text:
chg:dev: Updating sflow tool lib

Signed-off-by: arajosep pablo.araya@hpe.com

Please share an example of a parsed output with the new changes.

This is an output of the parser in detail form with new changes:
[OrderedDict([('datagramSourceIP', '10.10.12.1'), ('datagramSize', '784'), ('unixSecondsUTC', '1473282824'), ('datagramVersion', '5'), ('agentSubId', '0'), ('agent', '10.10.10.1'), ('packetSequenceNo', '15'), ('sysUpTime', '9000'), ('samplesInPacket', '6'), ('samples', [OrderedDict([('sampleType_tag', '0:1'), ('sampleType', 'FLOWSAMPLE'), ('sampleSequenceNo', '70'), ('sourceId', '2:1000'), ('meanSkipCount', '4096'), ('samplePool', '82058'), ('dropEvents', '0'), ('inputPort', '1'), ('outputPort', '0'), ('flowBlock_tag', '0:1'), ('flowSampleType', 'HEADER'), ('headerProtocol', '1'), ('sampledPacketSize', '1024'), ('strippedBytes', '4'), ('headerLen', '64'), ('headerBytes', '00-00-00-00-00-00-00-00-00-00-00-00-08-00-45-00-03-EE-00-00-00-00-40-11-4E-D7-0A-0A-0A-14-0A-0A-0A-01-00-07-00-07-03-DA-BC-08-00-01-02-03-04-05-06-07-08-09-0A-0B-0C-0D-0E-0F-10-11-12-13-14-15'), ('dstMAC', '000000000000'), ('srcMAC', '000000000000'), ('IPSize', '1006'), ('ip.tot_len', '1006'), ('srcIP', '10.10.10.20'), ('dstIP', '10.10.10.1'), ('IPProtocol', '17'), ('IPTOS', '0'), ('IPTTL', '64'), ('UDPSrcPort', '7'), ('UDPDstPort', '7'), ('UDPBytes', '986')]), OrderedDict([('sampleType_tag', '0:1'), ('sampleType', 'FLOWSAMPLE'), ('sampleSequenceNo', '71'), ('sourceId', '2:1000'), ('meanSkipCount', '4096'), ('samplePool', '82058'), ('dropEvents', '0'), ('inputPort', '1'), ('outputPort', '0'), ('flowBlock_tag', '0:1'), ('flowSampleType', 'HEADER'), ('headerProtocol', '1'), ('sampledPacketSize', '1024'), ('strippedBytes', '4'), ('headerLen', '64'), ('headerBytes', '00-00-00-00-00-00-00-00-00-00-00-00-00-01-02-03-04-05-06-07-08-09-0A-0B-0C-0D-0E-0F-10-11-12-13-14-15-16-17-18-19-1A-1B-1C-1D-1E-1F-20-21-22-23-24-25-26-27-28-29-2A-2B-2C-2D-2E-2F-30-31-32-33'), ('dstMAC', '000000000000'), ('srcMAC', '000000000000')]), OrderedDict([('sampleType_tag', '0:1'), ('sampleType', 'FLOWSAMPLE'), ('sampleSequenceNo', '72'), ('sourceId', '2:1000'), ('meanSkipCount', '4096'), ('samplePool', '82058'), ('dropEvents', '0'), ('inputPort', '1'), ('outputPort', '0'), ('flowBlock_tag', '0:1'), ('flowSampleType', 'HEADER'), ('headerProtocol', '1'), ('sampledPacketSize', '1024'), ('strippedBytes', '4'), ('headerLen', '64'), ('headerBytes', '00-00-00-00-00-00-00-00-00-00-00-00-00-01-02-03-04-05-06-07-08-09-0A-0B-0C-0D-0E-0F-10-11-12-13-14-15-16-17-18-19-1A-1B-1C-1D-1E-1F-20-21-22-23-24-25-26-27-28-29-2A-2B-2C-2D-2E-2F-30-31-32-33'), ('dstMAC', '000000000000'), ('srcMAC', '000000000000')]), OrderedDict([('sampleType_tag', '0:1'), ('sampleType', 'FLOWSAMPLE'), ('sampleSequenceNo', '73'), ('sourceId', '2:1000'), ('meanSkipCount', '4096'), ('samplePool', '82058'), ('dropEvents', '0'), ('inputPort', '1'), ('outputPort', '0'), ('flowBlock_tag', '0:1'), ('flowSampleType', 'HEADER'), ('headerProtocol', '1'), ('sampledPacketSize', '1024'), ('strippedBytes', '4'), ('headerLen', '64'), ('headerBytes', '00-00-00-00-00-00-00-00-00-00-00-00-00-01-02-03-04-05-06-07-08-09-0A-0B-0C-0D-0E-0F-10-11-12-13-14-15-16-17-18-19-1A-1B-1C-1D-1E-1F-20-21-22-23-24-25-26-27-28-29-2A-2B-2C-2D-2E-2F-30-31-32-33'), ('dstMAC', '000000000000'), ('srcMAC', '000000000000')]), OrderedDict([('sampleType_tag', '0:1'), ('sampleType', 'FLOWSAMPLE'), ('sampleSequenceNo', '74'), ('sourceId', '2:1000'), ('meanSkipCount', '4096'), ('samplePool', '82058'), ('dropEvents', '0'), ('inputPort', '1'), ('outputPort', '0'), ('flowBlock_tag', '0:1'), ('flowSampleType', 'HEADER'), ('headerProtocol', '1'), ('sampledPacketSize', '1024'), ('strippedBytes', '4'), ('headerLen', '64'), ('headerBytes', '00-00-00-00-00-00-00-00-00-00-00-00-00-01-02-03-04-05-06-07-08-09-0A-0B-0C-0D-0E-0F-10-11-12-13-14-15-16-17-18-19-1A-1B-1C-1D-1E-1F-20-21-22-23-24-25-26-27-28-29-2A-2B-2C-2D-2E-2F-30-31-32-33'), ('dstMAC', '000000000000'), ('srcMAC', '000000000000')]), OrderedDict([('sampleType_tag', '0:2'), ('sampleType', 'COUNTERSSAMPLE'), ('sampleSequenceNo', '2'), ('sourceId', '2:1002'), ('counterBlock_tag', '0:1'), ('ifIndex', '2'), ('networkType', '6'), ('ifSpeed', '1000000000'), ('ifDirection', '1'), ('ifStatus', '3'), ('ifInOctets', '3972'), ('ifInUcastPkts', '11'), ('ifInMulticastPkts', '1'), ('ifInBroadcastPkts', '1'), ('ifInDiscards', '0'), ('ifInErrors', '0'), ('ifInUnknownProtos', '0'), ('ifOutOctets', '6681'), ('ifOutUcastPkts', '10'), ('ifOutMulticastPkts', '4'), ('ifOutBroadcastPkts', '3'), ('ifOutDiscards', '3'), ('ifOutErrors', '0'), ('ifPromiscuousMode', '0')])])]), OrderedDict([('datagramSourceIP', '10.10.12.1'), ('datagramSize', '1308'), ('unixSecondsUTC', '1473282825'), ('datagramVersion', '5'), ('agentSubId', '0'), ('agent', '10.10.10.1'), ('packetSequenceNo', '16'), ('sysUpTime', '9000'), ('samplesInPacket', '10'), ('samples', [OrderedDict([('sampleType_tag', '0:1'), ('sampleType', 'FLOWSAMPLE'), ('sampleSequenceNo', '75'), ('sourceId', '2:1000'), ('meanSkipCount', '4096'), ('samplePool', '82058'), ('dropEvents', '0'), ('inputPort', '1'), ('outputPort', '0'), ('flowBlock_tag', '0:1'), ('flowSampleType', 'HEADER'), ('headerProtocol', '1'), ('sampledPacketSize', '1024'), ('strippedBytes', '4'), ('headerLen', '64'), ('headerBytes', '00-00-00-00-00-00-00-00-00-00-00-00-00-01-02-03-04-05-06-07-08-09-0A-0B-0C-0D-0E-0F-10-11-12-13-14-15-16-17-18-19-1A-1B-1C-1D-1E-1F-20-21-22-23-24-25-26-27-28-29-2A-2B-2C-2D-2E-2F-30-31-32-33'), ('dstMAC', '000000000000'), ('srcMAC', '000000000000')]), OrderedDict([('sampleType_tag', '0:1'), ('sampleType', 'FLOWSAMPLE'), ('sampleSequenceNo', '76'), ('sourceId', '2:1000'), ('meanSkipCount', '4096'), ('samplePool', '82058'), ('dropEvents', '0'), ('inputPort', '1'), ('outputPort', '0'), ('flowBlock_tag', '0:1'), ('flowSampleType', 'HEADER'), ('headerProtocol', '1'), ('sampledPacketSize', '1024'), ('strippedBytes', '4'), ('headerLen', '64'), ('headerBytes', '00-00-00-00-00-00-00-00-00-00-00-00-00-01-02-03-04-05-06-07-08-09-0A-0B-0C-0D-0E-0F-10-11-12-13-14-15-16-17-18-19-1A-1B-1C-1D-1E-1F-20-21-22-23-24-25-26-27-28-29-2A-2B-2C-2D-2E-2F-30-31-32-33'), ('dstMAC', '000000000000'), ('srcMAC', '000000000000')]), OrderedDict([('sampleType_tag', '0:1'), ('sampleType', 'FLOWSAMPLE'), ('sampleSequenceNo', '77'), ('sourceId', '2:1000'), ('meanSkipCount', '4096'), ('samplePool', '82058'), ('dropEvents', '0'), ('inputPort', '1'), ('outputPort', '0'), ('flowBlock_tag', '0:1'), ('flowSampleType', 'HEADER'), ('headerProtocol', '1'), ('sampledPacketSize', '1024'), ('strippedBytes', '4'), ('headerLen', '64'), ('headerBytes', '00-00-00-00-00-00-00-00-00-00-00-00-00-01-02-03-04-05-06-07-08-09-0A-0B-0C-0D-0E-0F-10-11-12-13-14-15-16-17-18-19-1A-1B-1C-1D-1E-1F-20-21-22-23-24-25-26-27-28-29-2A-2B-2C-2D-2E-2F-30-31-32-33'), ('dstMAC', '000000000000'), ('srcMAC', '000000000000')]), OrderedDict([('sampleType_tag', '0:1'), ('sampleType', 'FLOWSAMPLE'), ('sampleSequenceNo', '78'), ('sourceId', '2:1000'), ('meanSkipCount', '4096'), ('samplePool', '82058'), ('dropEvents', '0'), ('inputPort', '1'), ('outputPort', '0'), ('flowBlock_tag', '0:1'), ('flowSampleType', 