def get_longtime_data(self, args):
"""
get values from RAW Archive
parameters:
/<str>project/<str>tablename/<str>datestring/<str>key/<str>value_keys
keyids=hostname:srvszp2orb.tilak.cc means
this is only useful if keyids are unique
return data like this:
[
{
name: "name of this series" usually this is the counter name
data : [[ts, value], ...]
},
...
]
"""
assert len(args) == 5
project, tablename, monthstring, keys_str, value_key = args
if len(monthstring) > 7:
return "monthstring, has to be in YYYY-MM format"
# key_str should be a tuple string, convert to unicode tuple
keys = tuple([unicode(key_value) for key_value in eval(base64.b64decode(keys_str))])
logging.info("project : %s", project)
logging.info("tablename : %s", tablename)
logging.info("monthstring : %s", monthstring)
logging.info("keys : %s", keys)
logging.info("value_keys : %s", value_key)
datalogger = DataLogger(basedir, project, tablename)
data = datalogger.get_tsastats_longtime_hc(monthstring, keys, value_key)
#logging.info("got data: %s", data)
hc_data = [{"name" : funcname, "data" : data[funcname]} for funcname in data.keys()]
return json.dumps(hc_data)
def get_monthly_ts(self, project, tablename, monthstring, args):
"""
get monthly statistical values
TODO: should be combined with get_lt_ts
"""
index_key_enc = None
value_keyname = None
stat_func_name = "avg"
if len(args) == 2:
index_key_enc, value_keyname = args
else:
index_key_enc, value_keyname, stat_func_name = args
if len(monthstring) != 7:
web.internalerror()
return "monthstring, has to be in YYYY-MM format"
# key_str should be a tuple string, convert to unicode tuple
index_key = tuple([unicode(key_value) for key_value in eval(base64.b64decode(index_key_enc))])
logging.info("index_key : %s", index_key)
logging.info("value_keyname : %s", value_keyname)
logging.info("stat_func_name: %s", stat_func_name)
datalogger = DataLogger(basedir, project, tablename)
filterkeys = dict(zip(datalogger.index_keynames, index_key))
ret_data = []
for datestring in datalogger.monthwalker(monthstring):
logging.debug("getting tsatstats for %s", monthstring)
try:
tsastats = datalogger.load_tsastats(datestring, filterkeys=filterkeys)
ret_data.append([datestring, tsastats[index_key][value_keyname][stat_func_name]])
except DataLoggerRawFileMissing as exc:
logging.error("No Input File for datestring %s found, skipping this date", datestring)
except DataLoggerLiveDataError as exc:
logging.error("Reading from live data is not allowed, skipping this data, and ending loop")
break
return json.dumps(ret_data)
def get_quantile_web(self, args):
"""
return exported QuantileArray json formatted, special
version for use in webpages to render with tablesorter
in difference to get_quantile the value_keyname has to be given
ex: Datalogger/get_quantile/{projectname}/{tablename}/{datestring}
[
dict of index_keys : dict of quantile,
list of index_keys,
list of value_names,
]
returns:
json(quantile_dict)
"""
project, tablename, datestring, value_keyname = args[:4]
datalogger = DataLogger(basedir, project, tablename)
qa = datalogger.load_quantile(datestring)
ret_data = []
# build header
ret_data.append(list(datalogger.index_keynames) + ["Q0", "Q1", "Q2", "Q3", "Q4"])
# data part
for k, v in qa[value_keyname].quantile.items():
ret_data.append(list(k) + v.values())
return json.dumps(ret_data)
def archive(project, tablename, datestring):
datalogger = DataLogger(basedir, project, tablename)
caches = datalogger.get_caches(datestring)
suffix = "%s/%s/%s\t" % (datestring, project, tablename)
if caches["tsa"]["raw"] is None:
logging.debug("%s RAW Data not found", suffix)
else:
if not os.path.isfile(caches["tsa"]["raw"]):
logging.info("%s RAW does not exists, maybe archived or deleted", suffix)
return
logging.info("%s found raw file %s", suffix, caches["tsa"]["raw"])
filebasename = os.path.basename(caches["tsa"]["raw"])
parts= filebasename.split("_")
filetablename = filebasename.replace("_%s" % parts[-1], "")
filedatestring = parts[-1].split(".")[0]
filesuffix = ".".join(parts[-1].split(".")[1:])
logging.info("found tablename %s, datestring %s, ending %s", filetablename, filedatestring, filesuffix)
if (filetablename != tablename) or (filedatestring != datestring):
logging.error("the references raw file seems not to be the correct one")
else:
if filesuffix == "csv.gz":
logging.info("raw file already zipped, this seems not to be the actual one")
if (len(caches["tsa"]["keys"]) > 0) and (len(caches["tsastat"]["keys"]) > 0) and (len(caches["ts"]["keys"]) > 0) and (caches["quantile"]["exists"] is True):
logging.info("%s all generated archives found, raw data could be archived", suffix)
archivepath = os.path.join(args.archivedir, datestring, project, tablename)
archivefilename = os.path.join(archivepath, os.path.basename(caches["tsa"]["raw"]))
if not os.path.isdir(archivepath):
logging.info("creating directory %s", archivepath)
os.makedirs(archivepath)
logging.info("%s moving raw file to %s", suffix, archivefilename)
shutil.move(caches["tsa"]["raw"], archivefilename)
else:
logging.info("%s not all archives available, generate them first, before archiving raw data", suffix)
del caches
del datalogger
def get_scatter_data(args):
"""
gets scatter plot data of two value_keys of the same tablename
ex: Datalogger/{projectname}/{tablename}/{datestring}/{value_keyname1}/{value_keyname2}/{stat function name}
value_keyname{1/2} has to be one of get_value_keynames
stat function name has to be one of get_stat_func_names
returns:
json(highgraph data)
"""
assert len(args) == 6
project, tablename, datestring, value_key1, value_key2, stat_func_name = args
logging.info("project : %s", project)
logging.info("tablename : %s", tablename)
logging.info("datestring : %s", datestring)
logging.info("value_key1 : %s", value_key1)
logging.info("value_key2 : %s", value_key2)
datalogger = DataLogger(basedir, project, tablename)
tsastats = datalogger.load_tsastats(datestring)
hc_scatter_data = []
for key, tsstat in tsastats.items():
hc_scatter_data.append({
"name" : str(key),
"data" : ((tsstat[value_key1]["avg"], tsstat[value_key2]["avg"]), )
})
return json.dumps(hc_scatter_data)
def sr_hrstorage_unused(args):
"""
special report to get a report of unused SNMP Host Storage
