Earthquake

GODE
Author

SEOYEON CHOI

Published

November 24, 2022

Real analysis

imports

import tqdm
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt 
import plotly.express as px
import warnings
warnings.simplefilter("ignore", np.ComplexWarning)
from haversine import haversine
from IPython.display import HTML
import plotly.graph_objects as go
import rpy2
import rpy2.robjects as ro 
from rpy2.robjects.vectors import FloatVector 
from rpy2.robjects.packages import importr

load data and clean it

- load

df= pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/earthquakes-23k.csv')
df
Date Latitude Longitude Magnitude
0 01/02/1965 19.2460 145.6160 6.0
1 01/04/1965 1.8630 127.3520 5.8
2 01/05/1965 -20.5790 -173.9720 6.2
3 01/08/1965 -59.0760 -23.5570 5.8
4 01/09/1965 11.9380 126.4270 5.8
... ... ... ... ...
23407 12/28/2016 38.3917 -118.8941 5.6
23408 12/28/2016 38.3777 -118.8957 5.5
23409 12/28/2016 36.9179 140.4262 5.9
23410 12/29/2016 -9.0283 118.6639 6.3
23411 12/30/2016 37.3973 141.4103 5.5

23412 rows × 4 columns

df_korea= pd.read_csv('earthquake_korea2.csv').iloc[:,[1,2,5,6]].rename(columns={'규모':'Magnitude'})

https://www.weather.go.kr/w/eqk-vol/search/korea.do?schOption=&xls=0&startTm=2012-01-02&endTm=2022-06-17&startSize=2&endSize=&startLat=&endLat=&startLon=&endLon=&lat=&lon=&dist=&keyword=&dpType=m

df_global= pd.concat([pd.read_csv('00_05.csv'),pd.read_csv('05_10.csv'),pd.read_csv('10_15.csv'),pd.read_csv('15_20.csv')]).iloc[:,[0,1,2,4]].rename(columns={'latitude':'Latitude','longitude':'Longitude','mag':'Magnitude'}).reset_index().iloc[:,1:]

https://www.usgs.gov/programs/earthquake-hazards/lists-maps-and-statistics

- cleaning

df.Date[df.Date == '1975-02-23T02:58:41.000Z']
3378    1975-02-23T02:58:41.000Z
Name: Date, dtype: object
df.iloc[3378,0] = '02/03/1975'
df.Date[df.Date == '1985-04-28T02:53:41.530Z']
7512    1985-04-28T02:53:41.530Z
Name: Date, dtype: object
df.iloc[7512,0] = '04/28/1985'
df.Date[df.Date == '2011-03-13T02:23:34.520Z']
20650    2011-03-13T02:23:34.520Z
Name: Date, dtype: object
df.iloc[20650,0] = '03/13/2011'
df= df.assign(Year=list(map(lambda x: x.split('/')[-1], df.Date))).iloc[:,1:]
df
Latitude Longitude Magnitude Year
0 19.2460 145.6160 6.0 1965
1 1.8630 127.3520 5.8 1965
2 -20.5790 -173.9720 6.2 1965
3 -59.0760 -23.5570 5.8 1965
4 11.9380 126.4270 5.8 1965
... ... ... ... ...
23407 38.3917 -118.8941 5.6 2016
23408 38.3777 -118.8957 5.5 2016
23409 36.9179 140.4262 5.9 2016
23410 -9.0283 118.6639 6.3 2016
23411 37.3973 141.4103 5.5 2016

