async def async_measure_performance(self, prj, load_from_file=False):
# type: (BagProject, bool) -> Dict[str, Any]
"""A coroutine that performs measurement.
The measurement is done like a FSM. On each iteration, depending on the current
state, it creates a new testbench (or reuse an existing one) and simulate it.
It then post-process the simulation data to determine the next FSM state, or
if the measurement is done.
Parameters
----------
prj : BagProject
the BagProject instance.
load_from_file : bool
If True, then load existing simulation data instead of running actual simulation.
Returns
-------
output : Dict[str, Any]
the last dictionary returned by process_output().
"""
cur_state = self.get_initial_state()
prev_output = None
done = False
while not done:
# create and setup testbench
tb_name, tb_type, tb_specs, tb_sch_params = self.get_testbench_info(cur_state,
prev_output)
tb_package = tb_specs['tb_package']
tb_cls_name = tb_specs['tb_class']
tb_module = importlib.import_module(tb_package)
tb_cls = getattr(tb_module, tb_cls_name)
raw_data_fname = os.path.join(self.data_dir, '%s.hdf5' % cur_state)
tb_manager = tb_cls(raw_data_fname, tb_name, self.impl_lib, tb_specs,
self.sim_view_list, self.env_list)
if load_from_file:
print('Measurement %s in state %s, '
'load sim data from file.' % (self.meas_name, cur_state))
if os.path.isfile(raw_data_fname):
cur_results = load_sim_file(raw_data_fname)
else:
print('Cannot find data file, simulating...')
cur_results = await tb_manager.setup_and_simulate(prj, tb_sch_params)
else:
cur_results = await tb_manager.setup_and_simulate(prj, tb_sch_params)
# process and save simulation data
print('Measurement %s in state %s, '
'processing data from %s' % (self.meas_name, cur_state, tb_name))
done, next_state, prev_output = self.process_output(cur_state, cur_results, tb_manager)
with open_file(os.path.join(self.data_dir, '%s.yaml' % cur_state), 'w') as f:
yaml.dump(prev_output, f)
cur_state = next_state
return prev_output
def compute_ss():
output = 'ibias'
for mos_type in mos_list:
for thres in thres_list:
for lch in lch_list:
# load simulation results
tb_name = get_tb_name(mos_type, thres, lch)
fname = os.path.join(data_dir, '%s.data' % tb_name)
results = load_sim_file(fname)
info = compute_ss_helper(mos_type, results, output)
for key, val in info.items():
results[key] = val
results['sweep_params'][key] = results['sweep_params'][output]
save_sim_results(results, fname)
def print_data_info():
tb_name = get_tb_name(mos_list[0], thres_list[0], lch_list[0])
results = load_sim_file(os.path.join(data_dir, '%s.data' % tb_name))
# show that results is a dictionary
print('results type: %s' % type(results))
print('results keys: %s' % list(results.keys()))
# show values in the result dictionary
vds = results['vds']
vgs = results['vgs']
ibias = results['ibias']
# show how the sweep variables and outputs are structured
print('vds type: {}, vds shape: {}'.format(type(vds), vds.shape))
print('vgs type: {}, vgs shape: {}'.format(type(vgs), vgs.shape))
print('ibias type: {}, ibias shape: {}'.format(type(ibias), ibias.shape))
# show how to get sweep parameter order
print('sweep_params:')
pprint.pprint(results['sweep_params'])
def plot_data():
# output data name
output_names = ['ibias', 'gm', 'gds', 'vstar']
# plot all ibias data
fig_idx = 1
for mos_type in mos_list:
for thres in thres_list:
for lch in lch_list[:1]:
# load simulation results
tb_name = get_tb_name(mos_type, thres, lch)
results = load_sim_file(os.path.join(data_dir, '%s.data' % tb_name))
# plot data
for output in output_names:
plot_2d_data(mos_type, results, output, fig_idx)
fig_idx += 1
plt.show()