
SpikeDB.plotClear()
files = SpikeDB.getFiles(True)
for f in files:
	means = []
	err = []
	x = []
	raw = []
	for t in f['trials']:
		count = []
		x.append(t['xvalue'])	
		for p in t['passes']:
			if len(p) > 0:
				count.append(1)
			else:
				count.append(0)
		means.append(SpikeDB.mean(count))
		err.append(SpikeDB.stddev(count))
	SpikeDB.plotXLabel(f['xvar'])
	SpikeDB.plotYLabel('Spike Probability')
	SpikeDB.plotYMin(0)
	SpikeDB.plotYMax(1.0000001)
	SpikeDB.plotLine(x,means,err)



SpikeDB.plotClear()
files = SpikeDB.getFiles(True)
for f in files:
	means = []
	err = []
	x = []
	raw = []
	for t in f['trials']:
		count = []
		x.append(t['xvalue'])	
		for p in t['passes']:
			if len(p) > 0:
				count.append(1)
			else:
				count.append(0)
		means.append(SpikeDB.mean(count))
		err.append(SpikeDB.stddev(count))
	SpikeDB.plotXLabel(f['xvar'])
	SpikeDB.plotYLabel('Spike Probability')
	SpikeDB.plotYMin(0)
	SpikeDB.plotYMax(1.0000001)
	SpikeDB.plotLine(x,means,err)


SpikeDB.plotSetRGBA(0,0,0,0.25);
SpikeDB.plotSetPointSize(0);
SpikeDB.plotSetLineWidth(4);
SpikeDB.plotLine([x[first_index],x[-1]], [0.5,0.5], [])

Add line to spike prob
Add line to spike prob

  
  SpikeDB.plotClear()
  files = SpikeDB.getFiles(True)
  for f in files:
  	means = []
  	err = []
  	x = []
  	raw = []
  	for t in f['trials']:
  		count = []
  		x.append(t['xvalue'])	
  		for p in t['passes']:
  			if len(p) > 0:
  				count.append(1)
  			else:
  				count.append(0)
  		means.append(SpikeDB.mean(count))
  		err.append(SpikeDB.stddev(count))
  	SpikeDB.plotXLabel(f['xvar'])
  	SpikeDB.plotYLabel('Spike Probability')
  	SpikeDB.plotYMin(0)
  	SpikeDB.plotYMax(1.0000001)
  	SpikeDB.plotLine(x,means,err)
  
  
+ SpikeDB.plotSetRGBA(0,0,0,0.25);
+ SpikeDB.plotSetPointSize(0);
+ SpikeDB.plotSetLineWidth(4);
+ SpikeDB.plotLine([x[first_index],x[-1]], [0.5,0.5], [])
+ 
Add line to spike prob
## Code Before:

SpikeDB.plotClear()
files = SpikeDB.getFiles(True)
for f in files:
	means = []
	err = []
	x = []
	raw = []
	for t in f['trials']:
		count = []
		x.append(t['xvalue'])	
		for p in t['passes']:
			if len(p) > 0:
				count.append(1)
			else:
				count.append(0)
		means.append(SpikeDB.mean(count))
		err.append(SpikeDB.stddev(count))
	SpikeDB.plotXLabel(f['xvar'])
	SpikeDB.plotYLabel('Spike Probability')
	SpikeDB.plotYMin(0)
	SpikeDB.plotYMax(1.0000001)
	SpikeDB.plotLine(x,means,err)


## Instruction:
Add line to spike prob
## Code After:

SpikeDB.plotClear()
files = SpikeDB.getFiles(True)
for f in files:
	means = []
	err = []
	x = []
	raw = []
	for t in f['trials']:
		count = []
		x.append(t['xvalue'])	
		for p in t['passes']:
			if len(p) > 0:
				count.append(1)
			else:
				count.append(0)
		means.append(SpikeDB.mean(count))
		err.append(SpikeDB.stddev(count))
	SpikeDB.plotXLabel(f['xvar'])
	SpikeDB.plotYLabel('Spike Probability')
	SpikeDB.plotYMin(0)
	SpikeDB.plotYMax(1.0000001)
	SpikeDB.plotLine(x,means,err)


SpikeDB.plotSetRGBA(0,0,0,0.25);
SpikeDB.plotSetPointSize(0);
SpikeDB.plotSetLineWidth(4);
SpikeDB.plotLine([x[first_index],x[-1]], [0.5,0.5], [])
