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Commit 3d603296 authored by Andrey Filippov's avatar Andrey Filippov
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fixed last of the known so far errors in the DQ input delays vs DQS input delays LMA fitting

parent 07f11d45
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Showing with 84 additions and 56 deletions
py393/x393_lma.py
View file @ 3d603296
......@@ -116,7 +116,12 @@ class X393LMA(object):
'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': True, # False,# True, # False # Broke?
'tCDQS': False # True #False #True # list of 30
# 'tCDQS': False # True #False #True # list of 30
'tCDQS': [True, True, True, True, True, True, True, True, # 8
True, True, True, True, True, True, True, True, True, True, True, True, True, True, True,
# False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, #15
True, True, True, True, True, True, True]
# False, False, False, False, False, False, False] #7
}
"""
parameterMask={'tSDQS': True,
......@@ -144,8 +149,11 @@ class X393LMA(object):
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))
periods=None,
quiet=1):
if quiet < 3:
print ("createYandWvectors(): scale_w=%f"%(scale_w))
def pythIsNone(obj):
return obj is None
isNone=pythIsNone
......@@ -765,7 +773,7 @@ class X393LMA(object):
print()
return data_periods_map
def init_parameters(self,
def lma_fit(self,
lane, # byte lane
bin_size,
clk_period,
......@@ -782,6 +790,10 @@ class X393LMA(object):
each of 2x2 elements (DQ delay values) or null
Create data set template - for each DQS delay and inPhase
- branch - number of full periods to add
After initial parametersn are created - run LMA to find optimal ones,
then return up to 3 varints (early, nominal, late) providing the best
DQ input delay for each DQS one
@lane byte lane to process
@bin_size bin size for the histograms (should be 5/10/20/40)
@clk_period SDCLK period in ps
......@@ -791,9 +803,12 @@ class X393LMA(object):
@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
@quiet reduce output
@return 3-element dictionary of ('early','nominal','late'), each being None or a 160-element list,
each element being either None, or a list of 3 best DQ delay values for the DQS delay (some mey be None too)
"""
print ("init_parameters(): scale_w=%f"%(scale_w))
if quiet < 3:
print ("init_parameters(): scale_w=%f"%(scale_w))
self.clk_period=clk_period
hist_estimated=self.estimate_from_histograms(lane, # byte lane
......@@ -816,7 +831,8 @@ class X393LMA(object):
data_set,
compare_prim_steps,
scale_w,
data_periods_map)
data_periods_map,
quiet)
# print("ywp=%s"%(str(ywp)))
if quiet < 2:
print("\nY-vector:")
......@@ -839,7 +855,8 @@ class X393LMA(object):
for b, d in enumerate(hist_estimated['b_indiv']):
tDQSHL += (d[1][0]-d[0][0] +d[3][0]-d[2][0])*step_ps
tDQHL[b] = (d[0][0]-d[1][0] +d[3][0]-d[2][0])*step_ps
print ("%d: S=%f, D=%f"%(b, d[1][0]-d[0][0] +d[3][0]-d[2][0], d[0][0]-d[1][0] +d[3][0]-d[2][0]))
if quiet < 3:
print ("%d: S=%f, D=%f"%(b, d[1][0]-d[0][0] +d[3][0]-d[2][0], d[0][0]-d[1][0] +d[3][0]-d[2][0]))
tDQSHL /= 8.0
# calculate primary tDQ delays (primary - for the edges selected by 'primary_set'
tDQ=[0.0]*8
......@@ -860,9 +877,15 @@ class X393LMA(object):
"tCDQS": (0.0,)*30
# "anaScale":self.analog_scale
}
print ("parameters=%s"%(str(parameters)))
"""
Returns # best (early,nominal,late) for each bit for each delay ([3][160][8])
Outer list each has 160-element list, some of which are None, others hove 8 elements (including None ones)
"""
if quiet < 2:
print ("parameters=%s"%(str(parameters)))
self.normalizeParameters(parameters) #isMask=False)
print ("normalized parameters=%s"%(str(parameters)))
if quiet < 4:
print ("normalized parameters=%s"%(str(parameters)))
"""
both ways work:
self.parameterMask={}
......@@ -872,15 +895,16 @@ class X393LMA(object):
# self.parameterMask=self.normalizeParameters({},isMask=True)
self.parameterMask=self.normalizeParameters(self.parameterMask,isMask=True)
print ("parameters mask=%s"%(str(self.parameterMask)))
if quiet < 4:
print ("parameters mask=%s"%(str(self.parameterMask)))
create_jacobian=True
fxj= self.createFxAndJacobian(parameters,
ywp, # keep in self.variable?
primary_set,
jacobian=create_jacobian,
parMask=None,
quiet=1)
create_jacobian,
None, #parMask
quiet)
if create_jacobian:
fx=fxj['fx']
......@@ -893,19 +917,12 @@ class X393LMA(object):
print("\nfx (filtered):")
self.showYOrVector(ywp,True,fx)
SX=0.0
SX2=0.0
S0=0.0
for d,w in zip(fx,ywp['w']):
if w>0:
S0+=w
SX+=w*d
SX2+=w*d*d
avg= SX/S0
rms= math.sqrt(SX2/S0)
print ("average(fx)= %fps, rms(fx)=%fps"%(avg,rms))
if quiet < 4:
arms = self.getParAvgRMS(parameters,
ywp,
primary_set, # prima
quiet+1)
print ("average(fx)= %fps, rms(fx)=%fps"%(arms['avg'],arms['rms']))
if quiet < 3:
jByJT=np.dot(fxj['jacob'],np.transpose(fxj['jacob']))
print("\njByJT:")
......@@ -923,7 +940,7 @@ class X393LMA(object):
self.lambdas,
self.finalDiffRMS,
quiet)
if quiet < 4:
if (quiet < 4) or ((quiet < 5) and finished):
arms = self.getParAvgRMS(parameters,
ywp,
primary_set, # prima
......@@ -941,8 +958,8 @@ class X393LMA(object):
ywp, # keep in self.variable?
