Commit f36d5053 authored by Sebastian Heimann's avatar Sebastian Heimann

updated readme

parent a107c144
......@@ -30,17 +30,17 @@ sudo python setup.py install
Grond can be run as a command line tool or by calling Grond's library functions
from a Python script. To get a brief description on available options of
Grond's command line tool, run `grond --help` or `grond <subcommand> --help`.
Once dataset and configuration are ready, the command `grond go <configfile>
<eventname>` starts the optimization algorithm for a selected event. Before
running the optimization, to debug problems with the dataset and configuration,
use `grond check <configfile> <eventname>`. To get a list of event names
available in a configured setup, run `grond events <configfile>`. During the
optimization, results are aggregated in a directory, referred to in the
configuration as `<rundir>`. To visualize the results run `grond plot
<plotnames> <rundir>`. The results can be exported in various ways by running
the subcommand `grond export <what> <rundir>`. Finally, you may run `grond
report <rundir>` to aggregate results to a browsable summary, (by default)
under the directory `reports`.
Once dataset and configuration are ready, the command
`grond go <configfile> <eventname>` starts the optimization algorithm for a
selected event. Before running the optimization, to debug problems with the
dataset and configuration, use `grond check <configfile> <eventname>`. To get a
list of event names available in a configured setup, run
`grond events <configfile>`. During the optimization, results are aggregated in
a directory, referred to in the configuration as `<rundir>`. To visualize the
results run `grond plot <plotnames> <rundir>`. The results can be exported in
various ways by running the subcommand `grond export <what> <rundir>`. Finally,
you may run `grond report <rundir>` to aggregate results to a browsable
summary, (by default) under the directory `reports`.
## Example configuration file
......@@ -143,18 +143,18 @@ target_groups:
ffactor: 1.5
# Time window to include in the data fitting. Times can be defined offset
# to given phase arrivals. E.g. '{stored:begin}-100' would mean 100 s
# before arrival of the phase named 'begin', which must be defined in the
# to given phase arrivals. E.g. '{stored:begin}-100' would mean 100 s
# before arrival of the phase named 'begin', which must be defined in the
# travel time tables in the GF store.
tmin: '{stored:anyP_no_Pdiff}'
tmax: '{vel_surface:2.5}'
# Align traces by picks (will lose some control on origin time and
# location). Define the synthetic phasename, for which a travel time table
# must be available in the GF store,
# Align traces by picks (will lose some control on origin time and
# location). Define the synthetic phasename, for which a travel time table
# must be available in the GF store,
#pick_synthetic_traveltime: 'anyP_no_Pdiff'
# and the name of the picks to use in the picks file (defined in
# and the name of the picks to use in the picks file (defined in
# dataset_config)
#pick_phasename: 'P'
......@@ -172,7 +172,7 @@ target_groups:
norm_exponent: 1
# How to interpolate the Green's functions (available choices:
# 'nearest_neighbor', 'multilinear'). Note that the GFs have to be densely
# 'nearest_neighbor', 'multilinear'). Note that the GFs have to be densely
# sampled when using interpolation other than nearest_neighbor.
interpolation: 'nearest_neighbor'
......@@ -248,16 +248,16 @@ problem_config: !grond.CMTProblemConfig
# spreading etc.
apply_balancing_weights: true
# Under what norm to combine targets into the global misfit
# Under what norm to combine targets into the global misfit
# (exponent of norm, 1 or 2)
norm_exponent: 1
# -----------------------------------------------------------------------------
# Configuration of the optimization procedure
# Configuration of the optimization procedure. The following example setup will
# run a Bayesian bootstrap optimization (BABO).
# -----------------------------------------------------------------------------
# This configuration will run the BABO (Bayesian Bootstrap) optimization
optimizer_config: !grond.HighScoreOptimizerConfig
# Number of bootstrap realizations to be tracked simultaneously in the
......@@ -276,10 +276,10 @@ optimizer_config: !grond.HighScoreOptimizerConfig
# Number of iterations to operate in 'directed' phase
niterations: 10000
# Multiplicator for width of sampler distribution at end of this phase
# Multiplicator for width of sampler distribution to start with
scatter_scale_begin: 2.0
# Multiplicator for width of sampler distribution at end of this phase
# Multiplicator for width of sampler distribution at end of this phase
scatter_scale_end: 0.5
......
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