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Commit b7eab69e authored by Andrey Filippov's avatar Andrey Filippov
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debugging, adding more parameters to fit

parent 50aca841
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Showing with 978 additions and 269 deletions
py393/get_test_dq_dqs_data.py
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py393/test_mcntrl.py
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......@@ -42,7 +42,7 @@ import re
from argparse import ArgumentParser
#import argparse
from argparse import RawDescriptionHelpFormatter
import time
from import_verilog_parameters import ImportVerilogParameters
#from import_verilog_parameters import VerilogParameters
from verilog_utils import hx
......@@ -158,6 +158,7 @@ def getFuncArgsString(name):
#dflts
def main(argv=None): # IGNORE:C0111
tim=time.time()
'''Command line options.'''
global QUIET
if argv is None:
......@@ -361,7 +362,8 @@ USAGE
if (args.interactive):
line =""
while True:
line=raw_input('x393%s--> '%('','(simulated)')[args.simulated]).strip()
line=raw_input('x393%s +%3.3fs--> '%(('','(simulated)')[args.simulated],(time.time()-tim))).strip()
tim=time.time()
#remove comment from the input line
if line.find("#") > 0:
line=line[:line.find("#")]
......
py393/x393_lma.py
View file @ b7eab69e
......@@ -44,14 +44,35 @@ tFDQi - array of 5 fine delay steps (here in ps) for each bit - 5*8=40
error**2 here (y-tFDQi-fXi)**2
"""
PARAMETER_TYPES=(
{"name":"tSDQS", "size":1, "units":"ps","description":"DQS input delay per step (1/5 of the datasheet value)","en":1},
{"name":"tSDQ", "size":8, "units":"ps","description":"DQ input delay per step (1/5 of the datasheet value)","en":1},
{"name":"tDQSHL", "size":1, "units":"ps","description":"DQS HIGH minus LOW difference","en":1},
{"name":"tDQHL", "size":8, "units":"ps","description":"DQi HIGH minus LOW difference","en":1},
{"name":"tDQS", "size":1, "units":"ps","description":"DQS delay (not adjusted)","en":0},
{"name":"tDQ", "size":8, "units":"ps","description":"DQi delay","en":1},
{"name":"tFDQS", "size":4, "units":"ps","description":"DQS fine delays (mod 5)","en":1}, #only 4 are independent, 5-th is -sum of 4
{"name":"tFDQ", "size":32, "units":"ps","description":"DQ fine delays (mod 5)","en":1},
{"name":"anaScale","size":1, "dflt":20, "units":"ps","description":"Scale for non-binary measured results","en":1},
{"name":"tCDQS", "size":30, "units":"ps","description":"DQS primary dealays (all but 8 and 24","en":1}, #only 4 are independent, 5-th is -sum of 4
)
FINE_STEPS=5
DLY_STEPS =FINE_STEPS * 32 # =160
def test_data(meas_delays,
compare_prim_steps,
quiet=1):
halfStep=0.5
if compare_prim_steps:
halfStep*=FINE_STEPS
if quiet < 2:
print ("DQS",end=" ")
for f in ('ir','if','or','of'):
for b in range (16):
print ("%s_%d"%(f,b),end=" ")
print()
for ldly, data in enumerate(meas_delays):
print("%d"%ldly,end=" ")
if data:
......@@ -66,24 +87,14 @@ def test_data(meas_delays,
for typ in range(4):
for pData in data: # 16 DQs, each None nor a pair of lists for inPhase in (0,1), each a pair of edges, each a pair of (dly,diff)
if pData and (not pData[typ] is None):
print ("%d"%pData[typ],end=" ")
if pData[typ][1] is None:
print ("%d"%(pData[typ]+halfStep),end=" ")
else:
print ("%d"%(pData[typ]),end=" ")
else:
print ("x",end=" ")
print()
PARAMETER_TYPES=(
{"name":"tSDQS", "size":1, "units":"ps","description":"DQS input delay per step (1/5 of the datasheet value)","en":1},
{"name":"tSDQ", "size":8, "units":"ps","description":"DQ input delay per step (1/5 of the datasheet value)","en":1},
{"name":"tDQSHL", "size":1, "units":"ps","description":"DQS HIGH minus LOW difference","en":1},
{"name":"tDQHL", "size":8, "units":"ps","description":"DQi HIGH minus LOW difference","en":1},
{"name":"tDQS", "size":1, "units":"ps","description":"DQS delay (not adjusted)","en":0},
{"name":"tDQ", "size":8, "units":"ps","description":"DQi delay","en":1},
{"name":"tFDQS", "size":4, "units":"ps","description":"DQS fine delays (mod 5)","en":1}, #only 4 are independent, 5-th is -sum of 4
{"name":"tFDQ", "size":32, "units":"ps","description":"DQ fine delays (mod 5)","en":1},
{"name":"anaScale","size":1, "dflt":20, "units":"ps","description":"Scale for non-binary measured results","en":0},
)
FINE_STEPS=5
DLY_STEPS =FINE_STEPS * 32 # =160
def make_repeat(value,nRep):
if isinstance(value,(list,tuple)):
return value
......@@ -93,18 +104,19 @@ def make_repeat(value,nRep):
class X393LMA(object):
lambdas={"initial":0.1,"current":0.1,"max":100.0}
maxNumSteps=25
finalDiffRMS=0.0001
finalDiffRMS=0.001
parameters=None
# parameterMask={}
parameterMask={'tSDQS': True,
'tSDQ': [True, True, True, True, True, True, True, True],
'tDQSHL': True,
'tDQHL': [True, True, True, True, True, True, True, True],
'tDQHL': [True, True, True, True, True, True, True, True],# 23.523465ps -> 23.315524 - too little difference?