works only with snmp/hrStorageTable
"""
datestring, storage_type = args[:2]
datalogger = DataLogger(basedir, "snmp", "hrStorageTable")
tsastat = datalogger.load_tsastats(datestring)
data = []
data.append(("hostname", "hrStorageDescr", "hrStorageSizeKb", "hrStorageUsedKb", "hrStorageNotUsedKbMin", "hrStorageNotUsedPct"))
for index_key in tsastat.keys():
# (u'srvcacdbp1.tilak.cc', u'Physical Memory',
# u'HOST-RESOURCES-TYPES::hrStorageRam')
if (u"HOST-RESOURCES-TYPES::%s" % storage_type) not in index_key:
del tsastat[index_key]
if index_key[1][:4] in (u"/run", u"/dev", u"/sys"):
del tsastat[index_key]
for key, tsstat in tsastat.items():
sizekb = tsstat["hrStorageSize"]["min"] * tsstat["hrStorageAllocationUnits"]["max"] / 1024
usedkb = tsstat["hrStorageUsed"]["max"] * tsstat["hrStorageAllocationUnits"]["max"] / 1024
notused = sizekb - usedkb
notused_pct = 0.0
try:
notused_pct = 100.0 * notused / sizekb
except ZeroDivisionError:
pass
data.append((key[0], key[1], "%0.2f" % sizekb, "%0.2f" % usedkb, "%0.2f" % notused, "%0.2f" % notused_pct))
return json.dumps(data)
def __init__(self):
self.ramlog = collections.deque(maxlen=settings['ramlog']['length'])
self.fslog_last = 0
self.ramlog_last = 0
# avoid no data received error right at the beginning
self.commdead_last = time.time()
strace = settings['trace']
self.trace = ProzedaTrace(strace['dir'] + strace['prefix'],
strace['suffix'])
ProzedaLogdata.set_config(settings['system'])
ProzedaHistory.set_config(settings)
sserial = settings['serialport']
serialport = serial.Serial(sserial['port'], sserial['baudrate'])
self.prozeda = ProzedaReader(serialport)
sfslog = settings['fslog']
self.fslog = DataLogger(sfslog['dir'] + sfslog['prefix'],
sfslog['suffix'])
self.prozeda.evt_rawdata_received = self.trace.data_received
JsonrpcRequestHandler.pirozeda = self
def get_quantile(self, project, tablename, datestring, args):
"""
return exported QuantileArray json formatted
ex: Datalogger/get_quantile/{projectname}/{tablename}/{datestring}
[
dict of index_keys : dict of quantile,
list of index_keys,
list of value_names,
]
returns:
json(quantile_dict)
"""
logging.info("optional arguments received: %s", args)
datalogger = DataLogger(basedir, project, tablename)
quantile = datalogger.load_quantile(datestring)
if len(args) > 0:
value_keyname = args[0]
ret_data = []
# build header
ret_data.append(list(datalogger.index_keynames) + ["Q0", "Q1", "Q2", "Q3", "Q4"])
# data part
for k, v in quantile[value_keyname].quantile.items():
ret_data.append(list(k) + v.values())
return json.dumps(ret_data)
return quantile.to_json()
def record_logs(self, user_id, success=False):
"""
Special callback added to this callback plugin
Called by Runner object
:param user_id:
:return:
"""
log_storage = DataLogger()
return log_storage.save_log(user_id, self.logger.log,
self.logger.runtime, success)
def start(self, duration, comment=None):
if self.starttime == None:
self.starttime = time.time()
if duration is None:
duration = 0
self.stoptime = time.time() + int(duration)
if self.logger is None:
self.logger = DataLogger(self.fileprefix, self.filesuffix,
'%Y-%m-%d_%H.%M.%S', True)
if comment is not None and self.logger is not None:
self.logger.write(None, 'c', comment)
def get_tsastats_func(self, args):
"""
return json data to render html table from it
parameters:
<b>project</b> project string
<b>tablename</b> tablename string
<b>datestring</b> datestring in YYYY-MM-DD form
<b>stat_func_name</b> statistical function
"""
project, tablename, datestring, stat_func_name = args
datalogger = DataLogger(basedir, project, tablename)
tsastats = datalogger.load_tsastats(datestring)
return json.dumps(tsastats.to_csv(stat_func_name))
def loop(client, account_currency, payment_method_currency, transfers):
sell_target = 141.00
quote_sell_at = 0.95 # Ratio of sell price when quotes will be obtained (0.00 to 1.00)
exRates = MyExRates(client.client, account_currency,
payment_method_currency)
data_logger = DataLogger(exRates.dict, 'pricelog.csv')
while True:
exRates = MyExRates(client.client, account_currency,
payment_method_currency)
account = client.client.get_account(transfers.wallet)
spot_value = exRates.spot_price * float(account['balance']['amount'])
print()
print('Account Balance: ' + str(account['balance']['amount']) + ' ' +
account['balance']['currency'])
print('Spot Price: ' + str(exRates.spot_price))
print('Spot Value: ' + str(spot_value))
print('Spot value at ' + str("%.2f" %
(spot_value / sell_target * 100)) +
'% of target (' + str(sell_target) + ').')
if spot_value > sell_target * quote_sell_at:
quote = client.client.sell(transfers.wallet,
amount=str(
account['balance']['amount']),
currency='BTC',
payment_method=transfers.payment_method,
quote=True)
print('Spot price within ' + str(quote_sell_at * 100) +
'% of target - Getting quote')
if float(quote['total']['amount']) > sell_target:
print('Attempting Sell')
sell = client.client.sell(
transfers.wallet,
amount=str(account['balance']['amount']),
currency=account['balance']['currency'],
payment_method=transfers.payment_method,
quote=False)
print('Sold ' + sell['total']['amount'])
else:
print('Quote of ' + quote['total']['amount'] +
' too low - No sell')
data_logger.add_line(exRates.dict)
time.sleep(10)
def get_projects():
"""
get available projects for this Datalogger Server
ex: Datalogger/get_projects/...
there is no further argument needed
returns:
json(existing project names)
"""
ret_data = {
"projects" : DataLogger.get_projects(basedir),
"stat_func_names" : TimeseriesStats.stat_funcs.keys(),
"last_businessday_datestring" : DataLogger.get_last_business_day_datestring()
}
return json.dumps(ret_data)
def aggregate_data():
"""
Compute data aggregate for whole period (since last email report)
and save it to file (bad idea! it shouldn't)
:return: None
"""
# Load the entire data file
df = DataLogger.read_data_log(DATA_LOG_FILE, False)
# compute the average for all existing data
df[3].append(PollData.aggregate(df[2]))
# save a new file with ONLY the aggregate data
now = dt.now()
DataLogger.create_data_log(DATA_LOG_FILE, True, now, now, None, df[3])
logger.info("Aggregate data complete. New data file created.")
def data(logged_data):
if not os.path.exists(logged_data):
return '<br/>'.join(os.listdir('.'))
