idxLAA+= 1
idxLAM+= 1
idxLBA+= 1
idxLBM+= 1
idxLCA+= 1
idxLCM+= 1
# return V0 V+ V-
return[V0Ang,V0Mag,VpAng,VpMag,VnAng,VnMag,Vn_ubalance_seq,V0_ubalance_seq]
opts = { 'input_streams' : ['upmu/switch_a6/L1ANG','upmu/switch_a6/L1MAG','upmu/switch_a6/L2ANG',
'upmu/switch_a6/L2MAG','upmu/switch_a6/L3ANG','upmu/switch_a6/L3MAG','upmu/grizzly_new/L1ANG'], \
'input_uids' : ['adf13e17-44b7-4ef6-ae3f-fde8a9152ab7','df64af25-a389-4be9-8061-f87c3616f286',
'4f56a8f1-f3ca-4684-930e-1b4d9955f72c','6e6ad513-ddd2-47fb-98c1-16e6477504fc',
'2c07ccef-20c5-4971-87cf-2c187ce5f722','bcf38098-0e16-46f2-a9fb-9ce481d7d55b',
'b4776088-2f85-4c75-90cd-7472a949a8fa'], \
'start_date' : '2014-12-03T00:00:00.000000', \
'end_date' : '2014-12-03T23:59:59.000000', \
'output_streams' : ['VOLTAGE_ZERO_SEQ_ANG','VOLTAGE_ZERO_SEQ_MAG','VOLTAGE_POSITIVE_SEQ_ANG',
'VOLTAGE_POSITIVE_SEQ_MAG','VOLTAGE_NEGATIVE_SEQ_ANG','VOLTAGE_NEGATIVE_SEQ_MAG',
'VOLTAGE_UNBALANCE_NEG_SEQ','VOLTAGE_UNBALANCE_ZERO_SEQ'], \
'output_units' : ['Degree','V','Degree','V','Degree','V','Precent','Precent'], \
'author' : 'Refined Switch_a6', \
'name' : 'Sequence Components', \
'version' : 10, \
'algorithm' : compute }
qdf.register(Distillate(), opts)
qdf.begin()
# start date for input streams. Format must match: 'yyyy-mm-ddThh:mm:ss.ssssss'
'start_date' : '2014-10-06T00:00:00.000000',
# end date for input streams. Format must match: 'yyyy-mm-ddThh:mm:ss.ssssss'
'end_date' : '2014-10-06T02:30:00.000000',
# string names of output streams.
'output_streams' : ['out1, out2, out3'],
# List of units for each output stream
'output_units' : ['Unit', 'Unit', 'Unit'],
# The programmer who wrote this distillate. First level directory name
'author' : 'CAB',
# The name of the type algorithm implemented. Second level directory name
'name' : 'New Output Frequency',
# The version of the code. Increment this to overwrite existing data from an older
# version of this code
'version' : 6,
# Plaintext name of the function defined above. This passes a 'function reference'
# that can be called in another program
'algorithm' : algorithm }
# The following two lines instantiate the distillate class and begin begin the distillation
qdf.register(Distillate(),
opts) #instantiates an object with your parameters and algorithm
qdf.begin() # enters the program and runs the distillation program
continue
if VpAng[idxVp].time < CpAng[idxCp].time:
idxCp += 1
continue
# compute Total_dpf_pos_seq
total = np.cos(np.radians(VpAng[idxVp].value - CpAng[idxCp].value))
Total_dpf_pos_seq.append((VpAng[idxVp].time, total))
idxVp += 1
idxCp += 1
''' return Totalp_dpf_seq'''
return [Total_dpf_pos_seq]
opts = { 'input_streams' : ['Refined Grizzly/Sequence Components/VOLTAGE_POSITIVE_SEQ_ANG',
'Refined Grizzly/Sequence Components/CURRENT_POSITIVE_SEQ_ANG'], \
'input_uids' : ['b70fae9d-8275-4956-8fb0-cf42a2474c7c','f5e8010b-147c-4d9b-940c-57703f4d29e7'], \
'start_date' : '2014-12-03T00:00:00.000000', \
'end_date' : '2014-12-03T11:59:59.000000', \
'output_streams' : ['TOTAL_DPF_POS_SEQ'], \
'output_units' : ['Precent'], \
'author' : 'Calculated Grizzly', \
'name' : 'Sequence Components', \
'version' : 17, \
'algorithm' : compute }
qdf.register(Distillate(), opts)
qdf.begin()
values_1hz = []
values_2hz = []
while timestamp < target_end_date:
#Round down to the second to prevent cumulative error
timestamp /= 1000000000
timestamp *= 1000000000
for i in xrange(120):
delta = 8333333 #This corresponds with the one used by the uPMUs
values_1hz.append((timestamp + delta*i, np.sin( 2*np.pi * i*delta/1E9)))
values_2hz.append((timestamp + delta*i, np.sin( 2*2*np.pi * i*delta/1E9)))
if len(values_1hz) >= qdf.OPTIMAL_BATCH_SIZE:
yield self.stream_insert_multiple("1hz", values_1hz)
yield self.stream_insert_multiple("2hz", values_2hz)
values_1hz = []
values_2hz = []