23412 rows × 4 columns

df.Year = df.Year.astype(np.float64)
df_korea = df_korea.assign(Year=list(map(lambda x: x.split('/')[0], df_korea.발생시각))).iloc[:,1:]
df_korea = df_korea.assign(Latitude=list(map(lambda x: x.split(' ')[0], df_korea.위도))).iloc[:,[0,2,3,4]]
df_korea = df_korea.assign(Longitude=list(map(lambda x: x.split(' ')[0], df_korea.경도))).iloc[:,[0,2,3,4]]
df_global = df_global.assign(Year=list(map(lambda x: x.split('-')[0], df_global.time))).iloc[:,1:]
df_korea.Year = df_korea.Year.astype(np.float64)
df_korea.Latitude = df_korea.Latitude.astype(np.float64)
df_korea.Longitude = df_korea.Longitude.astype(np.float64)
df_global.Year = df_global.Year.astype(np.float64)

define class

class MooYaHo:
    def __init__(self,df):
        self.df = df 
        self.f = df.Magnitude.to_numpy()
        self.year = df.Year.to_numpy()
        self.lat = df.Latitude.to_numpy()
        self.long = df.Longitude.to_numpy()
        self.n = len(self.f)
        
        self.theta= None
    def get_distance(self):
        self.D = np.zeros([self.n,self.n])
        locations = np.stack([self.lat, self.long],axis=1)
        for i in tqdm.tqdm(range(self.n)):
            for j in range(i,self.n): 
                self.D[i,j]=haversine(locations[i],locations[j])
        self.D = self.D+self.D.T
    def get_weightmatrix(self,theta=1,beta=0.5,kappa=4000):
        self.theta = theta
        dist = np.where(self.D<kappa,self.D,0)
        self.W = np.exp(-(dist/self.theta)**2)

    def _eigen(self):
        d= self.W.sum(axis=1)
        D= np.diag(d)
        self.L = np.diag(1/np.sqrt(d)) @ (D-self.W) @ np.diag(1/np.sqrt(d))
        self.lamb, self.Psi = np.linalg.eigh(self.L)
        self.Lamb = np.diag(self.lamb)        
    def fit(self,m):
        self._eigen()
        self.fhat = self.Psi[:,0:m]@self.Psi[:,0:m].T@self.f
        self.df = self.df.assign(MagnitudeHat = self.fhat)
        self.df = self.df.assign(Residual = self.df.Magnitude- self.df.MagnitudeHat)
        plt.plot(self.f,'.')
        plt.plot(self.fhat,'x')
        
    def vis(self,MagThresh=7,ResThresh=1):
        fig = px.density_mapbox(self.df, 
                        lat='Latitude', 
                        lon='Longitude', 
                        z='Magnitude', 
                        radius=5,
                        center=dict(lat=37, lon=160), 
                        zoom=1.5,
                        height=900,
                        opacity = 0.4,
                        mapbox_style="stamen-terrain",
                        range_color=[-7,7])
        fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
        fig.add_scattermapbox(lat = self.df.query('Magnitude > @MagThresh')['Latitude'],
                      lon = self.df.query('Magnitude > @MagThresh')['Longitude'],
                      text = self.df.query('Magnitude > @MagThresh')['Magnitude'],
                      marker_size= 8,
                      marker_color= 'red',
                      opacity = 0.6
                      )
        fig.add_scattermapbox(lat = self.df.query('Residual**2 > @ResThresh')['Latitude'],
                      lon = self.df.query('Residual**2 > @ResThresh')['Longitude'],
                      text = self.df.query('Magnitude > @ResThresh')['Magnitude'],
                      marker_size= 8,
                      marker_color= 'blue',
                      opacity = 0.5
                      )
        return HTML(fig.to_html(include_mathjax=False, config=dict({'scrollZoom':False})))
    def visf(self):
        fig = px.density_mapbox(self.df, 
                        lat='Latitude', 
                        lon='Longitude', 
                        z='Magnitude', 
                        radius=5,
                        center=dict(lat=37, lon=160), 
                        zoom=1.5,
                        height=900,
                        opacity = 0.7,
                        mapbox_style="stamen-terrain",
                        range_color=[-7,7])
        fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
        return HTML(fig.to_html(include_mathjax=False, config=dict({'scrollZoom':False})))
    def visfhat(self):
        fig = px.density_mapbox(self.df, 
                        lat='Latitude', 
                        lon='Longitude', 
                        z='MagnitudeHat', 
                        radius=5,
                        center=dict(lat=37, lon=160), 
                        zoom=1.5,
                        height=900,
                        opacity = 0.7,
                        mapbox_style="stamen-terrain",
                        range_color=[-7,7])
        fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
        return HTML(fig.to_html(include_mathjax=False, config=dict({'scrollZoom':False})))
    def visres(self,MagThresh=7,ResThresh=1):
        fig = px.density_mapbox(self.df, 
                        lat='Latitude', 
                        lon='Longitude', 
                        z=[0] * len(self.df), 
                        radius=5,
                        center=dict(lat=37, lon=160), 
                        zoom=1.5,
                        height=900,
                        opacity = 0.7,
                        mapbox_style="stamen-terrain",
                        range_color=[-7,7])
        fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
        fig.add_scattermapbox(lat = self.df.query('Residual**2 > @ResThresh')['Latitude'],
                      lon = self.df.query('Residual**2 > @ResThresh')['Longitude'],
                      text = self.df.query('Magnitude > @ResThresh')['Magnitude'],
                      marker_size= 8,
                      marker_color= 'blue',
                      opacity = 0.7
                      )
        return HTML(fig.to_html(include_mathjax=False, config=dict({'scrollZoom':False})))
class MooYaHo2(MooYaHo): # ebayesthresh 기능추가
    def fit2(self,ref=0.5): # fit with ebayesthresh
        self._eigen()
        self.fbar = self.Psi.T @ self.f # fbar := graph fourier transform of f
        self.power = self.fbar**2 
        ebayesthresh = importr('EbayesThresh').ebayesthresh
        self.power_threshed=np.array(ebayesthresh(FloatVector(self.fbar**2)))
        self.fbar_threshed = np.where(self.power_threshed>0,self.fbar,0)
        self.fhat = self.Psi@self.fbar_threshed
        self.df = self.df.assign(MagnitudeHat = self.fhat)
        self.df = self.df.assign(Residual = self.df.Magnitude- self.df.MagnitudeHat)
        self.con = np.where(self.df.Residual>0.7,1,0)
        #plt.plot(self.f,'.')
        #plt.plot(self.fhat,'x')

