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 [1492]:
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 [1493]:
#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 [1494]:
#print (aniso_data['lat'])
In [1495]:
aniso_data['time'] = pd.to_datetime(aniso_data['time'])
In [1496]:
aniso_data.describe()
Out[1496]:
lat long depth mag elapse_diff evid viso vfast vslow azfast azslow azcoeff rms_coeff leng ddeg ns ne f1 f2 elapse_days
count 668.000000 668.000000 668.000000 668.000000 668.000000 6.680000e+02 668.000000 668.000000 668.000000 668.000000 668.000000 668.00000 668.000000 668.0 668.0 668.0 668.0 668.0 668.0 668.000000
mean 41.671623 142.864705 53.375494 4.938473 0.000253 2.011534e+13 498.993278 530.414132 467.572141 102.326662 44.669281 11.71024 15.834805 7.0 5.0 -1.0 0.0 1.0 15.0 -2436.542680
std 1.946132 1.771490 53.647269 1.006933 0.000247 4.942081e+10 40.691609 32.639445 68.752633 26.575657 52.721252 12.51657 30.214993 0.0 0.0 0.0 0.0 0.0 0.0 1811.936480
min 27.052000 131.064000 0.000000 2.800000 -0.000969 2.002062e+13 303.690000 427.240000 128.490000 1.030000 0.350000 0.86000 0.190000 7.0 5.0 -1.0 0.0 1.0 15.0 -5924.483602
25% 41.475150 141.993750 31.172500 4.200000 0.000077 2.008039e+13 500.042500 519.637500 479.035000 96.095000 14.370000 5.84750 1.360000 7.0 5.0 -1.0 0.0 1.0 15.0 -3799.177539
50% 42.185050 142.544500 47.215000 4.800000 0.000262 2.011112e+13 508.945000 528.410000 489.170000 106.200000 22.220000 7.47000 2.570000 7.0 5.0 -1.0 0.0 1.0 15.0 -2486.478269
75% 42.663450 143.305000 64.230000 5.600000 0.000435 2.016082e+13 516.700000 540.897500 497.602500 114.635000 37.205000 10.17000 4.737500 7.0 5.0 -1.0 0.0 1.0 15.0 -746.170941
max 49.183300 155.168000 681.710000 9.000000 0.000693 2.018122e+13 746.620000 840.050000 722.270000 179.440000 179.980000 80.90000 99.130000 7.0 5.0 -1.0 0.0 1.0 15.0 106.120111
In [1497]:
#print(aniso_data[aniso_data['vslow'] < 100])
In [1498]:
#aniso_data['azslow']
In [1499]:
statsOUT = aniso_data.describe()
In [1500]:
statsOUT = aniso_data.describe(percentiles=[0.01, 0.05, 0.1, 0.25, 0.75, 0.9, 0.95, 0.99])
In [1501]:
statsOUT.viso
Out[1501]:
count    668.000000
mean     498.993278
std       40.691609
min      303.690000
1%       366.813500
5%       413.941500
10%      435.370000
25%      500.042500
50%      508.945000
75%      516.700000
90%      529.476000
95%      539.921000
99%      560.611700
max      746.620000
Name: viso, dtype: float64
In [1502]:
print (statsOUT.viso['mean'], statsOUT.viso['10%'])

498.9932784431138 435.37
In [1503]:
viso_minplot = statsOUT.viso['10%']
viso_maxplot = statsOUT.viso['90%']
In [1504]:
viso_minplot = statsOUT.viso['5%']
viso_maxplot = statsOUT.viso['95%']
In [1505]:
viso_minplot = statsOUT.viso['1%']
viso_maxplot = statsOUT.viso['99%']
In [1506]:
azcoeff_minplot = statsOUT.azcoeff['5%']
azcoeff_maxplot = statsOUT.azcoeff['95%']
In [1507]:
rms_coeff_minplot = statsOUT.rms_coeff['5%']
rms_coeff_maxplot = statsOUT.rms_coeff['95%']
In [1508]:
azfast_minplot = statsOUT.azfast['5%']
azfast_maxplot = statsOUT.azfast['95%']
In [1509]:
azslow_minplot = statsOUT.azslow['5%']
azslow_maxplot = statsOUT.azslow['95%']
In [1510]:
vslow_minplot = statsOUT.vslow['5%']
vslow_maxplot = statsOUT.vslow['95%']
In [1511]:
vfast_minplot = statsOUT.vfast['5%']
vfast_maxplot = statsOUT.vfast['95%']
In [1512]:
#print(aniso_data.time)
In [1513]:
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[1513]:
<matplotlib.text.Text at 0x140516fd0>
In [1514]:
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[1514]:
<matplotlib.text.Text at 0x13eed9cf8>
In [1515]:
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[1515]:
<matplotlib.text.Text at 0x13ef72908>
In [1516]:
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[1516]:
<matplotlib.text.Text at 0x140946358>
In [1517]:
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[1517]:
<matplotlib.text.Text at 0x1409f1470>
In [1518]:
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[1518]:
<matplotlib.text.Text at 0x140b53f28>
In [1519]:
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[1519]:
<matplotlib.text.Text at 0x140cb6c50>