data = DataLogger(logged_data).read()
start_time = float(data[0][0])
data = [[float(a) - start_time, float(b)] for a, b in data]
return render_template('data.html', data=data, data_filename=logged_data)
def sr_vicenter_unused_mem(args):
"""
special resport to find virtual machine which are not used their ram entirely
on this machines there is a possibility to save some virtual memory
works only for VMware machine, in special virtualMachineMemoryStats
"""
datestring = args[0]
datalogger = DataLogger(basedir, "vicenter", "virtualMachineMemoryStats")
tsastat = datalogger.load_tsastats(datestring)
tsastat_g = datalogger.tsastat_group_by(tsastat, ("hostname", ))
data = []
data.append(("hostname", "avg_active_max", "avg_granted_min", "avg_notused_min"))
for key in tsastat_g.keys():
not_used = tsastat_g[key]["mem.granted.average"]["min"] - tsastat_g[key]["mem.active.average"]["max"]
data.append((key[0], "%0.2f" % tsastat_g[key]["mem.active.average"]["max"], "%0.3f" % tsastat_g[key]["mem.granted.average"]["min"], "%0.2f" % not_used))
return json.dumps(data)
def __init__(self):
self.config = Config()
self.mcu = MCU()
self.detecting = Detecting()
# fb = Firebase()
ws = webserver.WebServer()
dl = DataLogger()
def main():
for datestring in tuple(DataLogger.datewalker(startdate, args.enddate)):
start_ts, stop_ts = DataLogger.get_ts_for_datestring(datestring)
logging.debug("working on datestring %s (from %s to %s)", datestring, start_ts, stop_ts)
for project in DataLogger.get_projects(args.basedir):
if args.project is not None:
if project != args.project:
logging.debug("skipping project %s", project)
continue
logging.debug("working on project %s", project)
for tablename in DataLogger.get_tablenames(args.basedir, project):
if args.tablename is not None:
if tablename != args.tablename:
logging.debug("skipping tablename %s", tablename)
continue
logging.debug("working on tablename %s", tablename)
archive(project, tablename, datestring)
def __init__(self, name):
adc = ADC("Analog input",0)
self.pressure = Strain_PressureSensor("Pressure (kPa)",adc)
print "Pressure is %s kPa" % self.pressure.get_pressure_kpa()
# put Port 8 Pin 3&4 into mode 7 (GPIO_1_6 & GPIO_1_7)
open('/sys/kernel/debug/omap_mux/gpmc_ad6', 'wb').write("%X" % 7)
open('/sys/kernel/debug/omap_mux/gpmc_ad7', 'wb').write("%X" % 7)
gpio1_6 = GPIO(38,GPIO.OUTPUT) #p8_3
gpio1_7 = GPIO(39,GPIO.OUTPUT) #p8_4
self.vi = Valve("Inlet",gpio1_6)
self.vo = Valve("Outlet",gpio1_7)
# put Port 8 Pin 5 into mode 7 (GPIO_1_2)
open('/sys/kernel/debug/omap_mux/gpmc_ad2', 'wb').write("%X" % 7)
gpio1_2 = GPIO(34,GPIO.OUTPUT) #p8_5
self.pump = Pump("Water cooling pump", gpio1_2)
# put Port 9 Pin 12 into mode 7 (GPIO_1_28)
gpio1_28 = GPIO(60,GPIO.INPUT) #p9_12
self.waterlevel = WaterLevel("Waterlevel sensor", gpio1_28)
sck=GPIO(49,GPIO.OUTPUT) #p9_23
s0=GPIO(115,GPIO.INPUT) #p9_27
cs_t1=GPIO(48,GPIO.OUTPUT) #p9_15 GPIO1_16 48
cs_t2=GPIO(117,GPIO.OUTPUT) #p9_25
self.max1 = MAX31855(cs_t1, sck, s0) #Maxim IC No 1, connected to the cartidge heater TC
self.max2 = MAX31855(cs_t2, sck, s0) #Maxim IC No 2, connected to the TC at the bottom of the vessel
# PWMHeater
self.h = PWMHeater("Cartridge heater (%)", "ehrpwm1a")
self.h.setfrequency(3)
# DS18B20
T1="28-000003f5b1c9"
T2="28-000003f5baa4"
T3="28-000003f5be11"
self.ds1=DS18B20("T in (C)",T1)
self.ds2=DS18B20("T out (C)",T2)
self.ds3=DS18B20("T Env (C)",T3)
print "ds1: ",self.ds1.get_temperature()
print "ds2: ",self.ds2.get_temperature()
print "ds3: ",self.ds3.get_temperature()
# CS5490 power meter
self.cs=CS5490("Power In (W)")
print "start DataLogger"
self.log = DataLogger("my datalogger",500)
self.log.add_sensor(("T Vessel bottom (C)","Maxim 2 Cld jnct (C)"),self.max2.get_celsius)
self.log.add_sensor(("T Cartridge (C)","Maxim 1 Cld jnct (C)"),self.max1.get_celsius)
self.log.add_sensor(self.ds1.get_name(),self.ds1.get_temperature)
self.log.add_sensor(self.ds2.get_name(),self.ds2.get_temperature)
self.log.add_sensor(self.ds3.get_name(),self.ds3.get_temperature)
self.log.add_sensor(self.pressure.get_name(),self.pressure.get_pressure_kpa)
self.log.add_sensor(self.cs.get_name(),self.cs.get_average_power)
def report_group(project, tablename, datestring1, datestring2, value_key):
# get data, from datalogger, or dataloggerhelper
datalogger = DataLogger(BASEDIR, project, tablename)
dataloggerweb = DataLoggerWeb(DATALOGGER_URL)
print "loading data"
starttime = time.time()
# tsa1 = datalogger.load_tsa(datestring1)
tsa1 = dataloggerweb.get_tsa(project, tablename, datestring1)
tsa1 = datalogger.group_by(datestring1, tsa1, ("hostname",), lambda a, b: (a + b) / 2)
# tsa2 = datalogger.load_tsa(datestring2)
tsa2 = dataloggerweb.get_tsa(project, tablename, datestring2)
tsa2 = datalogger.group_by(datestring2, tsa2, ("hostname",), lambda a, b: (a + b) / 2)
print "Duration load %f" % (time.time() - starttime)
starttime = time.time()
cm = CorrelationMatrixTime(tsa1, tsa2, value_key)
print "TOP most differing keys between %s and %s" % (datestring1, datestring2)
for key, coefficient in sorted(cm.items(), key=lambda items: items[1], reverse=True)[:20]:
print key, coefficient
def get_tsstat_caches(args):
"""
DEPRECATED use get_caches instead
get a list of all available TimeseriesStats available
attention: there are only tsstat caches if raw data is already analyzed
ex: Datalogger/get_tsstat_caches/{projectname}/{tablename}/{datestring}
returns:
json(list of all available TimeseriesStats data)
"""
project, tablename, datestring = args[:3]
datalogger = DataLogger(basedir, project, tablename)
keys = []
for cache in datalogger.list_tsstat_caches(datestring):
keys.append(cache[1])
return json.dumps(keys)
def get_quantile(self, args):
"""
return exported QuantileArray json formatted
ex: Datalogger/get_quantile/{projectname}/{tablename}/{datestring}
[
dict of index_keys : dict of quantile,
list of index_keys,
list of value_names,
]
returns:
json(quantile_dict)
"""
project, tablename, datestring = args[:3]
datalogger = DataLogger(basedir, project, tablename)
quantile = datalogger.load_quantile(datestring)
return quantile.to_json()
def sr_vicenter_unused_cpu_cores(args):
"""
special report to find virtual machine which re not used their virtual core entirely
on this machine there is a possibility to save some virtual cores
works only for VMware machines, in special virtualMachineCpuStats
"""
datestring = args[0]
datalogger = DataLogger(basedir, "vicenter", "virtualMachineCpuStats")
tsastat = datalogger.load_tsastats(datestring)
tsastat_g = datalogger.tsastat_group_by(tsastat, ("hostname", ))
data = []
data.append(("hostname", "avg_idle_min", "avg_used_avg", "avg_used_max"))
for key in tsastat_g.keys():
num_cpu = sum([key[0] in index_key for index_key in tsastat.keys()])
if num_cpu < 3:
continue
data.append((key[0], "%0.2f" % tsastat_g[key]["cpu.idle.summation"]["min"], "%0.2f" % tsastat_g[key]["cpu.used.summation"]["avg"], "%0.2f" % tsastat_g[key]["cpu.used.summation"]["max"]))
return json.dumps(data)
def get_tsastats(self, args):
"""
return exported TimeseriesArrayStats json formatted
[
list of index_keys,
list of value_keys,
list of [
index_key : tsstat_dictionary
]
]
returns:
json(tsastats_dict)
"""
project, tablename, datestring = args[:3]
datalogger = DataLogger(basedir, project, tablename)
tsastats = datalogger.load_tsastats(datestring)
return tsastats.to_json()
def get_last_business_day_datestring(args):
"""
get datestring of last businessday Mo.-Fr.
ex: Dataloger/get_last_business_day_datestring/...
