def plot():
# Define filenames.
# `save_folder` is the full path to the directory containing the Pickle (.pkl) log files from the optimization.
save_folder = os.path.join(os.getcwd(), 'phc_test')
spec_folder = os.getcwd()
# Load the logged monitor data and monitor spec information.
# `df` is a pandas dataframe containing all the data loaded from the log Pickle (.pkl) files.
df = log_tools.create_log_data_frame(log_tools.load_all_logs(save_folder))
# `monitor_spec_filename` is the full path to the monitor spec yml file.
monitor_spec_filename = os.path.join(spec_folder,
"monitor_spec_dynamic.yml")
# `monitor_descriptions` now contains the information from the monitor_spec.yml file. It follows the format of
# the schema found in `log_tools.monitor_spec`.
monitor_descriptions = log_tools.load_from_yml(monitor_spec_filename)
# Plot all monitor data and save into a pdf file in the project folder.
# `summary_out_name` is the full path to the pdf that will be generated containing plots of all the log data.
summary_out_name = os.path.join(save_folder, "summary.pdf")
# This command plots all the monitor data contained in the log files, saves it to the specified pdf file, and
# displays to the screen.
log_tools.plot_monitor_data(df, monitor_descriptions, None)
def view_opt(save_folder: str) -> None:
"""Shows the result of the optimization.
This runs the auto-plotter to plot all the relevant data.
See `examples/wdm2` IPython notebook for more details on how to process
the optimization logs.
Args:
save_folder: Location where the log files are saved.
"""
log_df = log_tools.create_log_data_frame(
log_tools.load_all_logs(save_folder))
monitor_descriptions = log_tools.load_from_yml(
os.path.join(os.path.dirname(__file__), "monitor_spec.yml"))
log_tools.plot_monitor_data(log_df, monitor_descriptions)
def view_opt(save_folder: str) -> None:
"""Shows the result of the optimization.
This runs the auto-plotter to plot all the relevant data.
See `examples/wdm2` IPython notebook for more details on how to process
the optimization logs.
Args:
save_folder: Location where the log files are saved.
"""
log_df = log_tools.create_log_data_frame(log_tools.load_all_logs(save_folder))
monitor_descriptions = log_tools.load_from_yml(os.path.join(os.path.dirname(__file__), "monitor_spec_dual.yml"))
log_tools.plot_monitor_data(log_df, monitor_descriptions, show=False)
log_tools.plot_power_vs_wlen(log_df, monitor_descriptions, "port1_out", show=False)
log_tools.plot_power_vs_wlen(log_df, monitor_descriptions, "port2_out", show=False)
import matplotlib.pyplot as plt
wlen_goal, s11_fn, s21_fn = load_s_param_goal()
plt.plot(wlen_goal, 20*np.log10(s21_fn(wlen_goal)), ':r')
plt.show()