'HEADER'), ('headerProtocol', '1'), ('sampledPacketSize', '1024'), ('strippedBytes', '4'), ('headerLen', '64'), ('headerBytes', '00-00-00-00-00-00-00-00-00-00-00-00-00-01-02-03-04-05-06-07-08-09-0A-0B-0C-0D-0E-0F-10-11-12-13-14-15-16-17-18-19-1A-1B-1C-1D-1E-1F-20-21-22-23-24-25-26-27-28-29-2A-2B-2C-2D-2E-2F-30-31-32-33'), ('dstMAC', '000000000000'), ('srcMAC', '000000000000')]), OrderedDict([('sampleType_tag', '0:1'), ('sampleType', 'FLOWSAMPLE'), ('sampleSequenceNo', '79'), ('sourceId', '2:1000'), ('meanSkipCount', '4096'), ('samplePool', '82058'), ('dropEvents', '0'), ('inputPort', '1'), ('outputPort', '0'), ('flowBlock_tag', '0:1'), ('flowSampleType', 'HEADER'), ('headerProtocol', '1'), ('sampledPacketSize', '1024'), ('strippedBytes', '4'), ('headerLen', '64'), ('headerBytes', '00-00-00-00-00-00-00-00-00-00-00-00-00-01-02-03-04-05-06-07-08-09-0A-0B-0C-0D-0E-0F-10-11-12-13-14-15-16-17-18-19-1A-1B-1C-1D-1E-1F-20-21-22-23-24-25-26-27-28-29-2A-2B-2C-2D-2E-2F-30-31-32-33'), ('dstMAC', '000000000000'), ('srcMAC', '000000000000')]), OrderedDict([('sampleType_tag', '0:1'), ('sampleType', 'FLOWSAMPLE'), ('sampleSequenceNo', '80'), ('sourceId', '2:1000'), ('meanSkipCount', '4096'), ('samplePool', '82058'), ('dropEvents', '0'), ('inputPort', '1'), ('outputPort', '0'), ('flowBlock_tag', '0:1'), ('flowSampleType', 'HEADER'), ('headerProtocol', '1'), ('sampledPacketSize', '1024'), ('strippedBytes', '4'), ('headerLen', '64'), ('headerBytes', '00-00-00-00-00-00-00-00-00-00-00-00-00-01-02-03-04-05-06-07-08-09-0A-0B-0C-0D-0E-0F-10-11-12-13-14-15-16-17-18-19-1A-1B-1C-1D-1E-1F-20-21-22-23-24-25-26-27-28-29-2A-2B-2C-2D-2E-2F-30-31-32-33'), ('dstMAC', '000000000000'), ('srcMAC', '000000000000')]), OrderedDict([('sampleType_tag', '0:1'), ('sampleType', 'FLOWSAMPLE'), ('sampleSequenceNo', '81'), ('sourceId', '2:1000'), ('meanSkipCount', '4096'), ('samplePool', '82058'), ('dropEvents', '0'), ('inputPort', '1'), ('outputPort', '0'), ('flowBlock_tag', '0:1'), ('flowSampleType', 'HEADER'), ('headerProtocol', '1'), ('sampledPacketSize', '1024'), ('strippedBytes', '4'), ('headerLen', '64'), ('headerBytes', '00-00-00-00-00-00-00-00-00-00-00-00-00-01-02-03-04-05-06-07-08-09-0A-0B-0C-0D-0E-0F-10-11-12-13-14-15-16-17-18-19-1A-1B-1C-1D-1E-1F-20-21-22-23-24-25-26-27-28-29-2A-2B-2C-2D-2E-2F-30-31-32-33'), ('dstMAC', '000000000000'), ('srcMAC', '000000000000')]), OrderedDict([('sampleType_tag', '0:1'), ('sampleType', 'FLOWSAMPLE'), ('sampleSequenceNo', '82'), ('sourceId', '2:1000'), ('meanSkipCount', '4096'), ('samplePool', '82058'), ('dropEvents', '0'), ('inputPort', '1'), ('outputPort', '0'), ('flowBlock_tag', '0:1'), ('flowSampleType', 'HEADER'), ('headerProtocol', '1'), ('sampledPacketSize', '1024'), ('strippedBytes', '4'), ('headerLen', '64'), ('headerBytes', '00-00-00-00-00-00-00-00-00-00-00-00-00-01-02-03-04-05-06-07-08-09-0A-0B-0C-0D-0E-0F-10-11-12-13-14-15-16-17-18-19-1A-1B-1C-1D-1E-1F-20-21-22-23-24-25-26-27-28-29-2A-2B-2C-2D-2E-2F-30-31-32-33'), ('dstMAC', '000000000000'), ('srcMAC', '000000000000')]), OrderedDict([('sampleType_tag', '0:1'), ('sampleType', 'FLOWSAMPLE'), ('sampleSequenceNo', '83'), ('sourceId', '2:1000'), ('meanSkipCount', '4096'), ('samplePool', '82058'), ('dropEvents', '0'), ('inputPort', '1'), ('outputPort', '0'), ('flowBlock_tag', '0:1'), ('flowSampleType', 'HEADER'), ('headerProtocol', '1'), ('sampledPacketSize', '1024'), ('strippedBytes', '4'), ('headerLen', '64'), ('headerBytes', '00-00-00-00-00-00-00-00-00-00-00-00-00-01-02-03-04-05-06-07-08-09-0A-0B-0C-0D-0E-0F-10-11-12-13-14-15-16-17-18-19-1A-1B-1C-1D-1E-1F-20-21-22-23-24-25-26-27-28-29-2A-2B-2C-2D-2E-2F-30-31-32-33'), ('dstMAC', '000000000000'), ('srcMAC', '000000000000')]), OrderedDict([('sampleType_tag', '0:1'), ('sampleType', 'FLOWSAMPLE'), ('sampleSequenceNo', '84'), ('sourceId', '2:1000'), ('meanSkipCount', '4096'), ('samplePool', '82058'), ('dropEvents', '0'), ('inputPort', '1'), ('outputPort', '0'), ('flowBlock_tag', '0:1'), ('flowSampleType', 'HEADER'), ('headerProtocol', '1'), ('sampledPacketSize', '1024'), ('strippedBytes', '4'), ('headerLen', '64'), ('headerBytes', '00-00-00-00-00-00-00-00-00-00-00-00-00-01-02-03-04-05-06-07-08-09-0A-0B-0C-0D-0E-0F-10-11-12-13-14-15-16-17-18-19-1A-1B-1C-1D-1E-1F-20-21-22-23-24-25-26-27-28-29-2A-2B-2C-2D-2E-2F-30-31-32-33'), ('dstMAC', '000000000000'), ('srcMAC', '000000000000')])])]), OrderedDict([('datagramSourceIP', '10.10.12.1'), ('datagramSize', '656'), ('unixSecondsUTC', '1473282826'), ('datagramVersion', '5'), ('agentSubId', '0'), ('agent', '10.10.10.1'), ('packetSequenceNo', '17'), ('sysUpTime', '11000'), ('samplesInPacket', '5'), ('samples', [OrderedDict([('sampleType_tag', '0:1'), ('sampleType', 'FLOWSAMPLE'), ('sampleSequenceNo', '85'), ('sourceId', '2:1000'), ('meanSkipCount', '4096'), ('samplePool', '82058'), ('dropEvents', '0'), ('inputPort', '1'), ('outputPort', '0'), ('flowBlock_tag', '0:1'), ('flowSampleType', 'HEADER'), ('headerProtocol', '1'), ('sampledPacketSize', '1024'), ('strippedBytes', '4'), ('headerLen', '64'), ('headerBytes', '00-00-00-00-00-00-00-00-00-00-00-00-00-01-02-03-04-05-06-07-08-09-0A-0B-0C-0D-0E-0F-10-11-12-13-14-15-16-17-18-19-1A-1B-1C-1D-1E-1F-20-21-22-23-24-25-26-27-28-29-2A-2B-2C-2D-2E-2F-30-31-32-33'), ('dstMAC', '000000000000'), ('srcMAC', '000000000000')]), OrderedDict([('sampleType_tag', '0:1'), ('sampleType', 'FLOWSAMPLE'), ('sampleSequenceNo', '86'), ('sourceId', '2:1000'), ('meanSkipCount', '4096'), ('samplePool', '82058'), ('dropEvents', '0'), ('inputPort', '1'), ('outputPort', '0'), ('flowBlock_tag', '0:1'), ('flowSampleType', 'HEADER'), ('headerProtocol', '1'), ('sampledPacketSize', '1024'), ('strippedBytes', '4'), ('headerLen', '64'), ('headerBytes', '00-00-00-00-00-00-00-00-00-00-00-00-00-01-02-03-04-05-06-07-08-09-0A-0B-0C-0D-0E-0F-10-11-12-13-14-15-16-17-18-19-1A-1B-1C-1D-1E-1F-20-21-22-23-24-25-26-27-28-29-2A-2B-2C-2D-2E-2F-30-31-32-33'), ('dstMAC', '000000000000'), ('srcMAC', '000000000000')]), OrderedDict([('sampleType_tag', '0:1'), ('sampleType', 'FLOWSAMPLE'), ('sampleSequenceNo', '87'), ('sourceId', '2:1000'), ('meanSkipCount', '4096'), ('samplePool', '82058'), ('dropEvents', '0'), ('inputPort', '1'), ('outputPort', '0'), ('flowBlock_tag', '0:1'), ('flowSampleType', 'HEADER'), ('headerProtocol', '1'), ('sampledPacketSize', '1024'), ('strippedBytes', '4'), ('headerLen', '64'), ('headerBytes', '00-00-00-00-00-00-00-00-00-00-00-00-00-01-02-03-04-05-06-07-08-09-0A-0B-0C-0D-0E-0F-10-11-12-13-14-15-16-17-18-19-1A-1B-1C-1D-1E-1F-20-21-22-23-24-25-26-27-28-29-2A-2B-2C-2D-2E-2F-30-31-32-33'), ('dstMAC', '000000000000'), ('srcMAC', '000000000000')]), OrderedDict([('sampleType_tag', '0:1'), ('sampleType', 'FLOWSAMPLE'), ('sampleSequenceNo', '88'), ('sourceId', '2:1000'), ('meanSkipCount', '4096'), ('samplePool', '82058'), ('dropEvents', '0'), ('inputPort', '1'), ('outputPort', '0'), ('flowBlock_tag', '0:1'), ('flowSampleType', 'HEADER'), ('headerProtocol', '1'), ('sampledPacketSize', '1024'), ('strippedBytes', '4'), ('headerLen', '64'), ('headerBytes', '00-00-00-00-00-00-00-00-00-00-00-00-00-01-02-03-04-05-06-07-08-09-0A-0B-0C-0D-0E-0F-10-11-12-13-14-15-16-17-18-19-1A-1B-1C-1D-1E-1F-20-21-22-23-24-25-26-27-28-29-2A-2B-2C-2D-2E-2F-30-31-32-33'), ('dstMAC', '000000000000'), ('srcMAC', '000000000000')]), OrderedDict([('sampleType_tag', '0:2'), ('sampleType', 