params.nHidden, params.optimizer, params.learningRate,
params.momentum, params.trainingRatio, params.nEpochs,
params.batchSize, params.delay, params.standardization)
errors[graph_x_index][trials_index] = nn_model.error
losss[graph_x_index][trials_index][:] = nn_model.history.history[
'loss']
val_losss[graph_x_index][
trials_index][:] = nn_model.history.history['val_loss']
result = Result(params, errors, losss, val_losss, non_cancell_error_array,
previous_errors, previous_losss, previous_val_losss)
with open(output_dir + '/result.pkl', 'wb') as f:
pickle.dump(result, f)
ber_fig, ber_ax = graph.new_ber_canvas("phi", params.graph_x_min,
params.graph_x_max)
n_sum = params.previous_test_bits * params.trials
errors_sum = np.sum(non_cancell_error_array, axis=1)
bers = errors_sum / n_sum
ber_ax.plot(graph_x_array,
bers,
color="k",
marker='x',
linestyle='--',
label="w/o canceller")
n_sum = params.previous_test_bits * params.trials
errors_sum = np.sum(previous_errors, axis=1)
bers = errors_sum / n_sum
ber_ax.plot(graph_x_array,
params.TX_IQI, params.PA, params.LNA, params.RX_IQI,
params.nHidden, params.optimizer, params.learningRate,
params.momentum, params.trainingRatio, params.nEpochs,
params.batchSize, delay, params.standardization)
errors[delay_array_index][trials_index] = nn_model.error
losss[delay_array_index][
trials_index][:] = nn_model.history.history['loss']
val_losss[delay_array_index][
trials_index][:] = nn_model.history.history['val_loss']
result = Result(params, errors, losss, val_losss)
with open(output_dir + '/result.pkl', 'wb') as f:
pickle.dump(result, f)
ber_fig, ber_ax = graph.new_ber_canvas("delay", params.delay_min,
params.delay_max)
n_sum = params.test_bits * params.trials
errors_sum = np.sum(errors, axis=1)
bers = errors_sum / n_sum
ber_ax.plot(delay_array,
bers,
color="k",
marker='o',
linestyle='--',
label="OFDM")
plt.tight_layout()
plt.savefig(output_dir + '/SNR_BER.pdf', bbox_inches='tight')
# slack通知用
output_png_path = output_dir + '/SNR_BER.png'
if __name__ == '__main__':
SIMULATIONS_NAME = os.path.basename(__file__).split('.')[0]
load_files = 1 # 同じ条件で読み込む数
dirname = settings.dirname_current_datetime(SIMULATIONS_NAME)
# dirname = "../results/keep/ofdm_fde_system_model_chain_load"
settings.init_output(dirname)
param_path = "../results/comex/results/ofdm_fde_system_model_epoch/params.json"
params = settings.load_param(param_path)
# n_sum = params["test_bits"] * params['SNR_AVERAGE'] * load_files
n_sum = params['test_bits'] * params['trials']
snrs_db = np.linspace(params['graph_x_min'], params['graph_x_max'],
params['graph_x_num'])
fig, ax = graph.new_ber_canvas("Training Epoch", params['graph_x_min'],
params['graph_x_max'], -5)
ax.set_yticks([10**0, 10**-1, 10**-2, 10**-3, 10**-4, 10**-5])
# ax.set_xticks(np.linspace(-0.5, 0.5, 6))
pkl_path = "../results/comex/results/ofdm_fde_system_model_epoch/result.pkl"
result = load_pkl_file(pkl_path)
# previous_n_sum = params['previous_test_bits'] * params['trials']
# errors_sum = np.sum(result.non_cancell_error_array, axis=1)
# bers = errors_sum / previous_n_sum
# np.place(bers, bers == 0, None)
# ax.plot(snrs_db, bers, color='k', marker='x', linestyle='--', label="w/o canceller", ms=12)
#
# errors_sum = np.sum(result.previous_errors, axis=1)
# bers = errors_sum / previous_n_sum
# np.place(bers, bers == 0, None)
params.gamma, params.phi, params.PA_IBO, params.PA_rho,
params.LNA_IBO, params.LNA_rho, h_si, h_s, params.h_si_len,
params.h_s_len, params.receive_antenna, params.TX_IQI,
