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#!/usr/bin/env python3
import csv
import io
import numpy as np
import struct
import xml.etree.ElementTree as ET
from dfatool.loader.generic import ExternalTimerSync
class KeysightCSV:
"""Simple loader for Keysight CSV data, as exported by the windows software."""
def __init__(self):
"""Create a new KeysightCSV object."""
pass
def load_data(self, filename: str):
"""
Load log data from filename, return timestamps and currents.
Returns two one-dimensional NumPy arrays: timestamps and corresponding currents.
"""
with open(filename) as f:
for i, _ in enumerate(f):
pass
timestamps = np.ndarray((i - 3), dtype=float)
currents = np.ndarray((i - 3), dtype=float)
# basically seek back to start
with open(filename) as f:
for _ in range(4):
next(f)
reader = csv.reader(f, delimiter=",")
for i, row in enumerate(reader):
timestamps[i] = float(row[0])
currents[i] = float(row[2]) * -1
return timestamps, currents
class DLogChannel:
def __init__(self, desc_tuple):
self.slot = desc_tuple[0]
self.smu = desc_tuple[1]
self.unit = desc_tuple[2]
self.data = None
def __repr__(self):
return f"""<DLogChannel(slot={self.slot}, smu="{self.smu}", unit="{self.unit}", data={self.data})>"""
class DLog(ExternalTimerSync):
"""Loader for DLog files generated by Keysight power analyzers."""
def __init__(
self,
voltage: float,
state_duration: int,
with_traces=False,
skip_duration=None,
limit_duration=None,
):
"""
Create a new DLog object
:param voltage: Voltage in V
:type voltage: float
:param state_duration: Expected state duration in ms. Used to detect and ignore UART transmissions in captured energy data.
:type state_duration: int
:param with_traces: Provide traces and timestamps, default false
:type with_traces: bool
:param skip_duration: Ignore the first `skip_duration` seconds, default None (ignore nothing)
:type skip_duration: float
:param limit_duration: Ignore everything after `limit_duration` seconds, default none (ignore nothing)
:type limit_duration: float
"""
self.voltage = voltage
self.state_duration = state_duration
self.with_traces = with_traces
self.skip_duration = skip_duration
self.limit_duration = limit_duration
self.errors = list()
self.sync_min_duration = 0.7
self.sync_min_low_count = 3
self.sync_min_high_count = 3
# TODO auto-detect
self.sync_power = 10e-3
def load_data(self, content):
lines = []
line = ""
with io.BytesIO(content) as f:
while line != "</dlog>\n":
line = f.readline().decode()
lines.append(line)
xml_header = "".join(lines)
raw_header = f.read(8)
data_offset = f.tell()
raw_data = f.read()
xml_header = xml_header.replace("1ua>", "X1ua>")
xml_header = xml_header.replace("2ua>", "X2ua>")
dlog = ET.fromstring(xml_header)
channels = []
for channel in dlog.findall("channel"):
channel_id = int(channel.get("id"))
sense_curr = channel.find("sense_curr").text
sense_volt = channel.find("sense_volt").text
model = channel.find("ident").find("model").text
if sense_volt == "1":
channels.append((channel_id, model, "V"))
if sense_curr == "1":
channels.append((channel_id, model, "A"))
num_channels = len(channels)
self.channels = list(map(DLogChannel, channels))
self.interval = float(dlog.find("frame").find("tint").text)
self.sense_minmax = int(dlog.find("frame").find("sense_minmax").text)
self.planned_duration = int(dlog.find("frame").find("time").text)
self.observed_duration = self.interval * int(len(raw_data) / (4 * num_channels))
if self.sense_minmax:
raise RuntimeError(
"DLog files with 'Log Min/Max' enabled are not supported yet"
)
self.timestamps = np.linspace(
0, self.observed_duration, num=int(len(raw_data) / (4 * num_channels))
)
if (
self.skip_duration is not None
and self.observed_duration >= self.skip_duration
):
start_offset = 0
for i, ts in enumerate(self.timestamps):
if ts >= self.skip_duration:
start_offset = i
break
self.timestamps = self.timestamps[start_offset:]
raw_data = raw_data[start_offset * 4 * num_channels :]
if (
self.limit_duration is not None
and self.observed_duration > self.limit_duration
):
stop_offset = len(self.timestamps) - 1
for i, ts in enumerate(self.timestamps):
if ts > self.limit_duration:
stop_offset = i
break
self.timestamps = self.timestamps[:stop_offset]
self.observed_duration = self.timestamps[-1]
raw_data = raw_data[: stop_offset * 4 * num_channels]
self.data = np.ndarray(
shape=(num_channels, int(len(raw_data) / (4 * num_channels))),
dtype=np.float32,
)
iterator = struct.iter_unpack(">f", raw_data)
channel_offset = 0
measurement_offset = 0
for value in iterator:
if value[0] < -1e6 or value[0] > 1e6:
print(
f"Invalid data value {value[0]} at channel {channel_offset}, measurement {measurement_offset}. Replacing with 0."
)
self.data[channel_offset, measurement_offset] = 0
else:
self.data[channel_offset, measurement_offset] = value[0]
if channel_offset + 1 == num_channels:
channel_offset = 0
measurement_offset += 1
else:
channel_offset += 1
# An SMU has four slots
self.slots = [dict(), dict(), dict(), dict()]
for i, channel in enumerate(self.channels):
channel.data = self.data[i]
self.slots[channel.slot - 1][channel.unit] = channel
assert "A" in self.slots[0]
self.data = self.slots[0]["A"].data * self.voltage
def observed_duration_equals_expectation(self):
return int(self.observed_duration) == self.planned_duration
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