Metadata-Version: 2.1
Name: jrfapp
Version: 0.0.54
Summary: Joint inversion of Receiver function and Apparant velocity
Author-email: Mohammad Veisi <m.veisi9687@gmail.com>
License: MIT License
        
        Copyright (c) [year] [fullname]
        
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Project-URL: Homepage, https://github.com/mveisi/jrfapp
Classifier: Development Status :: 3 - Alpha
Classifier: Operating System :: POSIX :: Linux
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.5
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: obspy
Requires-Dist: rf
Requires-Dist: matplotlib

# Jrfapp Package
## Introduction:
### The Jrfapp stands for joint inversion of the Receiver Function and apparent velocity data. This is a python package to perform joint inversion of these datasets and outputs an estimated shear velocity model. For more info see "manuscript title".

## Installation:
To run this code, you will need the following software and tools:

- Computer Program in Seismology
- Python 3.8
- matplotlib
- numpy
- obspy
- rf
1. You can install Computer Program in Seismology (CPS) from [here](https://www.eas.slu.edu/eqc/eqccps.html).
> You need to path the binary of CPS in your .bashrc. Before proceeding to the next step make sure that this program
> is installed correctly.
> This package depends on the hrftn96. If you installed CPS correctly and included it in your .bashrc the output of
> hrftn96 in the terminal should look like this:
`
>> Model not specified
>> USAGE: hrftn96 [-P] [-S] [-2] [-r] [-z] -RAYP p -ALP alpha -DT dt -NSAMP nsamp -M model
>> -P           (default true )    Incident P wave
>> -S           (default false)    Incident S wave
>> -RAYP p      (default 0.05 )    Ray parameter in sec/km
>> -DT dt       (default 1.0  )    Sample interval for synthetic
>> -NSAMP nsamp (default 512  )    Number samples for synthetic
>> -M   model   (default none )    Earth model name
>> -ALP alp     (default 1.0  )    Number samples for synthetic
>>      H(f) = exp( - (pi freq/alpha)**2) 
>>      Filter corner ~ alpha/pi 
>> -2           (default false)    Use 2x length internally
>> -r           (default false)    Output radial   time series
>> -z           (default false)    Output vertical time series
>>      -2  (default false) use double length FFT to
>>      avoid FFT wrap around in convolution 
>> -D delay     (default 5 sec)    output delay sec before t=0
>> -?                   Display this usage message
>> -h                   Display this usage message
>>  SAC header values set by hrftn96
>>   B     :  delay
>>   USERO :  gwidth        KUSER0:  Rftn
>>   USER4 :  rayp (sec/km)
>>   USER5 :  fit in % (set at 100)
>>   KEVNM :  Rftn          KUSER1:  hrftn96
>> The program creates the file names hrftn96.sac
>> This is the receiver fucntion, Z or R trace according to the command line flag
`

2. All the python package requires for Jrfapp and this package can be installed by `pip install jrfapp`.

<div class="alert alert-block alert-info">
<b>Tip:</b> I highly recommend creating a conda environment and installing the package in this environment. 
</div>

## Examples:
I have included four tutorials on the GitHub page that explain the main usage of the package. 


