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accelplot_test.py
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accelplot_test.py
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import matplotlib.pyplot as plt
from datetime import datetime, timedelta
from scipy import interpolate
from scipy import fft
from scipy import signal
import numpy as np
from bokeh.plotting import figure, output_file, show
from bokeh.layouts import gridplot
from bokeh.models import ColumnDataSource, LinearColorMapper, Range1d
import statistics
import pandas as pd
#run accel_readfile first to import variables
def accelplot(quakelist,starttime_s,endtime_s,time_s,timeunits,accelunits,accelx,accely,accelz):
quakecounter = 0
for j in quakelist:
quakecounter = quakecounter + 1
if quakecounter > 2:
continue
print('Generating spectrogram for earthquake ' + str(quakecounter) + '/' + str(len(quakelist)) + ':')
print(' ' + str(round(j[2])) + 'M ' + j[0])
quaketime = j[3]
quaketime = datetime.utcfromtimestamp(quaketime)
quaketime = quaketime.strftime("%Y-%m-%d %H:%M:%S UTC")
windowstart = (j[5]) - 6 #start spectrogram 1min before arrival
windowend = windowstart + 24 #end spectrogram after 4min
if windowstart < starttime_s:
windowstart = starttime_s #if window starts before data, cut window to data
if windowend > endtime_s:
windowend = endtime_s #if window ends before data, cut window to data
windowindices = []
for kindex, k in enumerate(time_s): #find indices of times in window for
if k <= windowstart:
continue
if k >= windowend:
continue
#print(windowend)
windowindices.append(kindex)
window_accelx = accelx[windowindices] #cut down arrays to times in the window
window_accely = accely[windowindices]
window_accelz = accelz[windowindices]
window_time_s = []
for row in windowindices:
window_time_s.append(time_s[row])
def interpolateaccel(axis):
f = interpolate.interp1d(window_time_s,axis,kind='cubic') #make interpolation function
timelength = int((windowend - windowstart) * 1000) #max(window_time_s)
timenew = np.linspace(window_time_s[0],window_time_s[-1],timelength) #generate even time values
accelaxisnew = f(timenew) #actually use interpolation function
return accelaxisnew
f = interpolate.interp1d(window_time_s,window_accelx,kind='cubic') #make interpolation function
timelength = int((windowend - windowstart) * 1000)
timenew = np.linspace(window_time_s[0],window_time_s[-1],timelength) #generate even time values
accelxnew = f(timenew) #actually use interpolation function
#accelxnew = interpolateaccel(window_accelx)
accelynew = interpolateaccel(window_accely)
accelznew = interpolateaccel(window_accelz)
timenew = np.linspace(-6000,18000,24000)
windowname = j[0] #set name of window to location of quake
windowname = windowname.replace(" ", "") #strip whitespace to make a valid filename
#windowname = windowname + '_' + j[3] + '_'
windowfilename = windowname + '.png' #generate filename
def accelplot2(axis,axisname,axisnumber):
output_file(str(round(j[2])) + 'M_' + windowname +
axisname + '_acceleration.html')
axistop = max(axis)+0.2
axisbot = min(axis)-0.2
medianaxis = statistics.median(axis)
stddevaxis = statistics.stdev(axis)
#axishigh = medianaxis + (2 * stddevaxis)
#axislow = medianaxis - (2 * stddevaxis)
axishigh = medianaxis + stddevaxis
axislow = medianaxis - stddevaxis
p = figure(
plot_width=1900, plot_height=900,
