In [869]:
matplotlib inline
In [870]:
import matplotlib.pyplot as plt
import pandas as pd
import matplotlib.dates as mdates
In [871]:
from __future__ import print_function

%matplotlib inline
import matplotlib.pyplot as plt
#plt.style.use('ggplot')
plt.rcParams['figure.figsize'] = 11, 4
In [1604]:
#sta = "IBUH01" # velocity decrease, direction change, but increase aniso coeff, but  rms_coeff increase
#sta = "IBUH02" # velocoty degrease, no change direction, no change anino coeff. no increase aniso rms_coeff
#sta = "IBUH03"# Hokkaido velocoty degrease, change direction, no change anino coeff. no increase aniso rms_coeff
                           # Tohoku-oki velocity degrease, no change direction, no change anino coeff. no increase aniso rms_coeff
#sta = "IBUH04" # seismic data are not good
#sta = "IBUH05" # no change
sta = "IBUH06" # velocity increase, az all direction. but why az_coeff increase? all parameters are messy
#sta = "IBUH07" # no change


#sta = "SRCH09"  # velocoty degrease, no change direction, no change anino coeff. no increase aniso rms_coeff
#sta = "SRCH10" #  no change
#sta = "SRCH08" # no change
#sta = "SRCH07" # no chnage
 
#sta = "SBSH08" # no many data
#sta = "SBSH07" # small change?
#sta = "SBSH03" # no change? no many data


#sta = "HDKH04" # iso change no azimuth change, no coeff change
#sta = "HDKH01" # large variability, unclear velocity change
#sta = "HDKH03" # no change?
#sta = "HDKH05" # no change. 
#sta = "HDKH06" # no change?

#sta = "IKRH01"  # 
#sta = "IKRH02"# 
#sta = "IKRH03"#
In [1605]:
#IBUH01 2003-04-17T15:40:54.9600 42.55450 143.50700 72.40 4.40 0.0006361000 20030418004000 516.48 541.09 491.87 126.17 36.17 9.10 3.71 7 5 -1 0 1 15

aniso_fi = "http://ncedc.org/ftp/outgoing/taira/"+sta+".out2"
aniso_data = pd.read_csv(aniso_fi,   
                       sep=" ",names=["sta", "time", "lat", "long", "depth", "mag", "elapse_diff", "evid", "viso", "vfast", "vslow", "azfast", "azslow", "azcoeff", "rms_coeff", "leng", "ddeg", "ns", "ne", "f1", "f2","elapse_days"],header=None)
In [1606]:
#print (aniso_data['lat'])
In [1607]:
aniso_data['time'] = pd.to_datetime(aniso_data['time'])
In [1608]:
aniso_data.describe()
Out[1608]:
lat long depth mag elapse_diff evid viso vfast vslow azfast azslow azcoeff rms_coeff leng ddeg ns ne f1 f2 elapse_days
count 368.000000 368.000000 368.000000 368.000000 368.000000 3.680000e+02 368.000000 368.000000 368.000000 368.000000 368.000000 368.000000 368.000000 368.0 368.0 368.0 368.0 368.0 368.0 368.000000
mean 41.857671 142.789220 52.701495 4.930163 0.000236 2.012426e+13 900.623967 938.064321 863.183424 106.010109 76.661250 7.826386 24.121685 7.0 5.0 -1.0 0.0 1.0 15.0 -2110.619999
std 1.474040 1.673342 37.062739 1.014198 0.000246 4.939492e+10 41.506026 49.841308 57.928764 69.039082 32.509385 6.808797 29.790449 0.0 0.0 0.0 0.0 0.0 0.0 1804.313040
min 31.428000 138.566000 0.010000 2.900000 -0.000969 2.002071e+13 686.220000 745.130000 575.220000 0.000000 0.020000 0.640000 0.360000 7.0 5.0 -1.0 0.0 1.0 15.0 -5901.138431
25% 41.477225 141.987750 33.180000 4.200000 0.000072 2.009089e+13 890.267500 923.962500 852.255000 17.415000 52.612500 3.580000 1.770000 7.0 5.0 -1.0 0.0 1.0 15.0 -3280.618056
50% 42.030900 142.424000 48.160000 4.700000 0.000237 2.013102e+13 908.495000 929.020000 888.905000 128.530000 84.970000 4.435000 3.190000 7.0 5.0 -1.0 0.0 1.0 15.0 -1779.096579
75% 42.641600 143.043500 65.322500 5.500000 0.000415 2.017083e+13 912.490000 935.525000 894.960000 172.337500 93.090000 10.947500 45.895000 7.0 5.0 -1.0 0.0 1.0 15.0 -389.527048
max 49.183300 155.168000 397.260000 9.000000 0.000692 2.018122e+13 1365.570000 1562.110000 1169.030000 179.910000 171.760000 37.560000 98.990000 7.0 5.0 -1.0 0.0 1.0 15.0 108.356364
In [1609]:
#print(aniso_data[aniso_data['vslow'] < 100])
In [1610]:
#aniso_data['azslow']
In [1611]:
statsOUT = aniso_data.describe()
In [1612]:
statsOUT = aniso_data.describe(percentiles=[0.01, 0.05, 0.1, 0.25, 0.75, 0.9, 0.95, 0.99])
In [1613]:
statsOUT.viso
Out[1613]:
count     368.000000
mean      900.623967
std        41.506026
min       686.220000
1%        768.821900
5%        846.712500
10%       860.348000
25%       890.267500
50%       908.495000
75%       912.490000
90%       917.559000
95%       939.306500
99%      1001.573500
max      1365.570000
Name: viso, dtype: float64
In [1614]:
print (statsOUT.viso['mean'], statsOUT.viso['10%'])

