockGraphController(execution_model=self.exec_model)
self.canvas = BlockCanvas(graph_controller=self.controller)
self.controller.canvas = self.canvas
self._shared_context = shared_context
self._local_context = DataContext(name=self._LOCAL_NAME_TEMPLATE())
self._update_exec_context()
#---------------------------------------------------------------------
# Persistence
#---------------------------------------------------------------------
def load_code_from_file(self, filename):
# Clear out the old references
self.canvas = self.controller = self.exec_model = None
self.exec_model = ExecutionModel.from_file(filename)
self.controller = BlockGraphController(execution_model=self.exec_model)
self.canvas = BlockCanvas(graph_controller=self.controller)
self.controller.canvas = self.canvas
def load_from_config(self, config, dirname, project=None):
""" Loads the experiment. The existing state of the experiment is
completely modified.
Parameters
----------
config: a dict-like object
The keys should correspond to the configspec for experiments as
defined in project_config_spec.
dirname: a string
the absolute path to the subdirectory of the project that
holds the experiment's saved data
project: Project instance
If provided, the project is used to hook up references to
the shared context.
"""
join = os.path.join
self.name = config.get("name", "")
if "code_file" in config:
# Clear out the old references
self.canvas = self.controller = self.exec_model = None
self.exec_model = ExecutionModel.from_file(
join(dirname, config["code_file"]))
self.controller = BlockGraphController(
execution_model=self.exec_model)
self.canvas = BlockCanvas(graph_controller=self.controller)
self.controller.canvas = self.canvas
if "layout_file" in config:
self.canvas.load_layout(join(dirname, config["layout_file"]))
if "local_context" in config:
self._local_context = DataContext.load(
join(dirname, config["local_context"]))
shared_context = None
if project is not None:
name = config.get("shared_context")
if name != "":
shared_context = project.find_context(name)
self._shared_context = shared_context
self._update_exec_context()
def save(self, basename, dirname):
""" Saves the experiment into a directory.
Parameters
----------
basename: string
the name of the project directory
dirname: string
the name of the experiment's subdirectory
Returns
-------
A dict representing the saved state for this object. All path
references in the dict will be relative to the given root directory.
"""
# Make the directory using the absolute path.
fullpath = join(basename, dirname)
if not os.path.isdir(fullpath):
os.mkdir(fullpath)
config = {}
config["name"] = self.name
config["save_dir"] = dirname
# Save out the canvas
config["layout_file"] = "layout.dat"
self.canvas.save_layout(join(fullpath, "layout.dat"))
# Save the execution model's code
config["code_file"] = "code.txt"
f = file(join(fullpath, "code.txt"), "w")
try:
f.write(self.exec_model.code)
finally:
f.close()
# Clean context
self.exec_model._clean_old_results_from_context(
self.context.subcontext)
# Save the local context
config["local_context"] = "local_context.pickle"
self._local_context.save(join(fullpath, "local_context.pickle"))
# Store the name of the shared context
if self._shared_context is not None:
config["shared_context"] = self._shared_context.name
return config
def save_script(self, filename):
""" Save the execution model code as script. """
f = file(filename, "w")
try:
f.write(self.exec_model.code)
finally:
f.close()
def export_as_function(self, filename, func_name, mode="w"):
""" Export this experiment as a function for batch application.
It puts together the model (i.e. code) and the parameters
(i.e. context) to create a self-contained function to be used
in batch application.
"""
imports_and_locals = self.exec_model.imports_and_locals
sorted_statements = self.exec_model.sorted_statements
context = self.context
# Clean context
self.exec_model._clean_old_results_from_context(context.subcontext)
export_as_function(filename, func_name, \
imports_and_locals, sorted_statements, context, \
reserved_inputs=[], \
reserved_outputs=[], \
mode=mode)
def export_as_script(self, filename, mode="w"):
""" Export this experiment as a script for batch application.
It puts together the model (i.e. code) and the parameters
(i.e. context) to create a self-contained script to be used
in batch application.
"""
imports_and_locals = self.exec_model.imports_and_locals
sorted_statements = self.exec_model.sorted_statements
context = self.context
# Clean context
self.exec_model._clean_old_results_from_context(context.subcontext)
export_as_script(filename, \
imports_and_locals, sorted_statements, context, \
reserved_inputs=[], \
reserved_outputs=[], \
mode=mode)
#---------------------------------------------------------------------
# Trait Event Handlers
#---------------------------------------------------------------------
@on_trait_change('exec_model')
def _exec_model_changed(self, name, old, new):
""" Propagate the change to the objects under this one.
"""
if self.controller is not None:
self.controller.execution_model = new
if self.context is not None:
self.context.executable = new
#---------------------------------------------------------------------
# Property getters/setters
#---------------------------------------------------------------------
def _get_shared_context(self):
return self._shared_context
def _set_shared_context(self, newcontext):
subcontexts = self.context.subcontext.subcontexts
if self._shared_context is not None:
assert (self._shared_context in subcontexts)
if newcontext is None:
subcontexts.remove(self._shared_context)
self._shared_context = None
else:
# Swap out the new context for the old, in-place
ndx = subcontexts.index(self._shared_context)
subcontexts[ndx] = newcontext
self._shared_context = newcontext
elif newcontext is not None:
self._shared_context = newcontext
subcontexts.append(newcontext)
return
def _update_exec_context(self):
mc = MultiContext(
# Put the function filter in front so we don't dirty up the data
# context with function objects.
FunctionFilterContext(name='functions'),
self._local_context,
name='multi',
)
if self._shared_context is not None:
mc.subcontexts.append(self._shared_context)
self.context = ExecutingContext(
executable=self.exec_model,
subcontext=mc,
name='exec',
)