'tDQS': False,
'tDQ': [True, True, True, True, True, True, True, True],
'tFDQS': [True, True, True, True],
'tFDQ': [True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True],
'anaScale': False
'anaScale': False,# True, # False # Broke?
'tCDQS': True #False #True # list of 30
}
"""
parameterMask={'tSDQS': True,
......@@ -130,16 +142,35 @@ class X393LMA(object):
def createYandWvectors(self,
lane,
data_set,
compare_prim_steps,
scale_w=0.2, # multiply weight by this if fractions are undefined
periods=None):
print ("createYandWvectors(): scale_w=%f"%(scale_w))
def pythIsNone(obj):
return obj is None
isNone=pythIsNone
if isinstance(data_set,np.ndarray):
isNone=np.isnan
n=len(data_set)*32
# fx=np.zeros((DLY_STEPS*32,))
"""
use np.nan instead of the None data
np.isnan() test
, dtype=np.float
@compare_prim_steps while scanning, compare this delay with 1 less by primary(not fine) step,
save None for fraction in unknown (previous -0.5, next +0.5)
"""
halfStep=0.5
if compare_prim_steps:
halfStep*=FINE_STEPS
# extra_Y=(0.0,halfStep)
y=np.zeros((n,), dtype=np.int) #[0]*n
w=np.zeros((n,)) #[0]*n
f=np.full((n,),np.nan) # fractions
yf=np.zeros((n,)) # y with added fractions
if not periods is None:
p=np.zeros((n), dtype=np.int)#[0]*n
for dly,data in enumerate(data_set):
......@@ -152,40 +183,102 @@ class X393LMA(object):
for t,tData in enumerate(bData):
if not tData is None: #[dly],[b],[t] tData - int value
i=32*dly+8*t+b
y[i]=tData
y[i]=tData[0]
if not isNone(tData[1]):
f[i] = tData[1]
yf[i]=tData[0]
w[i]=1
else:
w[i]=scale_w
yf[i]=tData[0]+halfStep
if not periods is None:
p[i]=periods[dly][b][t]
vectors={'y':y,'w':w}
vectors={'y':y,'yf':yf,'w':w,'f':f} # yf - use for actual float value, y - integer
if not periods is None:
vectors['p']=p
return vectors
def showYOrVector(self,
ywp,
vector=None):
pass
filtered=False,
vector=None,
showMode="IA"):
def pythIsNone(obj):
return obj is None
isNone=pythIsNone
# If vector is None - print y vector (skipping zero mask),
# otherwise print vector (should be the same length, using the same 'w' weight mask
v=vector
if v is None:
v= ywp['y']
v= ywp['yf']
w=ywp['w']
try:
f=ywp['f']
noF=False
except:
f=None
noF=True
if not noF:
if isinstance(f,np.ndarray):
isNone=np.isnan
# print ("using np.isnan")
# print("filtered=",filtered)
n=len(v)/32
if 'A' in showMode.upper():
av=[]
for dly in range(n):
avd=[]
SAX=0.0
SA0=0.0
for t in range(4):
SX=0.0
S0=0.0
for b in range(8):
i=32*dly+8*t+b
if w[i] and ((not filtered) or noF or (not isNone(f[i]))):
SX+=w[i]*v[i]
S0+=w[i]
SAX+=SX
SA0+=S0
if S0>0:
SX/=S0
else:
SX=None
avd.append(SX)
if SA0>0:
SAX/=SA0
else:
SAX=None
avd.append(SAX)
av.append(avd)
print("DQS_dly", end= " ")
for f in ('ir','if','or','of'):
if "I" in showMode.upper():
for ft in ('ir','if','or','of'):
for b in range (8):
print ("%s_%d"%(f,b),end=" ")
print ("%s_%d"%(ft,b),end=" ")
if "A" in showMode.upper():
for ft in ('ir','if','or','of','all'):
print ("%s"%(ft),end=" ")
print()
n=len(v)/32
for dly in range(n):
print("%d"%dly,end=" ")
if "I" in showMode.upper():
for t in range(4):
for b in range(8):
i=32*dly+8*t+b
if w[i]:
if w[i] and ((not filtered) or noF or (not isNone(f[i]))):
print("%s"%(str(v[i])),end=" ")
else:
print("?",end=" ")
if "A" in showMode.upper():
for a in av[dly]:
if not a is None:
print("%f"%(a),end=" ")
else:
print("?",end=" ")
print()
def normalizeParameters(self,
......@@ -356,6 +449,7 @@ class X393LMA(object):
dly_step_ds,
primary_set,
data_set,
compare_prim_steps,
quiet=1):
"""
Prepare data by building and processing histograms to find
......@@ -376,14 +470,18 @@ class X393LMA(object):
@dly_step_ds IDELAY step (from the datasheet)
@primary_set which of the data edge series to use as leading (other will be trailing by 180)
@data_set measured data set
@compare_prim_steps while scanning, compare this delay with 1 less by primary(not fine) step,
save None for fraction in unknown (previous -0.5, next +0.5)
@quiet reduce output
"""
num_hist_steps=2*((DLY_STEPS+bin_size-1)//bin_size)