params.PA, params.LNA, params.RX_IQI, params.nHidden,
params.optimizer, params.learningRate, params.momentum,
params.trainingRatio, nEpochs, params.batchSize, params.delay,
params.standardization)
errors[graph_x_index][trials_index] = nn_model.error
result = Result(params, errors)
with open(output_dir + '/result.pkl', 'wb') as f:
pickle.dump(result, f)
ber_fig, ber_ax = graph.new_ber_canvas("Training Epoch",
params.graph_x_min,
params.graph_x_max)
n_sum = params.test_bits * params.trials
errors_sum = np.sum(errors, axis=1)
bers = errors_sum / n_sum
ber_ax.plot(graph_x_array,
bers,
color="k",
marker='o',
linestyle='--',
label="Proposal")
plt.tight_layout()
# ber_ax.legend()
plt.savefig(output_dir + '/SNR_BER.pdf', bbox_inches='tight')
params.PA, params.LNA, params.RX_IQI, params.nHidden,
params.optimizer, params.learningRate, params.momentum,
params.trainingRatio, params.nEpochs, params.batchSize,
params.delay, params.standardization)
errors[antennae_array_index][trials_index] = nn_model.error
losss[antennae_array_index][
trials_index][:] = nn_model.history.history['loss']
val_losss[antennae_array_index][
trials_index][:] = nn_model.history.history['val_loss']
result = Result(params, errors, losss, val_losss)
with open(output_dir + '/result.pkl', 'wb') as f:
pickle.dump(result, f)
ber_fig, ber_ax = graph.new_ber_canvas("antennae", params.antennae_min,
params.antennae_max)
n_sum = params.test_bits * params.trials
errors_sum = np.sum(errors, axis=1)
bers = errors_sum / n_sum
ber_ax.plot(antennae_array,
bers,
color="k",
marker='o',
linestyle='--',
label="OFDM")
plt.tight_layout()
plt.savefig(output_dir + '/SNR_BER.pdf', bbox_inches='tight')
# slack通知用
output_png_path = output_dir + '/SNR_BER.png'
params.trainingRatio,
params.nEpochs,
params.batchSize,
params.delay,
params.standardization
)
errors[gamma_array_index][trials_index] = nn_model.error
losss[gamma_array_index][trials_index][:] = nn_model.history.history['loss']
val_losss[gamma_array_index][trials_index][:] = nn_model.history.history['val_loss']
result = Result(params, errors, losss, val_losss)
with open(output_dir + '/result.pkl', 'wb') as f:
pickle.dump(result, f)
ber_fig, ber_ax = graph.new_ber_canvas("gamma", params.gamma_min, params.gamma_max)
n_sum = params.test_bits * params.trials
errors_sum = np.sum(errors, axis=1)
bers = errors_sum / n_sum
ber_ax.plot(gamma_array, bers, color="k", marker='o', linestyle='--', label="OFDM")
plt.tight_layout()
plt.savefig(output_dir + '/SNR_BER.pdf', bbox_inches='tight')
# slack通知用
output_png_path = output_dir + '/SNR_BER.png'
plt.savefig(output_png_path, bbox_inches='tight')
for gamma_index, gamma in enumerate(gamma_array):
learn_fig, learn_ax = graph.new_learning_curve_canvas(params.nEpochs)
loss_avg = np.mean(losss[gamma_index], axis=0).T
SIMULATIONS_NAME = os.path.basename(__file__).split('.')[0]
load_files = 1 # 同じ条件で読み込む数
dirname = settings.dirname_current_datetime(SIMULATIONS_NAME)
# dirname = "../results/keep/ofdm_fde_system_model_chain_load"
settings.init_output(dirname)
param_path = "../results/keep/fig031921/gamma/params.json"
params = settings.load_param(param_path)
# n_sum = params["test_bits"] * params['SNR_AVERAGE'] * load_files
n_sum = params['test_bits'] * params['trials']
snrs_db = np.linspace(params['graph_x_min'], params['graph_x_max'],
params['graph_x_num'])
fig, ax = graph.new_ber_canvas(
"amplitude error " + r'$\gamma^{\rm T}$, $\gamma^{\rm R}_i$', -0.5,
0.5, -5)
ax.set_yticks([10**0, 10**-1, 10**-2, 10**-3, 10**-4, 10**-5])
