""""Peak to Peak : calculate the peak to peak value of a Positive signal value of a signal segment and return the time of this pick """

#crop the signal on the interval considered

tr_copy = tr.copy()

L =[]

indexesSommet = peakutils.indexes(tr_copy.data,thres = 0.01,min_dist=0)

indexesVallee = peakutils.indexes(1000-tr_copy.data,thres = 0.01,min_dist=0)

i=0

#print 'pt', L

if indexesVallee[0]< indexesSommet[0]:

ptp0 = tr_copy[indexesSommet[0]]-abs(tr_copy[indexesVallee[0]])

L.append(ptp0)

for i in xrange(len(indexesSommet)-1):

Lvalley=[]

for valley in indexesVallee:

if valley>indexesSommet[i] and valley<indexesSommet[i+1] :

Lvalley.append(valley)

elif valley>indexesSommet[i+1]:

break

if len(Lvalley)==1 :

ptp1 = tr_copy[indexesSommet[i]] - abs(tr_copy[Lvalley[0]])

L.append(ptp1)

ptp2 = tr_copy[indexesSommet[i+1]] - abs(tr_copy[Lvalley[0]])

L.append(ptp2)

elif len(Lvalley)>1 :

ptp1 = tr_copy[indexesSommet[i]] - abs(tr_copy[Lvalley[0]])

L.append(ptp1)

ptp2 = tr_copy[indexesSommet[i+1]] - abs(tr_copy[Lvalley[len(Lvalley)-1]])

L.append(ptp2)

if indexesVallee[len(indexesVallee)-1]>indexesSommet[len(indexesSommet)-1]:

ptp3 = tr_copy[indexesSommet[i+1]] - abs(tr_copy[valley])

L.append(ptp3)

#L = np.array(L)

#print len(L),L

ptpmax = np.amax(L)

ptpFirst = L[0]

""""Peak to Peak : calculate the Pick to pick value of a signal segment and return the time of this pick

* Input :

-tr type : trace

- method : type int if 1 : peak to peak is the distance between the peak i and the first valley and the second ptp is the distance between the last valley and the peak i+1

if 2 : peak to peak is the distance between the peak i and the minimum valley and the second Peak to peak is the distance between the peak i+1 and the minimum valley.

-plot: type boool; if true the plot of peak to peak is plotted

* output :

-MaxptoP:type float; maximum of the peak to peak of the "complet (ie not decompoose in 3 components) of the signal

-pfirstPtoP type: float; First peak to Peak value : should be first p wave amplitude

*exemple :

ptpmax,ptpFirst = PeaktoPeak(tr,1,True)

"""

#0. Fuind peaks with a minimum distancebetween peaks in count of 20 and threshold for detcting a peak and valley of 0.001 ~~~~~~~~~~~~~~

tr_copy = tr.copy()

indexesSommet = peakutils.indexes(tr_copy.data,thres = 0.001,min_dist=20)

indexesVallee = peakutils.indexes(-tr_copy.data,thres = 0.001,min_dist=20)

#1. Plot ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

if plot==True :

time = np.arange(0, tr_copy.stats.npts / tr_copy.stats.sampling_rate, tr_copy.stats.delta)

plt.figure()

pplot(time,tr_copy.data,indexesSommet)

plt.plot(time,tr_copy.data)

pplot(time,tr_copy.data, indexesVallee)

plt.show()

i=0

#2. Maximum peak to peak calculation ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

L =[]

#2.0 Initialisation of the first peak to peak which is equal to the distance betwween the first valley and the first peak.

if indexesVallee[0]< indexesSommet[0]:

ptp0 = tr_copy[indexesSommet[0]]+abs(tr_copy[indexesVallee[0]])

L.append(ptp0)

# 3.0 measure peak to peak between 2 peaks ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

for i in xrange(len(indexesSommet)-1):

Lvalley=[]

#3.1 record all the valleys indexes that are between peaks i and i+1 to then take into account only the fist and the second peaks into account (maybe better to take the minimum! )

for valley in indexesVallee:

if valley>indexesSommet[i] and valley<indexesSommet[i+1] :

Lvalley.append(valley)

elif valley>indexesSommet[i+1]:

break

#3.2 calculate pêak to peak ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

if len(Lvalley)==1 :

ptp1 = tr_copy[indexesSommet[i]] + abs(tr_copy[Lvalley[0]])