est_step_period=(clk_period/dly_step_ds)*FINE_STEPS
est_bin_period=est_step_period/bin_size
halfStep=0.5
if compare_prim_steps:
halfStep*=FINE_STEPS
extra_Y=(0.0,halfStep)
hist=[0.0]* num_hist_steps
hist4=[]
for _ in range(4):
......@@ -401,10 +499,11 @@ class X393LMA(object):
if bData:
for t,tData in enumerate(bData):
if not tData is None:
binNum=(tData-dly+DLY_STEPS+1) // bin_size
binNum=int((tData[0]+extra_Y[tData[1] is None]-dly+DLY_STEPS+1) / bin_size)
hist8x4[b][t][binNum] += 1 # lowest bin will be 1 count shy
if binNum == 0:
hist8x4[b][t][binNum] += 1.0/(bin_size-1.0)
for t in range(4):
for i in range(num_hist_steps):
for b in range(8):
......@@ -571,9 +670,19 @@ class X393LMA(object):
def get_periods_map(self,
lane,
data_set,
compare_prim_steps,
hist_estimated,
quiet=1):
"""
@compare_prim_steps while scanning, compare this delay with 1 less by primary(not fine) step,
save None for fraction in unknown (previous -0.5, next +0.5)
"""
#assign most likely period shift for each data sample
halfStep=0.5
if compare_prim_steps:
halfStep*=FINE_STEPS
extra_Y=(0.0,halfStep)
period=hist_estimated['period']
data_periods_map=[]
for dly,data in enumerate(data_set):
......@@ -587,7 +696,7 @@ class X393LMA(object):
if not tData is None: #[dly],[b],[t] tData - int value
he=hist_estimated['b_indiv'][b][t] # tuple (b, periods)
#find most likely period shift
pm[b][t]=int(round((tData-dly-he[0])/period))+he[1]
pm[b][t]=int(round((tData[0]+extra_Y[tData[1] is None]-dly-he[0])/period))+he[1]
data_periods_map.append(pm)
else:
data_periods_map.append(None)
......@@ -606,7 +715,8 @@ class X393LMA(object):
for typ in range(4):
for b, bData in enumerate(data_lane): # 8 DQs, each ...
if bData and (not bData[typ] is None):
print ("%d"%(bData[typ]),end=" ")
d=bData[typ][0]+extra_Y[bData[typ][1] is None]
print ("%d"%(d),end=" ")
else:
print ("x",end=" ")
print()
......@@ -645,7 +755,8 @@ class X393LMA(object):
for typ in range(4):
for b, bData in enumerate(data_lane): # 8 DQs, each ...
if bData and (not bData[typ] is None):
print ("%f"%(bData[typ]-period*data_periods_map[dly][b][typ]),end=" ")
d=bData[typ][0]+extra_Y[bData[typ][1] is None]
print ("%f"%(d-period*data_periods_map[dly][b][typ]),end=" ")
else:
print ("x",end=" ")
print()
......@@ -658,6 +769,8 @@ class X393LMA(object):
dly_step_ds,
primary_set,
data_set,
compare_prim_steps,
scale_w,
quiet=1):
"""
Initialize parameters and y-vector
......@@ -672,8 +785,12 @@ class X393LMA(object):
@dly_step_ds IDELAY step (from the datasheet)
@primary_set which of the data edge series to use as leading (other will be trailing by 180)
@data_set measured data set
@compare_prim_steps while scanning, compare this delay with 1 less by primary(not fine) step,
save None for fraction in unknown (previous -0.5, next +0.5)
@scale_w weight for "uncertain" values (where samples chane from all 0 to all 1 in one step)
@quiet reduce output
"""
print ("init_parameters(): scale_w=%f"%(scale_w))
self.clk_period=clk_period
hist_estimated=self.estimate_from_histograms(lane, # byte lane
......@@ -682,24 +799,33 @@ class X393LMA(object):
dly_step_ds,
primary_set,
data_set,
compare_prim_steps,
quiet)
if quiet < 3:
print ("hist_estimated=%s"%(str(hist_estimated)))
data_periods_map=self.get_periods_map(lane,
data_set,
compare_prim_steps,
hist_estimated,
quiet) #+1)
ywp= self.createYandWvectors(lane,
data_set,
compare_prim_steps,
scale_w,
data_periods_map)
# print("ywp=%s"%(str(ywp)))
if quiet < 2:
print("\nY-vector:")
self.showYOrVector(ywp)
self.showYOrVector(ywp,False,None)
print("\nY-vector(filtered):")
self.showYOrVector(ywp,True,None)
if quiet < 2:
print("\nperiods map:")
self.showYOrVector(ywp,ywp['p'])
print("\nperiods_map:")
self.showYOrVector(ywp,False,ywp['p'])
if quiet < 2:
print("\nweights_map:")
self.showYOrVector(ywp,False,ywp['w'])
......@@ -722,12 +848,13 @@ class X393LMA(object):
parameters={
"tSDQS": step_ps,
"tSDQ": (step_ps,)*8,
"tDQSHL": tDQSHL, # 0.0, # improve
"tDQSHL": tDQSHL, # 0.0, # improve Seems that initial value does not match final by sign!
