In [26]:

    

%matplotlib inline
%config InlineBackend.figure_format = 'retina'


    

In [1]:

    

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import halfspace.projections as hsp


    

In [3]:

    

fdf = pd.read_csv('../data/fault_data/fault_df_inv.csv')


    

In [5]:

    

fdf.fault_name.unique()


    

    
Out[5]:





array(['gurla', 'tib_dog', nan, 'MHT_rupture'], dtype=object)

In [6]:

    

fdf[fdf.fault_name == 'MHT_rupture'].to_csv('../data/fault_data/mht_rup_stresses.csv')


    

In [52]:

    

fdf.head()


    

    
Out[52]:






  

    

      

      
EVENT_ID
      
depth
      
dip
      
east
      
fault_name
      
htm_ind
      
north
      
point_index
      
rake
      
sig_nn
      
...
      
xx_stress
      
xy_stress
      
xz_stress
      
yy_stress
      
yz_stress
      
zz_stress
      
txx
      
tyy
      
txy
      
tau_rake
    
  
  

    

      
0
      
NaN
      
-18166.666667
      
70.619256
      
-55729.125497
      
gurla
      
NaN
      
3341329.054816
      
0
      
-170.584876
      
142.473296
      
...
      
57.983532
      
38.709333
      
-3.130255
      
140.814060
      
-7.833799
      
122.365046
      
-9.231886
      
72.850762
      
37.446496
      
145.197086
    
    

      
1
      
NaN
      
-16527.333333
      
73.934283
      
-54657.964479
      
gurla
      
NaN
      
3341155.562365
      
1
      
-169.672261
      
133.973200
      
...
      
60.434603
      
35.630217
      
-2.546067
      
136.122469
      
-7.169587
      
122.802498
      
-8.398814
      
66.276816
      
34.067379
      
161.215385
    
    

      
2
      
NaN
      
-13288.333333
      
68.460230
      
-53918.655475
      
gurla
      
NaN
      
3341974.752790
      
2
      
-166.248248
      
125.511555
      
...
      
65.652053
      
29.637668
      
-1.578494
      
127.308890
      
-5.545806
      
123.452559
      
-6.752828
      
53.287993
      
27.390909
      
165.825389
    
    

      
3
      
NaN
      
-11725.000000
      
69.749617
      
-54321.288308
      
gurla
      
NaN
      
3343089.493691
      
3
      
-168.232733
      
124.935294
      
...
      
68.381670
      
26.899948
      
-1.128569
      
123.375707
      
-4.596917
      
123.926082
      
-5.958378
      
47.018818
      
24.168449
      
166.059318
    
    

      
4
      
NaN
      
-18510.000000
      
70.715283
      
-57216.150551
      
gurla
      
NaN
      
3342311.810013
      
4
      
-168.045877
      
137.953272
      
...
      
57.582461
      
39.310817
      
-3.483587
      
141.945827
      
-7.801336
      
122.374934
      
-9.406360
      
74.227575
      
38.154200
      
157.032293
    
  

5 rows × 24 columns

In [53]:

    

fdf.describe()


    

    
Out[53]:






  

    

      

      
EVENT_ID
      
depth
      
dip
      
east
      
htm_ind
      
north
      
point_index
      
rake
      
sig_nn
      
slip_m
      
...
      
xx_stress
      
xy_stress
      
xz_stress
      
yy_stress
      
yz_stress
      
zz_stress
      
txx
      
tyy
      
txy
      
tau_rake
    
  
  

    

      
count
      
7.600000e+01
      
796.000000
      
796.000000
      
796.000000
      
110
      
796.000000
      
7.960000e+02
      
796.000000
      
796.000000
      
791.000000
      
...
      
796.000000
      
796.000000
      
796.000000
      
796.000000
      
796.000000
      
796.000000
      
796.000000
      
796.000000
      
796.000000
      
796.000000
    
    

      
mean
      
3.935226e+07
      
-8596.994640
      
49.715302
      
103792.898077
      
9999
      
3264692.026692
      
3.757408e+06
      
-40.874890
      
99.106532
      
0.199132
      
...
      
62.803925
      
18.904720
      
-4.811861
      
101.751427
      
-5.099441
      
98.799977
      
-4.368797
      
34.475098
      
17.720770
      
-42.590003
    
    

      
std
      
1.334292e+08
      
6930.771604
      
19.525128
      
201118.793916
      
0
      
116465.472302
      
4.258484e+07
      
128.329104
      
36.540323
      
0.363824
      
...
      