L.append(ptp1)

ptp2 = tr_copy[indexesSommet[i+1]] + abs(tr_copy[Lvalley[0]])

L.append(ptp2)

elif len(Lvalley)>1 :

# peak to peak is the distance between the peak i and the first valley and the second ptp is the distance between the last valley and the peak i+1

if method == 1 :

ptp1 = tr_copy[indexesSommet[i]] + abs(tr_copy[Lvalley[0]])

L.append(ptp1)

ptp2 = tr_copy[indexesSommet[i+1]] + abs(tr_copy[Lvalley[len(Lvalley)-1]])

L.append(ptp2)

#peak to peak is the distance between the peak i and the minimum valley and the second Peak to peak is the distance between the peak i+1 and the minimum valley.

elif method == 2 :

Lvaluevalley = []

if j in Lvalley :

Lvaluevalley.append(abs(tr_copy[Lvalley[j]]))

Valleymax= max(Lvaluevalley)

ptp1 = tr_copy[indexesSommet[i]] + max(Lvaluevalley)

L.append(ptp1)

ptp2 = tr_copy[indexesSommet[i+1]] + max(Lvaluevalley)

L.append(ptp2)

#4.0 end if the signal finished with a valley the last peak to peak is the distance between the last peak and the last valley ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

if indexesVallee[len(indexesVallee)-1]>indexesSommet[len(indexesSommet)-1]:

ptp3 = tr_copy[indexesSommet[i+1]] + abs(tr_copy[valley])

L.append(ptp3)

#5. Take the maximum of the peak to peak ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

ptpmax = np.amax(L)

# 6. the first vallue of the peak to peak which is suppose to be the P wave amplitude ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

ptpFirst = L[0]

""" ruturn the index of the peaks that are 0.98 greater than the max peak

*input :

- tr: type : trace , trace of the signal to find max and min

*output :

- LPtP :type list; Liste of Peak to Peak values

- MaxPtP : Maximum of PtP values

*exemple :

LPtP, MaxPtP= PeaktoPeak_derivative(tr,True)

"""

#0. Sampling rate

df = tr.stats.sampling_rate

#1. Derivative of the signal ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Dsignal= []

for i in xrange(len(tr.data)-1) :

f1 = (tr.data[i+1]-tr.data[i])/df

Dsignal.append(f1)

#2. index where f1 == 0 ie index of extremumns~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

asign = np.sign(Dsignal)

signchange = ((np.roll(asign, 1) - asign) != 0).astype(int)

Zeros = np.argwhere(signchange==1)

indexmax= np.argmax(tr.data)

Peakselect = []

for index in Zeros :

if tr.data[index[0]]>=thresMax * tr.data[indexmax]:

Peakselect.append(index[0])

""" run peak to peak using the changing sign of the derivative of the signal

*input :

- tr: type : trace , trace of the signal to find max and min

*output :

- LPtP :type list; Liste of Peak to Peak values

- MaxPtP : Maximum of PtP values

*exemple :

LPtP, MaxPtP= PeaktoPeak_derivative(tr,True)

"""

#0. Sampling rate

df = tr.stats.sampling_rate

#1. Derivative of the signal ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Dsignal= []

for i in xrange(len(tr.data)-1) :

print 'derivative ', len(tr.data)

f1 = (tr.data[i+1]-tr.data[i])/df

Dsignal.append(f1)

#2. index where f1 == 0 ie index of extremumns~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

asign = np.sign(Dsignal)

signchange = ((np.roll(asign, 1) - asign) != 0).astype(int)

print type(signchange)

Zeros = np.argwhere(signchange==1)

print Zeros, 'zeros'

LPtP=[]

#3. Value of extremums ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

for i in xrange(len(Zeros)-1):

PtP = abs(tr.data[Zeros[i]])+abs(tr.data[Zeros[i+1]])

LPtP.append(PtP)

#4. Maximum~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

MaxPtP = max(LPtP)

#plot scatter, trace derivative and trace ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

if plot ==True :

time = np.arange(0, tr.stats.npts / tr.stats.sampling_rate, tr.stats.delta)

plt.figure()

plt.scatter(time[0:len(signchange)],signchange*100000, marker = '+', color = 'r')

plt.plot(time, tr.data)

plt.plot(time[0:len(Dsignal)], Dsignal, 'r')

"""Envelope gives the enveloppe of a signal (trace) and return the sum of this enveloppe

* input :

- tr : type trace; trace

-plot type bool; if True waves and envelope are plotted

*output :