"tDQHL": tDQHL, # (0.0)*8, # improve
"tDQS": 0.0,
"tDQ": tDQ,
"tFDQS": (0.0,)*4,
"tFDQ": (0.0,)*32#,
"tFDQ": (0.0,)*32,
"tCDQS": (0.0,)*30
# "anaScale":self.analog_scale
}
print ("parameters=%s"%(str(parameters)))
......@@ -759,7 +886,11 @@ class X393LMA(object):
if quiet < 2:
print("\nfx:")
self.showYOrVector(ywp,fx)
self.showYOrVector(ywp,False,fx)
print("\nfx (filtered):")
self.showYOrVector(ywp,True,fx)
SX=0.0
SX2=0.0
S0=0.0
......@@ -771,9 +902,9 @@ class X393LMA(object):
avg= SX/S0
rms= math.sqrt(SX2/S0)
print ("average(fx)= %fps, rms(fx)=%fps"%(avg,rms))
jByJT=np.dot(fxj['jacob'],np.transpose(fxj['jacob']))
if quiet < 3:
jByJT=np.dot(fxj['jacob'],np.transpose(fxj['jacob']))
print("\njByJT:")
for i,l in enumerate(jByJT):
print ("%d"%(i),end=" ")
......@@ -781,7 +912,7 @@ class X393LMA(object):
print ("%f"%(d),end=" ")
print()
self.lambdas ['current']=self.lambdas ['initial']
for _ in range(self.maxNumSteps):
for n_iter in range(self.maxNumSteps):
OK,finished=self.LMA_step(parameters,
ywp, # keep in self.variable?
primary_set, # prima
......@@ -789,9 +920,18 @@ class X393LMA(object):
self.lambdas,
self.finalDiffRMS,
quiet)
if OK:
print ("parameters=%s"%(str(parameters)))
if quiet < 4:
arms = self.getParAvgRMS(parameters,
ywp,
primary_set, # prima
quiet+1)
print ("%d: LMA_step %s average(fx)= %fps, rms(fx)=%fps"%(n_iter,("FAILURE","SUCCESS")[OK],arms['avg'],arms['rms']))
if OK and quiet < 2:
print ("updated parameters=%s"%(str(parameters)))
if finished:
if quiet < 4:
print ("final parameters=%s"%(str(parameters)))
break
fx= self.createFxAndJacobian(parameters,
......@@ -802,16 +942,121 @@ class X393LMA(object):
quiet=1)
if quiet < 3:
print("\nfx:")
self.showYOrVector(ywp,fx)
print("\nfx-postLMA:")
self.showYOrVector(ywp,False,fx)
print("\nfx-postLMA (filtered):")
self.showYOrVector(ywp,True,fx)
# calculate DQ[i] vs. DQS for -1, 0 and +1 period
DQvDQS=self.getBestDQforDQS(parameters,
primary_set,
quiet)
if quiet < 4:
enl_list=[]
for i in range(3):
if not DQvDQS[i] is None:
enl_list.append(i)
print("DQS", end=" ")
for enl in enl_list:
for b in range(8):
print("%s%d"%(('E','N','L')[enl],b),end=" ")
print()
for dly in range(DLY_STEPS):
print ("%d"%(dly),end=" ")
for enl in enl_list:
if DQvDQS[enl][dly] is None:
print ("? "*8,end="")
else:
for b in range(8):
if DQvDQS[enl][dly][b] is None:
print("?",end=" ")
else:
print("%d"%(DQvDQS[enl][dly][b]),end=" ")
print()
# print("delta=%s"%(str(delta)))
# for i,d in enumerate(delta):
# print ("%d %f"%(i,d))
def getBestDQforDQS(self,
parameters,
primary_set, # prima
quiet=1):
period=self.clk_period
tFDQS5=list(parameters['tFDQS'])
tFDQS5.append(-tFDQS5[0]-tFDQS5[1]-tFDQS5[2]-tFDQS5[3])
tSDQS=parameters['tSDQS']
tSDQ= parameters['tSDQ'] # list
tDQS =parameters['tDQS']#single value
tDQ= parameters['tDQ'] # list
tCDQS32=list(parameters['tCDQS'][0:8])+[0]+list(parameters['tCDQS'][8:23])+[0]+list(parameters['tCDQS'][23:30])
# tDQSHL =parameters['tDQSHL']#single value
# tDQHL= parameters['tDQHL'] # list
tFDQs=[]
for b in range(8):
tFDQi=list(parameters['tFDQ'][4*b:4*(b+1)])
tFDQi.append(-tFDQi[0]-tFDQi[1]-tFDQi[2]-tFDQi[3])
for i in range(5):
tFDQi[i]/=tSDQ[b]
tFDQs.append(tFDQi)
dqForDqs=[]
for enl in (0,1,2):
vDQ=[]
someData=False
for dly in range(DLY_STEPS):