#         fig, axs = plt.subplots(2,2,figsize=(16,10))

#         axs[0,0].plot(self.f,'b')
#         axs[0,0].set_title('Magnitude')
#         axs[0,0].set_ylim([4.5,9])

#         axs[0,1].plot(self.fhat,'k')
#         axs[0,1].set_title('MagnitudeHat')
#         axs[0,1].set_ylim([4.5,9])

#         axs[1,0].plot(self.con,'r*')
#         axs[1,0].set_title('Residual square')

#         axs[1,1].plot(self.f,'b')
#         axs[1,1].plot(self.fhat,'k')
#         axs[1,1].plot(self.con,'r*')
#         axs[1,1].set_title('Graph')
#         axs[1,1].set_ylim([4.5,9])

#         plt.tight_layout()
#         plt.show()
class MooYaHo3(MooYaHo2):
    def vis(self,MagThresh=7,ResThresh=1):
        fig = px.density_mapbox(self.df, 
                        lat='Latitude', 
                        lon='Longitude', 
                        z='Magnitude', 
                        radius=5,
                        center=dict(lat=37, lon=126), 
                        zoom=5.7,
                        height=900,
                        opacity = 0.3,
                        mapbox_style="stamen-terrain")
        fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
        fig.add_scattermapbox(lat = self.df.query('Magnitude > @MagThresh')['Latitude'],
                      lon = self.df.query('Magnitude > @MagThresh')['Longitude'],
                      text = self.df.query('Magnitude > @MagThresh')['Magnitude'],
                      marker_size= 8,
                      marker_color= 'red',
                      opacity = 0.5
                      )
        fig.add_scattermapbox(lat = self.df.query('Residual**2 > @ResThresh')['Latitude'],
                      lon = self.df.query('Residual**2 > @ResThresh')['Longitude'],
                      text = self.df.query('Magnitude > @ResThresh')['Magnitude'],
                      marker_size= 8,
                      marker_color= 'blue',
                      opacity = 0.5
                      )
        return HTML(fig.to_html(include_mathjax=False, config=dict({'scrollZoom':False})))
       ebayesthresh = importr('EbayesThresh').ebayesthresh

analysis_df_global(2010~2015)