timestamp += 1000000000 #Next second
#Make sure that we write out the values we generated
yield self.stream_insert_multiple("1hz", values_1hz)
yield self.stream_insert_multiple("2hz", values_2hz)
#Now that we are done, save the time we finished at
self.persist("end_timestamp", timestamp)
qdf.register(Example1HZ())
qdf.begin()
window_end = current + 15 * qdf.MINUTE
if window_end > end:
window_end = end
_, hz1_values = yield self.stream_get("1hz", current, window_end)
_, hz2_values = yield self.stream_get("2hz", current, window_end)
delta_values = []
idx1 = 0
idx2 = 0
while idx1 < len(hz1_values) and idx2 < len(hz2_values):
if hz1_values[idx1].time < hz2_values[idx2].time:
idx1 += 1
continue
if hz1_values[idx1].time > hz2_values[idx2].time:
idx2 += 1
continue
delta = hz1_values[idx1].value - hz2_values[idx2].value
delta_values.append((hz1_values[idx1].time, delta))
idx1 += 1
idx2 += 1
yield self.stream_insert_multiple("delta", delta_values)
current += 15 * qdf.MINUTE
# we don't need to use any persistence, because the latest versions we used are stored in the metadata
qdf.register(DistillateDriver())
qdf.begin()
#This is the final level. You can have multiple of these
self.use_stream("del", "589f0dbe-8855-49f2-b30e-3c64440673de")
#If this is incremented, it is assumed that the whole distillate is invalidated, and it
#will be deleted and discarded. In addition all 'persist' data will be removed
self.set_version(4)
@defer.inlineCallbacks
def compute(self):
"""
This is called to compute your algorithm.
This example generates the difference between two streams
"""
if self.unpersist("done",False):
print "Already done"
return
start_date = self.date("2014-10-01T00:00:00.000000")
end_date = self.date("2014-10-01T00:01:00.000000")
yield self.stream_delete_range("dev", start_date, end_date)
#Now that we are done, save the time we finished at
self.persist("done", True)
qdf.register(DeleteStream())
qdf.begin()
hz1_version, hz1_values = yield self.stream_get("1hz", start_date, end_date)
hz2_version, hz2_values = yield self.stream_get("2hz", start_date, end_date)
delta_values = []
idx1 = 0
idx2 = 0
while idx1 < len(hz1_values) and idx2 < len(hz2_values):
if hz1_values[idx1].time < hz2_values[idx2].time:
idx1 += 1
continue
if hz1_values[idx1].time > hz2_values[idx2].time:
idx2 += 1
continue
delta = hz1_values[idx1].value - hz2_values[idx2].value
delta_values.append((hz1_values[idx1].time, delta))
if len(delta_values) >= qdf.OPTIMAL_BATCH_SIZE:
yield self.stream_insert_multiple("delta", delta_values)
delta_values = []
idx1 += 1
idx2 += 1
yield self.stream_insert_multiple("delta", delta_values)
#Now that we are done, save the time we finished at
self.persist("done", True)
qdf.register(ExampleDelta())
qdf.begin()
idx1 = 0
idx2 = 0
while idx1 < len(vals_a) and idx2 < len(vals_b):
if vals_a[idx1].time < vals_b[idx2].time:
idx1 += 1
continue
if vals_a[idx1].time > vals_b[idx2].time:
idx2 += 1
continue
delta = vals_a[idx1].value - vals_b[idx2].value + pair[
0][2] + pair[1][2]
if delta < -180:
delta += 360
elif delta >= 180:
delta -= 360
delta_values.append((vals_a[idx1].time, delta))
idx1 += 1
idx2 += 1
print "Inserting values: ", len(delta_values)
yield self.stream_insert_multiple(streamname, delta_values)
current += 15 * qdf.MINUTE
# we don't need to use any persistence, because the latest versions we used are stored in the metadata
qdf.register(DistillateDriver())
qdf.begin()
#This is the final level. You can have multiple of these
self.use_stream("del", "589f0dbe-8855-49f2-b30e-3c64440673de")
#If this is incremented, it is assumed that the whole distillate is invalidated, and it
#will be deleted and discarded. In addition all 'persist' data will be removed
self.set_version(4)
@defer.inlineCallbacks
def compute(self):
"""
This is called to compute your algorithm.