tdqs=dly * tSDQS - tDQS - tFDQS5[dly % FINE_STEPS] # t - time from DQS pad to internal DQS clock with zero setup/hold times to DQ FFs
tdqs-=tCDQS32[dly // FINE_STEPS]
tdq3=tdqs +(-0.75+enl)*period # (early, nominal, late)
bDQ=[]
for b in range(8): # use all 4 variants
tdq=(tdq3+tDQ[b])/tSDQ[b]
itdq=int(round(tdq)) # in delay steps
bestDQ=None
if (itdq >= 0) and (itdq < DLY_STEPS):
bestDiff=None
for idq in range (max(itdq-FINE_STEPS,0),min(itdq+FINE_STEPS,DLY_STEPS-1)+1):
diff=idq-tFDQs[b][idq % FINE_STEPS]
if (bestDQ is None) or (abs(diff) < bestDiff):
bestDQ=idq
bestDiff=abs(diff)
if bestDQ is None:
bDQ=None
break
bDQ.append(bestDQ)
someData=True
vDQ.append(bDQ)
if someData:
dqForDqs.append(vDQ)
else:
dqForDqs.append(None)
return dqForDqs
"""
for dly in range(DLY_STEPS):
tdqs=dly * tSDQS - tDQS - tFDQS5[dly % FINE_STEPS] # t - time from DQS pad to internal DQS clock with zero setup/hold times to DQ FFs
tdq3=(tdqs-0.75*period, tdqs + 0.25*period,tdqs + 1.25*period) # (early, nominal, late)
bDQ=[]
allBits=[True,True,True]
for b in range(8): # use all 4 variants
vDQ=[]
for enl in (0,1,2):
tdq=(tdq3[enl]+tDQ[b])/tSDQ[b]
itdq=int(round(tdq)) # in delay steps
bestDQ=None
if (itdq >= 0) and (itdq < DLY_STEPS):
bestDiff=None
for idq in range (max(itdq-FINE_STEPS,0),min(itdq+FINE_STEPS,DLY_STEPS-1)+1):
diff=idq-tFDQs[b][idq % FINE_STEPS]
if (bestDQ is None) or (abs(diff) < bestDiff):
bestDQ=idq
bestDiff=abs(diff)
if bestDQ is None:
allBits[enl] = False
vDQ.append(bestDQ)
bDQ.append(vDQ)
dqForDqs.append(bDQ)
"""
"""
......@@ -835,6 +1080,7 @@ class X393LMA(object):
return obj is None
isNone=pythIsNone # swithch to np.isnan
y_vector = y_data['y']
yf_vector = y_data['yf'] # when no fractions available - half interval (0.5 or 2.5) is added, if available - nothing is added
periods_vector=y_data['p']
period=self.clk_period
try:
......@@ -856,6 +1102,9 @@ class X393LMA(object):
#self.clk_period
tFDQS5=list(parameters['tFDQS'])
tFDQS5.append(-tFDQS5[0]-tFDQS5[1]-tFDQS5[2]-tFDQS5[3])
tCDQS32=list(parameters['tCDQS'][0:8])+[0]+list(parameters['tCDQS'][8:23])+[0]+list(parameters['tCDQS'][23:30])
print("*****tCDQS32=",tCDQS32)
tFDQ=[]
for b in range(8):
tFDQi=list(parameters['tFDQ'][4*b:4*(b+1)])
......@@ -871,15 +1120,16 @@ class X393LMA(object):
tDQHL= parameters['tDQHL'] # list
for dly in range(DLY_STEPS):
tdqs=dly * tSDQS - tDQS - tFDQS5[dly % FINE_STEPS] # t - time from DQS pad to internal DQS clock with zero setup/hold times to DQ FFs
tdqs_r = tdqs + 0.25 * tDQSHL # sign opposite from: ir = ir0 - s/4 + d/4; or = or0 - s/4 - d/4
tdqs_f = tdqs - 0.25 * tDQSHL # sign opposite from: if = if0 + s/4 - d/4; of = of0 + s/4 + d/4
tdqs-=tCDQS32[dly // FINE_STEPS]
tdqs_r = tdqs - 0.25 * tDQSHL # sign opposite from: ir = ir0 - s/4 + d/4; or = or0 - s/4 - d/4 - NOT, but maybe other is wrong
tdqs_f = tdqs + 0.25 * tDQSHL # sign opposite from: if = if0 + s/4 - d/4; of = of0 + s/4 + d/4
tdqs_rf=(tdqs_r, tdqs_f)
#correct for DQS edge type
for b in range(8): # use all 4 variants
for t in range(4):
indx=32*dly+t*8+b
if (w_vector is None) or (w_vector[indx] > 0):
tdq=y_vector[indx] * tSDQ[b] - tDQ[b] - tFDQ[b][y_vector[indx] % FINE_STEPS]
tdq=yf_vector[indx] * tSDQ[b] - tDQ[b] - tFDQ[b][y_vector[indx] % FINE_STEPS]
# correct for periods
tdq -= period*periods_vector[indx] # or should it be minus here?