- make instance for analysis

moo_global=MooYaHo2(df_global.query("2010 <= Year < 2015"))

- get distance

moo_global.get_distance()
100%|██████████| 10762/10762 [05:32<00:00, 32.36it/s] 
moo_global.D[moo_global.D>0].mean()
8746.68756693945
plt.hist(moo_global.D[moo_global.D>0])
(array([10857854., 11686138., 15883516., 16913862., 14592080., 12938904.,
        11847394.,  9636694.,  9649504.,  1799146.]),
 array([8.97930163e-02, 2.00141141e+03, 4.00273303e+03, 6.00405465e+03,
        8.00537626e+03, 1.00066979e+04, 1.20080195e+04, 1.40093411e+04,
        1.60106627e+04, 1.80119844e+04, 2.00133060e+04]),
 <BarContainer object of 10 artists>)

- weight matrix

moo_global.get_weightmatrix(theta=(8810.865423093777),kappa=2500)

- fit

moo_global.fit2()
moo_global.df.sort_values("Residual",ascending=False).iloc[:40,:]
Latitude Longitude Magnitude Year MagnitudeHat Residual
30311 38.2970 142.3730 9.1 2011.0 7.625067 1.474933
11094 -36.1220 -72.8980 8.8 2010.0 7.638469 1.161531
32803 -36.1220 -72.8980 8.8 2010.0 7.789231 1.010769
32436 -36.2170 -73.2570 6.7 2010.0 5.841501 0.858499
26564 14.1290 -92.1640 6.5 2012.0 5.653542 0.846458
27513 0.8020 92.4630 8.2 2012.0 7.368113 0.831887
30347 38.4350 142.8420 7.3 2011.0 6.490603 0.809397
30296 36.2810 141.1110 7.9 2011.0 7.156346 0.743654
10292 7.8810 91.9360 7.5 2010.0 6.766319 0.733681
9193 26.9010 143.6980 7.4 2010.0 6.667897 0.732103
32554 -36.6650 -73.3740 6.6 2010.0 5.873441 0.726559
30301 38.9690 143.3700 6.7 2011.0 5.984234 0.715766
32001 7.8810 91.9360 7.5 2010.0 6.793936 0.706064
31229 -3.4870 100.0820 7.8 2010.0 7.100594 0.699406
28229 37.3650 141.3680 6.1 2011.0 5.403952 0.696048
9283 -56.4120 -25.7410 6.3 2010.0 5.607381 0.692619
9520 -3.4870 100.0820 7.8 2010.0 7.111591 0.688409
30827 -19.7020 167.9470 7.3 2010.0 6.615744 0.684256
30295 36.0230 142.2690 6.6 2011.0 5.917664 0.682336
27456 2.5810 90.2690 6.2 2012.0 5.521275 0.678725
30017 37.5940 142.6480 6.5 2011.0 5.828617 0.671383
10135 -22.1460 -68.2160 6.3 2010.0 5.631982 0.668018
24958 -60.2738 -46.4011 7.7 2013.0 7.041204 0.658796
10783 -34.2900 -71.8910 6.9 2010.0 6.241810 0.658190
29019 -29.3370 -177.0510 6.0 2011.0 5.347355 0.652645
32492 -34.2900 -71.8910 6.9 2010.0 6.250075 0.649925
30902 26.9010 143.6980 7.4 2010.0 6.758872 0.641128
28931 52.8810 108.4400 5.3 2011.0 4.661692 0.638308
29004 38.0340 143.2640 7.0 2011.0 6.368649 0.631351
30992 -56.4120 -25.7410 6.3 2010.0 5.680727 0.619273
30893 27.2030 143.4550 5.3 2010.0 4.681926 0.618074
9665 -4.9630 133.7600 7.0 2010.0 6.382904 0.617096
29640 38.2760 141.5880 7.1 2011.0 6.485066 0.614934
26354 55.2280 -134.8591 7.5 2013.0 6.890518 0.609482
27527 2.3270 93.0630 8.6 2012.0 7.992761 0.607239
25452 -60.8570 -25.0700 7.3 2013.0 6.693727 0.606273
29536 -10.3750 161.2000 6.8 2011.0 6.197847 0.602153
26468 37.8900 143.9490 7.3 2012.0 6.698942 0.601058
32231 -15.2710 -173.2190 6.1 2010.0 5.502608 0.597392
26234 -11.1830 164.8820 7.1 2013.0 6.507029 0.592971

