APSG tutorial - Part 5

from apsg import *

Pandas interface

To activate APSG interface for pandas you need to import it.

import pandas as pd
from apsg.pandas import *

We can use pandas to read and manage data. See pandas documentation for more information.

df = pd.read_csv('structures.csv')
df.head()

	site	structure	azi	inc
0	PB3	L3	113	47
1	PB3	L3	118	42
2	PB3	S1	42	79
3	PB3	S1	42	73
4	PB4	S0	195	10

We can split out dataset by type of the structure…

g = df.groupby('structure')

and select only one type…

l = g.get_group('L3')
l.head()

	site	structure	azi	inc
0	PB3	L3	113	47
1	PB3	L3	118	42
5	PB8	L3	167	17
6	PB9	L3	137	9
7	PB9	L3	147	14

Before we can use APSG interface, we need to create column with APSG features. For that we can use apsg accessor and it’s methods create_vecs, create_fols, create_lins or create_faults. Each of this method accepts keyword argument name to provide name of the new column.

l = l.apsg.create_lins(name='L3')
l.head()

	site	structure	azi	inc	L3
0	PB3	L3	113	47	L:113/47
1	PB3	L3	118	42	L:118/42
5	PB8	L3	167	17	L:167/17
6	PB9	L3	137	9	L:137/9
7	PB9	L3	147	14	L:147/14

Once we create column with APSG features, we can use accessors vec, fol, lin or fault providing methods for individual feature types, e.g. to calculate resultant vector

l.lin.R()

L:122/8

or to calculate orientation tensor…

l.lin.ortensor()

OrientationTensor3
[[ 0.29  -0.344 -0.067]
 [-0.344  0.644  0.088]
 [-0.067  0.088  0.065]]
(S1:0.932, S2:0.29, S3:0.216)

or to plot data on stereonet…

l.lin.plot(label=True)

You can also extract APSG column as featureset using accessor property getset

l.lin.getset

L(97) L3

To construct stereonets with more data, you can pass stereonet object using keyword argument snet

s = StereoNet()
l.lin.contour(snet=s)
l.lin.plot(snet=s, label=True)
pp = l.lin.ortensor().eigenfols
s.great_circle(*pp, label='principal\nplanes')
s.show()

f = g.get_group('S2').apsg.create_fols(name='S2')
f.fol.plot()

The fault features could be created from columns containing orientation of fault plane, fault striation and sense of shear (+/-1)

df = pd.read_csv('mele.csv')
df.head()

	fazi	finc	lazi	linc	sense
0	94.997	79.966	119.073	79.032	-1
1	65.923	84.972	154.087	20.008	-1
2	42.354	46.152	109.786	21.778	-1
3	14.093	61.963	295.917	21.045	1
4	126.138	77.947	40.848	21.033	-1

t = df.apsg.create_faults()
t.head()

	fazi	finc	lazi	linc	sense	faults
0	94.997	79.966	119.073	79.032	-1	F:95/80-119/79 -
1	65.923	84.972	154.087	20.008	-1	F:66/85-154/20 -
2	42.354	46.152	109.786	21.778	-1	F:42/46-110/22 -
3	14.093	61.963	295.917	21.045	1	F:14/62-296/21 +
4	126.138	77.947	40.848	21.033	-1	F:126/78-41/21 -

t[:5].fault.plot()