'COUNTERSSAMPLE'), ('sampleSequenceNo', '3'), ('sourceId', '2:1001'), ('counterBlock_tag', '0:1'), ('ifIndex', '1'), ('networkType', '6'), ('ifSpeed', '1000000000'), ('ifDirection', '1'), ('ifStatus', '3'), ('ifInOctets', '293721088'), ('ifInUcastPkts', '286837'), ('ifInMulticastPkts', '0'), ('ifInBroadcastPkts', '0'), ('ifInDiscards', '286749'), ('ifInErrors', '0'), ('ifInUnknownProtos', '0'), ('ifOutOctets', '467'), ('ifOutUcastPkts', '0'), ('ifOutMulticastPkts', '4'), ('ifOutBroadcastPkts', '0'), ('ifOutDiscards', '3'), ('ifOutErrors', '0'), ('ifPromiscuousMode', '0')])])]), OrderedDict([('datagramSourceIP', '10.10.12.1'), ('datagramSize', '1308'), ('unixSecondsUTC', '1473282827'), ('datagramVersion', '5'), ('agentSubId', '0'), ('agent', '10.10.10.1'), ('packetSequenceNo', '18'), ('sysUpTime', '11000'), ('samplesInPacket', '10'), ('samples', [OrderedDict([('sampleType_tag', '0:1'), ('sampleType', 'FLOWSAMPLE'), ('sampleSequenceNo', '89'), ('sourceId', '2:1000'), ('meanSkipCount', '4096'), ('samplePool', '286837'), ('dropEvents', '0'), ('inputPort', '1'), ('outputPort', '0'), ('flowBlock_tag', '0:1'), ('flowSampleType', 'HEADER'), ('headerProtocol', '1'), ('sampledPacketSize', '1024'), ('strippedBytes', '4'), ('headerLen', '64'), ('headerBytes', '00-00-00-00-00-00-00-00-00-00-00-00-00-01-02-03-04-05-06-07-08-09-0A-0B-0C-0D-0E-0F-10-11-12-13-14-15-16-17-18-19-1A-1B-1C-1D-1E-1F-20-21-22-23-24-25-26-27-28-29-2A-2B-2C-2D-2E-2F-30-31-32-33'), ('dstMAC', '000000000000'), ('srcMAC', '000000000000')]), OrderedDict([('sampleType_tag', '0:1'), ('sampleType', 'FLOWSAMPLE'), ('sampleSequenceNo', '90'), ('sourceId', '2:1000'), ('meanSkipCount', '4096'), ('samplePool', '286837'), ('dropEvents', '0'), ('inputPort', '1'), ('outputPort', '0'), ('flowBlock_tag', '0:1'), ('flowSampleType', 'HEADER'), ('headerProtocol', '1'), ('sampledPacketSize', '1024'), ('strippedBytes', '4'), ('headerLen', '64'), ('headerBytes', '00-00-00-00-00-00-00-00-00-00-00-00-00-01-02-03-04-05-06-07-08-09-0A-0B-0C-0D-0E-0F-10-11-12-13-14-15-16-17-18-19-1A-1B-1C-1D-1E-1F-20-21-22-23-24-25-26-27-28-29-2A-2B-2C-2D-2E-2F-30-31-32-33'), ('dstMAC', '000000000000'), ('srcMAC', '000000000000')]), OrderedDict([('sampleType_tag', '0:1'), ('sampleType', 'FLOWSAMPLE'), ('sampleSequenceNo', '91'), ('sourceId', '2:1000'), ('meanSkipCount', '4096'), ('samplePool', '286837'), ('dropEvents', '0'), ('inputPort', '1'), ('outputPort', '0'), ('flowBlock_tag', '0:1'), ('flowSampleType', 'HEADER'), ('headerProtocol', '1'), ('sampledPacketSize', '1024'), ('strippedBytes', '4'), ('headerLen', '64'), ('headerBytes', '00-00-00-00-00-00-00-00-00-00-00-00-00-01-02-03-04-05-06-07-08-09-0A-0B-0C-0D-0E-0F-10-11-12-13-14-15-16-17-18-19-1A-1B-1C-1D-1E-1F-20-21-22-23-24-25-26-27-28-29-2A-2B-2C-2D-2E-2F-30-31-32-33'), ('dstMAC', '000000000000'), ('srcMAC', '000000000000')]), OrderedDict([('sampleType_tag', '0:1'), ('sampleType', 'FLOWSAMPLE'), ('sampleSequenceNo', '92'), ('sourceId', '2:1000'), ('meanSkipCount', '4096'), ('samplePool', '286837'), ('dropEvents', '0'), ('inputPort', '1'), ('outputPort', '0'), ('flowBlock_tag', '0:1'), ('flowSampleType', 'HEADER'), ('headerProtocol', '1'), ('sampledPacketSize', '1024'), ('strippedBytes', '4'), ('headerLen', '64'), 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('headerBytes', '00-00-00-00-00-00-00-00-00-00-00-00-00-01-02-03-04-05-06-07-08-09-0A-0B-0C-0D-0E-0F-10-11-12-13-14-15-16-17-18-19-1A-1B-1C-1D-1E-1F-20-21-22-23-24-25-26-27-28-29-2A-2B-2C-2D-2E-2F-30-31-32-33'), ('dstMAC', '000000000000'), ('srcMAC', '000000000000')]), OrderedDict([('sampleType_tag', '0:1'), ('sampleType', 'FLOWSAMPLE'), ('sampleSequenceNo', '136'), ('sourceId', '2:1000'), ('meanSkipCount', '4096'), ('samplePool', '434240'), ('dropEvents', '0'), ('inputPort', '1'), ('outputPort', '0'), ('flowBlock_tag', '0:1'), ('flowSampleType', 'HEADER'), ('headerProtocol', '1'), ('sampledPacketSize', '1024'), ('strippedBytes', '4'), ('headerLen', '64'), ('headerBytes', '00-00-00-00-00-00-00-00-00-00-00-00-00-01-02-03-04-05-06-07-08-09-0A-0B-0C-0D-0E-0F-10-11-12-13-14-15-16-17-18-19-1A-1B-1C-1D-1E-1F-20-21-22-23-24-25-26-27-28-29-2A-2B-2C-2D-2E-2F-30-31-32-33'), ('dstMAC', '000000000000'), ('srcMAC', '000000000000')]), OrderedDict([('sampleType_tag', '0:1'), ('sampleType', 'FLOWSAMPLE'), ('sampleSequenceNo', '137'), ('sourceId', '2:1000'), ('meanSkipCount', '4096'), ('samplePool', '434240'), ('dropEvents', '0'), ('inputPort', '1'), ('outputPort', '0'), ('flowBlock_tag', '0:1'), ('flowSampleType', 'HEADER'), ('headerProtocol', '1'), ('sampledPacketSize', '1024'), ('strippedBytes', '4'), ('headerLen', '64'), ('headerBytes', '00-00-00-00-00-00-00-00-00-00-00-00-00-01-02-03-04-05-06-07-08-09-0A-0B-0C-0D-0E-0F-10-11-12-13-14-15-16-17-18-19-1A-1B-1C-1D-1E-1F-20-21-22-23-24-25-26-27-28-29-2A-2B-2C-2D-2E-2F-30-31-32-33'), ('dstMAC', '000000000000'), ('srcMAC', '000000000000')]), OrderedDict([('sampleType_tag', '0:1'), ('sampleType', 'FLOWSAMPLE'), ('sampleSequenceNo', '138'), ('sourceId', '2:1000'), ('meanSkipCount', '4096'), ('samplePool', '434240'), ('dropEvents', '0'), ('inputPort', '1'), ('outputPort', '0'), ('flowBlock_tag', '0:1'), ('flowSampleType', 'HEADER'), ('headerProtocol', '1'), ('sampledPacketSize', '1024'), ('strippedBytes', '4'), ('headerLen', '64'), ('headerBytes', '00-00-00-00-00-00-00-00-00-00-00-00-00-01-02-03-04-05-06-07-08-09-0A-0B-0C-0D-0E-0F-10-11-12-13-14-15-16-17-18-19-1A-1B-1C-1D-1E-1F-20-21-22-23-24-25-26-27-28-29-2A-2B-2C-2D-2E-2F-30-31-32-33'), ('dstMAC', '000000000000'), ('srcMAC', '000000000000')]), OrderedDict([('sampleType_tag', '0:1'), ('sampleType', 'FLOWSAMPLE'), ('sampleSequenceNo', '139'), ('sourceId', '2:1000'), ('meanSkipCount', '4096'), ('samplePool', '434240'), ('dropEvents', '0'), ('inputPort', '1'), ('outputPort', '0'), ('flowBlock_tag', '0:1'), ('flowSampleType', 'HEADER'), ('headerProtocol', '1'), ('sampledPacketSize', '1024'), ('strippedBytes', '4'), ('headerLen', '64'), ('headerBytes', '00-00-00-00-00-00-00-00-00-00-00-00-00-01-02-03-04-05-06-07-08-09-0A-0B-0C-0D-0E-0F-10-11-12-13-14-15-16-17-18-19-1A-1B-1C-1D-1E-1F-20-21-22-23-24-25-26-27-28-29-2A-2B-2C-2D-2E-2F-30-31-32-33'), ('dstMAC', '000000000000'), ('srcMAC', '000000000000')])])]), OrderedDict([('datagramSourceIP', '10.10.12.1'), ('datagramSize', '540'), ('unixSecondsUTC', '1473282833'), ('datagramVersion', '5'), ('agentSubId', '0'), ('agent', '10.10.10.1'), ('packetSequenceNo', '25'), ('sysUpTime', '17000'), ('samplesInPacket', '4'), ('samples', [OrderedDict([('sampleType_tag', '0:1'), ('sampleType', 'FLOWSAMPLE'), ('sampleSequenceNo', '140'), ('sourceId', '2:1000'), ('meanSkipCount', '4096'), ('samplePool', '434240'), ('dropEvents', '0'), ('inputPort', '1'), ('outputPort', '0'), ('flowBlock_tag', '0:1'), ('flowSampleType', 'HEADER'), ('headerProtocol', '1'), ('sampledPacketSize', '1024'), ('strippedBytes', '4'), ('headerLen', '64'), ('headerBytes', '00-00-00-00-00-00-00-00-00-00-00-00-00-01-02-03-04-05-06-07-08-09-0A-0B-0C-0D-0E-0F-10-11-12-13-14-15-16-17-18-19-1A-1B-1C-1D-1E-1F-20-21-22-23-24-25-26-27-28-29-2A-2B-2C-2D-2E-2F-30-31-32-33'), ('dstMAC', '000000000000'), ('srcMAC', '000000000000')]), OrderedDict([('sampleType_tag', '0:1'), ('sampleType', 