- Envelope : envelope of the signal

- t: type list; time where data are computed

- SumEnvelope : type float; Sum of the data values of the enveloppe

- MaxEnv ; type float; maximum of the envelope

*exemple :

Envelope, t, SumEnvelope = Envelope(tr)

"""

Envelope = envelope(tr.data)

npts = tr.stats.npts

fs = tr.stats.sampling_rate

t = np.arange(0,npts/fs,1/fs)

#integration of the envelope

SumEnvelope = sum(Envelope)

#max~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

EnvMax = max(Envelope)

indexmax= np.argwhere (Envelope==EnvMax)

if plot==True:

plt.figure()

plt.plot(t,tr.data )

plt.plot(t, Envelope, '.g')

print EnvMax,indexmax

"""Total envelope of the signal (high and lower) envelopes calculated using a cubic interpolation of the function between peak and idem between valleys

* input :

- tr : type trace; trace

-p lot type bool; if True waves and enevlopes are plotted

*output : , q_l, MaxEnv, indexMax

- q_u : type, array; upper envelope

- q_l : type array; lower envelope

- MaxEnv: type float; maximum of the envelope amplitude (max sul upper and abs (lower) envelopes)

- indexmax : type, int, index of the maximum of the envelope

*exemple :

Env_u, Env_l, MaxEnv, indexMax = EnvelopeTotal(tr, True)

"""

#0. initialisation ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

q_u = np.zeros(tr.data.shape)

q_l = np.zeros(tr.data.shape)

# 1. Prepend the first value of (s) to the interpolating values. This forces the model to use the same starting point for both the upper and lower envelope models.

#upper envelope

u_x = [0,]

u_y = [tr.data[0],]

#lower envelope

l_x = [0,]

l_y = [tr.data[0],]

# 2. find peaks and valleys and mark their location an dvalues in u_x,u_y,l_x,l_y respectively.~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

for k in xrange(1,len(tr.data)-1):

if (np.sign(tr.data[k]-tr.data[k-1])==1) and (np.sign(tr.data[k]-tr.data[k+1])==1):

u_x.append(k)

u_y.append(tr.data[k])

if (np.sign(tr.data[k]-tr.data[k-1])==-1) and ((np.sign(tr.data[k]-tr.data[k+1]))==-1):

l_x.append(k)

l_y.append(tr.data[k])

#3. Append the last value of (s) to the interpolating values.

#This forces the model to use the same ending point for both the upper and lower envelope models.~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

u_x.append(len(tr.data)-1)

u_y.append(tr.data[-1])

l_x.append(len(tr.data)-1)

l_y.append(tr.data[-1])

#4. Fit suitable models to the data. Here I am using cubic splines,

# similarly to the MATLAB example given in the question.~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

u_p = interp1d(u_x,u_y, kind = 'cubic',bounds_error = False, fill_value=0.0)

l_p = interp1d(l_x,l_y,kind = 'cubic',bounds_error = False, fill_value=0.0)

#5. Evaluate each model over the domain of (s)

for k in xrange(0,len(tr.data)):

q_u[k] = u_p(k)

q_l[k] = l_p(k)

#6. Calculate max envelope~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Sum = q_u+abs(q_l)

MaxEnv= max(Sum)

indexMax= np.argwhere(Sum == MaxEnv)

if plot==True:

#time ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

npts = tr.stats.npts

fs = tr.stats.sampling_rate

t = np.arange(0,npts/fs,1/fs)

#plot

plt.figure()

#trace plot

plt.plot(t, tr.data, 'k')

# upper and lower envelope plots

plt.plot(t, q_u,'.g',markersize=1 )

plt.plot(t, q_l, '.g',markersize=1 )

#max enveloppe locatiojn and value

plt.scatter(indexMax/fs, MaxEnv, marker= '+', color= 'g')

print MaxEnv, indexMax

""" Horizontal Vertical spectral ratio : if relaively constants there is no site effects

* input :

- SH : type array, horizontal spectrum

- SV: type array , vertical spectrum

* output :

- HVSR : type array; horizontal/vertical spectrum ratio

* exemple :

"""

HVSR = SH/SV

""" Smooth spectrum of a given trace with the Konno_omachi method

* inputs :

- tr: type: trace; seimic trace

- bandwidth : type int; number of point over wich the spectrum is smoothed, after testing 110 seems good

- plot : type Bool; if True smooth, and un smooth spectrum are plotted

* outputs :