primary_set,
False,
parMask=None,
quiet=1)
None,
quiet)
if quiet < 3:
print("\nfx-postLMA:")
......@@ -975,7 +992,15 @@ class X393LMA(object):
print("?",end=" ")
else:
print("%d"%(DQvDQS[enl][dly][b]),end=" ")
print()
print()
rslt={}
rslt_names=("early","nominal","late")
for i, d in enumerate(DQvDQS):
rslt[rslt_names[i]] = d
return rslt
# return DQvDQS
# Returns 3-element dictionary of ('early','nominal','late'), each being None or a 160-element list,
# each element being either None, or a list of 3 best DQ delay values for the DQS delay (some mey be None too)
def getBestDQforDQS(self,
......@@ -1032,7 +1057,7 @@ class X393LMA(object):
if someData:
dqForDqs.append(vDQ)
else:
dqForDqs.append(None)
dqForDqs.append(None)
return dqForDqs
"""
for dly in range(DLY_STEPS):
......@@ -1189,7 +1214,7 @@ class X393LMA(object):
if d >= 0:
dqs_delay32_index[i] = dqs_delay32_en[d]
print("*****dqs_delay32_index=",dqs_delay32_index)
# print("*****dqs_delay32_index=",dqs_delay32_index)
dq_finedelay_en=[None]*8
for b in range(8):
......@@ -1208,6 +1233,7 @@ class X393LMA(object):
dtdqs_dtFDQS = (-fineM5[0][dlyMod5],-fineM5[1][dlyMod5],-fineM5[2][dlyMod5],-fineM5[3][dlyMod5])
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
# dbg=[0.0]*32
for b in range(8): # use all 4 variants
for t in range(4):
indx=32*dly+t*8+b
......@@ -1223,9 +1249,10 @@ class X393LMA(object):
jacob[pIndx,indx]=-dtdqs_dtFDQS[i]
if dqs_delay32_en:
for i,pIndx in enumerate (dqs_delay32_en):
for i,pIndx in enumerate (dqs_delay32_index):
if pIndx >= 0:
jacob[pIndx,indx]=(0,1.0)[i==dlyDiv5]
# dbg[i]+=jacob[pIndx,indx]
if parInd['tDQSHL'] >= 0:
jacob[parInd['tDQSHL'],indx]=-dtdqs_dtDQSHL_rf[t & 1]
......@@ -1249,7 +1276,7 @@ class X393LMA(object):
if parInd['anaScale'] >= 0:
if anaScale and not isNone(y_fractions[indx]):
jacob[parInd['anaScale'],indx]=-y_fractions[indx]
# print("dbg: %d: "%(dly),dbg)
return {'fx':fx,'jacob':jacob}
def getParAvgRMS(self,
parameters,
......
py393/x393_mcntrl_adjust.py
View file @ 3d603296
......@@ -2042,10 +2042,20 @@ class X393McntrlAdjust(object):
quiet=1):
"""
@scale_w weight for "uncertain" values (where samples chane from all 0 to all 1 in one step)
Run DQ vs DQS fitting for one data lane (0 or 1) using earlier acquired hard-coded data
@lane byte lane to process
@bin_size bin size for the histograms (should be 5/10/20/40)
@primary_set which of the data edge series to use as leading (other will be trailing by 180)
@data_set_number select one of the hard-coded data sets (sets 0 and 1 use comparing with the data 1 fine step below
set #2 (default) used measurement with previous primary step measurement (will not suffer from
fine range wider than on primary step)
@scale_w weight for "uncertain" values (where samples change from all 0 to all 1 in one step)
For sufficient data 0.0 is OK (and seems to work better)- only "analog" samples are considered
@return 3-element dictionary of ('early','nominal','late'), each being None or a 160-element list,
each element being either None, or a list of 3 best DQ delay values for the DQS delay (some mey be None too)
"""
print ("proc_test_data(): scale_w=%f"%(scale_w))
if quiet < 3:
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=[]
......@@ -2054,14 +2064,6 @@ class X393McntrlAdjust(object):
bits=[None]*16
for b,pData in enumerate(data):
if pData:
"""
bits[b]=[[None,None],[None,None]]
for inPhase in (0,1):
if pData[inPhase]:
for e in (0,1):
if pData[inPhase][e]:
bits[b][inPhase][e]=pData[inPhase][e][0]
"""
bits[b]=[None]*4
for inPhase in (0,1):
if pData[inPhase]:
......@@ -2072,14 +2074,13 @@ class X393McntrlAdjust(object):
if quiet<1:
x393_lma.test_data(meas_delays,compare_prim_steps,quiet)
lma=x393_lma.X393LMA()
lma.init_parameters(
lane,
bin_size,
2500.0, # clk_period,
78.0, # dly_step_ds,
primary_set,
meas_delays,
compare_prim_steps,
scale_w,
quiet)
return lma.lma_fit(lane,
bin_size,
2500.0, # clk_period,
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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