'FLOWSAMPLE'), ('sampleSequenceNo', '141'), ('sourceId', '2:1000'), ('meanSkipCount', '4096'), ('samplePool', '434240'), ('dropEvents', '0'), ('inputPort', '1'), ('outputPort', '0'), ('flowBlock_tag', '0:1'), ('flowSampleType', 'HEADER'), ('headerProtocol', '1'), ('sampledPacketSize', '1024'), ('strippedBytes', '4'), ('headerLen', '64'), ('headerBytes', '00-00-00-00-00-00-00-00-00-00-00-00-00-01-02-03-04-05-06-07-08-09-0A-0B-0C-0D-0E-0F-10-11-12-13-14-15-16-17-18-19-1A-1B-1C-1D-1E-1F-20-21-22-23-24-25-26-27-28-29-2A-2B-2C-2D-2E-2F-30-31-32-33'), ('dstMAC', '000000000000'), ('srcMAC', '000000000000')]), OrderedDict([('sampleType_tag', '0:1'), ('sampleType', 'FLOWSAMPLE'), ('sampleSequenceNo', '142'), ('sourceId', '2:1000'), ('meanSkipCount', '4096'), ('samplePool', '434240'), ('dropEvents', '0'), ('inputPort', '1'), ('outputPort', '0'), ('flowBlock_tag', '0:1'), ('flowSampleType', 'HEADER'), ('headerProtocol', '1'), ('sampledPacketSize', '1024'), ('strippedBytes', '4'), ('headerLen', '64'), ('headerBytes', '00-00-00-00-00-00-00-00-00-00-00-00-00-01-02-03-04-05-06-07-08-09-0A-0B-0C-0D-0E-0F-10-11-12-13-14-15-16-17-18-19-1A-1B-1C-1D-1E-1F-20-21-22-23-24-25-26-27-28-29-2A-2B-2C-2D-2E-2F-30-31-32-33'), ('dstMAC', '000000000000'), ('srcMAC', '000000000000')]), OrderedDict([('sampleType_tag', '0:1'), ('sampleType', 'FLOWSAMPLE'), ('sampleSequenceNo', '143'), ('sourceId', '2:1000'), ('meanSkipCount', '4096'), ('samplePool', '434240'), ('dropEvents', '0'), ('inputPort', '1'), ('outputPort', '0'), ('flowBlock_tag', '0:1'), ('flowSampleType', 'HEADER'), ('headerProtocol', '1'), ('sampledPacketSize', '1024'), ('strippedBytes', '4'), ('headerLen', '64'), ('headerBytes', '00-00-00-00-00-00-00-00-00-00-00-00-00-01-02-03-04-05-06-07-08-09-0A-0B-0C-0D-0E-0F-10-11-12-13-14-15-16-17-18-19-1A-1B-1C-1D-1E-1F-20-21-22-23-24-25-26-27-28-29-2A-2B-2C-2D-2E-2F-30-31-32-33'), ('dstMAC', '000000000000'), ('srcMAC', '000000000000')])])])]
One packet:
OrderedDict([('datagramSourceIP', '10.10.12.1'), ('datagramSize', '784'), ('unixSecondsUTC', '1473282824'), ('datagramVersion', '5'), ('agentSubId', '0'), ('agent', '10.10.10.1'), ('packetSequenceNo', '15'), ('sysUpTime', '9000'), ('samplesInPacket', '6'), ('samples', [OrderedDict([('sampleType_tag', '0:1'), ('sampleType', 'FLOWSAMPLE'), ('sampleSequenceNo', '70'), ('sourceId', '2:1000'), ('meanSkipCount', '4096'), ('samplePool', '82058'), ('dropEvents', '0'), ('inputPort', '1'), ('outputPort', '0'), ('flowBlock_tag', '0:1'), ('flowSampleType', 'HEADER'), ('headerProtocol', '1'), ('sampledPacketSize', '1024'), ('strippedBytes', '4'), ('headerLen', '64'), ('headerBytes', '00-00-00-00-00-00-00-00-00-00-00-00-08-00-45-00-03-EE-00-00-00-00-40-11-4E-D7-0A-0A-0A-14-0A-0A-0A-01-00-07-00-07-03-DA-BC-08-00-01-02-03-04-05-06-07-08-09-0A-0B-0C-0D-0E-0F-10-11-12-13-14-15'), ('dstMAC', '000000000000'), ('srcMAC', '000000000000'), ('IPSize', '1006'), ('ip.tot_len', '1006'), ('srcIP', '10.10.10.20'), ('dstIP', '10.10.10.1'), ('IPProtocol', '17'), ('IPTOS', '0'), ('IPTTL', '64'), ('UDPSrcPort', '7'), ('UDPDstPort', '7'), ('UDPBytes', '986')]), OrderedDict([('sampleType_tag', '0:1'), ('sampleType', 'FLOWSAMPLE'), ('sampleSequenceNo', '71'), ('sourceId', '2:1000'), ('meanSkipCount', '4096'), ('samplePool', '82058'), ('dropEvents', '0'), ('inputPort', '1'), ('outputPort', '0'), ('flowBlock_tag', '0:1'), ('flowSampleType', 'HEADER'), ('headerProtocol', '1'), ('sampledPacketSize', '1024'), ('strippedBytes', '4'), ('headerLen', '64'), ('headerBytes', '00-00-00-00-00-00-00-00-00-00-00-00-00-01-02-03-04-05-06-07-08-09-0A-0B-0C-0D-0E-0F-10-11-12-13-14-15-16-17-18-19-1A-1B-1C-1D-1E-1F-20-21-22-23-24-25-26-27-28-29-2A-2B-2C-2D-2E-2F-30-31-32-33'), ('dstMAC', '000000000000'), ('srcMAC', '000000000000')]), OrderedDict([('sampleType_tag', '0:1'), ('sampleType', 'FLOWSAMPLE'), ('sampleSequenceNo', '72'), ('sourceId', '2:1000'), ('meanSkipCount', '4096'), ('samplePool', '82058'), ('dropEvents', '0'), ('inputPort', '1'), ('outputPort', '0'), ('flowBlock_tag', '0:1'), ('flowSampleType', 'HEADER'), ('headerProtocol', '1'), ('sampledPacketSize', '1024'), ('strippedBytes', '4'), ('headerLen', '64'), ('headerBytes', '00-00-00-00-00-00-00-00-00-00-00-00-00-01-02-03-04-05-06-07-08-09-0A-0B-0C-0D-0E-0F-10-11-12-13-14-15-16-17-18-19-1A-1B-1C-1D-1E-1F-20-21-22-23-24-25-26-27-28-29-2A-2B-2C-2D-2E-2F-30-31-32-33'), ('dstMAC', '000000000000'), ('srcMAC', '000000000000')]), OrderedDict([('sampleType_tag', '0:1'), ('sampleType', 'FLOWSAMPLE'), ('sampleSequenceNo', '73'), ('sourceId', '2:1000'), ('meanSkipCount', '4096'), ('samplePool', '82058'), ('dropEvents', '0'), ('inputPort', '1'), ('outputPort', '0'), ('flowBlock_tag', '0:1'), ('flowSampleType', 'HEADER'), ('headerProtocol', '1'), ('sampledPacketSize', '1024'), ('strippedBytes', '4'), ('headerLen', '64'), ('headerBytes', '00-00-00-00-00-00-00-00-00-00-00-00-00-01-02-03-04-05-06-07-08-09-0A-0B-0C-0D-0E-0F-10-11-12-13-14-15-16-17-18-19-1A-1B-1C-1D-1E-1F-20-21-22-23-24-25-26-27-28-29-2A-2B-2C-2D-2E-2F-30-31-32-33'), ('dstMAC', '000000000000'), ('srcMAC', '000000000000')]), OrderedDict([('sampleType_tag', '0:1'), ('sampleType', 'FLOWSAMPLE'), ('sampleSequenceNo', '74'), ('sourceId', '2:1000'), ('meanSkipCount', '4096'), ('samplePool', '82058'), ('dropEvents', '0'), ('inputPort', '1'), ('outputPort', '0'), ('flowBlock_tag', '0:1'), ('flowSampleType', 'HEADER'), ('headerProtocol', '1'), ('sampledPacketSize', '1024'), ('strippedBytes', '4'), ('headerLen', '64'), ('headerBytes', '00-00-00-00-00-00-00-00-00-00-00-00-00-01-02-03-04-05-06-07-08-09-0A-0B-0C-0D-0E-0F-10-11-12-13-14-15-16-17-18-19-1A-1B-1C-1D-1E-1F-20-21-22-23-24-25-26-27-28-29-2A-2B-2C-2D-2E-2F-30-31-32-33'), ('dstMAC', '000000000000'), ('srcMAC', '000000000000')]), OrderedDict([('sampleType_tag', '0:2'), ('sampleType', 'COUNTERSSAMPLE'), ('sampleSequenceNo', '2'), ('sourceId', '2:1002'), ('counterBlock_tag', '0:1'), ('ifIndex', '2'), ('networkType', '6'), ('ifSpeed', '1000000000'), ('ifDirection', '1'), ('ifStatus', '3'), ('ifInOctets', '3972'), ('ifInUcastPkts', '11'), ('ifInMulticastPkts', '1'), ('ifInBroadcastPkts', '1'), ('ifInDiscards', '0'), ('ifInErrors', '0'), ('ifInUnknownProtos', '0'), ('ifOutOctets', '6681'), ('ifOutUcastPkts', '10'), ('ifOutMulticastPkts', '4'), ('ifOutBroadcastPkts', '3'), ('ifOutDiscards', '3'), ('ifOutErrors', '0'), ('ifPromiscuousMode', '0')])])])
One sample:
OrderedDict([('sampleType_tag', '0:1'), ('sampleType', 'FLOWSAMPLE'), ('sampleSequenceNo', '70'), ('sourceId', '2:1000'), ('meanSkipCount', '4096'), ('samplePool', '82058'), ('dropEvents', '0'), ('inputPort', '1'), ('outputPort', '0'), ('flowBlock_tag', '0:1'), ('flowSampleType', 'HEADER'), ('headerProtocol', '1'), ('sampledPacketSize', '1024'), ('strippedBytes', '4'), ('headerLen', '64'), ('headerBytes', '00-00-00-00-00-00-00-00-00-00-00-00-08-00-45-00-03-EE-00-00-00-00-40-11-4E-D7-0A-0A-0A-14-0A-0A-0A-01-00-07-00-07-03-DA-BC-08-00-01-02-03-04-05-06-07-08-09-0A-0B-0C-0D-0E-0F-10-11-12-13-14-15'), ('dstMAC', '000000000000'), ('srcMAC', '000000000000'), ('IPSize', '1006'), ('ip.tot_len', '1006'), ('srcIP', '10.10.10.20'), ('dstIP', '10.10.10.1'), ('IPProtocol', '17'), ('IPTOS', '0'), ('IPTTL', '64'), ('UDPSrcPort', '7'), ('UDPDstPort', '7'), ('UDPBytes', '986')])