(2010~2014 시도) - 21번째 Ouest Department, Haiti 아이티 지진 2010년 진도 7.0 - 24번째 Puchuncavi, Valparaíso, Chile 칠레 지진 2014년 진도 6.4 - 28번째 Baoxing County, Yaan, Sichuan, China 중국 쓰촨성 지진 2013년 진도 6.6

(2010~2015 시도_결과 좋지 않음?!) - 23번째 2010년 West New Britain Province, Papua New Guinea 진도 7.3 - 24번째 2011년 Kuzawa Terayama, Tanagura, Higashishirakawa District, Fukushima 963-5671, Japan 진도 6.6 - 29번째 2015년 Kishim, Afghanistan 진도 7.5

- vis

#moo_global.visf()
#moo_global.visfhat()
#moo_global.visres()
#moo_global.vis(MagThresh=6.9,ResThresh=0.5)

analysis_df_global(2015~2020)

- make instance for analysis

moo_global=MooYaHo2(df_global.query("2015 <= Year <= 2020"))

- get distance

moo_global.get_distance()
100%|██████████| 11239/11239 [04:35<00:00, 40.84it/s] 
moo_global.D[moo_global.D>0].mean()
8814.318793468068
plt.hist(moo_global.D[moo_global.D>0])
(array([10894274., 13618924., 16426520., 17583818., 16025000., 15684642.,
        13794372., 10946494.,  9072574.,  2254138.]),
 array([2.54728455e-02, 2.00123511e+03, 4.00244475e+03, 6.00365439e+03,
        8.00486402e+03, 1.00060737e+04, 1.20072833e+04, 1.40084929e+04,
        1.60097026e+04, 1.80109122e+04, 2.00121218e+04]),
 <BarContainer object of 10 artists>)

- weight matrix

moo_global.get_weightmatrix(theta=(8814.318793468068),kappa=2500)

- fit

moo_global.fit2()
moo_global.df.sort_values("Residual",ascending=False).iloc[:30,:]
Latitude Longitude Magnitude Year MagnitudeHat Residual
21952 -31.5729 -71.6744 8.3 2015.0 7.294011 1.005989
36363 -21.9496 169.4266 7.5 2018.0 6.523828 0.976172
36993 -18.1125 -178.1530 8.2 2018.0 7.311720 0.888280
39932 -42.7373 173.0540 7.8 2016.0 6.951928 0.848072
36263 55.0999 164.6993 7.3 2018.0 6.470796 0.829204
35004 -30.1733 -177.8625 6.1 2019.0 5.285797 0.814203
41015 -4.9521 94.3299 7.8 2016.0 6.993888 0.806112
21719 -8.3381 124.8754 6.5 2015.0 5.698526 0.801474
33404 54.6020 -159.6258 7.6 2020.0 6.819466 0.780534
41735 -31.5729 -71.6744 8.3 2015.0 7.547595 0.752405
40113 -19.7819 -178.2443 6.9 2016.0 6.148672 0.751328
41695 -31.5173 -71.8040 6.7 2015.0 5.951922 0.748078
37927 56.0039 -149.1658 7.9 2018.0 7.170056 0.729944
36848 -18.4743 179.3502 7.9 2018.0 7.173844 0.726156
21945 -31.5622 -71.4262 7.0 2015.0 6.291422 0.708578
41502 -8.3381 124.8754 6.5 2015.0 5.812161 0.687839
33896 -33.2927 -177.8571 7.4 2020.0 6.715142 0.684858
22560 -5.4912 151.8715 6.0 2015.0 5.316904 0.683096
39584 -43.4064 -73.9413 7.6 2016.0 6.921775 0.678225
36777 -25.4150 178.1991 6.5 2018.0 5.833282 0.666718
35487 -30.6441 -178.0995 7.3 2019.0 6.638342 0.661658
39771 -10.6812 161.3273 7.8 2016.0 7.145371 0.654629
38827 -30.5156 -178.0563 6.0 2017.0 5.351325 0.648675
41963 52.3760 -169.4458 6.9 2015.0 6.256523 0.643477
22488 27.8087 86.0655 7.3 2015.0 6.662980 0.637020
21795 -54.4856 -135.7080 6.1 2015.0 5.468025 0.631975
22839 -10.7598 164.1216 6.1 2015.0 5.468139 0.631861
41423 38.6700 20.6000 6.5 2015.0 5.874436 0.625564
36101 -30.0404 -71.3815 6.7 2019.0 6.081398 0.618602
36602 -21.7427 169.5217 6.5 2018.0 5.882525 0.617475