tools="pan,box_zoom,reset,save",
y_range=[axisbot, axistop],
title=(axisname + ' Acceleration'),
x_axis_label='Time (' + timeunits + ')',
y_axis_label='Acceleration (' + accelunits + ')'
)
p.line(timenew, timenew, legend="y=x")
p.circle(timenew, timenew, legend="time", fill_color="white", size=8)
p.line(timenew, axis, legend=axisname, line_width=1)
p.line(timenew,statistics.mean(axis), legend='average value', line_color="red")
p.line(timenew,axishigh, line_color="orange", legend='1 std deviation')
p.line(timenew,axislow, line_color="orange")
show(p)
def accelplot3():
#set output file name
output_file(str(round(j[2])) + 'M_' + windowname + '_acceleration.html')
#create three plots
p1 = figure(plot_width=1800,plot_height=250,title='x',)
p1.line(timenew,accelxnew,legend='x',line_width=1)
p2 = figure(plot_width=1800,plot_height=250,title='y',)
p2.line(timenew,accelynew,legend='y',line_width=1)
p3 = figure(plot_width=1800,plot_height=250,title='z',)
p3.line(timenew,accelznew,legend='z',line_width=1)
#grid = gridplot([[p1],[p2],[p3]])
accelgrid = gridplot([p1,p2,p3], ncols=1)
#show(grid)
return accelgrid
accelfig = plt.figure(figsize=(12,6))
def accelplot(axis,axisname,axisnumber): #plot acceleration graphs in a column
plt.subplot(3,1,axisnumber)
plt.plot(timenew,axis,linewidth=0.5)
plt.title(axisname + ' Acceleration')
plt.xlabel('Time (' + timeunits + ')')
plt.ylabel('Acceleration (' + accelunits + ')')
axistop = max(axis)+0.2
#axistop = 2
axisbot = min(axis)-0.2
#axisbot = -2
plt.ylim(axisbot,axistop)
plt.set_cmap('magma')
#accelplot2(accelxnew,'x',1) #call accelplot
#accelplot2(accelynew,'y',2)
#accelplot2(accelznew,'z',3)
accelgrid = accelplot3()
#plt.suptitle(str(j[2]) + 'M ' + j[0] + '\n' + quaketime) # main plot title
#plt.tight_layout() #add padding between subplots
#plt.subplots_adjust(top=0.88)
#plt.savefig(str(round(j[2])) + 'M_' + windowname + '_acceleration.png', dpi = 300)
#plt.close('all')
#compute and plot fft of data in window
#start_time = 80 # seconds
#end_time = 90 # seconds
accelspec = plt.figure(figsize=(8,10))
def fftaccel(axis,axisname,axisnumber):
sampling_rate = 1000 # Hz
N = 24000 # array size
#accelxshort = accelxnew[(start_time*sampling_rate):(end_time*sampling_rate)]
# Nyquist Sampling Criteria (for interpolated data)
T = 1/sampling_rate
xfft = np.linspace(0.0, 1.0/(2.0*T), int(N/2))
# FFT algorithm
yr = fft(axis) # "raw" FFT with both + and - frequencies
yfft = 2/N * np.abs(yr[0:np.int(N/2)]) # positive freqs only
# Plotting the results
plt.subplot(3,2,axisnumber)
plt.plot(xfft, yfft)
plt.xlabel('Frequency (Hz)')
plt.ylabel('Vibration (g)')
#plt.xlim(0,20)
plt.title(axisname + ' Frequency Spectrum')
#plt.savefig(windowname + '_' + axisname + '_freq.png')
plt.subplot(3,2,axisnumber+1)
f, t2, Sxx = signal.spectrogram(axis, 1000, nperseg = 1500)
plt.pcolormesh(t2, f, np.log(Sxx))
plt.set_cmap('inferno')
plt.ylabel('Frequency [Hz]')
plt.xlabel('Time [sec]')
plt.title(axisname + ' Spectrogram')
plt.ylim(0,20)
#plt.savefig(windowname + '_' + axisname + '_spec.png')
return xfft,yfft,yr
def fftaccel2(axis,axisname,axisnumber):
sampling_rate = 1000 # Hz
N = 24000 # array size
#accelxshort = accelxnew[(start_time*sampling_rate):(end_time*sampling_rate)]
# Nyquist Sampling Criteria (for interpolated data)
T = 1/sampling_rate
xfft = np.linspace(0.0, 1.0/(2.0*T), int(N/2))