ax.set_xticks(np.linspace(-0.5, 0.5, 6))
pkl_path = "../results/keep/fig031921/gamma_wo/result.pkl"
result = load_pkl_file(pkl_path)
previous_n_sum = params['previous_test_bits'] * params['trials']
errors_sum = np.sum(result.non_cancell_error_array, axis=1)
bers = errors_sum / previous_n_sum
np.place(bers, bers == 0, None)
ax.plot(snrs_db,
bers,
color='k',
marker='x',
load_files = 1 # 同じ条件で読み込む数
dirname = settings.dirname_current_datetime(SIMULATIONS_NAME)
# dirname = "../results/keep/" + SIMULATIONS_NAME
settings.init_output(dirname)
# param_path = "../results/ofdm_fde_with_previous/2021/01/29/13_02_26/params.json"
param_path = "../results/keep/ofdm_fde_with_previous/12_35_55/params.json"
params_dict = settings.load_param(param_path)
params = Params.from_params_dict(params_dict)
n_sum = params.test_bits * params.trials
trials_num = params.trials
graph_x_array = np.linspace(1, params.trials, trials_num, dtype=int)
fig, ax = graph.new_ber_canvas('trials', 0, params.trials, -5)
# pkl_path = "../results/ofdm_fde_with_previous/2021/01/29/13_02_26/result.pkl"
# pkl_path = "../results/ofdm_fde_system_model/2021/01/22/01_25_45/result.pkl"
pkl_path = "../results/keep/ofdm_fde_with_previous/12_35_55/result.pkl"
result = load_pkl_file(pkl_path)
SNR = 25 # 使わないけど後からわかりやすいように定義しておく
SNR_errors = result.errors[-1]
errors_sum = np.sum(result.errors, axis=1)
bers = np.zeros((trials_num))
for i, trials in enumerate(graph_x_array):
error = np.sum(SNR_errors[:trials])
n_sum = params.test_bits * trials
ber = error / n_sum
if __name__ == '__main__':
SIMULATIONS_NAME = os.path.basename(__file__).split('.')[0]
load_files = 1 # 同じ条件で読み込む数
dirname = settings.dirname_current_datetime(SIMULATIONS_NAME)
# dirname = "../results/keep/ofdm_fde_system_model_chain_load"
settings.init_output(dirname)
param_path = "../results/comex/results/iqi_phi/params.json"
params = settings.load_param(param_path)
# n_sum = params["test_bits"] * params['SNR_AVERAGE'] * load_files
n_sum = params['test_bits'] * params['trials']
snrs_db = np.linspace(params['graph_x_min'], params['graph_x_max'], params['graph_x_num'])
fig, ax = graph.new_ber_canvas("phase error " + r'$\phi$', params['graph_x_min'], params['graph_x_max'], -4)
ax.set_yticks([10**0, 10**-1, 10**-2, 10**-3, 10**-4])
# ax.set_xticks(np.linspace(-0.5, 0.5, 6))
pkl_path = "../results/comex/results/iqi_phi/result.pkl"
result = load_pkl_file(pkl_path)
previous_n_sum = params['previous_test_bits'] * params['trials']
errors_sum = np.sum(result.non_cancell_error_array, axis=1)
bers = errors_sum / previous_n_sum
np.place(bers, bers == 0, None)
ax.plot(snrs_db, bers, color='k', marker='x', linestyle=':', label="w/o canceller", ms=12)
errors_sum = np.sum(result.previous_errors, axis=1)
bers = errors_sum / previous_n_sum
params.nHidden, params.optimizer, params.learningRate,
params.momentum, params.trainingRatio, params.nEpochs,
params.batchSize, params.delay, params.standardization)
errors[graph_x_index][trials_index] = nn_model.error
losss[graph_x_index][trials_index][:] = nn_model.history.history[
'loss']
val_losss[graph_x_index][
trials_index][:] = nn_model.history.history['val_loss']
result = Result(params, errors, losss, val_losss, non_cancell_error_array,
previous_errors, previous_losss, previous_val_losss)
with open(output_dir + '/result.pkl', 'wb') as f:
pickle.dump(result, f)
ber_fig, ber_ax = graph.new_ber_canvas("PA IBO [dB]", params.graph_x_min,
params.graph_x_max)
n_sum = params.previous_test_bits * params.trials
