A few more fixes and cleanups to try to get working for a single day

    return pd.concat(frames, ignore_index=True)

#def load_traces_by_day(trace_dir, start_dt, end_dt, colname):

#    """Load CPU or GPU traces between start_dt and end_dt.

#

#    Args:

#        trace_dir (str): Directory containing daily CSV files.

#        start_dt (datetime): Start datetime.

#        end_dt (datetime): End datetime.

#        colname (str): Name of the utilization column (e.g., 'cpu_util' or 'gpu_util').

#    """

#    frames = []

#    current = start_dt.date()

#

#    while current <= end_dt.date():

#        daily_file = os.path.join(trace_dir, f"{current}.csv")

#        if os.path.exists(daily_file):

#            df = pd.read_csv(

#                daily_file,

#                names=["time", "machineId", colname],  # no header in daily CSVs

#                parse_dates=["time"]

#            )

#            frames.append(df)

#        else:

#            print(f"⚠ No trace file for {current}")

#        current += timedelta(days=1)

#

#    if not frames:

#        return pd.DataFrame(columns=["time", "machineId", colname])

#

#    return pd.concat(frames, ignore_index=True)

#def load_gpu_traces_by_day(trace_dir, start_dt, end_dt):

#    """Load GPU traces only for the days between start_dt and end_dt."""

#    frames = []

#

#    current = start_dt.date()

#

#    while current <= end_dt.date():

#        daily_file = os.path.join(trace_dir, f"{current}.csv")

#        if os.path.exists(daily_file):

#            df = pd.read_csv(

#                daily_file,

#                names=["time", "machineId", "gpu_util"],  # no header in daily CSVs

#                parse_dates=["time"]

#            )

#            frames.append(df)

#        else:

#            print(f"⚠ No trace file for {current}")

#        current += timedelta(days=1)

#

#    if not frames:

#        return pd.DataFrame(columns=["time", "machineId", "gpu_util"])

#

#    return pd.concat(frames, ignore_index=True)

def parse_date(s):

    if not s or s == "None":

        return None

    cpu_util["time"] = cpu_util["time"].apply(parse_date)

    # --- 3. GPU util ---

    PDT = timezone(timedelta(hours=-7))

    start_dt = datetime.fromtimestamp(start_ts, tz=PDT)

    end_dt   = datetime.fromtimestamp(end_ts, tz=PDT)

    #PDT = timezone(timedelta(hours=-7))

    #start_dt = datetime.fromtimestamp(start_ts, tz=PDT)

    #end_dt   = datetime.fromtimestamp(end_ts, tz=PDT)

    start_dt = datetime.fromtimestamp(start_ts)  # naive datetime

    end_dt   = datetime.fromtimestamp(end_ts)

    cpu_trace_dir = os.path.join(trace_dir, "dist", "cpu_by_day")

    gpu_trace_dir = os.path.join(trace_dir, "dist", "gpu_by_day")

        # --- absolute datetimes (used for filtering traces) ---

        submitted_dt = parse_timestamp(raw.get("submitted_time"))

        # Clamp to global CLI window - this should be fixed later to include the actual

        # trace start and end times (trace_start_time? and trace_end_time?) 

        #job_start = max(start, start_dt) if start else start_dt

        #job_end   = min(end, end_dt) if end else end_dt

        job_start = start

        job_end = end

        # CPU utilization traces

        cpu_trace = load_traces_by_day(cpu_trace_dir, start, end, "cpu_util")

        cpu_trace = load_traces_by_day(cpu_trace_dir, job_start, job_end, "cpu_util")

        mask = (

            (cpu_trace["machineId"].isin(machine_ids)) &

        if len(machine_ids) > 1:

            job_cpu = job_cpu.groupby("time")["cpu_util"].mean().reset_index()

        # Convert from percentage to fraction

        job_cpu_trace = (job_cpu["cpu_util"].to_numpy() * 0.01).tolist()

        # Extract GPU utilization traces

        gpu_trace = load_traces_by_day(gpu_trace_dir, start, end, "gpu_util")

        gpu_trace = load_traces_by_day(gpu_trace_dir, job_start, job_end, "gpu_util")

        mask = (

            (gpu_trace["machineId"].isin(machine_ids)) &

            (gpu_trace["time"] <= end)

        )

        # Convert traces from percent to fraction of gpus_per_node, e.g., 8 gpus at 100% is 8, at 50% is 4, etc.

        job_gpu_trace = (gpu_trace.loc[mask, "gpu_util"].to_numpy() * 0.01 * gpus_per_node).tolist()

        job_gpu = gpu_trace.loc[mask].copy()

        # Aggregate across machines if >1 machine

        if len(machine_ids) > 1:

            job_gpu = job_gpu.groupby("time")["gpu_util"].mean().reset_index()

        job_gpu_trace = (job_gpu["gpu_util"].to_numpy() * 0.01 * gpus_per_node).tolist()

        if machine_ids:

            # Shift times relative to start_ts

            print(job)

            exit()

    # Find max end timestamp across jobs

    #end_ts = max(j.end_time for j in jobs_list if j.end_time is not None)

    end_ts = 3600