def exec_loop(
iterable,
settings,
matcode,
used_vars,
real_vars_indexes,
need_store_counter,
globals_dict,
locals_dict,
folded,
):
try:
# Check whether iterable for-loop argument has type "xrange"
range_desc = check_iterable(settings, iterable)
try:
# If so, retreive it's parameters
start, step, iters_count, last = range_desc
except TypeError:
return None
# Load necessary variables, check it's types and make vector
# for further operations with matrixes appending unit row
vector = load_vars(
settings,
used_vars,
globals_dict,
locals_dict,
) + [1]
except TypeError as err:
err.message = "Can't run optimized loop: " + err.message
if settings['profile']:
profiler.exc(settings, "Hook didn't allow optimization", err)
return None
# Define constant values in matrix code
matcode = define_values(matcode, folded, {
'start': start,
'step': step,
'iters_count': iters_count,
})
# Add last detail to matrix code before it's execution
matcode.append([END])
# Run matrix code
vector = run.run_matcode(settings, matcode, vector)
if settings['profile']:
profiler.success(settings,
'Optimized execution of %s iterations' % iters_count)
# Pack real variables' values to a list that will be unpacked in
# the main function to the values that would be assigned to the
# globals and the locals. We can't implement storing variables like
# its' loading because locals() dictionary is read-only.
packed = [vector[index] for index in real_vars_indexes]
if need_store_counter:
packed.append(last)
return packed
def exec_loop(
iterable,
settings,
matcode,
used_vars,
real_vars_indexes,
need_store_counter,
globals_dict,
locals_dict,
folded,
):
try:
# Check whether an iterable has type "xrange" and the required
# number of iterations
range_params = check_iterable(settings, iterable)
if range_params is None: # If the number of iterations is too little
return None
start, step, iters_count, last = range_params
# Load necessary variables, check their types and make a vector
# for further operations with matrixes (including a unit row)
vector = load_vars(
settings,
used_vars,
globals_dict,
locals_dict,
) + [1]
except TypeError as err:
generic_err = TypeError("Can't run optimized loop: %s" % err)
if settings['verbose']:
settings['logger'].debug(generic_err)
if settings['strict']:
raise generic_err
return None
# Define constant values in matrix code
matcode = define_values(matcode, folded, {
'start': start,
'step': step,
'iters_count': iters_count,
})
matcode.append([END])
# Run matrix code
vector = run.run_matcode(settings, matcode, vector)
if settings['verbose']:
settings['logger'].debug('Execution of %s iterations was optimized '
'successfully' % iters_count)
# Pack values of real variables to a list. It will be unpacked in
# a main function to values that will be assigned to the
# globals and the locals. We can't just modify `locals_dict`
# because locals() dictionary is read-only.
packed = [vector[index] for index in real_vars_indexes]
if need_store_counter:
packed.append(last)
return packed
def exec_loop(
iterable, settings, matcode, used_vars, real_vars_indexes,
need_store_counter, globals_dict, locals_dict, folded,
):
try:
# Check whether iterable for-loop argument has type "xrange"
range_desc = check_iterable(settings, iterable)
try:
# If so, retreive it's parameters
start, step, iters_count, last = range_desc
except TypeError:
return None
# Load necessary variables, check it's types and make vector
# for further operations with matrixes appending unit row
vector = load_vars(
settings, used_vars, globals_dict, locals_dict,
) + [1]
except TypeError as err:
err.message = "Can't run optimized loop: " + err.message
if settings['profile']:
profiler.exc(settings, "Hook didn't allow optimization", err)
return None
# Define constant values in matrix code
matcode = define_values(matcode, folded, {
'start': start, 'step': step, 'iters_count': iters_count,
})
# Add last detail to matrix code before it's execution
matcode.append([END])
# Run matrix code
vector = run.run_matcode(settings, matcode, vector)
if settings['profile']:
profiler.success(settings,
'Optimized execution of %s iterations' % iters_count)
# Pack real variables' values to a list that will be unpacked in
# the main function to the values that would be assigned to the
# globals and the locals. We can't implement storing variables like
# its' loading because locals() dictionary is read-only.
packed = [vector[index] for index in real_vars_indexes]
if need_store_counter:
packed.append(last)
return packed
def exec_loop(iterable, settings, matcode, used_vars, real_vars_indexes,
need_store_counter, globals_dict, locals_dict, folded):
try:
# Check whether an iterable has type "xrange" and the required
# number of iterations
range_params = check_iterable(settings, iterable)
if range_params is None: # If the number of iterations is too little
return None
start, step, iters_count, last = range_params
# Load necessary variables, check their types and make a vector
# for further operations with matrixes (including a unit row)
vector = load_vars(
settings, used_vars, globals_dict, locals_dict,
) + [1]
except TypeError as err:
generic_err = TypeError("Can't run optimized loop: %s" % err)
if settings['verbose']:
settings['logger'].debug(generic_err)
if settings['strict']:
raise generic_err
return None
# Define constant values in matrix code
matcode = define_values(matcode, folded, {
'start': start, 'step': step, 'iters_count': iters_count,
})
matcode.append([END])
# Run matrix code
vector = run.run_matcode(settings, matcode, vector)
if settings['verbose']:
settings['logger'].debug('Execution of %s iterations was optimized '
'successfully' % iters_count)
# Pack values of real variables to a list. It will be unpacked in
# a main function to values that will be assigned to the
# globals and the locals. We can't just modify `locals_dict`
# because locals() dictionary is read-only.
packed = [vector[index] for index in real_vars_indexes]
if need_store_counter:
packed.append(last)
return packed