# correct for edge types
......@@ -888,11 +1138,8 @@ class X393LMA(object):
else:
tdq += 0.25*tDQHL[b]
if anaScale:
# if y_fractions[indx] is None:
if isNone(y_fractions[indx]):
tdq+=2.5
else:
tdq+=anaScale*y_fractions[indx]
if not isNone(y_fractions[indx]):
tdq-=anaScale*y_fractions[indx] # negative values mean that actual zero-point is not yet reached
if (t ^ primary_set) & 2:
tdq -= 0.5*period
fx[indx] = tdq - tdqs_rf[t & 1] # odd are falling DQS, even are rising DQS
......@@ -904,19 +1151,43 @@ class X393LMA(object):
# numPars=len(pv)
# print("pv=%s"%(str(pv)))
parInd=self.createParameterIndex(parameters,parMask)
if quiet <2:
print("parInd=%s"%(str(parInd)))
numPars=parInd['numPars']
jacob=np.zeros((numPars,DLY_STEPS*32))
"""
fineM5=((1.0, 0.0, 0.0, 0.0, -0.25),
(0.0, 1.0, 0.0, 0.0, -0.25),
(0.0, 0.0, 1.0, 0.0, -0.25),
(0.0, 0.0, 0.0, 1.0, -0.25))
"""
fineM5=((1.0, 0.0, 0.0, 0.0, -1.0),
(0.0, 1.0, 0.0, 0.0, -1.0),
(0.0, 0.0, 1.0, 0.0, -1.0),
(0.0, 0.0, 0.0, 1.0, -1.0))
dqs_finedelay_en=parInd['tFDQS']
for e in dqs_finedelay_en:
if e>=0:
break
else:
dqs_finedelay_en=None
dqs_delay32_en=parInd['tCDQS']
for e in dqs_delay32_en:
if e>=0:
break
else:
dqs_delay32_en=None
# tCDQS32=list(parameters['tCDQS'][0:8])+[0]+list(parameters['tCDQS'][8:23])+[0]+list(parameters['tCDQS'][23:30])
if not dqs_delay32_en is None:
dqs_delay32_index=range(0,8)+[-1]+range(8,23)+[-1]+range(23,30)
for i,d in enumerate(dqs_delay32_index):
if d >= 0:
dqs_delay32_index[i] = dqs_delay32_en[d]
print("*****dqs_delay32_index=",dqs_delay32_index)
dq_finedelay_en=[None]*8
for b in range(8):
dq_finedelay_en[b]=parInd['tFDQ'][4*b:4*(b+1)]
......@@ -928,10 +1199,11 @@ class X393LMA(object):
for dly in range(DLY_STEPS):
dlyMod5=dly % FINE_STEPS
dlyDiv5=dly // FINE_STEPS
dtdqs_dtSDQS = dly
dtdqs_dtDQS = -1.0
dtdqs_dtFDQS = (-fineM5[0][dlyMod5],-fineM5[1][dlyMod5],-fineM5[2][dlyMod5],-fineM5[3][dlyMod5])
dtdqs_dtDQSHL_rf=(0.25,-0.25)
dtdqs_dtDQSHL_rf=(-0.25,+0.25) # ign opposite from: ir = ir0 - s/4 + d/4; or = or0 - s/4 - d/4, ... - NOT, but maybe other is wrong
#correct for DQS edge type
for b in range(8): # use all 4 variants
for t in range(4):
......@@ -946,6 +1218,12 @@ class X393LMA(object):
for i,pIndx in enumerate (dqs_finedelay_en):
if pIndx >= 0:
jacob[pIndx,indx]=-dtdqs_dtFDQS[i]
if dqs_delay32_en:
for i,pIndx in enumerate (dqs_delay32_en):
if pIndx >= 0:
jacob[pIndx,indx]=(0,1.0)[i==dlyDiv5]
if parInd['tDQSHL'] >= 0:
jacob[parInd['tDQSHL'],indx]=-dtdqs_dtDQSHL_rf[t & 1]
#dependencies of DQ delays
......@@ -981,6 +1259,7 @@ class X393LMA(object):
False, # jacobian
None,
quiet)
"""
SX=0.0
SX2=0.0
S0=0.0
......@@ -989,6 +1268,10 @@ class X393LMA(object):
S0+=w
SX+=w*d
SX2+=w*d*d
"""
S0=np.sum(ywp['w'])
SX=np.sum(fx*ywp['w'])
SX2=np.sum(fx*fx*ywp['w'])
avg= SX/S0
rms= math.sqrt(SX2/S0)
return {"avg":avg,"rms":rms}
......@@ -1008,7 +1291,7 @@ class X393LMA(object):
ywp,
primary_set, # prima
quiet+1)
if quiet < 3:
if quiet < 2:
print ("LMA_step <start>: average(fx)= %fps, rms(fx)=%fps"%(arms0['avg'],arms0['rms']))
delta=self.LMA_solve(parameters,
......@@ -1032,13 +1315,13 @@ class X393LMA(object):
newPars) # parameters=None):# if not None, will be updated
if quiet < 2:
print ("\n2: newPars=%s"%(str(newPars)))
print ("\nparameters=%s"%(str(parameters)))
# print ("\nparameters=%s"%(str(parameters)))
arms1 = self.getParAvgRMS(newPars,
ywp,
primary_set, # prima
quiet+1)
finished=False
if arms1['rms'] < arms0['rms']:
if arms1['rms'] <= arms0['rms']:
parameters.update(newPars)
lambdas["current"]*=.5
success=True
......@@ -1049,7 +1332,7 @@ class X393LMA(object):
success=False
if lambdas["current"] > lambdas["max"]:
finished=True
if quiet < 3:
if (quiet < 2) or ((quiet < 4) and (not success)):
print ("LMA_step %s: average(fx)= %fps, rms(fx)=%fps, lambda=%f"%(('FAILURE','SUCCESS')[success],arms1['avg'],arms1['rms'],lambdas["current"]))
return (success,finished)
......@@ -1069,12 +1352,26 @@ class X393LMA(object):
True, # jacobian
parMask,
quiet)
try:
w_vector = ywp['w']
except:
w_vector = np.full((len(fxj['fx']),),1.0)
# print("w_vector=",w_vector)
# print("fxj['jacob']=",fxj['jacob'])
# JT=np.transpose(fxj['jacob'])
# print("JT=",JT)
wJ=fxj['jacob'] *w_vector
# JT=np.transpose(wJ) # fxj['jacob'])
JT=np.transpose(fxj['jacob'])
jByJT=np.dot(fxj['jacob'],JT)
# print("wJ=",wJ)
jByJT=np.dot(wJ,JT)
# print("jByJT=",jByJT)
for i,_ in enumerate(jByJT):
jByJT[i,i] += lmbda*jByJT[i,i]
jByDiff= -np.dot(fxj['jacob'],fxj['fx'])
jByDiff= -np.dot(wJ,fxj['fx'])
delta=np.linalg.solve(jByJT,jByDiff)
# print("*****delta=",delta)
return delta
"""
......