authors: arajosep viswanav


text:
5.1.2.1.1	Allow volunteer to see tasks assigned to them

As an authenticated volunteer, I can:

[ ] See the list of tasks assigned to me

I'll start this

@bcbeatty Sounds good, this one is all yours!

After talking with @tonysurma, Scope for P1:
A Volunteer will only see the tasks are assigned to them from an Administrator. Anonymous users will not see tasks.
If an admin views is on the volunteer view(non admin area) in P1 they will be treated like a normal user.

source: gharchive/issue

authors: MisterJames bcbeatty tonysurma

repo: HTBox/allReady

license: MIT

license_type: permissive

license_source: github-api


text:
#42 - Volunteer can mark accepted tasks completed or not completed

:boom: Closes #42!
Accepted task

Completed task

"Unable to Complete" modal

"Unable to Complete" task

:boom: :shipit:

source: gharchive/pull-request

authors: dpaquette mheggeseth


text:
HDF representation

It was already decided to allow multiple representations (txt, json, csv, hdf...) of the new spectral library format, based on a common framework (required (meta)data, controlled vocabulary...). In this issue thread, we can discuss the best way to represent the spectral library format in HDF.
As a reference, the current TXT format looks like this:
MS:1008014|spectrum index=500
MS:1008013|spectrum name=AAAVDPTPAAPAR/2_0
MS:1008010|molecular mass=1208.6510
MS:1008015|spectrum aggregation type=MS:1008017|consensus spectrum
[1]MS:1008030|number of enzymatic termini=2
[1]MS:1001045|cleavage agent name=MS:1001251|Trypsin
MS:1001471|peptide modification details=0
...

And JSON (for one metadata item) would take the following shape:
    {
      "accession": "MS:1001045",
      "cv_param_group": "1",
      "name": "cleavage agent name",
      "value": "Trypsin",
      "value_accession": "MS:1001251"
    },

Discussion spun off from issue #12:
@bittremieux:

I think the design decisions can be quite different based on the data format, i.e. between text-based (CSV, TSV) and binary (HDF5).
Personally, with spectral libraries increasing in size, I'm strongly in favor of HDF5. HDF5 has built-in compression, resulting in much smaller files. Also, it's much easier and efficient to slice and dice the data.
With these big, repository-scale spectral libraries I think it's quite important to focus on IO and computational efficiency. Just the time required to read a multi-GB spectral library is non-negligible, making up a considerable part of the search time.
Taking that into consideration, the compact option seems considerably superior to me. You could go even further and just have two arrays per spectrum (m/z and intensity), which fits the HDF5 data model perfectly. This minimizes the query time (2 lookups to retrieve a spectrum versus 2 * k per spectrum with k peaks), and HDF5 was developed to store (binary) arrays.
Also, keep in mind that HDF5 performance can degrade quite significantly if millions of keys are used because the internal B-tree index can become quite unbalanced, leading to significant overhead during querying. Keeping the number of keys limited might thus be essential to achieve acceptable performance.
Related to your final question, the main consideration here is what the goal of this version of the format is. Is it readability, then CSV is obviously superior. But I don't care about readability here, as you mention in #11, there's already the text version (and to a lesser extent the JSON version). Instead, when going for HDF5 performance should be the main goal. And that means using HDF5 the way it was intended and storing values in arrays instead of each individual value separately. Make it as compact as possible, make spectrum reading efficient by storing the peaks in compact arrays.

@RalfG:

Thanks for the reply! I started looking into HDF and there's a lot more to it then I initially thought. Definitely the nested key system and metadata for each group would be very useful for the spectral library format. This means that an optimal HDF representation would look pretty different from this general tabular format. Since we want to make it possible with the specification to have multiple representations (txt, json, csv, hdf...), I propose that we keep this discussion about general tabular formats (such as csv/tsv) and move the discussion on the HDF format to a new issue.

@bittremieux, would you then propose a group system that looks like this:
.
└── library (group)
    ├── spectrum_001 (group)
    ├── spectrum_002 (group)
    ├── ...
    └── spectrum_n (group)
        ├── intensity (array)
        └── mz (array)

with metadata embedded as attributes for each relevant group?
That seems very structured and efficient to read a given spectrum, but that would mean that one library would have lot of keys (up a million or more) and you mentioned performance degradation in this case.

That seems a pretty logical layout. And yes, for large-scale spectral libraries the number of keys will be in the millions, but still significantly lower than the layout that was previously suggested where each peak would be stored separately.
This is a much better trade-off between ease of querying and performance, and I can't immediately come up with a better alternative. Also, by using groups the performance hit should be relatively modest, even for millions of keys. HDF5 was developed as a big data format after all.
Here are some relevant threads on HDF5 performance:

https://github.com/h5py/h5py/issues/1055
https://cyrille.rossant.net/moving-away-hdf5/ (section performance issues)
https://stackoverflow.com/questions/47624467/reading-hdf5-file-initially-fast-then-becomes-very-slow
https://stackoverflow.com/questions/35321093/limit-on-number-of-hdf5-datasets

Hi, the above structure library (group) ...  looks a lot like mzML to me. In fact, the OpenMS people already have a tool to search spectra in one mzML file against spectra in another mzML file for exactly that purpose. We used their prototype to export MassBank to mzML. With mz5 you get mzML encoded as HDF5 as well, and there are already tools to convert between mzML and mz5 forth and back. What's currently missing is the set of userParam and cvParam to keep the spectral-library specific attributes. Yours, Steffen

I also want to add that entity-attribute-values are a pain in the butt. Especially if you use it in a standard (SQL) setup.

AFAIK you can't query entries in an HDF5 based on the value of their attributes. You can use attributes to store metadata, but to filter on a specific value you'd need to walk the entire tree and filter manually. Not great.
Also +1 on the account that entity-attribute-values in SQL are pretty annoying.

This is always a problem, but the issue is that there are potentially hundreds of attributes of metadata that we want to be able to capture. So we need a system that can capture and store any attribute. BUT, it is usually the case that there is only a relatively small subset of attributes that one would want to filter or search on. So, the data model and archival/transmission format needs to be able to store anything. The optimized active-use format probably should be able to store anything but then optimize search and filter on a subset of terms. Some RDBMSs have SPARSE matrix support, maybe other systems, too. Otherwise we could implement a sparse matrix system in a custom binary format. Or just reuse SQLite or HDF5 where the most common attributes are indexed as columns and the rest are just stored. But all that is very usage-specific. metabolomics will want different columns that proteomics. So I think the best goal is to make a nice format that can store anything with controlled vocabulary, and then everyone can try to implement their idea of a fast storage mechanism using the technology they like, which history has shown we can't all agree on.

With SQLite3's JSON extension enabled, you can create partial indices over JSON-path expressions.
CREATE INDEX idx_spectrum_is_etd ON [spectrum_table](id)
    WHERE json_extract(params, '$.MS:1000598') != NULL;

Then searching for ETD spectra will incur one index traversal followed by a fast series of reads from the spectrum table, whatever it is called. This does not require that every row in the spectrum table store a value in params under the key "MS:1000598", but it does essentially replicate whether or not that value is present for all spectra in the index.
Of course, we can solve this problem in HDF5 by simply building an index array ourselves and storing it in the library too. This complicates library writing code because now we have to manage building and updating all the indices whenever we add a new entry or decide we want to add/remove an index. It also complicates reading code because it requires that the library reading code manually manipulate these index arrays.