returns:
json(datestring of last businessday)
"""
return json.dumps(DataLogger.get_last_business_day_datestring())
def get_lt_ts(self, project, tablename, args):
"""
get longtime statistical values
"""
# datestringStart + "/" + datestringStop + "/" + Base64.encode(indexKey) + "/" + valueKeyname + "/" + statFuncName
start, stop, index_key_enc, value_keyname, stat_func_name = args
index_key = tuple([unicode(key_value) for key_value in eval(base64.b64decode(index_key_enc))])
datalogger = DataLogger(basedir, project, tablename)
filterkeys = dict(zip(datalogger.index_keynames, index_key))
ret_data = []
for datestring in datalogger.datewalker(start, stop):
try:
tsastats = datalogger.load_tsastats(datestring, filterkeys=filterkeys)
ret_data.append([datestring, tsastats[index_key][value_keyname][stat_func_name]])
except DataLoggerRawFileMissing as exc:
logging.error("No Input File for datestring %s found, skipping this date", datestring)
except DataLoggerLiveDataError as exc:
logging.error("Reading from live data is not allowed, skipping this data, and ending loop")
break
return json.dumps(ret_data)
def get_projects(args):
"""
get available projects for this Datalogger Server
ex: Datalogger/get_projects/...
there is no further argument needed
returns:
json(existing project names)
"""
return json.dumps(DataLogger.get_projects(basedir))
def get_ts_caches(args):
"""
DEPRECATED use get_caches instead
get name of all index keys found in one specific TimeseriesArray
useful to build autofill input fields
attention: there are only ts caches if the raw data is already converted
ex: Datalogger/get_ts_caches/{projectname}/{tablename}/{datestring}
datstring has to be in format YYYY-MM-DD
returns:
json(list of all index keys)
"""
project, tablename, datestring = args[:3]
datalogger = DataLogger(basedir, project, tablename)
keys = []
for cache in datalogger.list_ts_caches(datestring):
keys.append(cache[1])
return json.dumps(keys)
def get_tsastats_table(self, args):
"""
return html renderer table from tsatstats data
"""
def csv_to_table(csvdata, keys):
outbuffer = []
outbuffer.append("<thead><tr>")
[outbuffer.append("<th>%s</th>" % header) for header in csvdata[0]]
outbuffer.append("</tr></thead><tbody>")
for values in csvdata[1:]:
outbuffer.append("<tr>")
[outbuffer.append("<td >%s</td>" % value) for value in values[0:keys]]
[outbuffer.append("<td type=numeric>%0.2f</td>" % value) for value in values[keys:]]
outbuffer.append("</tr>")
outbuffer.append("</tbody>")
return outbuffer
project, tablename, datestring, stat_func_name = args
datalogger = DataLogger(basedir, project, tablename)
tsastats = datalogger.load_tsastats(datestring)
return json.dumps("\n".join(csv_to_table(tsastats.to_csv(stat_func_name), len(tsastats.index_keys))))
def webControl(x, y):
x = int(x)
y = int(y)
usrMsgLogger = DataLogger()
evobot = EvoBot("/dev/tty.usbmodemfa131", usrMsgLogger)
head = Head(evobot)
syringe = Syringe(evobot, 9)
syringe.plungerSetConversion(1)
evobot.home()
head.move(x, y)
def get_caches_dict(args):
"""
DEPRECATED use get_caches instead
get name of all index keys found in one specific TimeseriesArray
parameters:
/<str>project/<str>tablename/<str>datestring
returns:
<json><list> of all index combinations
"""
# the same for all vicenter data
project, tablename, datestring = args[:3]
datalogger = DataLogger(basedir, project, tablename)
keys = []
for cache in datalogger.list_ts_caches(datestring):
key = dict(zip(datalogger.index_keynames, cache[1][1]))
keys.append(key)
return json.dumps(keys)
def get_caches(args):
"""
return dictionary of caches available for this project/tablename/datestring combination
ex: Datalogger/get_caches/{project}/{tablename}/{datestring}
{
"tsastat" : {
"keys" : dictionary of available keys,
"pattern" : filename pattern,
},
"tsstat" : {
"keys" : dictionary of available keys,
"pattern" : filename pattern,
},
"tsa":
"keys" : dictionary of available keys,
"pattern" : filename pattern,
},
"ts" : {
"keys" : dictionary of available keys,
"pattern" : filename pattern,
},
"raw" : None or filename of raw data,
}
if return_date["raw"] == null it means, there is no raw data available
else if something (tsa,ts,tsastat,tsstat) is missing you can call get_tsastat to generate all caches
returns:
json(dictionary of caches and available data)
"""
project, tablename, datestring = args[:3]
datalogger = DataLogger(basedir, project, tablename)
caches = {}
try:
caches = datalogger.get_caches(datestring)
except StandardError as exc:
logging.exception(exc)
logging.error(caches)
return json.dumps(caches)
def get_datewalk(args):
"""
get list of datestrings between two datestrings
ex: Datalogger/get_datewalk/{datestring1}/{datestring2}
returns:
json(list of datestrings)
"""
datestring1, datestring2 = args[:2]
data = tuple(DataLogger.datewalker(datestring1, datestring2))
return json.dumps(data)
def get_tablenames(project):
"""
get available tablenames, for one particular project
uses directory listing in raw subdirectory for this purpose
ex: Datalogger/get_tablenames/{projectname}
<projectname> has to be something from Datalogger/get_projects
returns:
json(list of possible tablenames for given project)
"""
return json.dumps(DataLogger.get_tablenames(basedir, project))
def get_tsa_adv(self, args):
"""
return exported TimeseriesArray json formatted
"""
group_funcs = {
"avg" : lambda a, b: (a+b)/2,
"min" : min,
"max" : max,
"sum" : lambda a, b: a+b,
}
logging.info(args)
project, tablename, datestring, groupkeys_enc, group_func_name, index_pattern_enc = args
groupkeys_dec = eval(base64.b64decode(groupkeys_enc)) # should be tuple
logging.info("groupkeys_dec: %s", groupkeys_dec)
index_pattern = base64.b64decode(index_pattern_enc)
if index_pattern == "None":
index_pattern = None
logging.info("index_pattern: %s", index_pattern)
assert group_func_name in group_funcs.keys()
datalogger = DataLogger(basedir, project, tablename)
tsa = None
# gete data
if groupkeys_dec is not None:
logging.info("groupkeys is %s", groupkeys_dec)
groupkeys = tuple([unicode(key_value) for key_value in groupkeys_dec])
tsa1 = datalogger.load_tsa(datestring, index_pattern=index_pattern)
tsa = datalogger.group_by(datestring, tsa1, groupkeys, group_funcs[group_func_name])
else:
logging.info("groupkeys is None, fallback to get ungrouped tsa")
tsa = datalogger.load_tsa(datestring, index_pattern=index_pattern)