# FFT algorithm
yr = fft(axis) # "raw" FFT with both + and - frequencies
yfft = 2/N * np.abs(yr[0:np.int(N/2)]) # positive freqs only
#plotname = 'p' + axisname
#plotname = figure(plot_width=1800,plot_height=250,title=axisname)
f, t2, Sxx = signal.spectrogram(axis, 1000, nperseg = 1000)
i=0
df_spectogram = pd.DataFrame()
for freq in range(f.shape[0]):
for time in range(t2.shape[0]):
df_spectogram.loc[i] = [f[freq],t2[time],Sxx[freq][time]]
i = i+1
TOOLS = "hover,save,pan,box_zoom,reset,wheel_zoom"
PALETTE = ['#081d58', '#253494', '#225ea8', '#1d91c0', '#41b6c4', '#7fcdbb', '#c7e9b4', '#edf8b1', '#ffffd9']
mapper = LinearColorMapper(palette=PALETTE, low=np.min(Sxx), high=np.max(Sxx))
spectogram_figure = figure(title="Spectogram",x_axis_location="below", plot_width=900, plot_height=400,
tools=TOOLS)
spectogram_figure.background_fill_color = "#eaeaea"
spectrogram_source = ColumnDataSource(data=dict(Sxx=df_spectogram['Sxx'],Frequency=df_spectogram['Frequency'],Time=df_spectogram['Time']))
#spectogram_figure.circle(x="Time", y="Frequency", source=spectrogram_source, fill_color={'field': 'Sxx', 'transform': mapper}, line_color=None)
spectrogram_figure.quad(color=={'field': 'Sxx', 'transform': mapper},source=spectrogram_source,width="Time",height="Frequency")
spectrogram_figure.grid.visible = False
show(spectrogram_figure)
def fftaccel3(axis,axisname):
sampling_rate = 1000 # Hz
#N = windowend - windowstart # array size
N = 24000
#accelxshort = accelxnew[(start_time*sampling_rate):(end_time*sampling_rate)]
# Nyquist Sampling Criteria (for interpolated data)
T = 1/sampling_rate
xfft = np.linspace(0.0, 1.0/(2.0*T), int(N/2))
# FFT algorithm
yr = fft(axis) # "raw" FFT with both + and - frequencies
yfft = 2/N * np.abs(yr[0:np.int(N/2)]) # positive freqs only
p1=figure(tooltips=[("x", "$x"), ("y", "$y")])
p1.x_range=Range1d(0,200)
#p.line(timenew,np.real(yr))
p1.line(xfft,yfft)
#show(p1)
f, t2, Sxx = signal.spectrogram(axis, fs=1000, nperseg = 1000)
p = figure(tooltips=[("time", "$t2"), ("freq.", "$f"), ("value", "@image")])
p.x_range.range_padding = p.y_range.range_padding = 0
p.image(image=[np.log(Sxx)], x=0, y=0, dw=10, dh=10, palette="Spectral11")
output_file(str(round(j[2])) + 'M_' + windowname +
axisname + '_fft.html')
fftgrid = gridplot([p1,p], ncols=2)
#show(grid)
return fftgrid
def meshgrid_example():
#import numpy as np
#from bokeh.plotting import figure, show, output_file
N = 500
x = np.linspace(0, 10, N)
y = np.linspace(0, 10, N)
xx, yy = np.meshgrid(x, y)
d = np.sin(xx)*np.cos(yy)
p = figure(tooltips=[("x", "$x"), ("y", "$y"), ("value", "@image")])
p.x_range.range_padding = p.y_range.range_padding = 0
# must give a vector of image data for image parameter
p.image(image=[d], x=0, y=0, dw=10, dh=10, palette="Spectral11")
output_file("image.html", title="image.py example")
show(p) # open a browser
fftgrid = fftaccel3(accelxnew,'x')
#fftaccel(accelxnew,'x',1)
l = gridplot([
[accelgrid],
[fftgrid]
])
show(l)
#fftaccel(accelynew,'y',3)
#fftaccel(accelznew,'z',5)
#plt.suptitle(str(j[2]) + 'M ' + j[0] + '\n' + quaketime) # main plot title
#plt.tight_layout() #add padding between subplots
#plt.subplots_adjust(top=0.88)
#plt.savefig(str(round(j[2])) + 'M_' + windowname + '_spectrogram.png',dpi = 300)
#plt.close('all')
accelplot(quakelist,starttime_s,endtime_s,time_s,timeunits,accelunits,accelx,accely,accelz)