바다 아닌 거 - 8번째 2016년 Rotherham, New Zealand 뉴질랜드 카이코우라 지진 진도 7.8 - 9번째 2015년 Langkuru Utara, Pureman, Alor Regency, East Nusa Tenggara, Indonesia 수마트라 진도 6.5 - 15번째 2018년 Hela Province, Papua New Guinea 파푸아뉴기니 진도 7.5 - 20번째 2015년 Kalinchok, Nepal 네팔 진도 7.3 - 26번째 2019년 Coquimbo, Chile 칠레 코킴보주 진도 6.7

- vis

#moo_global.vis(MagThresh=7,ResThresh=0.3)
pd.read_html('https://en.wikipedia.org/wiki/Lists_of_21st-century_earthquakes',encoding='utf-8')[0].query('Magnitude<=7')# List of deadliest earthquakes
pd.read_html('https://en.wikipedia.org/wiki/Lists_of_21st-century_earthquakes',encoding='utf-8')[3] # Deadliest earthquakes by year
class eachlocation(MooYaHo2):
    def haiti(self,MagThresh=7,ResThresh=1,adjzoom=5,adjmarkersize = 40):
        fig = px.density_mapbox(self.df, 
                        lat='Latitude', 
                        lon='Longitude', 
                        z='Magnitude', 
                        radius=15,
                        center=dict(lat=18.4430, lon=-72.5710), 
                        zoom= adjzoom,
                        height=900,
                        opacity = 0.8,
                        mapbox_style="stamen-terrain",
                        range_color=[-3,3])
        fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
        fig.add_scattermapbox(lat = self.df.query('Magnitude > @MagThresh')['Latitude'],
                      lon = self.df.query('Magnitude > @MagThresh')['Longitude'],
                      text = self.df.query('Magnitude > @MagThresh')['Magnitude'],
                      marker_size= 5,
                      marker_color= 'blue',
                      opacity = 0.1
                      )
        fig.add_scattermapbox(lat = self.df.query('Residual**2 > @ResThresh')['Latitude'],
                      lon = self.df.query('Residual**2 > @ResThresh')['Longitude'],
                      text = self.df.query('Magnitude > @ResThresh')['Magnitude'],
                      marker_size= adjmarkersize,
                      marker_color= 'red',
                      opacity = 0.8
                      )
        fig.add_trace(go.Scattermapbox(
                    lat=self.df.query('Residual**2 > @ResThresh')['Latitude'],
                    lon=self.df.query('Residual**2 > @ResThresh')['Longitude'],
                    mode='markers',
                    marker=go.scattermapbox.Marker(
                        size=20,
                        color='rgb(255, 255, 255)',
                        opacity=0.4
                    )
                ))
        return fig 
    def lquique(self,MagThresh=7,ResThresh=1,adjzoom=5, adjmarkersize= 40):
        fig = px.density_mapbox(self.df, 
                        lat='Latitude', 
                        lon='Longitude', 
                        z='Magnitude', 
                        radius=15,
                        center=dict(lat=-32.6953, lon=-71.4416), 
                        zoom=adjzoom,
                        height=900,
                        opacity = 0.8,
                        mapbox_style="stamen-terrain",
                        range_color=[-7,7])
        fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
        fig.add_scattermapbox(lat = self.df.query('Magnitude > @MagThresh')['Latitude'],
                      lon = self.df.query('Magnitude > @MagThresh')['Longitude'],
                      text = self.df.query('Magnitude > @MagThresh')['Magnitude'],
                      marker_size= 5,
                      marker_color= 'blue',