logging.info(tsa.keys()[0])
web.header('Content-type', 'text/html')
# you must not set this option, according to
# http://stackoverflow.com/questions/11866333/ioerror-when-trying-to-serve-file
# web.header('Transfer-Encoding','chunked')
yield "[" + json.dumps(tsa.export().next())
for chunk in tsa.export():
#logging.info("yielding %s", chunk)
yield "," + json.dumps(chunk)
yield "]"
def gen_caches(project, tablename, datestring):
datalogger = DataLogger(basedir, project, tablename)
caches = datalogger.get_caches(datestring)
suffix = "%s/%s/%s\t" % (datestring, project, tablename)
data = None
if caches["tsa"]["raw"] is None:
if len(caches["tsa"]["keys"]) == 0:
logging.info("%s RAW Data not availabale maybe archived, tsa exists already", suffix)
else:
logging.debug("%s RAW Data is missing, no tsa archive exists", suffix)
else:
if len(caches["tsa"]["keys"]) == 0:
logging.info("%s TSA Archive missing, calling get_tsa and load_tsastats", suffix)
data = datalogger.load_tsa(datestring)
else:
if len(caches["tsastat"]["keys"]) == 0:
logging.info("%s TSASTAT Archive missing, calling load_tsastats", suffix)
data = datalogger.load_tsastats(datestring)
else:
if len(caches["ts"]["keys"]) == 0:
logging.info("%s there are no ts archives, something went wrong, or tsa is completely empty, calling load_tsastats", suffix)
data = datalogger.load_tsastats(datestring)
else:
logging.debug("%s All fine", suffix)
if caches["quantile"]["exists"] is not True:
logging.info("%s Quantile archive is missing, calling load_quantile", suffix)
data = datalogger.load_quantile(datestring)
del data
del caches
del datalogger
def get_tsastats(self, project, tablename, datestring, args):
"""
return exported TimeseriesArrayStats json formatted
[
list of index_keys,
list of value_keys,
list of [
index_key : tsstat_dictionary
]
]
if optional args is given, only one specific statistical function is returned
returns:
json(tsastats_dict)
"""
logging.info("optional arguments received: %s", args)
datalogger = DataLogger(basedir, project, tablename)
tsastats = datalogger.load_tsastats(datestring)
if len(args) > 0:
return json.dumps(tsastats.to_csv(args[0]))
return tsastats.to_json()
def sr_hrstorageram_unused(args):
"""
special report to find servers which are not using their ram entirely
specially on virtual machines are is a huge saving potential
works only for snmp data especially hrStorageTable
"""
datestring = args[0]
datalogger = DataLogger(basedir, "snmp", "hrStorageTable")
tsastat = datalogger.load_tsastats(datestring)
data = []
data.append(("hostname", "hrStorageSizeKb", "hrStorageUsedKb", "hrStorageNotUsedKbMin", "hrStorageNotUsedPct"))
for index_key in tsastat.keys():
# (u'srvcacdbp1.tilak.cc', u'Physical Memory',
# u'HOST-RESOURCES-TYPES::hrStorageRam')
if u'HOST-RESOURCES-TYPES::hrStorageRam' not in index_key:
del tsastat[index_key]
for key, tsstat in datalogger.tsastat_group_by(tsastat, ("hostname", )).items():
sizekb = tsstat["hrStorageSize"]["min"] * tsstat["hrStorageAllocationUnits"]["max"] / 1024
usedkb = tsstat["hrStorageUsed"]["max"] * tsstat["hrStorageAllocationUnits"]["max"] / 1024
notused = sizekb - usedkb
notused_pct = 100.0 * notused / sizekb
data.append((key[0], "%0.2f" % sizekb, "%0.2f" % usedkb, "%0.2f" % notused, "%0.2f" % notused_pct))
return json.dumps(data)
def main():
# Parse command line arguments
parser = optparse.OptionParser()
parser.add_option('-c', '--config',
help = 'read configuration from FILE [default: %default]',
metavar = 'FILE',
default = 'campbell.conf')
(options, args) = parser.parse_args()
# Read configuration file
cf = ConfigParser.SafeConfigParser()
print 'configuration read from %s' % cf.read(options.config)
for pakbus_id in cf.get('pakbus', 'dataloggers').split(','):
pakbus_id = int(pakbus_id, base = 0)
dl = DataLogger(cf.get('pakbus', 'host'),
pakbus_id,
int(cf.get('pakbus', 'my_node_id'), base = 0),
cf.getint('pakbus', 'timeout'))
print "ringing node {}...".format(pakbus_id)
dl.ring()
dl.hello()
def main():
if len(sys.argv) < 2:
print 'USAGE: runtracker.py participant_id [save_location]'
return
S = MARKER_SIZE
F = 1
participant_id = sys.argv[1]
save_location = DATA_DIR
if len(sys.argv) >= 3:
save_location = sys.argv[2]
# Mouse callback for setting origin point
def mouse_callback(event, x, y, flags, param):
if datalog.is_running():
return
if event == cv2.EVENT_LBUTTONDOWN:
# Set new origin_y
tracker.set_origin(y)
# Open webcam
cap = cv2.VideoCapture(TRACKER_CAM)
if not cap.isOpened():
print 'Error opening tracker camera!'
return
# Open facecam (for separate recording)
facecap = None
if FACE_CAM is not None:
facecap = cv2.VideoCapture(FACE_CAM)
if not facecap.isOpened():
print 'Error opening face camera!'
facecap = None
# Get video parameters (try to retain same attributes for output video)
width = float(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = float(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = float(cap.get(cv2.CAP_PROP_FPS))
# Create instances
track_finder = mk.MarkerFinder(GREEN_COLOR_MIN, GREEN_COLOR_MAX)
tracker = WristTracker(track_finder, S, F)
tracker.set_origin(int(height / 2))
cprstatus = CPRStatus(CPR_BUFFER_SIZE)
statussender = StatusSender(SOCK_ADDR, SOCK_PORT)
datalog = DataLogger(fps if (0 < fps <= 60) else 30, width, height,
save_location, facecap is not None)
trainer_on = True
cur_position = 0
cur_size = 0
last_rate, last_depth, last_recoil, last_code = 0, 0, 0, 0
while True:
# Get frame
ret, frame = cap.read()
if ret is False or frame is None:
break
faceframe = None
if facecap:
faceret, faceframe = facecap.read()
# Get dimensions
h, w, _ = frame.shape
center = (w / 2, h / 2)
'''
Output Drawing
'''
# Make output image
output = frame.copy()
# Display program status
cv2.putText(
output,
'<NOT RUNNING>' if not datalog.is_running() else 'RECORDING ' +
datalog.get_filename() + ' [' + str(datalog.get_index()) + ']',
(0, 30), cv2.FONT_HERSHEY_PLAIN, 1.5, (0, 0, 255), 2)
cv2.putText(
output, 'R: ' + str(last_rate) + ' D: ' + str(last_depth) +
' C: ' + str(last_recoil) + ' S: ' + str(last_code), (0, 60),
cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 255), 2)
# Draw center and origin lines
cv2.line(output, (center[0], 0), (center[0], h), (0, 0, 255), 1)
cv2.line(output, (center[0] - 20, int(tracker.get_origin())),
(center[0] + 20, int(tracker.get_origin())), (0, 0, 255), 1)