21.930522
      
13.745025
      
5.699524
      
39.933768
      
7.668224
      
37.958111
      
3.522060
      
27.793321
      
14.286226
      
106.300174
    
    

      
min
      
2.440890e+05
      
-25418.900000
      
4.000000
      
-125465.156335
      
9999
      
2957849.113062
      
0.000000e+00
      
-179.958757
      
5.157330
      
0.001200
      
...
      
2.621615
      
-7.982639
      
-26.264985
      
9.425106
      
-34.373000
      
4.583153
      
-12.917306
      
-11.753702
      
-6.041597
      
-179.931587
    
    

      
25%
      
1.176039e+06
      
-13780.833333
      
41.316580
      
-59747.377654
      
9999
      
3189377.300000
      
8.075000e+01
      
-159.755383
      
85.179862
      
0.100000
      
...
      
50.487581
      
7.557628
      
-8.751820
      
78.720090
      
-8.519040
      
75.089044
      
-7.003105
      
10.268282
      
5.278066
      
-136.728661
    
    

      
50%
      
9.889522e+06
      
-7360.833333
      
55.937968
      
99214.916667
      
9999
      
3218741.016667
      
1.850000e+02
      
-82.604206
      
111.446053
      
0.100000
      
...
      
68.475905
      
17.159029
      
-2.991214
      
108.946627
      
-3.537304
      
118.206600
      
-3.740608
      
29.517926
      
15.172702
      
-74.160090
    
    

      
75%
      
1.339640e+07
      
-2560.583333
      
64.100759
      
127861.083333
      
9999
      
3379423.573963
      
3.040000e+02
      
80.164675
      
120.417991
      
0.100000
      
...
      
76.790727
      
29.405069
      
-0.511337
      
131.812505
      
-0.741059
      
125.378995
      
-1.301230
      
55.262984
      
28.406087
      
36.896209
    
    

      
max
      
6.014569e+08
      
2931.000000
      
89.000000
      
990874.017750
      
9999
      
3429747.093679
      
6.014569e+08
      
179.959805
      
179.956163
      
3.106400
      
...
      
110.600214
      
51.766289
      
7.764514
      
177.686089
      
28.835635
      
167.383188
      
1.489467
      
101.933187
      
52.395343
      
179.894104
    
  

8 rows × 23 columns

In [54]:

    

fdf.columns


    

    
Out[54]:





Index(['EVENT_ID', 'depth', 'dip', 'east', 'fault_name', 'htm_ind', 'north',
       'point_index', 'rake', 'sig_nn', 'slip_m', 'strike', 'tau_dd', 'tau_ss',
       'xx_stress', 'xy_stress', 'xz_stress', 'yy_stress', 'yz_stress',
       'zz_stress', 'txx', 'tyy', 'txy', 'tau_rake'],
      dtype='object')

In [55]:

    

plt.figure(figsize=(15,10))

plt.scatter(fdf.rake, 
            fdf.tau_rake,
            c=fdf.depth,
            s=fdf.slip_m * 130, lw=0)
plt.colorbar()
plt.xlabel('slip rake')
plt.ylabel('shear stress rake')
plt.show()


    

In [56]:

    

fdf.head()


    

    
Out[56]:






  

    

      

      
EVENT_ID
      
depth
      
dip
      
east
      
fault_name
      
htm_ind
      
north
      
point_index
      
rake
      
sig_nn
      
...
      
xx_stress
      
xy_stress
      
xz_stress
      
yy_stress
      
yz_stress
      
zz_stress
      
txx
      
tyy
      
txy
      
tau_rake
    
  
  

    

      
0
      
NaN
      
-18166.666667
      
70.619256
      
-55729.125497
      
gurla
      
NaN
      
3341329.054816
      
0
      
-170.584876
      
142.473296
      
...
      
57.983532
      
38.709333
      
-3.130255
      
140.814060
      
-7.833799
      
122.365046
      
-9.231886
      
72.850762
      
37.446496
      
145.197086
    
    

      
1
      
NaN
      
-16527.333333
      
73.934283
      
-54657.964479
      
gurla
      
NaN
      
3341155.562365
      
1
      
-169.672261
      
133.973200
      
...
      
60.434603
      
35.630217
      
-2.546067
      
136.122469
      
-7.169587
      
122.802498
      
-8.398814
      
66.276816
      
34.067379
      
161.215385
    
    

      
2
      
NaN
      
-13288.333333
      
68.460230
      
-53918.655475
      
gurla
      
NaN
      
3341974.752790
      
2
      
-166.248248
      
125.511555
      
...
      