                      opacity = 0.1
                      )
        fig.add_scattermapbox(lat = self.df.query('Residual**2 > @ResThresh')['Latitude'],
                      lon = self.df.query('Residual**2 > @ResThresh')['Longitude'],
                      text = self.df.query('Magnitude > @ResThresh')['Magnitude'],
                      marker_size= adjmarkersize,
                      marker_color= 'red',
                      opacity = 0.8
                      )
        fig.add_trace(go.Scattermapbox(
                    lat=self.df.query('Residual**2 > @ResThresh')['Latitude'],
                    lon=self.df.query('Residual**2 > @ResThresh')['Longitude'],
                    mode='markers',
                    marker=go.scattermapbox.Marker(
                        size=20,
                        color='rgb(255, 255, 255)',
                        opacity=0.8
                    )
                ))
        return fig 
    def sichuan(self,MagThresh=7,ResThresh=1,adjzoom=5,adjmarkersize=40):
        fig = px.density_mapbox(self.df, 
                        lat='Latitude', 
                        lon='Longitude', 
                        z='Magnitude', 
                        radius=15,
                        center=dict(lat=30.3080, lon=102.8880), 
                        zoom=adjzoom,
                        height=900,
                        opacity = 0.6,
                        mapbox_style="stamen-terrain",
                        range_color=[-7,7])
        fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
        fig.add_scattermapbox(lat = self.df.query('Magnitude > @MagThresh')['Latitude'],
                      lon = self.df.query('Magnitude > @MagThresh')['Longitude'],
                      text = self.df.query('Magnitude > @MagThresh')['Magnitude'],
                      marker_size= 5,
                      marker_color= 'blue',
                      opacity = 0.1
                      )
        fig.add_scattermapbox(lat = self.df.query('Residual**2 > @ResThresh')['Latitude'],
                      lon = self.df.query('Residual**2 > @ResThresh')['Longitude'],
                      text = self.df.query('Magnitude > @ResThresh')['Magnitude'],
                      marker_size= adjmarkersize,
                      marker_color= 'red',
                      opacity = 0.8
                      )
        fig.add_trace(go.Scattermapbox(
                    lat=self.df.query('Residual**2 > @ResThresh')['Latitude'],
                    lon=self.df.query('Residual**2 > @ResThresh')['Longitude'],
                    mode='markers',
                    marker=go.scattermapbox.Marker(
                        size=20,
                        color='rgb(255, 255, 255)',
                        opacity=0.8
                    )
                ))
        return fig 
each_location=eachlocation(df_global.query("2010 <= Year < 2015"))

- get distance

each_location.get_distance()
100%|██████████| 12498/12498 [05:55<00:00, 35.16it/s] 
each_location.D[each_location.D>0].mean()
8810.865423093777
plt.hist(each_location.D[each_location.D>0])
(array([14176290., 16005894., 21186674., 22331128., 19394182., 17548252.,
        16668048., 13316436., 12973260.,  2582550.]),
 array([8.97930163e-02, 2.00141141e+03, 4.00273303e+03, 6.00405465e+03,
        8.00537626e+03, 1.00066979e+04, 1.20080195e+04, 1.40093411e+04,
        1.60106627e+04, 1.80119844e+04, 2.00133060e+04]),
 <BarContainer object of 10 artists>)

