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Patagonia ambient noise tomography

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do0_get_ENAP_mseed.py
    get ENAP files from /DB/, clean ANMA bad timing days, resample to 1Hz, and write day files

do0_make_alt_response_file.py
    read stationxml files for each network, pull stations that are used here, write a single 
    xml file and a single dataless file with all station info combined

do1_cat_and_convert_mseed.py
    list dates that we have data for, all networks, and cat into single-day, all-station
    miniseed files (pulling from ENAP and IRIS downloads). Call rdseed with combined
    dataless file to make seed for seed2cor.

do2_generate_seedlist.py
    make a list of seedfiles to run through seed2cor

do2_generate_station_list.py
    make a list of stations and their coordinates for seed2cor. *note* lat/lon order changed
    when version of seed2cor changed.

do3_cor.sh
    RUN seed2cor with hardcorded number of threads

do4_stack_cor.py
    read sac files from seed2cor, stack monthly cross-correlations into one stack per
    station pair. Do the same for overlapping month triplets for later std calc.

do5_symmetrize_cor.py
    symmetrize stacked cross correlations (for SNR calc)

do6_calc_snr.sh
    run the spectral_snr_s2c code to get SNR for full xcorr and 3-month xcorr

do7_ftan_input_list.py
    Write list input file for aftan: pulls filenames and uses snr for period bounds, 
    the rest is basically hard-coded

do8_aftan.sh
    run aftan

**do9_make_disp_curves.py [** -> run locally, not on server]
    read aftan output and make/pickle DispersionCurve python objects

**do10_params_tomo.py
    test tomo parameters

**do10_jackknife_stations.py
    run tomography with one station excluded in each pass

**do10_tomo_npass.py
    actually run tomography with 3-pass, with one set of parameters, and pickle vmaps

ak135_phvel.dat
    disp curve reference for aftan (hard-coded name/path)

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plot_aftan.py
    plot dispersion curves and amplitude maps from aftan outputs
        [note that station coord format may be messed up for mapping; easy to fix]

plot_lcurves.py
    plot some of the L-curve stuff in a 3D scatter. Doesn't actually do much but
        can be a reminder of relevant commands for parsing that info.

station_disp_curve.py
    read EQ and ANT maps, get dispersion curves at each station location and plot

compare_ant_eq.py
    plot EQ and ANT tomo maps side by side, on the same velocity scale, to see just how
        far off they are :(

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seed/
    miniseed files with all the input data
        1P/ and YJ/ are all the IRIS stuff (unpacked but not organized)
        ENAP/ is the resampled miniseed files pulled from /DB/
        dayfiles/ is the catted single-day, all-station files used for seed2cor
            (both miniseed and fullseed)
        datalesss/ is the dataless seed headers for ALL stations, all networks

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requests/
    Data_request_*.py
        script to automate breq_fast requests to IRIS
    there are two scripts because one was used for the discrete 1P and YJ periods and 
        the other dealt with the longer-term permanent network data

        For later: when more 1P data is available, you'll want _1_

    decrypt_untar.sh
        use openssl key to decrypt the embargoed 1P data
        Note that openssl version matters in interesting ways

    just_untar.sh
        YJ data (and permanent stations on their own) are not embargoed, hence not encrypted

    northchile_sta.lst
        YJ stations, for breq_fast request script

    southchile_sta.lst
        1P and permanent stations, for breq_fast request script

    permanent_sta.lst
        just the permanent stations, for breq_fast request script

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old/
    bits and pieces, some pysismo stuff and some other stuff that's all no longer used