import numpy as np
import numpy as np
import matplotlib.pylab as plt
import scipy.signal as signal
from analysis import smooth
from console import utils as ndaq
# Get data and items
data = ndaq.get_data()
items = ndaq.get_items()
dt = items[0].attrs['dt']
dt = .04
# Get peaks, decays and peak times
decayTimes = []
for trace in data:
peak = trace.min()
decay = peak*0.37
peakTime = trace.argmin()
decayTrace = trace[peakTime:]
idx = decayTrace<decay
decayTime = np.sum(idx)*dt
#plt.plot(decayTrace[idx])
print decayTime
decayTimes.append(decayTime)
#plt.show()
ndaq.store_data(decayTimes, name='decay_times')
# temp script to tranform velocity traces into something useful
import numpy as np
from analysis import smooth
from console import utils as ndaq
# Parameters
posThs = 1.66
negThs = 1.62
final_smth_window = 100
# Get data and items
data = ndaq.get_data()
item = ndaq.get_items()
dt = item[0].attrs['dt']
# Smooth
data = smooth.smooth(data, window_len=4, window='hanning')
# Threshold detection functions
compPositive = lambda a, b: a > b
compNegative = lambda a, b: a < b
# Go through data
i = 0
eStart, eEnd = [], []
while i < len(data):
if compPositive(data[i], posThs):
eStart.append(i)
while i < len(data) and compPositive(data[i], posThs - 0.02):
i += 1
import numpy as np
import matplotlib.pylab as plt
import scipy.signal as signal
from analysis import smooth
from console import utils as ndaq
# Add single PSCs at a defined frequency and number
# Get data and items
data = ndaq.get_data()
items = ndaq.get_items()
dt = items[0].attrs['dt']
dt = 0.04
# Lag traces and add them
number = 20
freq = 20.
isi = 1 / freq * 1000 # in ms
result = []
for n in range(number):
trace = data
npad = int(n * isi / dt)
laggedTrace = np.pad(trace, (npad, 0), 'constant', constant_values=(0, 0))
#if n>0: laggedTrace = laggedTrace[:-npad]
result.append(laggedTrace)
maxshape = result[len(result) - 1].shape
alignedTraces = []
c = 0
for trace in result:
# temp script to tranform velocity traces into something useful
import numpy as np
from analysis import smooth
from console import utils as ndaq
# Parameters
posThs = 1.66
negThs = 1.62
final_smth_window = 100
# Get data and items
data = ndaq.get_data()
item = ndaq.get_items()
dt = item[0].attrs['dt']
# Smooth
data = smooth.smooth(data, window_len=4, window='hanning')
# Threshold detection functions
compPositive = lambda a, b: a > b
compNegative = lambda a, b: a < b
# Go through data
i = 0
eStart, eEnd = [], []
while i<len(data):
if compPositive(data[i], posThs):
eStart.append(i)
while i<len(data) and compPositive(data[i], posThs-0.02):
i+=1