- weight matrix

each_location.get_weightmatrix(theta=(8810.865423093777),kappa=2500)

- fit

each_location.fit2()
each_location.haiti(MagThresh=6.9,ResThresh=0.5)
each_location.lquique(MagThresh=8,ResThresh=0.4,adjzoom=4.3)
each_location.sichuan(MagThresh=6.5,ResThresh=0.4)

칠레

df_chile_ex= pd.concat([pd.read_csv('05_10.csv'),pd.read_csv('10_15.csv')]).iloc[:,[0,1,2,4]].rename(columns={'latitude':'Latitude','longitude':'Longitude','mag':'Magnitude'}).reset_index().iloc[:,1:]
df_chile = df_chile_ex.assign(Year=list(map(lambda x: x.split('-')[0], df_chile_ex.time))).iloc[:,1:]
df_chile = df_chile.assign(Month=list(map(lambda x: x.split('-')[1], df_chile_ex.time)))
df_chile.Year = df_chile.Year.astype(np.float64)
df_chile.Month = df_chile.Month.astype(np.float64)
chile_location=eachlocation(df_chile.query("2010 <= Year < 2015"))
chile_location.get_distance()
100%|██████████| 12498/12498 [06:28<00:00, 32.19it/s] 
chile_location.get_weightmatrix(theta=(chile_location.D[chile_location.D>0].mean()),kappa=2500) 
chile_location.fit2()

아이티

chile_location.df.assign(Residual2 = chile_location.df.Residual**2).reset_index().iloc[2324:2330,:]
index Latitude Longitude Magnitude Year Month MagnitudeHat Residual Residual2
2324 2324 18.463 -72.626 5.0 2010.0 1.0 5.105168 -0.105168 0.011060
2325 2325 18.387 -72.784 6.0 2010.0 1.0 5.703460 0.296540 0.087936
2326 2326 18.443 -72.571 7.0 2010.0 1.0 6.659386 0.340614 0.116018
2327 2327 -5.417 133.731 5.5 2010.0 1.0 5.604103 -0.104103 0.010837
2328 2328 15.437 -88.761 5.1 2010.0 1.0 4.964642 0.135358 0.018322
2329 2329 -16.861 -174.228 5.3 2010.0 1.0 5.340902 -0.040902 0.001673

칠레

chile_location.df.assign(Residual2 = chile_location.df.Residual**2).reset_index().query("-56.5 < Latitude & Latitude <-17.4 & -81.5 < Longitude & Longitude < -61.5 & Year == 2014 & Month == 8")
index Latitude Longitude Magnitude Year Month MagnitudeHat Residual Residual2
2997 14603 -32.6953 -71.4416 6.4 2014.0 8.0 6.088353 0.311647 0.097124
2999 14605 -20.1745 -69.0385 5.6 2014.0 8.0 5.553419 0.046581 0.002170
3032 14638 -20.1580 -70.0230 5.3 2014.0 8.0 5.251934 0.048066 0.002310
3046 14652 -23.9047 -66.7371 5.0 2014.0 8.0 5.210380 -0.210380 0.044260
3057 14663 -33.7770 -72.2030 5.2 2014.0 8.0 5.065422 0.134578 0.018111

중국

chile_location.df.assign(Residual2 = chile_location.df.Residual**2).reset_index().iloc[5136:5142,:]
index Latitude Longitude Magnitude Year Month MagnitudeHat Residual Residual2
5136 16742 30.209 102.862 5.0 2013.0 4.0 4.943022 0.056978 0.003247
5137 16743 30.308 102.888 6.6 2013.0 4.0 5.904218 0.695782 0.484113
5138 16744 39.693 143.258 5.0 2013.0 4.0 5.118611 -0.118611 0.014068
5139 16745 49.965 157.652 6.1 2013.0 4.0 5.852776 0.247224 0.061120
5140 16746 -11.976 121.632 5.8 2013.0 4.0 5.809708 -0.009708 0.000094
5141 16747 -14.966 166.857 5.2 2013.0 4.0 5.145590 0.054410 0.002960