Read Instrument Log

1: log figure

from pyspecProcScripts import logobj
import pyspecdata as psd
import h5py
import matplotlib.pyplot as plt
from matplotlib.transforms import blended_transform_factory
import datetime

coupler_atten = 22
myfilename = psd.search_filename(
    "230626_batch230515_E37_Ras_B10_ODNP_1.h5",
    exp_type="ODNP_NMR_comp/ODNP",
    unique=True,
)
with psd.figlist_var() as fl:
    # {{{ open h5 file to real log
    with h5py.File(myfilename, "r") as f:
        thislog = logobj.from_group(f["log"])
    # }}}
    # In order to properly set the time axis to start at 0
    # both the log's start time will be subtracted from the
    # the relative time recorded
    thislog.total_log["time"] -= thislog.total_log["time"][0]
    # }}}
    # {{{ plot the output power and reflection
    fig, (ax_Rx, ax_power) = plt.subplots(2, 1, figsize=(10, 8))
    fl.next("log figure", fig=fig)
    ax_Rx.set_ylabel("Rx / mV")
    ax_Rx.set_xlabel("Time / ms")
    ax_Rx.plot(thislog.total_log["time"], thislog.total_log["Rx"], ".")
    ax_power.set_ylabel("power / dBm")
    ax_power.set_xlabel("Time / ms")
    ax_power.plot(
        thislog.total_log["time"],
        10
        ** (
            (thislog.total_log["power"] + coupler_atten) / 10 - 3
        ),  # -3 for mW to W
        ".",
    )
    # }}}
    mask = thislog.total_log["cmd"] != 0
    position = 0
    npositions = 20
    for j, thisevent in enumerate(thislog.total_log[mask]):
        # {{{ Add a vertical line at the time the data acquisition for the
        #     set power began
        event_name = thislog.log_dict[thisevent["cmd"]]
        if event_name.lower().startswith("get_power"):
            continue  # ignore "get power" commands
        position = (
            position % npositions
        )  # use npositions positions top to bottom, then roll over
        for thisax in [ax_Rx, ax_power]:
            thisax.axvline(x=thisevent["time"], color="g", alpha=0.5)
            thisax.text(
                s=event_name,
                x=thisevent["time"],
                y=0.1 + (0.9 - 0.1) * position / npositions,
                transform=blended_transform_factory(
                    thisax.transData, thisax.transAxes
                ),
                alpha=0.5,
                color="g",
                size=8,  # really tiny!
            )
        position += 1
        # }}}
    for thisax in [ax_Rx, ax_power]:
        thisax.xaxis.set_major_formatter(
            plt.FuncFormatter(lambda x, _: str(datetime.timedelta(seconds=x)))
        )
    plt.tight_layout()