errors_sum = np.sum(non_cancell_error_array, axis=1)
bers = errors_sum / n_sum
ber_ax.plot(graph_x_array,
bers,
color="k",
marker='x',
linestyle='--',
label="w/o canceller")
n_sum = params.previous_test_bits * params.trials
errors_sum = np.sum(previous_errors, axis=1)
bers = errors_sum / n_sum
ber_ax.plot(graph_x_array,
SIMULATIONS_NAME = os.path.basename(__file__).split('.')[0]
load_files = 1 # 同じ条件で読み込む数
dirname = settings.dirname_current_datetime(SIMULATIONS_NAME)
# dirname = "../results/keep/ofdm_fde_system_model_chain_load"
settings.init_output(dirname)
param_path = "../results/keep/fig031921/delay/params.json"
params = settings.load_param(param_path)
# n_sum = params["test_bits"] * params['SNR_AVERAGE'] * load_files
n_sum = params['test_bits'] * params['trials']
snrs_db = np.linspace(params['delay_min'], params['delay_max'],
params['delay_num'])
fig, ax = graph.new_ber_canvas("decision delay " + r"$\delta$",
params['delay_min'], params['delay_max'],
-5)
# ax.set_yticks([10 ** 0, 10 ** -1, 10 ** -2, 10 ** -3, 10 ** -5])
pkl_path = "../results/keep/fig031921/delay/result.pkl"
result = load_pkl_file(pkl_path)
errors_sum = np.sum(result.errors, axis=1)
bers = errors_sum / n_sum
np.place(bers, bers == 0, None)
ax.plot(snrs_db,
bers,
color='k',
marker='d',
linestyle='-',
label="decision delay",
ms=12)
params.trainingRatio,
params.nEpochs,
params.batchSize,
params.delay,
params.standardization
)
errors[PA_IBO_array_index][trials_index] = nn_model.error
losss[PA_IBO_array_index][trials_index][:] = nn_model.history.history['loss']
val_losss[PA_IBO_array_index][trials_index][:] = nn_model.history.history['val_loss']
result = Result(params, errors, losss, val_losss)
with open(output_dir + '/result.pkl', 'wb') as f:
pickle.dump(result, f)
ber_fig, ber_ax = graph.new_ber_canvas("PA_IBO [dB]", params.PA_IBO_MIN, params.PA_IBO_MAX,)
n_sum = params.test_bits * params.trials
errors_sum = np.sum(errors, axis=1)
bers = errors_sum / n_sum
ber_ax.plot(PA_IBO_array, bers, color="k", marker='o', linestyle='--', label="OFDM")
plt.tight_layout()
plt.savefig(output_dir + '/SNR_BER.pdf', bbox_inches='tight')
# slack通知用
output_png_path = output_dir + '/SNR_BER.png'
plt.savefig(output_png_path, bbox_inches='tight')
for PA_IBO_index, PA_IBO in enumerate(PA_IBO_array):
learn_fig, learn_ax = graph.new_learning_curve_canvas(params.nEpochs)
loss_avg = np.mean(losss[PA_IBO_index], axis=0).T
if __name__ == '__main__':
SIMULATIONS_NAME = os.path.basename(__file__).split('.')[0]
load_files = 1 # 同じ条件で読み込む数
dirname = settings.dirname_current_datetime(SIMULATIONS_NAME)
# dirname = "../results/keep/ofdm_fde_system_model_chain_load"
settings.init_output(dirname)
param_path = "../results/keep/fig031921/pa_ibo/params.json"
params = settings.load_param(param_path)
# n_sum = params["test_bits"] * params['SNR_AVERAGE'] * load_files
n_sum = params['test_bits'] * params['trials']
snrs_db = np.linspace(params['graph_x_min'], params['graph_x_max'],
params['graph_x_num'])
fig, ax = graph.new_ber_canvas("IBO [dB]", params['graph_x_min'],
params['graph_x_max'], -5)
ax.set_yticks([10**0, 10**-1, 10**-2, 10**-3, 10**-4, 10**-5])
# ax.set_xticks(np.linspace(-0.5, 0.5, 6))
pkl_path = "../results/keep/fig031921/pa_ibo_wo/result.pkl"
result = load_pkl_file(pkl_path)
previous_n_sum = params['previous_test_bits'] * params['trials']
errors_sum = np.sum(result.non_cancell_error_array, axis=1)
bers = errors_sum / previous_n_sum
np.place(bers, bers == 0, None)
ax.plot(snrs_db,
bers,
color='k',
marker='x',
linestyle=':',