py393/x393_mcntrl_adjust.py
View file @ b7eab69e
......@@ -1655,6 +1655,8 @@ class X393McntrlAdjust(object):
return rdict
def adjust_pattern(self,
compare_prim_steps=True, # while scanning, compare this delay with 1 less by primary(not fine) step,
# save None for fraction in unknown (previous -0.5, next +0.5)
limit_step=0.125, # initial delay step as a fraction of the period
max_phase_err=0.1,
quiet=1,
......@@ -1849,31 +1851,61 @@ class X393McntrlAdjust(object):
# scan ranges, find closest solutions
#compare_prim_steps
best_dly= [[],[]]
best_diff=[[],[]]
for inPhase in range(2):
if not d_high[inPhase] is None:
patt=None
for dly in range(d_low[inPhase],d_high[inPhase]+1):
patt_prev=patt
patt=measure_patt(dly) # ,force_meas=False) - will be stored in cache
if patt_prev is None: #first run
# patt=None
best_dly[inPhase]=[d_low[inPhase]]*32
for p in patt:
best_diff[inPhase].append(p-0.5)
else: # all the rest
best_diff[inPhase]=[None]*32
# for b,p in enumerate(patt):
# positiveJump=((not inPhase) and (b<16)) or (inPhase and (b >= 16)) # may be 0, False, True
# if positiveJump:
# best_diff[inPhase].append(p-0.5)
# else:
# best_diff[inPhase].append(0.5-p)
for dly in range(d_low[inPhase]+1,d_high[inPhase]+1):
# patt_prev=patt
#as measured data is cached, there is no need to specially maintain patt_prev from earlier measurement
dly_prev= max(0,dly-(1,NUM_FINE_STEPS)[compare_prim_steps])
patt_prev=measure_patt(dly_prev) # ,force_meas=False) - will be stored in cache
patt= measure_patt(dly) # ,force_meas=False) - will be stored in cache
for b in range(32):
positiveJump=((not inPhase) and (b<16)) or (inPhase and (b >= 16)) # may be 0, False, True
signs=((-1,1)[patt_prev[b]>0.5],(-1,1)[patt[b]>0.5])
if (positiveJump and (signs==(-1,1))) or (not positiveJump and (signs==(1,-1))):
if positiveJump:
diffs_prev_this=(patt_prev[b]-0.5,patt[b]-0.5)
else:
diffs_prev_this=(0.5-patt_prev[b],0.5-patt[b])
"""
if abs(patt_prev[b]-0.5) < abs(patt[b]-0.5): # store previos sample
best_dly[inPhase][b]=dly-1
best_dly[inPhase][b]=dly_prev # dly-1
best_diff[inPhase][b]=patt_prev[b]-0.5
else:
best_dly[inPhase][b]=dly
best_diff[inPhase][b]=patt[b]-0.5
if not positiveJump:
best_diff[inPhase][b] *= -1 # inver sign, so sign always means <0 - delay too low, >0 - too high
best_diff[inPhase][b] *= -1 # invert sign, so sign always means <0 - delay too low, >0 - too high
"""
if abs(diffs_prev_this[0]) <= abs(diffs_prev_this[1]): # store previos sample
if (best_diff[inPhase][b] is None) or (abs (diffs_prev_this[0])<abs(best_diff[inPhase][b])):
best_dly[inPhase][b]=dly_prev # dly-1
best_diff[inPhase][b]=diffs_prev_this[0]
if quiet < 1:
print ("*%d:%0.3f:%0.3f%s"%(b,diffs_prev_this[0],diffs_prev_this[1],str(signs)),end="")
else:
if (best_diff[inPhase][b] is None) or (abs (diffs_prev_this[1])<abs(best_diff[inPhase][b])):
best_dly[inPhase][b]=dly # dly-1
best_diff[inPhase][b]=diffs_prev_this[1]
if quiet < 1:
print ("?%d:%0.3f:%0.3f%s"%(b,diffs_prev_this[0],diffs_prev_this[1],str(signs)),end="")
if quiet < 1:
print("\n dly=%d dly_prev=%d:"%(dly,dly_prev),end=" ")
for b in range(32):
if best_diff[inPhase][b] == -0.5:
best_diff[inPhase][b] = None # will have to add half-interval (0.5 or 2.5)
# rslt=[None]*16 # each bit will have [inphase][dqs_falling], each - a pair of (delay,diff)
for b in range(16):
rslt[b]=[[None]*2,[None]*2] # [inphase][dqs_falling]
......@@ -1904,6 +1936,7 @@ class X393McntrlAdjust(object):
print ()