- fr : type array; frequencies of the spectrum

- SmoothSpectre : type array; Smooth spectrum

* exemple :

"""

# 0. Spectre of the trace ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

t = np.arange(0, tr.stats.npts / tr.stats.sampling_rate, tr.stats.delta)

trFreq= np.fft.fft(tr.data, n= 10000)

fs = tr.stats.sampling_rate

N = len(trFreq)

#half spectre since fft is symmetric over is center

xF = trFreq[0:N/2] #

#frequencies

fr = np.linspace(0,fs/2,N/2)

#1. smoothing Spectrum ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

#CAREFULL SMOTTHING GONNA CHANGE A LOT THE SPECTRUM BUT DOING THE SPECTRUML RATIO IS OK!

SmoothSpectre = konno_ohmachi_smoothing(np.abs(xF), fr, bandwidth=bandwidth, count=1, enforce_no_matrix=False, normalize=False)

if plot== True :

plt.figure()

plt.plot(fr,np.abs(xF), 'r',label = 'non Smooth')

plt.plot(fr,SmoothSpectre , 'k', label='Smooth')

plt.ylabel("Amplitude")

plt.xlabel("Frequency Hz")

plt.legend()

"""smooth the data using a window with requested size.

This method is based on the convolution of a scaled window with the signal

The signal is prepared by introducing reflected copies of the signal

(with the window size) in both ends so that transient parts are minimized

in the begining and end part of the output signal.

input:

- x: the input spectrum

- window_len: the dimension of the smoothing window; should be an odd integer

- window: the type of window from 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'

flat window will produce a moving average smoothing.

-plot : type Bool; if plot == True, plot of the smooth spectra will be return

output:

the smoothed signal

example:

x=sin(t)+randn(len(t))*0.1

y=smooth(x)

see also:

NOTE: length(output) != length(input), to correct this: return y[(window_len/2-1):-(window_len/2)] instead of just y.

"""

if x.ndim != 1:

raise ValueError, "Input vector needs to be bigger than window size."

if window_len<3:

return x

if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:

raise ValueError, "Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'"

#build an array quickly : numpy_r

s=np.r_[x[window_len-1:0:-1],x,x[-1:-window_len:-1]]

f = np.r_[fr[window_len-1:0:-1],fr,fr[-1:-window_len:-1]]

if window == 'flat': #moving average

w=np.ones(window_len,'d')

else:

w=eval('numpy.'+window+'(window_len)')

y=np.convolve(w/w.sum(),s,mode='valid')

f2=np.convolve(w/w.sum(),f,mode='valid')

if plot== True :

plt.figure()

plt.plot(f2, y,'.g')

"""Amplitude spectrum of a given trace

* inputs :

- tr: type: trace; seimic trace

- plot : type Bool; if True smooth, and un smooth spectrum are plotted

* outputs :

- fr : type array; frequencies of the spectrum

-Spectre : type array; amplitude spectrum

* exemple :

fr, spectrum = Spectrum(tr, True)

"""

# 0. Spectre of the trace ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

t = np.arange(0, trf.stats.npts / trf.stats.sampling_rate, trf.stats.delta)

trFreq= np.fft.fft(trf.data, n=1000)

fs = trf.stats.sampling_rate

N = len(trFreq)

#half spectre since fft is symmetric over is center

xF = trFreq[0:N/2] #

#frequencies

fr = np.linspace(0,fs/2,N/2)

if plot==True:

plt.figure()

plt.plot(fr,np.abs(xF), 'k')

plt.ylabel("Amplitude")

plt.xlabel("Frequency Hz")

plt.show()

"""Amplitude spectrum of a given trace

* inputs :

- tr: type: trace; seimic trace

- plot : type Bool; if True smooth, and un smooth spectrum are plotted

* outputs :

- fr : type array; frequencies of the spectrum

-Spectre : type array; amplitude spectrum

* exemple :

fr, spectrum = Spectrum(tr, True)

"""

# 0. Spectre of the trace ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

t = np.arange(0, tr.stats.npts / tr.stats.sampling_rate, tr.stats.delta)

#set the niumber of poitn or else each spectrum for a given event won't have the same frquency content and erros will happened

trFreq= np.fft.fft(tr.data, n=100000)

fs = tr.stats.sampling_rate

N = len(trFreq)

#half spectre since fft is symmetric over is center

xF = trFreq[0:N/2] #

#frequencies

fr = np.linspace(0,fs/2,N/2)

