hashwrap
Wrap of HASHv1.2 Fortran library (see Hardebeck & Shearer, 2002)
Getting Started
The HASH f77 subroutines use fixed dimension arrays for efficiency. These array dimensions must be known at compile time, and hence when this module is installed. The dimensions must also be shared with the python side in order to ensure the numpy arrays are correctly sized to match their fortran counterparts.
The relevant parameters are:
- npick0: maximum number of observations (polarities + amp ratios) for an event
- nmc0: maximum number of trials (ray takeoff angles and azims are perturbed for each trial)
- nmax0: maximum number of acceptable mechanisms to output for an event
- dang0: minimum angular spacing (degrees) for mechanism grid search
- ncoor: maximum number of gridpoints
If you do nothing, these values will automatically get set to the default values indicated in hashwrap/fortran_arrays.toml:
parameter(npick0=500, nmc0=500, nmax0=500, dang0=5.0, ncoor=31032)
If you clone the repo (see below), then you can edit fortran_arrays.toml directly to change the array sizes before you install.
However, if you install directly without cloning, then you won't ever see the fortran_arrays.toml file. You can still modify them by setting env vars before running pip install, e.g.,
>export npick0=500
>export nmc0=500
...
>export ncoor=31032
The env vars will override the fortran_arrays.toml values.
Installing
The easiest way is to install directly from either pypi or github:
If you wish to change the default max array sizes (see above), you should set these vals in shell environment vars first.
Install toml
This needs to be installed before you install hashwrap
>pip install toml
Install directly from pypi
>pip install hashwrap
Or, Install from github
>pip install git+https://github.com/mikehagerty/hashwrap.git
Alternatively you can clone the repo and install it:
If you wish to change the default max array sizes (see above), you can either set these vals in shell environment vars first, or change the vals directly in hashwrap/fortran_arrays.toml.
>git clone https://github.com/mikehagerty/hashwrap.git
>cd hashwrap
>pip install .
Using hashwrap
In general, hashwrap exists to support other modules (e.g., the focalmech app in the aqms-pdl repo) and there is no need to interact directly with it.
Nevertheless, you may want to check out the shared Fortran lib and/or run the equivalent of the hash_driver{1,2,3}.f tests that come with HASH v1.2.
Checking everything is kosher
While you won't generally want/need to interact directly with the compiled Fortran shared object library, you can see what it contains:
> python
>>> from hashwrap import libhashpy
>>> print(libhashpy.__doc__)
This module 'libhashpy' is auto-generated with f2py (version:1.23.1).
Functions:
nf,strike,dip,rake,faults,slips = focalamp_mc(p_azi_mc,p_the_mc,sp_amp,p_pol,nmc,dang,maxout,nextra,ntotal,qextra,qtotal,npsta=shape(sp_amp, 0))
mfrac,mavg,stdr = get_misf_amp(p_azi_mc,p_the_mc,sp_ratio,p_pol,str_avg,dip_avg,rak_avg,npol=shape(p_azi_mc, 0))
nf,strike,dip,rake,faults,slips = focalmc(p_azi_mc,p_the_mc,p_pol,p_qual,nmc,dang,maxout,nextra,ntotal,npsta=shape(p_pol, 0))
nsltn,str_avg,dip_avg,rak_avg,prob,rms_diff = mech_prob(norm1in,norm2in,cangle,prob_max,nf=shape(norm1in, 1))
norm1_avg,norm2_avg = mech_avg(norm1,norm2,nf=shape(norm1, 1))