if quiet < 3:
print("\n\nMeasured data, integer portion, measured with %s steps"%(("fine","primary")[compare_prim_steps]))
print ("DQS",end=" ")
for f in ('ir','if','or','of'):
for b in range (16):
......@@ -1929,6 +1962,32 @@ class X393McntrlAdjust(object):
print ("x",end=" ")
print()
if quiet < 2:
print("\n\nMasked measured data, integer portion, measured with %s steps"%(("fine","primary")[compare_prim_steps]))
for f in ('ir','if','or','of'):
for b in range (16):
print ("%s_%d"%(f,b),end=" ")
print()
for ldly, data in enumerate(meas_data):
print("%d"%ldly,end=" ")
if data:
for typ in ((0,0),(0,1),(1,0),(1,1)):
for pData in data: # 16 DQs, each None nor a pair of lists for inPhase in (0,1), each a pair of edges, each a pair of (dly,diff)
if pData:
if pData[typ[0]] and pData[typ[0]][typ[1]] and (not pData[typ[0]][typ[1]][1] is None):
print ("%d"%pData[typ[0]][typ[1]][0],end=" ")
'''
try:
print ("%d"%pData[typ[0]][typ[1]][0],end=" ")
except:
print (".", end=" ")
'''
else:
print ("?", end=" ")
else:
print ("x",end=" ")
print()
if quiet < 2:
print ("\nDifferences from 0.5:")
......@@ -1944,7 +2003,7 @@ class X393McntrlAdjust(object):
for typ in ((0,0),(0,1),(1,0),(1,1)):
for pData in data: # 16 DQs, each None nor a pair of lists for inPhase in (0,1), each a pair of edges, each a pair of (dly,diff)
if pData:
if pData[typ[0]] and pData[typ[0]][typ[1]]:
if pData[typ[0]] and pData[typ[0]][typ[1]] and (not pData[typ[0]][typ[1]][1] is None):
print ("%.2f"%pData[typ[0]][typ[1]][1],end=" ")
'''
try:
......@@ -1958,8 +2017,10 @@ class X393McntrlAdjust(object):
print ("x",end=" ")
print()
print("\n\n")
# print ("meas_data=%s"%str(meas_data))
if quiet < 3:
print("\n\nMeasured data, comparing current data with the earlier by one %s step"%(("fine","primary")[compare_prim_steps]))
print("When the fractional (second in the tuple) data is exactly -0.5, the actual result is in the range %s from the integer delay"%
(("+0.0..+1.0","+0.0..+%d"%NUM_FINE_STEPS)[compare_prim_steps]))
print ("meas_data=[")
for d in meas_data:
print("%s,"%(str(d)))
......@@ -1974,8 +2035,19 @@ class X393McntrlAdjust(object):
lane=0,
bin_size=5,
primary_set=2,
# compare_prim_steps=True, # while scanning, compare this delay with 1 less by primary(not fine) step,
# # save None for fraction in unknown (previous -0.5, next +0.5)
data_set_number=2,
scale_w=0.2, # weight for "uncertain" values (where samples chane from all 0 to all 1 in one step)
quiet=1):
meas_data=get_test_dq_dqs_data.get_data()
"""
@scale_w weight for "uncertain" values (where samples chane from all 0 to all 1 in one step)
"""
print ("proc_test_data(): scale_w=%f"%(scale_w))
compare_prim_steps=get_test_dq_dqs_data.get_compare_prim_steps(data_set_number)
meas_data=get_test_dq_dqs_data.get_data(data_set_number)
meas_delays=[]
for data in meas_data:
if data:
......@@ -1995,10 +2067,10 @@ class X393McntrlAdjust(object):
if pData[inPhase]:
for e in (0,1):
if pData[inPhase][e]:
bits[b][inPhase*2+e]=pData[inPhase][e][0]
bits[b][inPhase*2+e]=pData[inPhase][e]# [0]
meas_delays.append(bits)
if quiet<1:
x393_lma.test_data(meas_delays,quiet)
x393_lma.test_data(meas_delays,compare_prim_steps,quiet)
lma=x393_lma.X393LMA()
lma.init_parameters(
lane,
......@@ -2007,5 +2079,7 @@ class X393McntrlAdjust(object):
78.0, # dly_step_ds,
primary_set,
meas_delays,
compare_prim_steps,
scale_w,
quiet)
\ No newline at end of file
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