My idea for HDF5 was indeed to have an additional index array with the most common attributes as columns. We could make a small set of required attributes (e.g. precursor mz, charge...) and make it possible for the user to expand that list. Writing does indeed get a little more complex, but should still be manageable.
SQLite with the JSON extension also looks nice, but that could easily be another representation of the format.

I think I came into the middle of this without knowing the data model, and just engaged in pedantics over data format engineering. Is there something I can read that goes over the data model formally? I saw some Google Docs links, but it wasn't clear if they were current/authoritative.
Wouldn't reading be trickier too, since there would need to be a way to introspect read requests for which indices to use? This would be tightly coupled to the reading interface of course. The more general/compose-able the interface is, the closer the implementation needs to get to implementing a query optimizer. It seems tempting to say indices are an implementation detail, but it makes library exchange more difficult unless you completely re-build the file on receipt. On the other hand, that might be necessary with a proliferation of back-ends in the first place, requiring libraries be exchanged in one of the less "optimized" formats before being tuned for searching by the application consuming it. Is this the intent?

Over the last weeks I heard several comments offline from people asking about the current data model, not knowing the current state. Can we make a push to collect that first ? I'd expect that this would also form basis of reporting & discussions @ PSI-MS in San Diego next month. Yours, Steffen

@RalfG any interest in giving this a try since most things are settled now?

Are there any other binary implementation/representations planned?

authors: RalfG bittremieux edeutsch hechth mobiusklein sneumann tkschmidt

repo: HUPO-PSI/mzSpecLib


text:
Changes to Monster Overlay

I have a few ideas as to how the monster overlay could be changed/improved, as while having a heath bar for the monsters does provide a useful tool, having it show for all of the monsters in the map as soon as you load in can seem a bit too hacky, and ruin some of the fun of the game.
One solution would be to only show the monsters who have been discovered/sighted, but I don't know how difficult or possible that would be to even track.
Another, potentially better solution would be to only show the bar when locked onto a monster as that would mechanically make the most sense, though again I don't know if it would be possible to track from outside the game.
This method would also add the extra steps to move the position to differ from the current setup, while opening up the avenue to add more options to customize it such as size as there would only be one bar on the screen at any given time.

I don't know if there's a way to track wether the monster was discovered or not and so far I haven't found the memory addresses that handle which monster you're locking onto (I know there is one, just need to find where it is).
I will add more customization options to the monsters HP overlay in the future though, just need to finish implementing rich presence. Thanks for the suggestions though :)

I belive I have seen other tools handle it by checking if monsters hp change or not and when they do not for a certain time just stop displaying them

Yea, that would work too. I might do that after finishing all the other stuff in my priority list

Done!


text:
base64 Encoding

To complete a Category for NSString, it owned base64 Encoding transform from string to encoding, or opposite.
Touch two files, NSString+Base64.m and NSString+Base64.h

Complete!😎

repo: HackSwjtu/HackRunningGo-iOS


text:
🛑 HackTJ 7.0 is down

In b37f255, HackTJ 7.0 (https://hacktj.org/2020) was down:

HTTP code: 0
Response time: 0 ms

Resolved: HackTJ 7.0 is back up in ca44789 after 6 hours, 10 minutes.

repo: HackTJ/status


text:
changed fetch method to add candidates

Description
Replaced postData fetch method with global fetchFromDb
Fixes #282
How to test?
Add candidates on a board
Checklist

[x] I have performed a self-review of my own code
[x] I have followed the name conventions for CSS Classnames and filenames, Components names and filenames, Style filenames, if you are in doubt check the the project README.MD and here https://github.com/HackYourFuture-CPH/curriculum/blob/master/review/review-checklist.md
[x] I have commented my code, particularly in hard-to-understand areas, if you code was simple enough mark the box anyway
[x] I have made corresponding changes to the documentation, if you code was simple enough mark the box anyway
[x] This PR is ready to be merged and not breaking any other functionality

We're using async almost everywhere now. I think it'd be better to standardize on that and remove the Promise resolution here. Also, any error will be caught at the error boundary anyway, right? Or do you want to catch it here?

Please note that using async/await syntax also returns Promise objects.
The good thing about using async/await is that it is ubiquitous across languages even though the implementation underneath differs slightly.

Made a change to return data from fetch method since the response.json throws an error if the method is not get.


text:
Timeout function executes twice

Hi. I just realized that the $scope.$on('IdleTimeout') event is calling my logout function twice, resulting in error since the second time, obviously, because the user is already logged out.
Any advice?
Thanks.

Facing the same issue. Have you found any solution? @alexandroscgb

repo: HackedByChinese/ng-idle

license: mit

license_source: bigquery


text:
Calendar is not exposed anywhere in the npm package

Expected behavior
The line below used to work in 0.27.0:
import Calendar from 'react-datepicker/lib/calendar';
Actual behavior
Now, Calendar is not exposed anywhere. I want to use just this component, but can't.
There is no node_modules/react-datepicker/lib/calendar.jsx file after running npm i --save react-datepicker.

Bump for justic

@stale this is still an issue. Don't close it.

This is still an issue. Don't close it.


text:
PHP Language - Hello World From Indonesian

What does the program do ?
Hello World
In what programming language it is written?
PHP

Thank you for participating  in hacktoberfest ❣ if you liked working on this repo you can share it with friends and star this project for helping others


text:
🛑 Myspleen is down

In dce325a, Myspleen (https://www.myspleen.org) was down:

HTTP code: 502
Response time: 204 ms

Resolved: Myspleen is back up in 58a54a1 after 23 minutes.

repo: Hagabard/IsMyspleenDown


text:
App does not give options for places with same name or postal code

Open Weather
Go to Change Location
Enter a name that has 2 cities in different places

Expected Result: The app should give options to select desired city
Actual Result: The App chooses 1 location without giving options
Haiku-2018-01-04T03-55-59-392554000Z.webm.zip

So I tested with 2 french cities of same name
Bordeaux, France, Aquitaine (the well known city)
Bordeaux, France, Ile de France (another one I even didn't know of before going to Yahoo page)
Note that about two years ago french regions were rearranged, some were grouped & renamed; API seems to use old names for now.
If you put Bordeaux, France only, API picks weather for Bordeaux, France, Aquitaine probably because it is first in alphabetic order.
If you enter full infos, you obtain indeed different weathers but location is shown both times as
"Bordeaux, France" so it's difficult then to distinguish which one is where. Since there are also other cities named Bordeaux around the world, if possible, it would be nice to be able to chose to display: City, country or City, region.

Fixed in 1cb655c05b0c1662f78b25214fa15735536757e8

repo: HaikuArchives/Weather


text:
提个小建议：图标选择器组件的dialog内部可以使用grid布局

我之前也写过图标选择器，发现其中图标的名称有自动换行的问题，不美观
发现element文档中是用grid布局实现的，自己试了一下，可以比较好的解决

还没咋写过 grid 布局，后面有空学习下，或者大佬可以给我提个pr

已更新为 grid 布局

authors: HalseySpicy Zong-FatStar

repo: HalseySpicy/Geeker-Admin


text:
请问数据集下载不下来是怎么回事呢？

您的这些数据集都链接到Github上，我把代码上传到服务器并配置好环境，但是在获取数据集的时候连接超时，请问怎么解决呢？

如果下载一直超时，可以去pyg的官网上下载数据集。

repo: HanChen-HUST/CSGCL


text:
Disable autoScale of TileSparklineSkin

Hi!
I'm trying to disable the autoscale of averege on TileSparklineSkin gauge.
It's possible ?
Thanks !

What exactly do you mean with autoscaling related to the averaging? Can you provide an example or screenshot?

authors: BrokenFire HanSolo

repo: HanSolo/Medusa


text:
Added CircleProgressNumberTile a modification of the CircleProgressTile. Gives the ability to …

…show different units in the larger center text with the percentage shown in the lower unit. CircleProgressTile has the percentage hard coded in the larger text. Other modifications swap the placement of value and unit. The Larger Center text displays the Value in its actual number. For example the number of hours. The smaller lower text shows the percent of the maxValue in a numeric value that is also displayed in the circular progress bar.

Hello, Making a small contribution to your very beautiful tiles library. Hopefully this is useful to others. Cheers.

Usage:
`
private static int TILE_WIDTH = 200;
private static int TILE_HEIGHT = 200;
public void start(Stage stage) {
Tile softwareHrs = TileBuilder.create()
.skinType(Tile.SkinType.CIRCLE_PROGRESS_NUM)
.prefSize(TILE_WIDTH, TILE_HEIGHT)
.title("Software Engineering")
.text("STUDY HOURS")
.unit("Hrs")
.maxValue(50)
.build();
}
`

Awesome...thx a lot 👍🏻

Added the new skin also to the jdk17 branch but renamed it there to CircularProgressNumberTileSkin

Awesome!

authors: HanSolo runnermann

repo: HanSolo/tilesfx


text:
Render time

Hi, thanks  for your good work!
I tested it, the render time is about 1s, right?  it is too slow, can be improved?

On Nvidia tesla v100 card, testing 1 image, rendering needs 0.5s, and forwarding network needs 0.2s.
As this process is a little slow, we use many parallelization tricks to speed up the testing process.

