fixed last of the known so far errors in the DQ input delays vs DQS input delays LMA fitting (3d603296) · Commits · Elphel / x393

py393/x393_lma.py

+62 −35

Original line number	Diff line number	Diff line
		@@ -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,
		@@ -144,7 +149,10 @@ class X393LMA(object):
		data_set,
		compare_prim_steps,
		scale_w=0.2, # multiply weight by this if fractions are undefined
		periods=None):
		periods=None,
		quiet=1):

		if quiet < 3:
		print ("createYandWvectors(): scale_w=%f"%(scale_w))
		def pythIsNone(obj):
		return obj is None
		@@ -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
		@@ -792,7 +804,10 @@ class X393LMA(object):
		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
		@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)
		"""
		if quiet < 3:
		print ("init_parameters(): scale_w=%f"%(scale_w))
		self.clk_period=clk_period

		@@ -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:")
		@@ -839,6 +855,7 @@ 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
		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'
		@@ -860,8 +877,14 @@ class X393LMA(object):
		"tCDQS": (0.0,)*30
		# "anaScale":self.analog_scale
		}
		"""
		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)
		if quiet < 4:
		print ("normalized parameters=%s"%(str(parameters)))
		"""
		both ways work:
		@@ -872,15 +895,16 @@ class X393LMA(object):
		# self.parameterMask=self.normalizeParameters({},isMask=True)
		self.parameterMask=self.normalizeParameters(self.parameterMask,isMask=True)

		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+=wdd
		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:")
		@@ -976,6 +993,14 @@ class X393LMA(object):
		else:
		print("%d"%(DQvDQS[enl][dly][b]),end=" ")
		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,
		@@ -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=32dly+t8+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

+22 −21

Original line number	Diff line number	Diff line
		@@ -2042,9 +2042,19 @@ 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)
		"""
		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)
		@@ -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]:
		@@ -2072,8 +2074,7 @@ class X393McntrlAdjust(object):
		if quiet<1:
		x393_lma.test_data(meas_delays,compare_prim_steps,quiet)
		lma=x393_lma.X393LMA()
		lma.init_parameters(
		lane,
		return lma.lma_fit(lane,
		bin_size,
		2500.0, # clk_period,
		78.0, # dly_step_ds,

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