# Draw instructions
if not datalog.is_running():
cv2.putText(
output, 'c - set clr at orig | t - trainer is ' +
('on' if trainer_on else 'off') + ' | space - tgl tracker',
(0, h - 4), cv2.FONT_HERSHEY_PLAIN, 1, (255, 0, 0), 1)
'''
Tracking
'''
# Get tracked marker from image
tracked_marker = tracker.get_marker(frame, output)
if tracked_marker:
draw_marker(output, tracked_marker.marker, tracked_marker.size,
tracked_marker.distance, tracked_marker.position)
# Update cur position/size (for calibration)
cur_position = tracked_marker.y
cur_size = tracked_marker.size
'''
Analysis
'''
if datalog.is_running():
if tracked_marker:
# Analyze CPR status
rate, depth, recoil, code = cprstatus.update(tracked_marker)
if code is not None:
last_rate, last_depth, last_recoil, last_code = rate, depth, recoil, code # Update
print 'R: ' + str(last_rate) + ' D: ' + str(
last_depth) + ' C: ' + str(last_recoil) + ' S: ' + str(
last_code)
# Send status if trainer is turned on
if trainer_on:
if not (last_rate == 0 and last_depth == 0
and last_recoil == 0):
print 'send status ' + str(code)
statussender.send_status(code)
datalog.log(frame, output, tracker.get_origin(),
tracked_marker.position if tracked_marker else 0,
rate, depth, recoil, code, faceframe)
'''
Show Output
'''
# Resize frame
#output = cv2.resize(output, (OUT_WIDTH, OUT_HEIGHT))
# Show frame
#cv2.imshow('Frame', frame)
cv2.imshow('Output', output)
cv2.setMouseCallback('Output', mouse_callback)
if faceframe is not None:
cv2.imshow('Face', faceframe)
'''
Process Keypresses
'''
k = cv2.waitKey(1)
if k == 27:
datalog.stop()
break
elif k == ord('v'):
track_finder.set_color(VIOLET_COLOR_MIN, VIOLET_COLOR_MAX)
elif k == ord('g'):
track_finder.set_color(GREEN_COLOR_MIN, GREEN_COLOR_MAX)
elif k == ord('y'):
track_finder.set_color(YELLOW_COLOR_MIN, YELLOW_COLOR_MAX)
elif k == ord('c'):
# Calibrate tracker (color)
color = (0, 0, 0)
# Get origin pixel color
pixel = frame[int(tracker.get_origin())][center[0]]
hsv = cvt_bgr2hsv(pixel)
print 'Calibrate color: ' + str(hsv) + ' | BGR: ' + str(pixel)
# Apply HSV range
'''
lower_bound = tuple(map(lambda x, y: max(0, x - y), hsv, COLOR_VARIANCE))
upper_bound = tuple(map(lambda x, y: min(255, x + y), hsv, COLOR_VARIANCE))
'''
lower_bound = (max(0, hsv[0] - COLOR_MIN[0]), COLOR_MIN[1],
COLOR_MIN[2])
upper_bound = (min(255, hsv[0] + COLOR_MAX[0]), COLOR_MAX[1],
COLOR_MAX[2])
#'''
track_finder.set_color(lower_bound, upper_bound)
print 'Color range: ' + str(lower_bound) + ' to ' + str(
upper_bound)
elif k == ord('t'):
# Toggle trainer
trainer_on = not trainer_on
elif k == 32:
# Toggle on/off
if datalog.is_running():
datalog.stop()
else:
# Reset logger
cur_time = datetime.now()
time_str = cur_time.strftime('%m-%d-%y_%H%M%S')
datalog.start('CPR_' + str(participant_id) + '_' + time_str)
cprstatus.reset()
# Set tracker origin/size
tracker.set_origin(cur_position)
tracker.set_force_px_size(cur_size)
cap.release()
opts.video = int(opts.video)
except ValueError:
pass
frmbuf = VideoBuffer(opts.video,
opts.start,
opts.stop,
historysize=LAST_DAY + 1,
loop=opts.loop)
else:
frmbuf = ROSCamBuffer(opts.camtopic + "/image_raw",
historysize=LAST_DAY + 1,
buffersize=30)
# start the node and control loop
rospy.init_node("flownav", anonymous=False)
datalog = DataLogger() if opts.publish else None
kbctrl = None
if opts.camtopic == "/ardrone" and not opts.video:
kbctrl = KeyboardController(max_speed=0.5, cmd_period=100)
if kbctrl:
FlatTrim = rospy.ServiceProxy("/ardrone/flattrim", Empty())
Calibrate = rospy.ServiceProxy("/ardrone/imu_recalib", Empty())
gmain_win = frmbuf.name
cv2.namedWindow(gmain_win, flags=cv2.WINDOW_OPENGL | cv2.WINDOW_NORMAL)
if opts.showmatches:
cv2.namedWindow(gtemplate_win, flags=cv2.WINDOW_OPENGL | cv2.WINDOW_NORMAL)
smatch.MAIN_WIN = gmain_win
smatch.TEMPLATE_WIN = gtemplate_win
from datetime import datetime
import pandas as pd
import matplotlib.pyplot as plt
import pandas as pd
from pandas import ExcelWriter
from datalogger import DataLogger
# establish general constants and parameters
number_reads_per_calib = 20
span_O2_concentration = 20.95
span_CO_concentration = 422
span_CO2_concentration = 2350
# create instance of the data logger and check connection
print("\nConnection to data logger")
rm, logger = DataLogger().new_instrument()
print("Starting HRR calibration procedure.\n")
# calibrate DPT
dpt_input = input("Ready to calibrate DPT?\n")
dpt_zero = np.zeros(number_reads_per_calib)
if dpt_input.lower() in ["y", "yes"]:
for i in range(number_reads_per_calib):
response, _ = DataLogger.query_data_for_HRR(logger)
dpt_zero[i] = response[1]
print(f" - Mean zero value in volts for:\n DPT: {dpt_zero.mean()}")
# calibrate zero levels for the gases
gases_zero_input = input("\nReady to calibrate zero for gases?\n")
oxygen_zero = np.zeros_like(dpt_zero)
from pandas import ExcelWriter
# add path to import functions and classes (absolute path on the FPA's computer)
sys.path.insert(
1,
r"C:\\Users\\FireLab\\Desktop\\Simon\\FeedbackControl_MassExperiments\\classes_and_functions"
)
from datalogger import DataLogger
#####
# CONNECT TO LOAD CELL AND DATA LOGGER AND INSTANTIATE CLASSES
#####
# create instance of the data logger and check connection
print("\nConnection to data logger")
rm, logger = DataLogger().new_instrument()
#####
# CALIBRATE THE LAMPS.
#####
# define constants
lamp_voltage_limit = 4.5
# define arrays used for polynomial fitting
hf_gauge_factor = 0.0001017 # V/kW/m2
nmbr_readings_pervoltage = 20
output_voltages = np.linspace(0, lamp_voltage_limit, 20)
all_output_voltages = np.zeros(nmbr_readings_pervoltage *
len(output_voltages) * 2)
all_input_voltages = np.zeros_like(all_output_voltages)
def main():
args = vars(ap.parse_args())
# create frame counter
fps_counter = FPSCounter()
# total number of blinks
TOTAL = 0
# initialize dlib's face detector (HOG-based) and then create
# the facial landmark predictor
print("[INFO] loading facial landmark predictor...")
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(args["shape_predictor"])
# grab the indexes of the facial landmarks for the left and
# right eye, respectively
(lStart, lEnd) = face_utils.FACIAL_LANDMARKS_IDXS["left_eye"]
(rStart, rEnd) = face_utils.FACIAL_LANDMARKS_IDXS["right_eye"]
# start the video stream thread
print("[INFO] starting video stream thread...")
print("[INFO] print q to quit...")