Hi, I want  to know why use the neural renderer method? are there other faster renderer methods?
thanks a lot!

repo: Hangz-nju-cuhk/Rotate-and-Render


text:
🛑 Hungarian is down

In 5f75df5, Hungarian (https://hu.scratch-wiki.info) was down:

HTTP code: 508
Response time: 476 ms

Resolved: Hungarian is back up in 3a8ff92 after 5 minutes.


text:
🛑 Russian is down

In 6a57e94, Russian (https://ru.scratch-wiki.info) was down:

HTTP code: 508
Response time: 479 ms

Resolved: Russian is back up in c765731 after 9 minutes.


text:
🛑 Japanese is down

In 839a6be, Japanese (https://ja.scratch-wiki.info) was down:

HTTP code: 508
Response time: 415 ms

Resolved: Japanese is back up in 410c695 after 7 minutes.


text:
🛑 Japanese is down

In 9fd20dc, Japanese (https://ja.scratch-wiki.info) was down:

HTTP code: 508
Response time: 1680 ms

Resolved: Japanese is back up in 9ab7d77 after 7 minutes.


text:
🛑 Drop is down

In cc1e138, Drop (https://drop.hao77.repl.co/) was down:

HTTP code: 502
Response time: 10451 ms

Resolved: Drop is back up in 199ed33.


text:
🛑 Google Scholar is down

In f23f281, Google Scholar (https://scholar.haoqiyun.tk/) was down:

HTTP code: 403
Response time: 561 ms

Resolved: Google Scholar is back up in 1a76973.


text:
🛑 Happy Vibes Bot 4 is down

In 2d1838e, Happy Vibes Bot 4 ($BOT4) was down:

HTTP code: 0
Response time: 0 ms

Resolved: Happy Vibes Bot 4 is back up in bb96874.

repo: Happy-Vibes-Bot/status


text:
🛑 Happy Vibes Bot 5 is down

In bc677aa, Happy Vibes Bot 5 ($BOT5) was down:

HTTP code: 0
Response time: 0 ms

Resolved: Happy Vibes Bot 5 is back up in bad316f.


text:
🛑 Happy Vibes Bot 4 is down

In ed61c0e, Happy Vibes Bot 4 ($BOT4) was down:

HTTP code: 0
Response time: 0 ms

Resolved: Happy Vibes Bot 4 is back up in d3ae740.


text:
🛑 Happy Vibes Bot 5 is down

In 734aefe, Happy Vibes Bot 5 ($BOT5) was down:

HTTP code: 0
Response time: 0 ms

Resolved: Happy Vibes Bot 5 is back up in f94c2db after 6 minutes.


text:
🛑 Happy Vibes Bot 5 is down

In 8a87e94, Happy Vibes Bot 5 ($BOT5) was down:

HTTP code: 404
Response time: 6069 ms

Resolved: Happy Vibes Bot 5 is back up in ff01d8e.


text:
Some characters get url encoded in the cluster name

% is a valid character for names that may include brackets. eg [i] is %5Bi%5D.
A better solution might be to do the encoding within the library but this is enough to make it function with bracket characters.

Thanks for the pull request!

authors: HariSekhon davidgibbons

repo: HariSekhon/lib


text:
导入png图片之后和原图会有细微差距

第一张是原图，第二张是我用demo上传的图片，第三张是下载马良app贴纸上传功能添加的图片效果，
下面是我添加图片调用的代码
let image = UIImage.init(named: "test")
let chartlet = try? self.canvas.makeTexture(with: image!.pngData()!,id: "test")
self.canvas.renderChartlet(at: self.view.center, size: chartlet!.size, textureID: chartlet!.id)
马良app上传的贴纸效果和原图保持，请教下您是如何上传图片呢，是否有什么参数或者模式我这边没设置，谢谢 。

如果图片打不开，复制链接到浏览器地址栏查看图片
https://nkhigh-1253881638.cos.ap-shanghai.myqcloud.com/images/1.PNG
https://nkhigh-1253881638.cos.ap-shanghai.myqcloud.com/images/2.PNG
https://nkhigh-1253881638.cos.ap-shanghai.myqcloud.com/images/3.PNG

如果图片打不开，复制链接到浏览器地址栏查看图片
https://nkhigh-1253881638.cos.ap-shanghai.myqcloud.com/images/1.PNG
https://nkhigh-1253881638.cos.ap-shanghai.myqcloud.com/images/2.PNG
https://nkhigh-1253881638.cos.ap-shanghai.myqcloud.com/images/3.PNG

修改了源码导致该问题，现已恢复

修改了源码导致该问题，现已恢复

repo: Harley-xk/MaLiang


text:
[Help] 🤔 登录后智力变低

登录前

登录后

换成精确模式试试

有空我试试啥情况

切精确模式后好了。。好奇这是什么原理，总不能登录和非登录用户的temperature不一样吧

直接官网 bing 不登录也是傻的，只有 go-proxy-bingai 不登录智商高一点。。

用别的逻辑问题试了一下，也是这个结论。

GPB我调过参数，可能登录账号会把参数覆盖了，我看看能不能调一下

哪个commit？指个路呗

哪个commit？指个路呗

https://github.com/Harry-zklcdc/go-proxy-bingai/blob/206d9e82b36a8ad1a545f7db56f56828e345213d/frontend/src/views/chat/components/Chat/Chat.vue#L246-L285

我这登录前后都是正常的，请尝试更新到最新版本

猜测可能和点击了「新话题」或者选择了「对话风格」，导致注入的参数失效

不过我这边试了一下，点击「新话题」或选择「对话风格」的时候，并不会导致 智力下降 的情况

破案了，登录之后必须把GPT-4 Turbo的开关关掉，再打开，就有效果了，看看是不是代码逻辑有点问题？

破案了，登录之后必须把GPT-4 Turbo的开关关掉，再打开，就有效果了，看看是不是代码逻辑有点问题？

我这边开不开都一样，都能正常回答出问题

那奇怪了，也许高峰期微软ban了部分用户的GPT-4权限？我看Copilot Pro的宣传就说“在高峰期能够使用GPT-4或GPT-4-Turbo”。没事了

same as me

authors: Harry-zklcdc RayGicEFL ningguoxu sdferttt

repo: Harry-zklcdc/go-proxy-bingai


text:
Fixed issue #34

Under this issue, I have made the following changes:
Fixed Navbar Position:
Made the navbar fixed to the top of the page for better accessibility while scrolling.

Added Hover Effects on Navbar Links:
Implemented hover effects for better user interaction with navbar items.

Adjusted Slider Image Display:
Fixed the issue where slider images were being cut off, ensuring all images are fully visible.

Updated Footer Links:
Corrected the links in the footer for "About" and "Services" to ensure they navigate to the appropriate sections or pages.

Justified Text in Subheadings:
Justified the text of "Student" and "Stakeholder" subheadings under the "Register" heading for better alignment and readability.

Justified Text of "Progress Tracking," "AI Research Templates," and "Explore Top Papers":

Okay, Thankyou for the feedback, will work on it

The problem is
this image is getting cut from top


text:
Hotkey for 5th weapon slot and armor sets

Add a hotkey for 5th 6th 7th (and so on) weapon slot, armor sets and armor pieces.

This is not going to work as you or most people expect. Equipping items is slow and it's blocked by skill casts, so an hotkey to equip weapons or armors would be slow and clunky. I would never try to sell this as "weapon slot 5+" as it just creates expectation for something we simply cannot provide.
That said, functionality to equip an armor would be nice, but it's not trivial, mostly because of allowing the user to select the right pieces, and for toolbox later to find them. We have to read, display and remember armor mods.

You can't just use the item ID of the armour pieces? o_O_o

No. For example my mesmer has 4 armor sets, and several extra headpiece and parts, all of which are the same armor type, only difference being color (sometimes) and mods.

reporting you to the fashion police...

Would be too clunky.

authors: DarkManic HasKha Hour-of-the-Owl reduf

repo: HasKha/GWToolboxpp


text:
[Design] Token Metadata API

Feature Name: metadata_api
Start Date: 2021-07-13
RFC PR: (leave this empty)
Hathor Issue: (leave this empty)
Author:

Giovane Costa gigio.coder@gmail.com
André Carneiro andreluizmrcarneiro@gmail.com

Summary
Metadata API will provide metadata information about Transactions, Tokens, Blocks and other things on Network.
Motivation
We need to have some data to be used on Public Explorer and Wallets that can't be stored on the blockchain about
Transactions, Tokens, Blocks and other things on Network.
So, we store somewhere else and retrieve them through MetadataAPI.
This data can also be used by community as they please.
Guide-level explanation
Endpoint
Get dag entity metadata
GET /metadata/dag return 200 | 404
Query params

id - id of the dag entity

Success Response

Code 200
Content {"<id>": <metadata content>}

Error Response

Code 404 NOT FOUND
Content {"error": "not_found"}
Meaning: no metadata for this entity.

Content format
Different dag elements may have different metadata associated with them.
For now we will describe the transaction and token metadata structures.
If any fields are missing from the actual response the caller should interpret as the "falsy" value for the field (bool is false, string is "" and dict is {}) or null when applicable.
Transaction

Field name
type
Description

id
str
Transaction id

context
str
Message to be shown on frontend

genesis
bool
If this transaction is genesis

Example:
{
  "id": "000000005e4849855eeb967c6891162cc6a185f7ba68fe47ee4cecbe3e590bad",
  "context": "First transaction made with the hathor ledger app"
}

Token

Field name
type
Description

id
str
Token unique id

verified
bool
If token is verified

banned
bool
If token is banned

reason
str
Ban reason

nft
bool
If token is a nft or not

nft_media
dict
NFT media data, if any

nft_media structure:

Field name
type
Description

type
enum(video|image|audio|pdf)
NFT media type

file
str
Media file of the NFT

loop
bool
If media will play in loop

autoplay
bool
If media will play automatically

Example:
{
  "id": "00003aa356c9493464c657873b115c5e8667adf58cceeb4b37a1cdae0ddc9536",
  "verified": false,
  "banned": true,
  "reason": "some reason",
  "nft": true,
  "nft_media": {
    "type": "video",
    "file": "https://vimeo.com/68231446",
    "loop": false,
    "autoplay": true
  }
}

Reference-level explanation
Information will be stored on a file dag/[id].json inside an S3 bucket.
The endpoint will read the json from the file and return it as described on the guide-level.
If the file was not found, 404 will be returned.
Drawbacks
We still store data manually in S3. It would be good to have a better way to do it, like a CMS or something like that.
Rationale and alternatives
Store in S3 in JSON format was the fastest we could do when we needed to have metadata for Token.
We could store in a database too.
Unresolved questions
Every entity that potentialy have some metadata will make a request and only a few will have, so, the massive majority of requests will return 404.
Future possibilities
The API could be more complex, allowing the caller to request a list of ids instead of a single one, or a more complex search like retrieve verified tokens with pagination.
We could store metadata on a database like mongodb or AWS DocumentDB and have a CronJob read the database, validate the data and compute any aggregations then write the S3 files.
This would allow a simple service to manage and interact with the database (maybe with an API) and we would still have the benefits of the public S3 with the metadata.