900.6239673913043 860.348
In [1615]:
viso_minplot = statsOUT.viso['10%']
viso_maxplot = statsOUT.viso['90%']
In [1616]:
viso_minplot = statsOUT.viso['5%']
viso_maxplot = statsOUT.viso['95%']
In [1617]:
viso_minplot = statsOUT.viso['1%']
viso_maxplot = statsOUT.viso['99%']
In [1618]:
azcoeff_minplot = statsOUT.azcoeff['5%']
azcoeff_maxplot = statsOUT.azcoeff['95%']
In [1619]:
rms_coeff_minplot = statsOUT.rms_coeff['5%']
rms_coeff_maxplot = statsOUT.rms_coeff['95%']
In [1620]:
azfast_minplot = statsOUT.azfast['5%']
azfast_maxplot = statsOUT.azfast['95%']
In [1621]:
azslow_minplot = statsOUT.azslow['5%']
azslow_maxplot = statsOUT.azslow['95%']
In [1622]:
vslow_minplot = statsOUT.vslow['5%']
vslow_maxplot = statsOUT.vslow['95%']
In [1623]:
vfast_minplot = statsOUT.vfast['5%']
vfast_maxplot = statsOUT.vfast['95%']
In [1624]:
#print(aniso_data.time)
In [1625]:
fig, ax = plt.subplots()
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y/%m/%d\n%H:%M'))
    
#plt.plot(aniso_data.time, aniso_data.viso, "o", label = 'Viso')
plt.plot(aniso_data['time'], aniso_data['viso'], "o", label = 'Viso')
#plt.ylim(350,600)
plt.ylim(viso_minplot, viso_maxplot)
plt.xlabel("Time UTC")
plt.ylabel("Velocitu (m/s)")
plt.legend(loc="upper left") 
plt.title(""+sta+" Viso")

#plt.xlim("2011-01-01 00:00:00","2018-10-01 0:00:00")
Out[1625]:
<matplotlib.text.Text at 0x14308c080>
In [1626]:
fig, ax = plt.subplots()
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y/%m/%d\n%H:%M'))
    
#plt.plot(aniso_data.time, aniso_data.viso, "o", label = 'Viso')
plt.plot(aniso_data['time'], aniso_data['rms_coeff'], "o", label = 'rms_coeff')
plt.ylim(rms_coeff_minplot, rms_coeff_maxplot)
plt.xlabel("Time UTC")
plt.ylabel("RMS_Coeff(%)")
plt.legend(loc="upper left") 
plt.title(""+sta+" RMS_Coeff")
Out[1626]:
<matplotlib.text.Text at 0x1431ed048>
In [1627]:
fig, ax = plt.subplots()
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y/%m/%d\n%H:%M'))
    
#plt.plot(aniso_data.time, aniso_data.viso, "o", label = 'Viso')
plt.plot(aniso_data['time'], aniso_data['azcoeff'], "o", label = 'azcoeff')
plt.ylim(azcoeff_minplot, azcoeff_maxplot)
plt.xlabel("Time UTC")
plt.ylabel("Az coeff(%)")
plt.legend(loc="upper left") 
plt.title(""+sta+" AzCoeff")
Out[1627]:
<matplotlib.text.Text at 0x143291b70>
In [1628]:
fig, ax = plt.subplots()
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y/%m/%d\n%H:%M'))
    
#plt.plot(aniso_data.time, aniso_data.viso, "o", label = 'Viso')
plt.plot(aniso_data['time'], aniso_data['azfast'], "o", label = 'azfast')
plt.ylim(azfast_minplot, azfast_maxplot)
plt.xlabel("Time UTC")
plt.ylabel("Az fast(deg)")
plt.legend(loc="upper left") 
plt.title(""+sta+" Az fast")
Out[1628]:
<matplotlib.text.Text at 0x143338710>
In [1629]:
fig, ax = plt.subplots()
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y/%m/%d\n%H:%M'))
    
#plt.plot(aniso_data.time, aniso_data.viso, "o", label = 'Viso')
plt.plot(aniso_data['time'], aniso_data['azslow'], "o", label = 'azslow')
plt.ylim(azslow_minplot, azslow_maxplot)
plt.xlabel("Time UTC")
plt.ylabel("Az slow(deg)")
plt.legend(loc="upper left") 
plt.title(""+sta+" Az slow")
Out[1629]:
<matplotlib.text.Text at 0x1433e30f0>
In [1630]:
fig, ax = plt.subplots()
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y/%m/%d\n%H:%M'))
    
#plt.plot(aniso_data.time, aniso_data.viso, "o", label = 'Viso')
plt.plot(aniso_data['time'], aniso_data['vfast'], "o", label = 'vfast')
plt.ylim(vfast_minplot, vfast_maxplot)
plt.xlabel("Time UTC")
plt.ylabel("v fast (m/s)")
plt.legend(loc="upper left") 
plt.title(""+sta+" v fast")
Out[1630]:
<matplotlib.text.Text at 0x1435450b8>
In [1631]:
fig, ax = plt.subplots()
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y/%m/%d\n%H:%M'))
    
#plt.plot(aniso_data.time, aniso_data.viso, "o", label = 'Viso')
plt.plot(aniso_data['time'], aniso_data['vslow'], "o", label = 'vslow')
plt.ylim(vslow_minplot, vslow_maxplot)
plt.xlabel("Time UTC")
plt.ylabel("v slow (m/s)")
plt.legend(loc="upper left") 
plt.title(""+sta+" v slow")
Out[1631]:
<matplotlib.text.Text at 0x1436af9e8>