# abs = module d'un nombre complexe sous python!!!!!

xfsmooth,frsmooth = Smooth(np.array(abs(xF)),fr,plot,window_len=300, window='flat')

if plot==True:

plt.figure()

plt.plot(fr,np.abs(xF), 'k')

plt.ylabel("Amplitude")

plt.xlabel("Frequency Hz")

""" return true and the component if the trace is satured find if there is a plateau formed

input :

tr: type trace , seimic trace to analyse

output :

Stat type bool, if true the signal is considerd as satured"""

indexesSommet = peakutils.indexes(tr.data,thres = 0.01,min_dist=20)

indexMax= np.argmax(tr.data)

indexesSommet=np.delete(indexesSommet,np.argwhere((indexesSommet==indexMax)))

if len(indexesSommet)<>0:

MAx2=np.argwhere(tr.data==np.max([tr.data[i] for i in indexesSommet]))

else :

MAx2 =[indexMax]

if len(MAx2)==0:

MAx2 =indexMax

else :

MAx2 = MAx2[0]

if indexMax<len(tr.data)-2 and MAx2<len(tr.data)-2:

if tr.data[indexMax+1]==tr.data[indexMax] or tr.data[MAx2+1]==tr.data[MAx2] :

Sat = True

else :

Sat= False

else :

Sat = True

""" return true and the component if the trace is satured find if there is a plateau formed

input :

tr: type trace , seimic trace to analyse

output :

Stat type bool, if true the signal is considerd as satured min_dist min poiunts between 2 peaks"""

#selection of peaks that have asignal greater than 0.7*np.max(tr.data)

indexselected = Peakindex_derivative(tr,0.7)

indexselected.sort()

indexselectedbis = copy.copy(indexselected)

#initialisation of the list of saturated peaks index that are greater than 0.95 than tr.data+1

Lsat =[]

for i in xrange(len(indexselected)-1) :

if tr.data[indexselected[i+1]] == tr.data[indexselected[i]]:

Lsat.append(True)

elif tr.data[indexselected[i+1]]>=0.997 * tr.data[indexselected[i]] and tr.data[indexselected[i+1]]<=(1+0.013)*tr.data[indexselected[i]] and abs(indexselected[i+1]-indexselected[i])<3:

Lsat.append(True)

else:

Lsat.append(False)

indexselectedbis.remove(indexselected[i])

#Thje siganl is satured if one peak have a neigbourg with an amplitude greater than 0.98 of his amplitude

a = len(np.where(Lsat)[0])

if a>=2:

Sat = True

else :

Sat = False

#plot

if plot==True :

time = np.arange(0, tr.stats.npts / tr.stats.sampling_rate, tr.stats.delta)

plt.figure()

pplot(time,tr.data,indexselected)

plt.plot(time,tr.data)

#pplot(time,tr.data,Listindex60)

plt.show()

MeanPSignal = np.mean(trTrim.data**2)

Maxsignal = np.max(trTrim.data)**2

tr1 = tr.copy()

trnoise = tr1.trim(starttime =tr1.stats.starttime+Second-190,endtime=tr1.stats.starttime+Second-100)

MeanPnoise = np.mean((trnoise.data)**2)

SNR = Maxsignal/MeanPnoise

#SNR = MeanPSignal/MeanPnoise

if SNR>LimSNR :

valid = True

else :

valid = False

if plot==True:

figurenoise =plt.figure()

trnoise.plot(fig=figurenoise,color='b',linewidth=2)

trTrim.plot(fig=figurenoise,color='r',linewidth=2)

tr.plot(fig=figurenoise,starttime =tr.stats.starttime ,endtime =tr.stats.starttime +Second +90 )

STDSignal = np.std(trTrim.data)

tr1 = tr.copy()

trnoise = tr1.trim(starttime =tr1.stats.starttime+Second-190,endtime=tr1.stats.starttime+Second-100)

STDnoise = np.std(trnoise.data)

SNR = STDSignal/STDnoise

#SNR = MeanPSignal/MeanPnoise

if SNR>LimSNR :

valid = True

else :

valid = False

if plot==True:

figurenoise =plt.figure()

trnoise.plot(fig=figurenoise,color='b',linewidth=2)

trTrim.plot(fig=figurenoise,color='r',linewidth=2)

tr.plot(fig=figurenoise,starttime =tr.stats.starttime ,endtime =tr.stats.starttime +Second +90 )