if args['video'] == "camera":
vs = VideoStream(src=0).start()
vs.stream.set(cv2.CAP_PROP_FPS, 15)
fileStream = False
else:
vs = FileVideoStream(args["video"]).start()
fileStream = True
fps = vs.stream.get(cv2.CAP_PROP_FPS)
# create dataloggers
datalogger = DataLogger(columns=['ear', 'adr'])
# blink detector
blink_detector = BlinkDetector(time_window=5,
plot=args['graph'],
frame_delay=10)
# loop over frames from the video stream
frame_cnt = 0
INIT_TIME = None
while True:
# if this is a file video stream, then we need to check if
# there any more frames left in the buffer to process
if fileStream and not vs.more():
break
# get timestamp
if fileStream:
timestamp = frame_cnt / fps
else:
if INIT_TIME is None:
INIT_TIME = time.time()
timestamp = time.time() - INIT_TIME
fps = fps_counter.tick()
# get the new frame
frame = vs.read()
frame_cnt += 1
if frame is None:
break
frame = imutils.resize(frame, width=450)
# it, and convert it to grayscale channels)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# detect faces in the grayscale frame
rects = detector(gray, 0)
# loop over the face detections
for rect in rects:
# determine the facial landmarks for the face region, then
# convert the facial landmark (x, y)-coordinates to a NumPy
# array
shape = predictor(gray, rect)
shape = face_utils.shape_to_np(shape)
# extract the left and right eye coordinates, then use the
# coordinates to compute the eye aspect ratio for both eyes
leftEye = shape[lStart:lEnd]
rightEye = shape[rStart:rEnd]
leftEAR = eye_aspect_ratio(leftEye)
rightEAR = eye_aspect_ratio(rightEye)
# compute the area-over-distance metric
adr = AreaDistanceRatio.compute(leftEye, rightEye)
# log ADR
datalogger.log(adr, 'adr', timestamp)
# average the eye aspect ratio together for both eyes
ear = (leftEAR + rightEAR) / 2.0
# log EAR
datalogger.log(ear, 'ear', timestamp)
# compute the convex hull for the left and right eye, then
# visualize each of the eyes
leftEyeHull = cv2.convexHull(leftEye)
rightEyeHull = cv2.convexHull(rightEye)
cv2.drawContours(frame, [leftEyeHull], -1, (0, 255, 0), 1)
cv2.drawContours(frame, [rightEyeHull], -1, (0, 255, 0), 1)
# send new data to blink detector and check if it detected new blinks
blink_detector.send(adr, timestamp)
blink = blink_detector.get_blink()
if blink is not None:
blink_time, blink_dur = blink
TOTAL += 1
print(f"[BLINK] time: {blink_time:.2f} dur: {blink_dur:.2f}")
# draw the total number of blinks on the frame along with
# the computed eye aspect ratio for the frame
cv2.putText(frame, "Blinks: {}".format(TOTAL), (10, 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
cv2.putText(frame, "ADR: {:.2f}".format(ear), (300, 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
cv2.putText(frame, "FPS: {:.2f}".format(fps), (300, 60),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
# show the frame
cv2.imshow("Frame", frame)
key = cv2.waitKey(1) & 0xFF
# if the `q` key was pressed, break from the loop
if key == ord("q"):
break
# save datafile
output_file = args['output_file']
if output_file == 'ask':
output_file = input("Enter filename to save: ")
if output_file is not None:
datalogger.save(output_file)
# do a bit of cleanup
cv2.destroyAllWindows()
vs.stop()
# Set up pressure pump switch GUI
pressure_pump_switch_gui = PressurePumpUim(pressure_pump_switch,
main_window_ui)
# Set up picarro code GUI
picarro_code_gui = PicarroCodeUim(picarro_code, main_window_ui)
# Set up state GUI
state_gui = StateUim(state, main_window_ui, state_ui)
# Set up maintenanceTools GUI
maintenancetools_gui = MaintenanceToolsUim(maintenancetools,
maintenancetools_ui)
# Set up Debug log GUI
debuglog_gui = DebuglogUim(debug_logger, debuglog_ui,
main_window.debuglog_window)
########################################################################################################################
# DATA LOGGER
########################################################################################################################
data_logger = DataLogger("data", humgen)
########################################################################################################################
# LAUNCH APPLICATION
########################################################################################################################
main_window.show()
sys.exit(app.exec_())
def index():
global datalogger
datalogger = DataLogger('data_%d.csv' % int(time.time()))
return render_template('index.html')
elif roll < 360:
orientation = "Roll: NorthWest-North"
return orientation
def find_compass():
north = sense.get_compass()
north = round(north, 1)
return north
# initialize text
text.init_text()
# initialize dataloggers
logger = DataLogger()
sense_logger = SenseLogger()
# set timedelta
time_between = timedelta(minutes=10)
# get initial orientations
base_orientation_yaw = find_orientation_by_yaw()
logger.log_orientation(base_orientation_yaw)
base_orientation_roll = find_orientation_by_roll()
logger.log_orientation(base_orientation_roll)
# get baseline for acceleration
base_acc = baseline_acceleration()
last_baseline = datetime.now()
def main():
config = configparser.ConfigParser(inline_comment_prefixes=(";", "#"))
config.read(APP_CONFIG_FILE)
# fetch interesting system data that we will use for statistics
swo = sw.SystemWatcher()
swo._update_host_fs()
swo._update_cpu_internet_uptime()
data = PollData.from_system_info(swo.system_info)
logger.info("fetched system data")
# TODO - Poll machines
# m = Machine("RPI-A", "192.168.1.170", 443, "TCP/ping", True)
# data.machines.append(m)
# Write system data to permanent storage
DataLogger.writeline_to_data_log(DATA_LOG_FILE, data)
logger.info("wrote data to file ./" + DATA_LOG_FILE)
# Does any value exceed a threshold?
# In that case we need to send a report immediately
send_warning = False
if data.cpu_load >= config.getint("warning_thresholds",
"cpu_utilization_percent"):
send_warning = True
logger.info("CPU load threshold exceeded!")
if data.cpu_temp >= config.getint("warning_thresholds", "cpu_temp"):
send_warning = True
logger.info("CPU temp threshold exceeded!")
if data.disk_usage_percent >= config.getint("warning_thresholds",
"disk_used_percent"):
send_warning = True
logger.info("Disk usage threshold exceeded!")
# Get the last time we created a report
dl = DataLogger.read_data_log(DATA_LOG_FILE, True)
# Is it time to publish to MQTT broker?
mqtt_enabled, mqtt_interval = ch.get_report(config, "publish_to_mqtt")
if mqtt_enabled and (dt.now() - dl[1]).total_seconds() > mqtt_interval:
to_pub = dict()
to_pub[config.get("MQTT", "topic_alive")] = "on"
to_pub[config.get("MQTT", "topic_cpu_temp")] = data.cpu_temp
to_pub[config.get("MQTT", "topic_cpu_load")] = data.cpu_load
to_pub[config.get("MQTT", "topic_cpu_internet")] = data.internet
to_pub[config.get(
"MQTT", "topic_diskusagepercent")] = data.disk_usage_percent
mqtt_publish(to_pub, config.get("MQTT", "broker_ip"),
int(config.get("MQTT", "broker_port")),
config.get("MQTT", "broker_user"),
config.get("MQTT", "broker_pwd"))