The design is approved for me

I think the design is good. Here are my comments:

The error response can benefit from a different response: {"<id>": "not_found"} (instead of using the key "error". This content supports queries with more than one tx_id.

Future possibilities: I think we are mixing two separate things here: using a database and running a validator. I mean, we can run a validator regardless of the storage.

Unresolved questions: I didn't understand why it is an open question. What is the problem to be solved or question to be answered?

The error response can benefit from a different response: {"<id>": "not_found"} (instead of using the key "error". This content supports queries with more than one tx_id.

Then the caller will have to check if the response.body["<id>"] is a string or an object. We could just not return any "not found" and the caller must check if "<id>" in response.body. This seems more intuitive, and if none are found, 404 with default error message.

Future possibilities: I think we are mixing two separate things here: using a database and running a validator. I mean, we can run a validator regardless of the storage.

Yes, they are separate things but i was implying that a validator would be more appropriate when writing data to the bucket (as opposed to when reading it).

Unresolved questions: I didn't understand why it is an open question. What is the problem to be solved or question to be answered?

There isn't a problem, but it would be better to minimize the misses (404's) on this API.

The error response can benefit from a different response: {"<id>": "not_found"} (instead of using the key "error". This content supports queries with more than one tx_id.

Then the caller will have to check if the response.body["<id>"] is a string or an object. We could just not return any "not found" and the caller must check if "<id>" in response.body. This seems more intuitive, and if none are found, 404 with default error message.

That's a good point. Maybe add an error key instead? E.g.: {"<id>": {"error": "not found"}}?

Future possibilities: I think we are mixing two separate things here: using a database and running a validator. I mean, we can run a validator regardless of the storage.

Yes, they are separate things but i was implying that a validator would be more appropriate when writing data to the bucket (as opposed to when reading it).

I got it. Just improve the text and maybe splitting into two sentences might be clearer.

Unresolved questions: I didn't understand why it is an open question. What is the problem to be solved or question to be answered?

There isn't a problem, but it would be better to minimize the misses (404's) on this API.

I guess you should move it to the Drawbacks section then.

repo: HathorNetwork/hathor-explorer-service


text:
Failed building wheel for flash-attn

creating build/lib.linux-x86_64-cpython-38/flash_attn/losses
copying flash_attn/losses/cross_entropy_parallel.py -> build/lib.linux-x86_64-cpython-38/flash_attn/losses
copying flash_attn/losses/init.py -> build/lib.linux-x86_64-cpython-38/flash_attn/losses
copying flash_attn/losses/cross_entropy.py -> build/lib.linux-x86_64-cpython-38/flash_attn/losses
copying flash_attn/losses/cross_entropy_apex.py -> build/lib.linux-x86_64-cpython-38/flash_attn/losses
running build_ext
/data/home/xuncai/anaconda3/envs/llm/lib/python3.8/site-packages/torch/utils/cpp_extension.py:387: UserWarning: The detected CUDA version (11.0) has a minor version mismatch with the version that was used to compile PyTorch (11.6). Most likely this shouldn't be a problem.
warnings.warn(CUDA_MISMATCH_WARN.format(cuda_str_version, torch.version.cuda))
/data/home/xuncai/anaconda3/envs/llm/lib/python3.8/site-packages/torch/utils/cpp_extension.py:397: UserWarning: There are no g++ version bounds defined for CUDA version 11.0
warnings.warn(f'There are no {compiler_name} version bounds defined for CUDA version {cuda_str_version}')
building 'flash_attn_cuda' extension
creating /tmp/pip-install-vtz3lk_i/flash-attn_38dfe6a6d0904a369d24c5ba53f9d63c/build/temp.linux-x86_64-cpython-38
creating /tmp/pip-install-vtz3lk_i/flash-attn_38dfe6a6d0904a369d24c5ba53f9d63c/build/temp.linux-x86_64-cpython-38/csrc
creating /tmp/pip-install-vtz3lk_i/flash-attn_38dfe6a6d0904a369d24c5ba53f9d63c/build/temp.linux-x86_64-cpython-38/csrc/flash_attn
creating /tmp/pip-install-vtz3lk_i/flash-attn_38dfe6a6d0904a369d24c5ba53f9d63c/build/temp.linux-x86_64-cpython-38/csrc/flash_attn/src
error: [Errno 8] Exec format error: 'c++'
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
ERROR: Failed building wheel for flash-attn
Running setup.py clean for flash-attn
Failed to build flash-attn
ERROR: Could not build wheels for flash-attn, which is required to install pyproject.toml-based projects

Sounds like some mismatched CUDA version as mentioned in the warning (CUDA 11.0 vs 11.6).
We require CUDA 11.4 or later.
We recommend the Pytorch container from Nvidia, which has all the required tools to install FlashAttention.

Which Pytorch container? Is there a minimum version? I tried with 20.10 and it did not work

I usually use the newest one (e.g. 23.05). I think anything from 22.05 (when FlashAttention was released) should work.

I use nvcr.io/nvidia/pytorch:23.02-py3, still error.

repo: HazyResearch/flash-attention


text:
FlashDepthWiseConv1d is not handling padding properly?

Hi Dan & Hermann,
I am trying to implement a short causal conv.  I am doing it this way:
        short_conv_slow = nn.Conv1d(
            self.d_inner * self.order,
            self.d_inner * self.order,
            bias=True,
            kernel_size=self.shortconv_ks,
            groups=self.d_inner * self.order,
            padding=self.shortconv_ks - 1)

        # FlashFFTConv Wrapper
        self.short_conv = FlashDepthWiseConv1d(
            channels=self.d_inner * self.order,
            kernel_size=self.shortconv_ks,
            padding=self.shortconv_ks - 1,
            weights=short_conv_slow.weight,
            bias=short_conv_slow.bias,
        )

...

out = self.short_conv(x.contiguous())[..., :x.shape[-2]]

However, I noticed an error that during the backward (self.shortconv_ks=5 in this example):
Traceback (most recent call last):
  File "/home/Projects/imaginaire4/projects/mesh2mesh/modules/hyena.py", line 395, in <module>
    grad = torch.autograd.grad(y[:, 10, :].sum(), x)[0]
  File "/usr/local/lib/python3.10/dist-packages/torch/autograd/__init__.py", line 399, in grad
    result = Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
  File "/usr/local/lib/python3.10/dist-packages/torch/autograd/function.py", line 289, in apply
    return user_fn(self, *args)
  File "/usr/local/lib/python3.10/dist-packages/flashfftconv/depthwise_1d.py", line 20, in backward
    du, dk, dbias = conv1d_backward(dout, input, weight, bias, ctx.padding, ctx.is_bhl)
RuntimeError: Expected size for first two dimensions of batch2 tensor to be: [768, 1024] but got: [768, 1028].

Note that, given the padding, [768, 1024] is indeed the shape that is to be expected.
Any idea of what might be the cause / how to solve it / how to implement properly a causal short1dconv with FlashFFTConv?
Thank you in advance! :)
Best,
David

hello, I got the same bug. Have you fixed it?

Hi Moriarty,
I do not really remember, tbh. If I remember correctly, the model handles padding automatically. Which means that it should be set to zero (or default). I think with this, the out shapes match.

repo: HazyResearch/flash-fft-conv


text:
Fields do not appear in the same order as in source data/SR

Is there an existing issue for this?

[X] I have searched the existing issues

Is your proposal related to a problem or functionality gap?
The fields are not appearing in the same order as in scan report and by default are ordered alphabetically A-Z.
Describe your proposal
Could this be changed back to how it was previously where you could only sort fields when needed via the sorting filter.
Describe alternatives you've considered
No response
I'm part of a Project Team
No response
Anything else?
No response
Are you willing to contribute to developing this feature?
None

Yep we'll change this back to the default order by id. Should have a release early next week for this.

authors: AndyRae erummas

repo: Health-Informatics-UoN/Carrot-Mapper


text:
Code=3840 "The operation couldn’t be completed."

Hi,
Impossible to parse my Json string in -  parseJSONString(JSON: String) -> AnyObject?
Error:
Error Domain=NSCocoaErrorDomain Code=3840 "The operation couldn’t be completed. (Cocoa error 3840.)" (Garbage at end.) UserInfo=0x7f8730567000 {NSDebugDescription=Garbage at end.}

That error means your string is not JSON it's a invalid format.

i have pass the same string twice. first time it execute perfectly but in second time it show the error Error Domain=NSCocoaErrorDomain Code=3840 "The operation couldn’t be completed. (Cocoa error 3840.)" (Garbage at end.) UserInfo=0x7f8730567000 {NSDebugDescription=Garbage at end.}

authors: Bill-Niz SinghBabita tobiasoleary

repo: Hearst-DD/ObjectMapper


text:
Feature Request: Add timer hud for spells

That would be great if the wheels of fate or spells u use show you a timer hud telling you how many seconds left before it end...
Can show that below of Current Pumpkins: x hud
sry for my bad english...
cheers

Good idea! I'll think it over!

I've investigated this feature and tried to implement it, but many spells have no duration, and wheel of fate spells have different logic, which is why the implementation looks inconsistent and kinda weird. This implementation will confuse players, so I'll not add this to the next version.

repo: Hedgefog/cs-halloween-mod


text:
highlight line height doesn't match line height

Could you please tell me how can I change the highlight line height? What's parameter it's related in the style.css?

I have try to change the --hk-codeblock-vertical-padding: 8px; .But this operation seems to have no effect, changing to 6 px and 10 px, the interface remains unchanged

Hi, were you able to solve this issue? @David-JA

adding this style fixes it
div.hk-codeblock div.hk-codeblock-highlight > span.line {
  font-family: var(--hk-codeblock-linenos-text-font);
  font-size: var(--hk-codeblock-linenos-text-size);
  line-height: var(--line-height-normal);
}

authors: David-JA chkhk vizelada15648lviki

repo: HeekangPark/obsidian-hk-code-block