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Commit 86e61d04 authored by Roman Trüb's avatar Roman Trüb
Browse files

improved parsing of multiple trace values in same local timestamp epoch

parent a6fea50c
......@@ -66,7 +66,7 @@ class SwoParser():
self._complete = False
self._format2 = (self._header & 0b10001111 == 0) # format 2 (single-byte packet)
self._tc = (header >> 4) & 0b11 if not self._format2 else None
self._tc = (header >> 4) & 0b11 if not self._format2 else 0b00 ## format 2 can only occur if timestamp is synchronous (i.e. tc=0b00)
def addByte(self, byteVal):
......@@ -164,7 +164,7 @@ class SwoParser():
# data value; otherweise: reserved
ret += "\n writeAccess: {}".format(self.writeAccess)
raise Exception("ERROR: DataTracePkt with reserved packetType!")
raise Exception("ERROR: DatatracePkt with reserved packetType!")
if self.isComplete():
ret += "\n value: {}".format(self.value)
......@@ -228,6 +228,7 @@ class SwoParser():
if discriminator_id in [0, 1, 2]:
# 0 Event counter wrapping, 1 Exception tracing, 2 PC sampling
self._ignoreBytes = plSize
print('WARNING: Hardware source packet with discriminator_id={} ignored!'.format(discriminator_id))
elif discriminator_id >= 8 and discriminator_id <= 23:
# Data tracing
......@@ -326,14 +327,23 @@ def read_fun(read_queue, input_file):
def parse_fun(read_queue):
Parses packets from the queue
Relevant passage from ARMv7-M Architecture Reference Manual:
"Local timestamping is differential, meaning each timestamp gives the time since the previous local timestamp.
When local timestamping is enabled and a DWT or ITM event transfers a packet to the appropriate output FIFO,
and the timestamp counter is non-zero, the ITM:
* Generates a Local timestamp packet.
* Resets the timestamp counter to zero."
columns = ['global_ts', 'comparator', 'data', 'PC', 'operation', 'local_ts']
out_list = []
completedDataPkts = []
completedLocalTimestampPkts = []
completedPkts = []
completedOverflowPkts = []
# completedDataPkts = []
# completedLocalTimestampPkts = []
swoParser = SwoParser()
while read_queue:
......@@ -345,61 +355,113 @@ def parse_fun(read_queue):
if ret:
if type(ret) == SwoParser.LocalTimestampPkt:
elif type(ret) == SwoParser.DatatracePkt:
elif type(ret) == SwoParser.OverflowPkt:
raise Exception['ERROR: Packet completed but type is unknown!']
elif elemType == 'global_ts':
if not swoParser._streamStarted:
global_ts = elemData
if not completedLocalTimestampPkts:
# cannot create rows from current data since there is no complete local timestamp pkt
print('WARNING: cannot create rows from current data since there is no complete local timestamp pkt')
if not swoParser._streamStarted:
# use collected packets to create rows with data
if completedDataPkts:
for comparatorId in [0, 1, 2, 3]:
# go through list of completed packts and try to create new rows
overflowPkts = []
while completedPkts:
# find next set of packtes for creating one row
idx = None
for i, pkt in enumerate(completedPkts):
if type(pkt) == SwoParser.DatatracePkt:
elif type(pkt) == SwoParser.LocalTimestampPkt:
idx = i
raise Exception('ERROR: Unrecognized packet type!')
if idx is None:
# no (more) complete set found -> wait for more data
elif idx == 0:
# single LocalTimestampPkt
assert type(pkt) == SwoParser.LocalTimestampPkt
dataPkt = completedPkts.pop(0)
new_row = collections.OrderedDict(zip(columns, [None]*len(columns)))
for dataPkt in completedDataPkts:
if dataPkt.comparator == comparatorId:
new_row['local_ts'] = dataPkt.ts
new_row['global_ts'] = global_ts
out_list += [new_row]
# Set with datatrace packets found
dataPkts = []
ltsPkts = []
for i in range(idx+1):
pkt = completedPkts.pop(0)
if type(pkt) == SwoParser.DatatracePkt:
elif type(pkt) == SwoParser.LocalTimestampPkt:
raise Exception('ERROR: Unexpected packet type {}'.format(type(pkt)))
assert len(dataPkts) >= 1
assert len(ltsPkts) == 1
# FIXME handle LocalTimestamp pkts with tc!=0 properly
# assert ltsPkts[0].tc == 0
# FIXME support case where multiple comparators output data!
comparators = [e.comparator for e in dataPkts]
if len(set(comparators)) > 1:
raise Exception('ERROR: More than one comparator in set for creating a row!')
comparatorId = comparators[0]
ltsUsed = False # indicate whether LocalTimestampPkt has been used for logging a row or not (releveant since local timestamp is a delta timestamp and logged value should therefore not always be incremented when LocalTimestampPkt is used multiple times)
while dataPkts:
# decide whether to process one (data only) or two (data and PC) dataPkts
use2Pkts = False
if len(dataPkts) >= 2:
if (dataPkts[0].comparator == dataPkts[1].comparator) and (dataPkts[0].pktType != dataPkts[1].pktType):
use2Pkts = True
# create row
new_row = collections.OrderedDict(zip(columns, [None]*len(columns)))
for i in range(use2Pkts+1):
dataPkt = dataPkts.pop(0)
if dataPkt.pktType == 1: # data trace pc value pkt
if not dataPkt.addressPkt: # we want PC value
new_row['PC'] = hex(dataPkt.value)
elif dataPkt.pktType == 2: # data trace data value pkt
new_row['operation'] = 'w' if dataPkt.writeAccess else 'r'
new_row['data'] = dataPkt.value
if not new_row['data'] is None:
new_row['comparator'] = comparatorId
new_row['local_ts'] = completedLocalTimestampPkts[-1].ts
new_row['global_ts'] = elemData
new_row['comparator'] = int(comparatorId)
# only log LocalTimestamp if this is the first row logged for this LocalTimestamp epoch (local timestamps are delta tiestamps!)
if not ltsUsed:
new_row['local_ts'] = ltsPkts[0].ts
ltsUsed = True
new_row['local_ts'] = 0
new_row['global_ts'] = global_ts
out_list += [new_row]
elif completedLocalTimestampPkts:
# no completedDataPkts -> we have overflow timestamp -> add it to use it for regression
# output overflow packats
for pkt in completedOverflowPkts:
new_row = collections.OrderedDict(zip(columns, [None]*len(columns)))
new_row['local_ts'] = completedLocalTimestampPkts[-1].ts
new_row['global_ts'] = elemData
new_row['global_ts'] = global_ts
out_list += [new_row]
if completedOverflowPkts:
for overflowPkt in completedOverflowPkts:
new_row = collections.OrderedDict(zip(columns, [None]*len(columns)))
new_row['global_ts'] = elemData
out_list += [new_row]
# clear all collected packets
# Unrecognized elemType
raise Exception('ERROR: Unknown element type!')
return pd.DataFrame(out_list)
ret = pd.DataFrame(out_list)
# FIXME: comparator and data should be ints not floats
# return ret.astype({'comparator': 'int32', 'data': 'int32'})
return ret
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