# Is it time to send an email report?
email_enabled, email_interval = ch.get_report(config, "send_emails")
if send_warning or email_enabled and (
dt.now() - dl[0]).total_seconds() > email_interval:
# Save report date because it will be overwritten when doing Aggregate
last_report = dl[0]
# aggregate data (an unfortunate side-affect is that the last report date will be set to Now()
aggregate_data()
# Load data
dl = DataLogger.read_data_log(DATA_LOG_FILE, False)
# get email credentials
email_config = ch.config_section_map(config, "email")
credentials = gmail.get_email_credentials()
http = credentials.authorize(httplib2.Http())
service = discovery.build('gmail',
'v1',
http=http,
cache_discovery=False)
# build html email from a template
html_email = gmail.apply_email_template(
last_report, config.get("app", "name"), data, dl,
path_join("email_templates/default.html"))
gmail.send_email_message(
service, email_config["user"],
gmail.create_email_message(email_config["from_address"],
email_config["to_address"],
email_config["subject"],
html_email))
logger.info("Sent email report to " + email_config["to_address"])
astro_status = True
return astro_status
# main program
sense = SenseHat()
# set correct rotation for astropi
sense.set_rotation(270)
# initialize text strings
tekster.init_tekst()
# initialize datalogger
logger = DataLogger()
# read baseline humidity until less than 1% variation
baseline = baseline_humidity()
last_baseline = datetime.now()
# set timedelta between each new baseline
time_between = timedelta(minutes=10)
while True:
# find astronaut
astronaut_status = find_astronaut(baseline)
# write to the datalog
logger.log_data(astronaut_status)
class Pirozeda(object):
def __init__(self):
self.ramlog = collections.deque(maxlen=settings['ramlog']['length'])
self.fslog_last = 0
self.ramlog_last = 0
# avoid no data received error right at the beginning
self.commdead_last = time.time()
strace = settings['trace']
self.trace = ProzedaTrace(strace['dir'] + strace['prefix'],
strace['suffix'])
ProzedaLogdata.set_config(settings['system'])
ProzedaHistory.set_config(settings)
sserial = settings['serialport']
serialport = serial.Serial(sserial['port'], sserial['baudrate'])
self.prozeda = ProzedaReader(serialport)
sfslog = settings['fslog']
self.fslog = DataLogger(sfslog['dir'] + sfslog['prefix'],
sfslog['suffix'])
self.prozeda.evt_rawdata_received = self.trace.data_received
JsonrpcRequestHandler.pirozeda = self
def run(self):
JsonrpcRequestHandler.startserver()
try:
while True:
time.sleep(0.1)
ts = time.time()
if not self.prozeda.communication_alive():
# nag only every 10 seconds
if ts >= (self.commdead_last + 10):
self.commdead_last = ts
print(str(datetime.now()) + ": no data received")
if ts >= (self.fslog_last + settings['fslog']['interval']):
self.fslog_last = ts
logentry = self.prozeda.get_latest_logentry()
# TODO: check if logentry is old
if logentry is not None:
self.fslog.write(logentry.timestamp, "d",
base64.b64encode(logentry.data))
# fslog.flush() # only enable flush for development
# (log will be written to file system for every line)
else:
self.fslog.write(time.time(), "w", "no data")
if ts >= (self.ramlog_last + settings['ramlog']['interval']):
self.ramlog_last = ts
logentry = self.prozeda.get_latest_logentry()
# TODO: check if logentry is old
if logentry is not None:
self.ramlog.append(logentry)
except KeyboardInterrupt:
print("quitting...")
self.prozeda.stop()
exit()
class ProzedaTrace(object):
def __init__(self, fileprefix, filesuffix):
self.fileprefix = fileprefix
self.filesuffix = filesuffix
self.starttime = None
self.stoptime = None
self.logger = None
self.livetrace = collections.deque(maxlen=100)
def start(self, duration, comment=None):
if self.starttime == None:
self.starttime = time.time()
if duration is None:
duration = 0
self.stoptime = time.time() + int(duration)
if self.logger is None:
self.logger = DataLogger(self.fileprefix, self.filesuffix,
'%Y-%m-%d_%H.%M.%S', True)
if comment is not None and self.logger is not None:
self.logger.write(None, 'c', comment)
def stop(self):
if self.logger is not None:
self.logger.close()
self.logger = None
self.starttime = None
self.stoptime = None
def stats(self):
result = {
'file': None,
'start': self.starttime,
'stop': self.stoptime,
'remaining': None,
'size': None
}
if self.stoptime is not None:
result['remaining'] = self.stoptime - time.time()
if self.logger is not None:
result['file'] = self.logger.filename
result['size'] = self.logger.get_filesize()
return result
def data_received(self, prd, timestamp, line):
if time.time() >= self.stoptime:
self.stop()
if self.logger is not None:
self.logger.write(timestamp, 'd', line)
self.livetrace.append([timestamp, line])
def livetrace_get(self, last_timestamp=0):
result = []
for item in self.livetrace:
if item[0] > last_timestamp:
result.append(item)
return result
from datalogger import DataLogger
from time import sleep
import animasjoner
import tekster
logger = DataLogger()
tekster.init_tekst()
while True:
logger.log_data()
sleep(1)
animasjoner.vis_animasjon()
tekster.vis_tekst()
import sys
from qtpy import QtWidgets
import os.path as osp
dir_path = osp.split(osp.dirname(osp.realpath(__file__)))[0]
sys.path.append(dir_path) # In case datalogger not
# accessible in normal PYTHONPATH
if __name__ == '__main__':
directory = sys.argv[1]
APP = QtWidgets.QApplication(sys.argv)
from datalogger import DataLogger
DLG = DataLogger(directory)
APP.exec_()
# Set up logging
if config.getboolean('monitor', 'debug', fallback=False):
print("Debug enabled")
level = logging.DEBUG
else:
level = logging.INFO
duallog.setup('solar-monitor',
minLevel=level,
fileLevel=level,
rotation='daily',
keep=30)
# Set up data logging
# datalogger = None
datalogger = DataLogger(config)
# Set up device manager and adapter
device_manager = SolarDeviceManager(adapter_name=config['monitor']['adapter'])
logging.info("Adapter status - Powered: {}".format(
device_manager.is_adapter_powered))
if not device_manager.is_adapter_powered:
logging.info("Powering on the adapter ...")
device_manager.is_adapter_powered = True
logging.info("Powered on")
# Run discovery
device_manager.update_devices()
logging.info("Starting discovery...")
# scan all the advertisements from the services list
device_manager.start_discovery()
def run(self):
sensors = None
datalogger = None
try:
# initialise objects
sensors = Sensors(self.T_OFFSET,self.P_OFFSET,self.H_OFFSET)
datalogger = DataLogger(self.DATALOGFILE, self.DATALOGKEYS, self.DATALOGMAXFILESIZE)
self.scheduler = HeaterScheduler()
self.modemanager = ModeManager( scheduler=self.scheduler,
callbackfunc=self.modemanagercallbackfunc)
self.mqttclient = MqttClient(subscribelist=self.SUBSCRIBELIST)
self.heatercontroller = HeaterController()
# initialise state
self.modemanager.setMode(self.modemanager.AUTO)
# initial data
t_avg,p_avg,h_avg = sensors.getData()
# present ourselves
self.mqttclient.publish({topics.IOTHERMSTATUS:'Active'})
self.mqttclient.publish({topics.IOTHERMVERSION:__version__ +' '+ __status__})
self.mqttclient.publish({topics.MODE:self.modemanager.currentmode.name})
self.mqttclient.publish({topics.TARGETTEMPERATURE:self.modemanager.currentmode.targettemperature})
t = 0
while True:
t_,p,h = sensors.getData()
messages = self.mqttclient.getData()
# use temperature value from mqtt
if self.USE_OTHERTEMPERATURE and topics.OTHERTEMPERATURE in messages:
t = float( messages[topics.OTHERTEMPERATURE] )
if not self.USE_OTHERTEMPERATURE:
t = t_
# calculate averages
t_avg = (t_avg + t)/2
p_avg = (p_avg + p)/2
h_avg = (h_avg + h)/2
# calculate derivatives
d_t = (t - t_avg)/self.REFRESH_SECONDS
d_p = (p - p_avg)/self.REFRESH_SECONDS
d_h = (h - h_avg)/self.REFRESH_SECONDS
# process data from subscribed topics
self.processMessages( messages )
# prepare for publishing
messages = self.prepareMessages(t, p, h, d_t, d_p, d_h)
datalogger.log(messages)
self.mqttclient.publish(messages)
# update the heatercontroller with the current and target temperatures
#print('targettemperature = {}'.format(self.modemanager.currentmode.targettemperature))
self.heatercontroller.update(t,self.modemanager.currentmode.targettemperature)
sleep(self.REFRESH_SECONDS)
finally:
print('IOThermostat: Stopping IOThermostat..')
if datalogger:
datalogger.close()
if sensors:
sensors.close()
if self.scheduler:
self.scheduler.close()
if self.heatercontroller:
self.heatercontroller.close()
if self.mqttclient:
self.mqttclient.close()