rota = mech_rot(norm1,norm2,slip1,slip2)
v3 = cross(v1,v2)
to_car(the,phi,r,x,y,z)
fpcoor(strike,dip,rake,fnorm,slip,idir)
fran = ran_norm()
mfrac,stdr = get_misf(p_azi_mc,p_the_mc,p_pol,p_qual,str_avg,dip_avg,rak_avg,npol=shape(p_azi_mc, 0))
magap,mpgap = get_gap(p_azi_mc,p_the_mc,npol=shape(p_azi_mc, 0))
sort(ra,n=shape(ra, 0))
ntab = mk_table(ntab)
tt,iflag = get_tts(ip,del,qdep)
layertrace(p1,h1,utop1,ubot1,imth,dx1,dt1,irtr)
flat,flon,felev = getstat_tri(stlfile,snam,scom,snet)
stpol = check_pol(polfile,snam,evyr,evmon,evdy,evhr)
qcor = get_cor(stlfile,snam,scom,snet)
ntab = mk_table_add(ind,vmodel)
COMMON blocks:
/angtable/ table(101,14,10),delttab(101),deptab(14),ndel,ndep
Run the hash_driver{1,2,3} examples
>>> from hashwrap import hashwrapper
>>> ex = hashwrapper.Examples() # Init the Examples class
>>> ex.run_example(1) # Equivalent to hash_driver1.f
You can printout the results in a format very similar to what hash_driver1.f prints out, for direct comparison. By default Examples.printout() only prints the first 10 events:
>>> ex.printout()
icusp event_info str dip rke [str2 dip2 rke2] agap pgap stdr msf rms_ang npol nspr mavg Q useAmp
3143312 1994-01-21 11:04 <34.242, -118.618> h:18.130000 [cusp id:3143312] 134 48 143 [250 63 47] 84.0 17.0 66 0.090 24.51 30 0 N/A [Q: A]
3145744 1994-01-25 10:05 <34.241, -118.621> h:18.540000 [cusp id:3145744] 280 47 54 [147 53 122] 44.0 15.0 63 0.172 31.77 33 0 N/A [Q: B]
3146815 1994-01-28 07:44 <34.239, -118.621> h:18.960000 [cusp id:3146815] 137 45 132 [264 58 55] 32.0 11.0 50 0.130 15.44 73 0 N/A [Q: A]
3146907 1994-01-28 15:47 <34.238, -118.611> h:17.790000 [cusp id:3146907] 297 39 99 [105 51 82] 67.0 20.0 61 0.069 33.02 23 0 N/A [Q: B]
3147167 1994-01-29 07:52 <34.238, -118.625> h:19.200000 [cusp id:3147167] 141 54 107 [292 38 67] 36.0 16.0 57 0.097 21.14 55 0 N/A [Q: A]
3148047 1994-01-31 07:13 <34.242, -118.615> h:17.920000 [cusp id:3148047] 139 49 111 [288 45 66] 35.0 15.0 61 0.070 28.07 39 0 N/A [Q: B]
3149674 1994-02-05 07:11 <34.240, -118.617> h:18.200000 [cusp id:3149674] 131 47 112 [280 47 67] 47.0 15.0 59 0.146 25.57 50 0 N/A [Q: B]
3150936 1994-02-10 04:19 <34.242, -118.620> h:18.310000 [cusp id:3150936] 143 59 130 [264 48 42] 43.0 15.0 49 0.053 18.96 57 0 N/A [Q: B]
3150947 1994-02-10 04:46 <34.241, -118.620> h:18.450000 [cusp id:3150947] 144 54 130 [268 51 47] 35.0 15.0 54 0.084 19.93 50 0 N/A [Q: A]
3151649 1994-02-13 09:04 <34.238, -118.613> h:17.740000 [cusp id:3151649] 129 47 110 [279 46 68] 42.0 16.0 54 0.101 24.09 33 0 N/A [Q: A]
By default, when sending the hash_driver output to a file, all events are included. To run hash_driver3.f and send all rows to a file, do:
>>> ex.run_example(3)
>>> ex.printout('example3.out')
HASH references
- Hardebeck, Jeanne L. and Peter M. Shearer, A new method for determining first- motion focal mechanisms, Bulletin of the Seismological Society of America, 92, 2264-2276, 2002.
- Hardebeck, Jeanne L. and Peter M. Shearer, Using S/P Amplitude Ratios to Constrain the Focal Mechanisms of Small Earthquakes, Bulletin of the Seismological Society of America, 93, 2434-2444, 2003.
Acknowledgments
- This code began as a fork of Mark Williams' HASHpy repo. It has diverged quite a bit but still retains several of his ideas and utility functions.
