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"""
Copyright 2008 Free Software Foundation, Inc.
This file is part of GNU Radio
GNU Radio Companion is free software; you can redistribute it and/or
modify it under the terms of the GNU General Public License
as published by the Free Software Foundation; either version 2
of the License, or (at your option) any later version.
GNU Radio Companion is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA
"""
from utils import expr_utils
from .. base.FlowGraph import FlowGraph as _FlowGraph
from Block import Block
from Connection import Connection
def get_variable_code(variable):
"""
Get the code representation for a variable.
Normally this is the value parameter.
For the variable chooser, use the index and choices.
Avoid using the to_code method of the variables,
as this forces evaluation before the variables are evaluated.
@param variable the variable block
@return the code string
"""
if variable.get_key() == 'variable_chooser':
choices = variable.get_param('choices').get_value()
value_index = variable.get_param('value_index').get_value()
return "(%s)[%s]"%(choices, value_index)
return variable.get_param('value').get_value()
class FlowGraph(_FlowGraph):
_eval_cache = dict()
def _eval(self, code, namespace):
"""
Evaluate the code with the given namespace.
@param code a string with python code
@param namespace a dict representing the namespace
@return the resultant object
"""
#check cache
if self._eval_cache.has_key(code) and self._eval_cache[code][0] == namespace:
return self._eval_cache[code][1]
#evaluate
result = eval(code, namespace, namespace)
self._eval_cache[code] = (namespace.copy(), result)
return result
def _get_io_signature(self, pad_key):
"""
Get an io signature for this flow graph.
The pad key determines the directionality of the io signature.
@param pad_key a string of pad_source or pad_sink
@return a dict with: type, nports, vlen, size
"""
pads = filter(lambda b: b.get_key() == pad_key, self.get_enabled_blocks())
if not pads: return {
'nports': '0',
'type': '',
'vlen': '0',
'size': '0',
}
pad = pads[0] #take only the first, user should not have more than 1
#load io signature
return {
'nports': str(pad.get_param('nports').evaluate()),
'type': str(pad.get_param('type').evaluate()),
'vlen': str(pad.get_param('vlen').evaluate()),
'size': pad.get_param('type').get_opt('size'),
}
def get_input_signature(self):
"""
Get the io signature for the input side of this flow graph.
The io signature with be "0", "0" if no pad source is present.
@return a string tuple of type, num_ports, port_size
"""
return self._get_io_signature('pad_source')
def get_output_signature(self):
"""
Get the io signature for the output side of this flow graph.
The io signature with be "0", "0" if no pad sink is present.
@return a string tuple of type, num_ports, port_size
"""
return self._get_io_signature('pad_sink')
def get_imports(self):
"""
Get a set of all import statments in this flow graph namespace.
@return a set of import statements
"""
imports = sum([block.get_imports() for block in self.get_enabled_blocks()], [])
imports = sorted(set(imports))
return imports
def get_variables(self):
"""
Get a list of all variables in this flow graph namespace.
Exclude paramterized variables.
@return a sorted list of variable blocks in order of dependency (indep -> dep)
"""
variables = filter(lambda b: b.get_key() in (
'variable', 'variable_slider', 'variable_chooser', 'variable_text_box'
), self.get_enabled_blocks())
#map var id to variable block
id2var = dict([(var.get_id(), var) for var in variables])
#map var id to variable code
#variable code is a concatenation of all param code (without the id param)
id2expr = dict([(var.get_id(), get_variable_code(var)) for var in variables])
#sort according to dependency
sorted_ids = expr_utils.sort_variables(id2expr)
#create list of sorted variable blocks
variables = [id2var[id] for id in sorted_ids]
return variables
def get_parameters(self):
"""
Get a list of all paramterized variables in this flow graph namespace.
@return a list of paramterized variables
"""
parameters = filter(lambda b: b.get_key() == 'parameter', self.get_enabled_blocks())
return parameters
def evaluate(self, expr):
"""
Evaluate the expression.
@param expr the string expression
@throw Exception bad expression
@return the evaluated data
"""
if self.is_flagged():
self.deflag()
#reload namespace
n = dict()
#load imports
for imp in self.get_imports():
try: exec imp in n
except: pass
#load parameters
np = dict()
for parameter in self.get_parameters():
try:
e = self._eval(parameter.get_param('value').to_code(), n)
np[parameter.get_id()] = e
except: pass
n.update(np) #merge param namespace
#load variables
for variable in self.get_variables():
try:
e = self._eval(get_variable_code(variable), n)
n[variable.get_id()] = e
except: pass
#make namespace public
self.n = n
#evaluate
e = self._eval(expr, self.n)
return e
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