This example generates the difference between two streams
"""
if self.unpersist("done", False):
print "Already done"
return
start_date = self.date("2014-10-01T00:00:00.000000")
end_date = self.date("2014-10-01T00:01:00.000000")
yield self.stream_delete_range("dev", start_date, end_date)
#Now that we are done, save the time we finished at
self.persist("done", True)
qdf.register(DeleteStream())
qdf.begin()
"1hz", start_date, end_date)
hz2_version, hz2_values = yield self.stream_get(
"2hz", start_date, end_date)
delta_values = []
idx1 = 0
idx2 = 0
while idx1 < len(hz1_values) and idx2 < len(hz2_values):
if hz1_values[idx1].time < hz2_values[idx2].time:
idx1 += 1
continue
if hz1_values[idx1].time > hz2_values[idx2].time:
idx2 += 1
continue
delta = hz1_values[idx1].value - hz2_values[idx2].value
delta_values.append((hz1_values[idx1].time, delta))
if len(delta_values) >= qdf.OPTIMAL_BATCH_SIZE:
yield self.stream_insert_multiple("delta", delta_values)
delta_values = []
idx1 += 1
idx2 += 1
yield self.stream_insert_multiple("delta", delta_values)
#Now that we are done, save the time we finished at
self.persist("done", True)
qdf.register(ExampleDelta())
qdf.begin()
values_2hz = []
while timestamp < target_end_date:
#Round down to the second to prevent cumulative error
timestamp /= 1000000000
timestamp *= 1000000000
for i in xrange(120):
delta = 8333333 #This corresponds with the one used by the uPMUs
values_1hz.append((timestamp + delta * i,
np.sin(2 * np.pi * i * delta / 1E9)))
values_2hz.append((timestamp + delta * i,
np.sin(2 * 2 * np.pi * i * delta / 1E9)))
if len(values_1hz) >= qdf.OPTIMAL_BATCH_SIZE:
yield self.stream_insert_multiple("1hz", values_1hz)
yield self.stream_insert_multiple("2hz", values_2hz)
values_1hz = []
values_2hz = []
timestamp += 1000000000 #Next second
#Make sure that we write out the values we generated
yield self.stream_insert_multiple("1hz", values_1hz)
yield self.stream_insert_multiple("2hz", values_2hz)
#Now that we are done, save the time we finished at
self.persist("end_timestamp", timestamp)
qdf.register(Example1HZ())
qdf.begin()
# start date for input streams. Format must match: 'yyyy-mm-ddThh:mm:ss.ssssss'
'start_date' : '2014-10-06T00:00:00.000000',
# end date for input streams. Format must match: 'yyyy-mm-ddThh:mm:ss.ssssss'
'end_date' : '2014-10-06T02:30:00.000000',
# string names of output streams.
'output_streams' : ['out1, out2, out3'],
# List of units for each output stream
'output_units' : ['Unit', 'Unit', 'Unit'],
# The programmer who wrote this distillate. First level directory name
'author' : 'CAB',
# The name of the type algorithm implemented. Second level directory name
'name' : 'New Output Frequency',
# The version of the code. Increment this to overwrite existing data from an older
# version of this code
'version' : 6,
# Plaintext name of the function defined above. This passes a 'function reference'
# that can be called in another program
'algorithm' : algorithm }
# The following two lines instantiate the distillate class and begin begin the distillation
qdf.register(Distillate(), opts) #instantiates an object with your parameters and algorithm
qdf.begin() # enters the program and runs the distillation program