65.652053
      
29.637668
      
-1.578494
      
127.308890
      
-5.545806
      
123.452559
      
-6.752828
      
53.287993
      
27.390909
      
165.825389
    
    

      
3
      
NaN
      
-11725.000000
      
69.749617
      
-54321.288308
      
gurla
      
NaN
      
3343089.493691
      
3
      
-168.232733
      
124.935294
      
...
      
68.381670
      
26.899948
      
-1.128569
      
123.375707
      
-4.596917
      
123.926082
      
-5.958378
      
47.018818
      
24.168449
      
166.059318
    
    

      
4
      
NaN
      
-18510.000000
      
70.715283
      
-57216.150551
      
gurla
      
NaN
      
3342311.810013
      
4
      
-168.045877
      
137.953272
      
...
      
57.582461
      
39.310817
      
-3.483587
      
141.945827
      
-7.801336
      
122.374934
      
-9.406360
      
74.227575
      
38.154200
      
157.032293
    
  

5 rows × 24 columns

In [57]:

    

fdf['']


    

    




---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-57-e7551f52d670> in <module>()
----> 1 fdf['']

/Users/itchy/src/anaconda/lib/python3.4/site-packages/pandas/core/frame.py in __getitem__(self, key)
   1795             return self._getitem_multilevel(key)
   1796         else:
-> 1797             return self._getitem_column(key)
   1798 
   1799     def _getitem_column(self, key):

/Users/itchy/src/anaconda/lib/python3.4/site-packages/pandas/core/frame.py in _getitem_column(self, key)
   1802         # get column
   1803         if self.columns.is_unique:
-> 1804             return self._get_item_cache(key)
   1805 
   1806         # duplicate columns & possible reduce dimensionaility

/Users/itchy/src/anaconda/lib/python3.4/site-packages/pandas/core/generic.py in _get_item_cache(self, item)
   1082         res = cache.get(item)
   1083         if res is None:
-> 1084             values = self._data.get(item)
   1085             res = self._box_item_values(item, values)
   1086             cache[item] = res

/Users/itchy/src/anaconda/lib/python3.4/site-packages/pandas/core/internals.py in get(self, item, fastpath)
   2849 
   2850             if not isnull(item):
-> 2851                 loc = self.items.get_loc(item)
   2852             else:
   2853                 indexer = np.arange(len(self.items))[isnull(self.items)]

/Users/itchy/src/anaconda/lib/python3.4/site-packages/pandas/core/index.py in get_loc(self, key, method)
   1570         """
   1571         if method is None:
-> 1572             return self._engine.get_loc(_values_from_object(key))
   1573 
   1574         indexer = self.get_indexer([key], method=method)

pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:3824)()

pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:3704)()

pandas/hashtable.pyx in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12280)()

pandas/hashtable.pyx in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12231)()

KeyError: ''

In [58]:

    

tdf = fdf[fdf.fault_name=='tib_dog']


    

In [61]:

    

plt.figure(figsize=(15,8))
plt.scatter(tdf.east, tdf.north, c=tdf.tau_rake, 
            s=50, lw=0)
plt.colorbar()

plt.axis('equal')
plt.show()


    

In [60]:

    

plt.figure(figsize=(15,8))
plt.scatter(tdf.east, tdf.north, c=tdf.tau_dd, 
            s=50, lw=0)
plt.colorbar()

plt.axis('equal')
plt.show()


    

In [39]:

    

plt.figure(figsize=(15,8))
plt.scatter(tdf.east, tdf.north, c=tdf.rake, lw=0)
plt.colorbar()
plt.show()


    

In [40]:

    

plt.figure(figsize=(15,8))
plt.scatter(tdf.east, tdf.north, c=tdf.strike, lw=0)
plt.colorbar()
plt.show()


    

In [41]:

    

plt.figure(figsize=(15,8))
plt.scatter(tdf.east, tdf.north, c=tdf.dip, lw=0)
plt.colorbar()
plt.show()


    

In [42]:

    

gdf = fdf[fdf.fault_name=='gurla']


    

In [43]:

    

plt.figure(figsize=(15,8))
plt.scatter(gdf.east, gdf.north, c=gdf.dip, lw=0)
plt.colorbar()
plt.show()


    

In [44]:

    

plt.figure(figsize=(15,8))
plt.scatter(gdf.east, gdf.north, c=hsp.unwrap_angle(gdf.strike), lw=0)
plt.colorbar()
plt.show()


    

In [48]:

    

plt.figure(figsize=(15,8))
plt.scatter(gdf.east, gdf.north, c=gdf.tau_rake, lw=0)
plt.colorbar()
plt.show()


    

In [ ]: