adapting log and exception convention

2 years ago · 5ef64c565c
--- a/examples/grond-playground-insar/config/insar_rectangular.gronf
+++ b/examples/grond-playground-insar/config/insar_rectangular.gronf
@ -1,197 +0,0 @@
 --- !grond.Config

 # All file paths referenced below are treated relative to the location of this
 # configuration file, here we may give a common prefix. E.g. setting it to '..'
 # if the configuration file is in the sub-directory '${project_root}/config'
 # allows us to give the paths below relative to '${project_root}'.
 path_prefix: '..'

 # Path, where to store output (run directories). The placeholder
 # '${problem_name}' will be expanded to a name configured below in
 # problem_config.name_template and will typically include a config identifier
 # and the event name.
 rundir_template: runs/${problem_name}.grun

 # If given, restrict to processing of listed events
 #event_names:
 #- 'gfz2018pmjk'


 # -----------------------------------------------------------------------------
 # Configuration section for dataset (input data)
 #
 # The placeholder '${event_name}' will be expanded to the current event. This
 # enables us to use the same configuration for multiple events. The available 
 # events are detected by looking into possible expansions of
 # dataset_config.events_path
 # -----------------------------------------------------------------------------

 dataset_config: !grond.DatasetConfig
  
  # File with hypocenter information and possibly reference solution
  events_path: 'data/events/${event_name}/event.txt'

  # List of directories for the InSAR scenes
  kite_scene_paths: ['data/events/${event_name}/insar']

 # -----------------------------------------------------------------------------
 # Configuration section for the observational data fitting
 #
 # This defines the objective function to be minimized in the optimisation. It
 # can be composed of one or more contributions, each represented by a
 # !grond.*TargetGroup section.
 # -----------------------------------------------------------------------------

 target_groups:
 - !grond.SatelliteTargetGroup

  # Normalisation family (see the Grond documentation for how it works).
  # Use distinct normalisation families when mixing misfit contributors with
  # different magnitude scaling, like e.g. cross-correlation based misfit and 
  # time-domain Lx norm.
  normalisation_family: 'static'

  # Just a name used to identify targets from this group. Use dot-separated path
  # notation to group related contributors
  path: 'insar'

  # How to weight contributions from this group in the global misfit
  weight: 1.0

  # Selector for kite scene ids, '*all' is a wildcard and load all scenes present
  kite_scenes: ['*all']

  # Subsection on how to fit the traces
  misfit_config: !grond.SatelliteMisfitConfig

    # Optimise a planar orbital ramp
    optimise_orbital_ramp: false

    # Parameters for the orbital ramp
    ranges:

      # Vertical offset in [m]
      offset: -0.5 .. 0.5

      # Ranges for the East-West and North-South inclination of the ramp
      ramp_east: -1e-4 .. 1e-4
      ramp_north: -1e-4 .. 1e-4

  # How to interpolate the Green's functions (available choices:
  # 'nearest_neighbor', 'multilinear'). Choices other than 'nearest_neighbor'
  # may require dense GF stores to avoid aliasing artifacts in the forward 
  # modelling.
  interpolation: multilinear

  # Name of the GF Store to use
  store_id: crust2_ib_static


 # -----------------------------------------------------------------------------
 # Definition of the problem to be solved
 #
 # In this section the source model to be fitted is chosen, the parameter space
 # defined, and how to combine the misfit contributions defined in the
 # target_groups section above.
 # 
 # The marker !grond.RectangularProblemConfig selects a finite rectancular
 # source model. 
 # -----------------------------------------------------------------------------
 problem_config: !grond.RectangularProblemConfig

  # Name used to identify the output
  name_template: rect_2009LaAquila

  # How to combine the target misfits. For L1 norm: 1, L2 norm: 2, etc.
  norm_exponent: 2

  # Definition of model parameter space to be searched in the optimisation
  ranges:

    # Scaling ranges in [m]
    depth: 2500 .. 7000
    east_shift: 0 .. 20000
    north_shift: 0 .. 20000
    length: 5000 .. 10000
    width: 2000 .. 10000
    slip: .2 .. 2

    # Orientation ranges in [deg]
    dip: 10 .. 50
    rake: 120 .. 360
    strike: 220 .. 360

  # We are using a dense GF store and will reduce the number of discrete
  # subsources by this factor. Decrease the decimation for a finer sub-source
  # resolution, and increased computational time
  decimation_factor: 12

  # Clearance distance around stations (no models with origin closer than this
  # distance to any station are produced by the sampler)
  distance_min: 0.0

 # -----------------------------------------------------------------------------
 # Configuration of pre-optimisation analysis phases. 
 # determined during this phase.
 # -----------------------------------------------------------------------------
 #
 analyser_configs:

 # DOES NOT APPLY FOR INSAR! Balancing weights are determined with this analyser
 - !grond.TargetBalancingAnalyserConfig
  niterations: 1000

 # -----------------------------------------------------------------------------
 # Configuration of the optimisation procedure
 # -----------------------------------------------------------------------------
 optimiser_config: !grond.HighScoreOptimiserConfig
  # Number of bootstrap realisations to be tracked simultaneously in the
  # optimisation
  nbootstrap: 25

  # Stages of the sampler then narrow down to the interesting regions
  # (!grond.DirectedSamplerPhase).
  sampler_phases:

  # Start with uniform sampling of the model space
  - !grond.UniformSamplerPhase
    # Number of iterations
    niterations: 10000

    # How often we shall try to find a valid sample
    ntries_preconstrain_limit: 1000

  # Narrow down to the interesting regions
  - !grond.DirectedSamplerPhase
    # Number of iterations
    niterations: 40000

    # How often we shall try to find a valid sample
    ntries_preconstrain_limit: 1000

    # Multiplicator for width of sampler distribution at start of this phase
    scatter_scale_begin: 2.0

    # Multiplicator for width of sampler distribution at end of this phase
    scatter_scale_end: 0.5

    starting_point: excentricity_compensated
    sampler_distribution: normal
    standard_deviation_estimator: median_density_single_chain
    ntries_sample_limit: 2000

  # This parameter determines the length of the chains
  chain_length_factor: 8.0

 # -----------------------------------------------------------------------------
 # Configuration section for synthetic seismogram engine
 #
 # Configures where to look for GF stores.
 # -----------------------------------------------------------------------------
 engine_config: !grond.EngineConfig

  # Whether to use GF store directories listed in ~/.pyrocko/config.pf
  gf_stores_from_pyrocko_config: true

  # List of directories with GF stores
  gf_store_superdirs: ['gf_stores']
--- a/examples/grond-playground-insar/config/insar_rectangular.gronf
+++ b/examples/grond-playground-insar/config/insar_rectangular.gronf
@ -0,0 +1 @@
 ../../../src/data/init/example_insar_rectangular.gronf
--- a/examples/grond-playground-regional/config/regional_cmt.gronf
+++ b/examples/grond-playground-regional/config/regional_cmt.gronf
@ -1,295 +0,0 @@
 %YAML 1.1
 --- !grond.Config

 # All file paths referenced below are treated relative to the location of this
 # configuration file, here we may give a common prefix. E.g. setting it to '..'
 # if the configuration file is in the sub-directory '${project_root}/config'
 # allows us to give the paths below relative to '${project_root}'.
 path_prefix: '..'

 # Path, where to store output (run directories). The placeholder
 # '${problem_name}' will be expanded to a name configured below in
 # problem_config.name_template and will typically include a config identifier
 # and the event name.
 rundir_template: 'runs/${problem_name}.grun'

 # If given, restrict to processing of listed events
 #event_names:
 #- 'gfz2018pmjk'


 # -----------------------------------------------------------------------------
 # Configuration section for dataset (input data)
 #
 # The placeholder '${event_name}' will be expanded to the current event. This
 # enables us to use the same configuration for multiple events. The available 
 # events are detected by looking into possible expansions of
 # dataset_config.events_path
 # -----------------------------------------------------------------------------

 dataset_config: !grond.DatasetConfig

  # List of files with station coordinates.
  stations_stationxml_paths: 
  - 'data/events/${event_name}/waveforms/stations.geofon.xml'
  - 'data/events/${event_name}/waveforms/stations.iris.xml'

  # File with hypocenter information and possibly reference solution
  events_path: 'data/events/${event_name}/event.txt'

  # List of directories with raw waveform data
  waveform_paths: ['data/events/${event_name}/waveforms/raw']

  # List of stations/components to be excluded according to their STA, NET.STA,
  # NET.STA.LOC, or NET.STA.LOC.CHA codes
  blacklist: ['GE.UGM', 'GE.PLAI']

  # List of files with additional exclusion lists (one entry per line, same 
  # format as above)
  blacklist_paths:
  - 'data/events/${event_name}/waveforms/blacklist.txt'

  # List of files with instrument response information (can be the same as in
  # stations_stationxml_paths above)
  responses_stationxml_paths:
  - 'data/events/${event_name}/waveforms/stations.geofon.xml'
  - 'data/events/${event_name}/waveforms/stations.iris.xml'


 # -----------------------------------------------------------------------------
 # Configuration section for the observational data fitting
 #
 # This defines the objective function to be minimized in the optimisation. It
 # can be composed of one or more contributions, each represented by a
 # !grond.*TargetGroup section.
 # -----------------------------------------------------------------------------

 target_groups:
 - !grond.WaveformTargetGroup

  # Normalisation family (see the Grond documentation for how it works).
  # Use distinct normalisation families when mixing misfit contributors with
  # different magnitude scaling, like e.g. cross-correlation based misfit and 
  # time-domain Lx norm.
  normalisation_family: 'td'

  # Just a name used to identify targets from this group. Use dot-separated path
  # notation to group related contributors
  path: 'td.love'

  # Epicentral distance range of stations to be considered in meter
  distance_min: 1e3
  distance_max: 900e3

  # Names of components to be included. Available: N=north, E=east, Z=vertical
  # (up), R=radial (away), T=transverse (right)
  channels: ['T']

  # How to weight contributions from this group in the global misfit
  weight: 1.0

  # subsection on how to fit the traces
  misfit_config: !grond.WaveformMisfitConfig

    # Frequency band [Hz] of acausal filter (flat part of frequency taper)
    fmin: 0.01
    fmax: 0.05

    # Factor defining fall-off of frequency taper (zero at fmin/ffactor and
    # fmax*ffactor)
    ffactor: 1.5

    # Time window to include in the data fitting. Times can be defined offset
    # to given phase arrivals. E.g. '{stored:P}-600' would mean 600 s
    # before arrival of the phase named 'P'. The phase must be defined in the
    # travel time tables in the GF store.
    tmin: '{stored:any_P}'
    tmax: '{vel_surface:2.5}'
    # tfade: 120.0

    # How to fit the data (available: 'time_domain', 'frequency_domain',
    # 'log_frequency_domain', 'absolute', 'envelope', 'cc_max_norm')
    domain: 'time_domain'
    tautoshift_max: 4.0
    autoshift_penalty_max: 0.05
    
    # For L1 norm: 1, L2 norm: 2, etc.
    norm_exponent: 1

  # How to interpolate the Green's functions (available choices:
  # 'nearest_neighbor', 'multilinear'). Choices other than 'nearest_neighbor'
  # may require dense GF stores to avoid aliasing artifacts in the forward 
  # modelling.
  interpolation: 'nearest_neighbor'

  # Name of the GF Store to use
  store_id: 'crust2_j3'


 - !grond.WaveformTargetGroup

  # Normalisation family (see the Grond documentation for how it works).
  # Use distinct normalisation families when mixing misfit contributors with
  # different magnitude scaling, like e.g. cross-correlation based misfit and 
  # time-domain Lx norm.
  normalisation_family: 'td'

  # Just a name used to identfy targets from this group. Use dot-separated path
  # notation to group related contributors
  path: 'td.rayleigh'

  # Epicentral distance range of stations to be considered in meter
  distance_min: 1e3
  distance_max: 900e3

  # Names of components to be included. Available: N=north, E=east, Z=vertical
  # (up), R=radial (away), T=transverse (right)
  channels: ['R', 'Z']

  # How to weight contributions from this group in the global misfit
  weight: 1.0

  # subsection on how to fit the traces
  misfit_config: !grond.WaveformMisfitConfig

    # Frequency band [Hz] of acausal filter (flat part of frequency taper)
    fmin: 0.01
    fmax: 0.05

    # Factor defining fall-off of frequency taper (zero at fmin/ffactor and
    # fmax*ffactor)
    ffactor: 1.5

    # Time window to include in the data fitting. Times can be defined offset
    # to given phase arrivals. E.g. '{stored:P}-600' would mean 600 s
    # before arrival of the phase named 'P'. The phase must be defined in the
    # travel time tables in the GF store.
    tmin: '{stored:any_P}'
    tmax: '{vel_surface:2.5}'
    # tfade: 120.0

    # How to fit the data (available: 'time_domain', 'frequency_domain',
    # 'log_frequency_domain', 'absolute', 'envelope', 'cc_max_norm')
    domain: 'time_domain'
    tautoshift_max: 4.0
    autoshift_penalty_max: 0.05
    
    # For L1 norm: 1, L2 norm: 2, etc.
    norm_exponent: 1

  # How to interpolate the Green's functions (available choices:
  # 'nearest_neighbor', 'multilinear'). Choices other than 'nearest_neighbor'
  # may require dense GF stores to avoid aliasing artifacts in the forward 
  # modelling.
  interpolation: 'nearest_neighbor'

  # Name of the GF Store to use
  store_id: 'crust2_j3'


 # -----------------------------------------------------------------------------
 # Definition of the problem to be solved
 #
 # In this section the source model to be fitted is chosen, the parameter space
 # defined, and how to combine the misfit contributions defined in the
 # target_groups section above.
 # 
 # The marker !grond.CMTProblemConfig selects a centroid moment tensor source
 # model. 
 # -----------------------------------------------------------------------------
 problem_config: !grond.CMTProblemConfig

  # Name used to identify the output
  name_template: 'cmt_${event_name}'

  # Definition of model parameter space to be searched in the optimisation
  ranges:
    # Time relative to hypocenter origin time [s]
    time: '-10 .. 10 | add'

    # Centroid location with respect to hypocenter origin [m]
    north_shift: '-15e3 .. 15e3'
    east_shift: '-15e3 .. 15e3'
    depth: '5e3 .. 30e3'

    # Range of magnitudes to allow
    magnitude: '5.7 .. 6.2'

    # Relative moment tensor component ranges (don't touch)
    rmnn: '-1.41421 .. 1.41421'
    rmee: '-1.41421 .. 1.41421'
    rmdd: '-1.41421 .. 1.41421'
    rmne: '-1 .. 1'
    rmnd: '-1 .. 1'
    rmed: '-1 .. 1'

    # Source duration range [s]
    duration: '1. .. 5.'

  # Clearance distance around stations (no models with origin closer than this
  # distance to any station are produced by the sampler)
  distance_min: 1e3

  # Type of moment tensor to restrict to (choices: 'full', 'deviatoric', 'dc')
  mt_type: 'deviatoric'

  # How to combine the target misfits. For L1 norm: 1, L2 norm: 2, etc.
  norm_exponent: 1


 # -----------------------------------------------------------------------------
 # Configuration of pre-optimisation analysis phases. 
 # determined during this phase.
 # -----------------------------------------------------------------------------
 #
 analyser_configs:

 # Balancing weights are determined with this analyser
 - !grond.TargetBalancingAnalyserConfig

  # Number of models to forward model in the analysis, larger number -> better
  # statistics)
  niterations: 1000

 # -----------------------------------------------------------------------------
 # Configuration of the optimisation procedure
 # -----------------------------------------------------------------------------
 optimiser_config: !grond.HighScoreOptimiserConfig

  # Number of bootstrap realisations to be tracked simultaneously in the
  # optimisation
  nbootstrap: 100

  # stages of the sampler. Start with uniform sampling of the model space
  # (!grond.UniformSamplerPhase), then narrow down to the interesting regions
  # (!grond.DirectedSamplerPhase).
  sampler_phases:

  - !grond.UniformSamplerPhase
      # Number of iterations
      niterations: 1000

  - !grond.DirectedSamplerPhase
      # Number of iterations
      niterations: 20000

      # Multiplicator for width of sampler distribution at start of this phase
      scatter_scale_begin: 2.0

      # Multiplicator for width of sampler distribution at end of this phase
      scatter_scale_end: 0.5


 # -----------------------------------------------------------------------------
 # Configuration section for synthetic seismogram engine
 #
 # Configures where to look for GF stores.
 # -----------------------------------------------------------------------------

 engine_config: !grond.EngineConfig

  # Whether to use GF store directories listed in ~/.pyrocko/config.pf
  gf_stores_from_pyrocko_config: false

  # List of directories with GF stores
  gf_store_superdirs: ['gf_stores']
--- a/examples/grond-playground-regional/config/regional_cmt.gronf
+++ b/examples/grond-playground-regional/config/regional_cmt.gronf
@ -0,0 +1 @@
 ../../../src/data/init/example_regional_cmt.gronf
--- a/examples/grond-playground-wphase/config/wphase_cmt.gronf
+++ b/examples/grond-playground-wphase/config/wphase_cmt.gronf
@ -1,239 +0,0 @@
 %YAML 1.1
 --- !grond.Config

 # All file paths referenced below are treated relative to the location of this
 # configuration file, here we may give a common prefix. E.g. setting it to '..'
 # if the configuration file is in the sub-directory '${project_root}/config'
 # allows us to give the paths below relative to '${project_root}'.
 path_prefix: '..'

 # Path, where to store output (run directories). The placeholder
 # '${problem_name}' will be expanded to a name configured below in
 # problem_config.name_template and will typically include a config identifier
 # and the event name.
 rundir_template: 'runs/${problem_name}.grun'

 # If given, restrict to processing of listed events
 #event_names:
 #- 'gfz2015sfdd'


 # -----------------------------------------------------------------------------
 # Configuration section for dataset (input data)
 #
 # The placeholder '${event_name}' will be expanded to the current event. This
 # enables us to use the same configuration for multiple events. The available 
 # events are detected by looking into possible expansions of
 # dataset_config.events_path
 # -----------------------------------------------------------------------------

 dataset_config: !grond.DatasetConfig

  # List of files with station coordinates.
  stations_stationxml_paths: 
  - 'data/events/${event_name}/waveforms/stations.geofon.xml'
  - 'data/events/${event_name}/waveforms/stations.iris.xml'
  - 'data/events/${event_name}/waveforms/stations.orfeus.xml'

  # File with hypocenter information and possibly reference solution
  events_path: 'data/events/${event_name}/event.txt'

  # List of directories with raw waveform data
  waveform_paths: ['data/events/${event_name}/waveforms/raw']

  # List of stations/components to be excluded according to their STA, NET.STA,
  # NET.STA.LOC, or NET.STA.LOC.CHA codes
  blacklist: []

  # List of files with additional exclusion lists (one entry per line, same 
  # format as above)
  blacklist_paths:
  - 'data/events/${event_name}/waveforms/blacklist.txt'

  # List of files with instrument response information (can be the same as in
  # stations_stationxml_paths above)
  responses_stationxml_paths:
  - 'data/events/${event_name}/waveforms/stations.geofon.xml'
  - 'data/events/${event_name}/waveforms/stations.iris.xml'
  - 'data/events/${event_name}/waveforms/stations.orfeus.xml'


 # -----------------------------------------------------------------------------
 # Configuration section for the observational data fitting
 #
 # This defines the objective function to be minimized in the optimisation. It
 # can be composed of one or more contributions, each represented by a
 # !grond.*TargetGroup section.
 # -----------------------------------------------------------------------------

 target_groups:

 - !grond.WaveformTargetGroup

  # Normalisation family (see the Grond documentation for how it works).
  # Use distinct normalisation families when mixing misfit contributors with
  # different magnitude scaling, like e.g. cross-correlation based misfit and 
  # time-domain Lx norm.
  normalisation_family: 'td'

  # Just a name used to identfy targets from this group. Use dot-separated path
  # notation to group related contributors
  path: 'td.wphase'

  # Epicentral distance range of stations to be considered
  distance_min: 3000e3
  distance_max: 10000e3

  # Names of components to be included. Available: N=north, E=east, Z=vertical
  # (up), R=radial (away), T=transverse (right)
  channels: ['Z','R']

  # How to weight contributions from this group in the global misfit
  weight: 1.0

  # subsection on how to fit the traces
  misfit_config: !grond.WaveformMisfitConfig

    # Frequency band [Hz] of acausal filter (flat part of frequency taper)
    fmin: 0.001
    fmax: 0.005

    # Factor defining fall-off of frequency taper (zero at fmin/ffactor and
    # fmax*ffactor)
    ffactor: 1.5

    # Time window to include in the data fitting. Times can be defined offset
    # to given phase arrivals. E.g. '{stored:P}-600' would mean 600 s
    # before arrival of the phase named 'P'. The phase must be defined in the
    # travel time tables in the GF store.
    tmin: '{stored:P}-600'
    tmax: '{stored:S}-60'
    # tfade: 120.0

    # How to fit the data (available: 'time_domain', 'frequency_domain',
    # 'log_frequency_domain', 'absolute', 'envelope', 'cc_max_norm')
    domain: 'time_domain'
    
    # For L1 norm: 1, L2 norm: 2, etc.
    norm_exponent: 1

  # How to interpolate the Green's functions (available choices:
  # 'nearest_neighbor', 'multilinear'). Choices other than 'nearest_neighbor'
  # may require dense GF stores to avoid aliasing artifacts in the forward 
  # modelling.
  interpolation: 'nearest_neighbor'

  # Name of the GF Store to use
  store_id: 'global_20s_shallow'


 # -----------------------------------------------------------------------------
 # Definition of the problem to be solved
 #
 # In this section the source model to be fitted is chosen, the parameter space
 # defined, and how to combine the misfit contributions defined in the
 # target_groups section above.
 # 
 # The marker !grond.CMTProblemConfig selects a centroid moment tensor source
 # model. 
 # -----------------------------------------------------------------------------

 problem_config: !grond.CMTProblemConfig

  # Name used to identify the output
  name_template: 'wphase_cmt_${event_name}'

  # Definition of model parameter space to be searched in the optimisation
  ranges:
    # Time relative to hypocenter origin time [s]
    time: '-100 .. 100 | add'

    # Centroid location with respect to hypocenter origin [m]
    north_shift: '-200e3 .. 200e3'
    east_shift: '-200e3 .. 200e3'
    depth: '5e3 .. 40e3'

    # Range of magnitudes to allow
    magnitude: '7.5 .. 8.5'

    # Relative moment tensor component ranges (don't touch)
    rmnn: '-1.41421 .. 1.41421'
    rmee: '-1.41421 .. 1.41421'
    rmdd: '-1.41421 .. 1.41421'
    rmne: '-1 .. 1'
    rmnd: '-1 .. 1'
    rmed: '-1 .. 1'

    # Source duration range [s]
    duration: '50. .. 200.'

  # Clearance distance around stations (no models with origin closer than this
  # distance to any station are produced by the sampler)
  distance_min: 0.

  # Type of moment tensor to restrict to (choices: 'full', 'deviatoric', 'dc')
  mt_type: 'deviatoric'

  # How to combine the target misfits. For L1 norm: 1, L2 norm: 2, etc.
  norm_exponent: 1


 # -----------------------------------------------------------------------------
 # Configuration of pre-optimisation analysis phases. 
 # determined during this phase.
 # -----------------------------------------------------------------------------
 #
 analyser_configs:

 # Balancing weights are determined with this analyser
 - !grond.TargetBalancingAnalyserConfig

  # Number of models to forward model in the analysis, larger number -> better
  # statistics)
  niterations: 1000

 # -----------------------------------------------------------------------------
 # Configuration of the optimisation procedure
 # -----------------------------------------------------------------------------

 optimiser_config: !grond.HighScoreOptimiserConfig

  # Number of bootstrap realisations to be tracked simultaneously in the
  # optimisation
  nbootstrap: 100

  # stages of the sampler. Start with uniform sampling of the model space
  # (!grond.UniformSamplerPhase), then narrow down to the interesting regions
  # (!grond.DirectedSamplerPhase).
  sampler_phases:

  - !grond.UniformSamplerPhase

      # Number of iterations
      niterations: 1000

  - !grond.DirectedSamplerPhase

      # Number of iterations
      niterations: 20000

      # Multiplicator for width of sampler distribution at start of this phase
      scatter_scale_begin: 2.0

      # Multiplicator for width of sampler distribution at end of this phase
      scatter_scale_end: 0.5


 # -----------------------------------------------------------------------------
 # Configuration section for synthetic seismogram engine
 #
 # Configures where to look for GF stores.
 # -----------------------------------------------------------------------------

 engine_config: !grond.EngineConfig

  # Whether to use GF store directories listed in ~/.pyrocko/config.pf
  gf_stores_from_pyrocko_config: false

  # List of directories with GF stores
  gf_store_superdirs: ['gf_stores']
--- a/examples/grond-playground-wphase/config/wphase_cmt.gronf
+++ b/examples/grond-playground-wphase/config/wphase_cmt.gronf
@ -0,0 +1 @@
 ../../../src/data/init/example_wphase_cmt.gronf
--- a/setup.py
+++ b/setup.py
@ -133,7 +133,11 @@ setup(
            'report/app/favicon.png',
            'report/app/templates/*.html',
            'report/app/css/*.css',
            'report/app/js/*.js']},
            'report/app/js/*.js',

            'data/init/*.gronf',
            'data/init/*.txt'
            ]},

    data_files=[],

--- a/src/analysers/noise_analyser/analyser.py
+++ b/src/analysers/noise_analyser/analyser.py
@ -112,7 +112,7 @@ def seismic_noise_variance(traces, engine, event, targets,
                            stat_w = 0.
                            logger.info(
                                'Noise analyser found event %s phase onset of '
                                '%s for %s' % (
                                '%s for %s.' % (
                                    ev.name, phase_def, target.name))

                        if arrival_time_pre > arrival_time-30.*60.\
@ -121,7 +121,7 @@ def seismic_noise_variance(traces, engine, event, targets,
                            stat_w *= 1.
                            logger.info(
                                'Noise analyser found event %s possibly '
                                'contaminating the noise' % ev.name)
                                'contaminating the noise.' % ev.name)

                            # this should be magnitude dependent
                    except Exception:
@ -161,10 +161,10 @@ class NoiseAnalyser(Analyser):
    within a 30 min time window before the start of the set pre-event noise
    window, only a warning is thrown.

    It is further possible to disregard data with a noise level exceeding the 
    median by a given ``cutoff`` factor. These weights are set to 0. This can be 
    done exclusively (``mode='weeding'``) such that noise weights are either
    1 or 0, or in combination with weighting below the median-times-cutoff 
    It is further possible to disregard data with a noise level exceeding the
    median by a given ``cutoff`` factor. These weights are set to 0. This can
    be done exclusively (``mode='weeding'``) such that noise weights are either
    1 or 0, or in combination with weighting below the median-times-cutoff
    noise level (``mode='weighting'``).
    '''

@ -235,7 +235,7 @@ class NoiseAnalyser(Analyser):
        norm_noise = meta.nanmedian(noise)
        if norm_noise == 0:
            logger.info(
                'Noise Analyser returned a weight of 0 for all stations')
                'Noise Analyser returned a weight of 0 for all stations.')

        ok = num.isfinite(noise)
        assert num.all(noise[ok] >= 0.0)
--- a/src/apps/cmd_init.py
+++ b/src/apps/cmd_init.py
@ -1,201 +1,55 @@
 import grond
 import logging

 import math
 import shutil
 import os
 from os import path as op
 from pyrocko import util
 from pyrocko.gf import Range, Timing
 import glob
 import os.path as op

 logger = logging.getLogger('grond.init')
 km = 1e3

 INIT_EVENT = '''name = myanmar_2011
 time = 2011-03-24 13:55:12.010
 latitude = 20.687
 longitude = 99.822
 magnitude = 6.9
 moment = 1.9228e+19
 depth = 8000
 region = Myanmar
 --------------------------------------------
 '''

 class GrondInit(object):

 class GrondProject(object):
    folder = op.join(op.dirname(__file__), '..', 'data', 'init')

    def __init__(self):
        self.dataset_config = grond.DatasetConfig(
            events_path='events.txt')

        self.project_config = None
        self.target_groups = []

        self.sub_dirs = ['gf_stores', 'config']
        self.empty_files = []

    def add_waveforms(self):
        logger.info('Added waveforms')
        dsc = self.dataset_config

        wd = op.join('data', 'waveforms')
        self.sub_dirs.append(wd)
        dsc.waveform_paths = [wd]

        sp = op.join(wd, 'stations.xml')
        dsc.stations_stationxml_paths = [sp]
        dsc.responses_stationxml_paths = [sp]

        self.target_groups.append(
            grond.WaveformTargetGroup(
                normalisation_family='time_domain',
                path='sw',
                distance_min=10*km,
                distance_max=1000*km,
                channels=['Z', 'R', 'T'],
                interpolation='nearest_neighbor',
                store_id='gf_store_id',
                misfit_config=grond.WaveformMisfitConfig(
                    tmin=Timing(phase_defs=['stored:any_P'], offset=-10.0),
                    tmax=Timing(phase_defs=['vel_surface:2.5']),
                    domain='time_domain',
                    tautoshift_max=4.0,
                    autoshift_penalty_max=0.05,
                    norm_exponent=2,
                    fmin=0.01,
                    fmax=0.05,
                    ffactor=1.5)))

    def add_insar(self):
        logger.info('Added InSAR')
        dsc = self.dataset_config

        sd = op.join('data', 'insar')
        self.sub_dirs.append(sd)
        dsc.kite_scene_paths = [sd]

        self.target_groups.append(
            grond.SatelliteTargetGroup(
                normalisation_family='static',
                path='insar',
                interpolation='multilinear',
                store_id='gf_static_store_id',
                kite_scenes=['*all'],
                misfit_config=grond.SatelliteMisfitConfig(
                    optimise_orbital_ramp=True,
                    ranges={
                        'offset': '-0.5 .. 0.5',
                        'ramp_north': '-1e-4 .. 1e-4',
                        'ramp_east': '-1e-4 .. 1e-4'
                        }
                    ))
            )

    def add_gnss(self):
        logger.info('Added GNSS')
        dsc = self.dataset_config

        gd = op.join('data', 'gnss')
        self.sub_dirs.append(gd)
        gp = op.join(gd, 'my_campaign.yaml')
        dsc.gnss_campaign_paths = [gp]
        self.target_groups.append(
            grond.GNSSCampaignTargetGroup(
                normalisation_family='static',
                path='gnss',
                interpolation='multilinear',
                store_id='gf_static_store_id',
                misfit_config=grond.GNSSCampaignMisfitConfig()
                )
            )

    def set_cmt_source(self):
        logger.info('Set problem to CMTProblem')
        pi2 = math.pi/2
        self.problem_config = grond.CMTProblemConfig(
            name_template='cmt_${event_name}',
            distance_min=2.*km,
            mt_type='deviatoric',
            ranges=dict(
                time=Range(-10.0, 10.0, relative='add'),
                north_shift=Range(-16*km, 16*km),
                east_shift=Range(-16*km, 16*km),
                depth=Range(1*km, 11*km),
                magnitude=Range(4.0, 6.0),
                rmnn=Range(-pi2, pi2),
                rmee=Range(-pi2, pi2),
                rmdd=Range(-pi2, pi2),
                rmne=Range(-1.0, 1.0),
                rmnd=Range(-1.0, 1.0),
                rmed=Range(-1.0, 1.0),
                duration=Range(1.0, 15.0))
            )

    def set_rectangular_source(self):
        logger.info('Set problem to RectangularProblem')
        self.problem_config = grond.RectangularProblemConfig(
            name_template='rect_source',
            ranges=dict(
                north_shift=Range(-20*km, 20*km),
                east_shift=Range(-20*km, 20*km),
                depth=Range(0*km, 10*km),
                length=Range(20*km, 40*km),
                width=Range(5*km, 12*km),
                dip=Range(20, 70),
                strike=Range(0, 180),
                rake=Range(0, 90),
                slip=Range(1, 3))
            )

    def check(self):
        assert self.target_groups, 'No target_groups set!'
        assert self.problem_config, 'No problem set!'

    def get_config(self):
        self.check()

        engine_config = grond.EngineConfig(
            gf_store_superdirs=['gf_stores'])

        optimiser_config = grond.HighScoreOptimiserConfig()

        return grond.Config(
            rundir_template=op.join('rundir', '${problem_name}.grun'),
            dataset_config=self.dataset_config,
            problem_config=self.problem_config,
            target_groups=self.target_groups,
            optimiser_config=optimiser_config,
            engine_config=engine_config)

    def build(self, project_dir, force=False):
        if op.exists(project_dir) and not force:
            raise grond.GrondError(
                'Directory "%s" already exists! Use --force to overwrite'
                % project_dir)
        elif op.exists(project_dir) and force:
            logger.info('Overwriting directory %s.' % project_dir)
            shutil.rmtree(project_dir)

        logger.info('Creating empty project in folder %s' % project_dir)
        config = self.get_config()

        def p(*fn):
            return op.join(project_dir, *fn)

        config.set_basepath(p())

        os.mkdir(op.abspath(project_dir))
        for d in self.sub_dirs:
            util.ensuredir(p(d))

        grond.write_config(config, p('config', 'config.gronf'))

        with open(p('events.txt'), 'w') as f:
            f.write(INIT_EVENT)

        for fn in self.empty_files:
            open(p(fn), 'w').close()

    def dump(self):
        return self.get_config().dump()
        pass

    def available_inits(self):
        return {
            'examples': tuple((self.filename_to_abbrv(fn),
                               self._get_description(fn))
                              for fn in self.example_files),
            'events': tuple((self.filename_to_abbrv(fn),
                             self._get_description(fn))
                            for fn in self.event_files),
        }

    @staticmethod
    def filename_to_abbrv(filename):
        return op.basename(filename).split('.')[0]

    @property
    def example_files(self):
        return self._get_files('example_*.gronf')

    @property
    def section_files(self):
        return self._get_files('section_*.gronf')

    @property
    def snippet_files(self):
        return self._get_files('snippet_*.gronf')

    @property
    def event_files(self):
        return self._get_files('event_*.txt')

    def _get_files(self, name):
        return glob.glob(op.join(self.folder, name))

    def _get_description(self, filename):
        with open(filename, 'rt') as f:
            for ln in f.readlines():
                if ln.startswith('#'):
                    return ln.split(':')[-1].strip('# \n')
            return 'No description!'
--- a/src/apps/grond.py
+++ b/src/apps/grond.py
@ -311,7 +311,7 @@ def multiple_choice(option, opt_str, value, parser, choices):
    options = value.split(',')
    for opt in options:
        if opt not in choices:
            raise OptionValueError('invalid option %s - valid options are: %s'
            raise OptionValueError('Invalid option %s - valid options are: %s'
                                   % (opt, ', '.join(choices)))
    setattr(parser.values, option.dest, options)

@ -320,14 +320,14 @@ def magnitude_range(option, opt_str, value, parser):
    mag_range = value.split('-')
    if len(mag_range) != 2:
        raise OptionValueError(
            'invalid magnitude %s - valid range is e.g. 6-7' % value)
            'Invalid magnitude %s - valid range is e.g. 6-7.' % value)
    try:
        mag_range = tuple(map(float, mag_range))
    except ValueError:
        raise OptionValueError('magnitudes must be numbers.')
        raise OptionValueError('Magnitudes must be numbers.')

    if mag_range[0] > mag_range[1]:
        raise OptionValueError('minimum magnitude must be larger than'
        raise OptionValueError('Minimum magnitude must be larger than'
                               ' maximum magnitude.')
    setattr(parser.values, option.dest, mag_range)

@ -444,6 +444,32 @@ def command_scenario(args):

 def command_init(args):

    from .cmd_init import GrondInit

    grond_init = GrondInit()

    def setup(parser):
        parser.add_option(
            '--force', dest='force', action='store_true')

    parser, options, args = cl_parse('init', args, setup)

    if len(args) not in (1, 2):
        help_and_die(parser, '1 or 2 arguments required')

    if args[0] == 'list':
        print('List of available configuration examples for grond:')

        for section, entries in grond_init.available_inits().items():
            padding = max([len(n) for n, _ in entries])
            print('\n{title}:'.format(title=section.title()))
            for name, desc in entries:
                print('  {name:<{padding}}: {desc}'.format(
                      name=name, desc=desc, padding=padding))


 def command_init_old(args):

    from . import cmd_init as init

    def setup(parser):
@ -937,7 +963,7 @@ def command_tag(args):
            logger.error(e)

    if errors:
        die('errors occurred, see log messages above.')
        die('Errors occurred, see log messages above.')


 def make_report(env_args, event_name, conf, update_without_plotting):
@ -1126,7 +1152,7 @@ def command_report(args):

    else:
        if not entries_generated and not options.index_only:
            logger.info('nothing to do, see: grond report --help')
            logger.info('Nothing to do, see: grond report --help')

    if entries_generated and not (options.serve or options.serve_external):
        logger.info(CLIHints('report', config=s_conf))
--- a/src/config.py
+++ b/src/config.py
@ -137,10 +137,10 @@ def read_config(path):
        config = guts.load(filename=path)
    except OSError:
        raise GrondError(
            'cannot read Grond configuration file: %s' % path)
            'Cannot read Grond configuration file: %s' % path)

    if not isinstance(config, Config):
        raise GrondError('invalid Grond configuration in file "%s"' % path)
        raise GrondError('Invalid Grond configuration in file "%s".' % path)

    config.set_basepath(op.dirname(path) or '.')
    return config
@ -160,7 +160,7 @@ def write_config(config, path):

    except OSError:
        raise GrondError(
            'cannot write Grond configuration file: %s' % path)
            'Cannot write Grond configuration file: %s' % path)


 def diff_configs(path1, path2):
--- a/src/core.py
+++ b/src/core.py
@ -151,7 +151,7 @@ def harvest(rundir, problem=None, nbest=10, force=False, weed=0):
        xs, misfits, bootstrap_misfits, _ = \
            load_problem_data(rundir, problem, nchains=nchains)

    logger.info('harvesting problem %s...' % problem.name)
    logger.info('Harvesting problem %s...' % problem.name)

    optimiser = load_optimiser_info(rundir)
    dumpdir = op.join(rundir, 'harvest')
@ -198,7 +198,7 @@ def harvest(rundir, problem=None, nbest=10, force=False, weed=0):
    for i in ibests:
        problem.dump_problem_data(dumpdir, xs[i], misfits[i, :, :])

    logger.info('done harvesting problem %s' % problem.name)
    logger.info('Done harvesting problem %s.' % problem.name)


 def cluster(rundir, clustering, metric):
@ -212,7 +212,7 @@ def cluster(rundir, clustering, metric):
    from grond.clustering import metrics

    if metric not in metrics.metrics:
        raise GrondError('unknown metric: %s' % metric)
        raise GrondError('Unknown metric: %s' % metric)

    mat = metrics.compute_similarity_matrix(events, metric)

@ -239,7 +239,7 @@ def get_event_names(config):

 def check_problem(problem):
    if len(problem.targets) == 0:
        raise GrondError('no targets available')
        raise GrondError('No targets available')


 def check(
@ -259,9 +259,9 @@ def check(
            problem = config.get_problem(event)

            _, nfamilies = problem.get_family_mask()
            logger.info('problem: %s' % problem.name)
            logger.info('number of target families: %i' % nfamilies)
            logger.info('number of targets (total): %i' % len(problem.targets))
            logger.info('Problem: %s' % problem.name)
            logger.info('Number of target families: %i' % nfamilies)
            logger.info('Number of targets (total): %i' % len(problem.targets))

            if target_string_ids:
                problem.targets = [
@ -269,7 +269,7 @@ def check(
                    if util.match_nslc(target_string_ids, target.string_id())]

            logger.info(
                'number of targets (selected): %i' % len(problem.targets))
                'Number of targets (selected): %i' % len(problem.targets))

            check_problem(problem)

@ -410,7 +410,7 @@ def check(
                        (target.string_id() + ':', sok)))

        except GrondError as e:
            logger.error('event %i, %s: %s' % (
            logger.error('Event %i, %s: %s' % (
                ievent,
                event.name or util.time_to_str(event.time),
                str(e)))
@ -474,18 +474,18 @@ def process_event(ievent, g_data_id):
        elif force:
            shutil.rmtree(rundir)
        else:
            logger.warn('skipping problem %s: rundir already exists: %s' %
            logger.warn('Skipping problem %s: rundir already exists: %s' %
                        (problem.name, rundir))
            return

    util.ensuredir(rundir)

    logger.info(
        'starting event %i / %i' % (ievent+1, nevents))
        'Starting event %i / %i' % (ievent+1, nevents))

    logger.info('rundir: %s' % rundir)
    logger.info('Rundir: %s' % rundir)

    logger.info('analysing problem %s' % problem.name)
    logger.info('Analysing problem %s.' % problem.name)

    for analyser_conf in config.analyser_configs:
        analyser = analyser_conf.get_analyser()
@ -537,10 +537,10 @@ def process_event(ievent, g_data_id):

    tstop = time.time()
    logger.info(
        'stop %i / %i (%g min)' % (ievent+1, nevents, (tstop - tstart)/60.))
        'Stop %i / %i (%g min)' % (ievent+1, nevents, (tstop - tstart)/60.))

    logger.info(
        'done with problem %s, rundir is %s' % (problem.name, rundir))
        'Done with problem %s, rundir is "%s".' % (problem.name, rundir))


 class ParameterStats(Object):
@ -614,16 +614,16 @@ def export(what, rundirs, type=None, pnames=None, filename=None):
        shortform = False

    if what == 'stats' and type is not None:
        raise GrondError('invalid argument combination: what=%s, type=%s' % (
        raise GrondError('Invalid argument combination: what=%s, type=%s' % (
            repr(what), repr(type)))

    if what != 'stats' and shortform:
        raise GrondError('invalid argument combination: what=%s, pnames=%s' % (
        raise GrondError('Invalid argument combination: what=%s, pnames=%s' % (
            repr(what), repr(pnames)))

    if what != 'stats' and type != 'vector' and pnames is not None:
        raise GrondError(
            'invalid argument combination: what=%s, type=%s, pnames=%s' % (
            'Invalid argument combination: what=%s, type=%s, pnames=%s' % (
                repr(what), repr(type), repr(pnames)))

    if filename is None:
@ -656,7 +656,7 @@ def export(what, rundirs, type=None, pnames=None, filename=None):
            guts.dump_all([ev], stream=out)

        else:
            raise GrondError('invalid argument: type=%s' % repr(type))
            raise GrondError('Invalid argument: type=%s' % repr(type))

    header = None
    for rundir in rundirs:
@ -708,7 +708,7 @@ def export(what, rundirs, type=None, pnames=None, filename=None):
                print(rs, file=out)

        else:
            raise GrondError('invalid argument: what=%s' % repr(what))
            raise GrondError('Invalid argument: what=%s' % repr(what))

    if out is not sys.stdout:
        out.close()
--- a/src/data/init/example_insar_rectangular.gronf
+++ b/src/data/init/example_insar_rectangular.gronf
@ -0,0 +1,200 @@
 %YAML 1.1
 # Example: Inversion of planar RectangularSource from local InSAR Observations

 --- !grond.Config

 # All file paths referenced below are treated relative to the location of this
 # configuration file, here we may give a common prefix. E.g. setting it to '..'
 # if the configuration file is in the sub-directory '${project_root}/config'
 # allows us to give the paths below relative to '${project_root}'.
 path_prefix: '..'

 # Path, where to store output (run directories). The placeholder
 # '${problem_name}' will be expanded to a name configured below in
 # problem_config.name_template and will typically include a config identifier
 # and the event name.
 rundir_template: runs/${problem_name}.grun

 # If given, restrict to processing of listed events
 #event_names:
 #- 'gfz2018pmjk'


 # -----------------------------------------------------------------------------
 # Configuration section for dataset (input data)
 #
 # The placeholder '${event_name}' will be expanded to the current event. This
 # enables us to use the same configuration for multiple events. The available 
 # events are detected by looking into possible expansions of
 # dataset_config.events_path
 # -----------------------------------------------------------------------------

 dataset_config: !grond.DatasetConfig
  
  # File with hypocenter information and possibly reference solution
  events_path: 'data/events/${event_name}/event.txt'

  # List of directories for the InSAR scenes
  kite_scene_paths: ['data/events/${event_name}/insar']

 # -----------------------------------------------------------------------------
 # Configuration section for the observational data fitting
 #
 # This defines the objective function to be minimized in the optimisation. It
 # can be composed of one or more contributions, each represented by a
 # !grond.*TargetGroup section.
 # -----------------------------------------------------------------------------

 target_groups:
 - !grond.SatelliteTargetGroup

  # Normalisation family (see the Grond documentation for how it works).
  # Use distinct normalisation families when mixing misfit contributors with
  # different magnitude scaling, like e.g. cross-correlation based misfit and 
  # time-domain Lx norm.
  normalisation_family: 'static'

  # Just a name used to identify targets from this group. Use dot-separated path
  # notation to group related contributors
  path: 'insar'

  # How to weight contributions from this group in the global misfit
  weight: 1.0

  # Selector for kite scene ids, '*all' is a wildcard and load all scenes present
  kite_scenes: ['*all']

  # Subsection on how to fit the traces
  misfit_config: !grond.SatelliteMisfitConfig

    # Optimise a planar orbital ramp
    optimise_orbital_ramp: false

    # Parameters for the orbital ramp
    ranges:

      # Vertical offset in [m]
      offset: -0.5 .. 0.5

      # Ranges for the East-West and North-South inclination of the ramp
      ramp_east: -1e-4 .. 1e-4
      ramp_north: -1e-4 .. 1e-4

  # How to interpolate the Green's functions (available choices:
  # 'nearest_neighbor', 'multilinear'). Choices other than 'nearest_neighbor'
  # may require dense GF stores to avoid aliasing artifacts in the forward 
  # modelling.
  interpolation: multilinear

  # Name of the GF Store to use
  store_id: crust2_ib_static


 # -----------------------------------------------------------------------------
 # Definition of the problem to be solved
 #
 # In this section the source model to be fitted is chosen, the parameter space
 # defined, and how to combine the misfit contributions defined in the
 # target_groups section above.
 # 
 # The marker !grond.RectangularProblemConfig selects a finite rectancular
 # source model. 
 # -----------------------------------------------------------------------------
 problem_config: !grond.RectangularProblemConfig

  # Name used to identify the output
  name_template: rect_2009LaAquila

  # How to combine the target misfits. For L1 norm: 1, L2 norm: 2, etc.
  norm_exponent: 2

  # Definition of model parameter space to be searched in the optimisation
  ranges:

    # Scaling ranges in [m]
    depth: 2500 .. 7000
    east_shift: 0 .. 20000
    north_shift: 0 .. 20000
    length: 5000 .. 10000
    width: 2000 .. 10000
    slip: .2 .. 2

    # Orientation ranges in [deg]
    dip: 10 .. 50
    rake: 120 .. 360
    strike: 220 .. 360

  # We are using a dense GF store and will reduce the number of discrete
  # subsources by this factor. Decrease the decimation for a finer sub-source
  # resolution, and increased computational time
  decimation_factor: 12

  # Clearance distance around stations (no models with origin closer than this
  # distance to any station are produced by the sampler)
  distance_min: 0.0

 # -----------------------------------------------------------------------------
 # Configuration of pre-optimisation analysis phases. 
 # determined during this phase.
 # -----------------------------------------------------------------------------
 #
 analyser_configs:

 # DOES NOT APPLY FOR INSAR! Balancing weights are determined with this analyser
 - !grond.TargetBalancingAnalyserConfig
  niterations: 1000

 # -----------------------------------------------------------------------------
 # Configuration of the optimisation procedure
 # -----------------------------------------------------------------------------
 optimiser_config: !grond.HighScoreOptimiserConfig
  # Number of bootstrap realisations to be tracked simultaneously in the
  # optimisation
  nbootstrap: 25

  # Stages of the sampler then narrow down to the interesting regions
  # (!grond.DirectedSamplerPhase).
  sampler_phases:

  # Start with uniform sampling of the model space
  - !grond.UniformSamplerPhase
    # Number of iterations
    niterations: 10000

    # How often we shall try to find a valid sample
    ntries_preconstrain_limit: 1000

  # Narrow down to the interesting regions
  - !grond.DirectedSamplerPhase
    # Number of iterations
    niterations: 40000

    # How often we shall try to find a valid sample
    ntries_preconstrain_limit: 1000

    # Multiplicator for width of sampler distribution at start of this phase
    scatter_scale_begin: 2.0

    # Multiplicator for width of sampler distribution at end of this phase
    scatter_scale_end: 0.5

    starting_point: excentricity_compensated
    sampler_distribution: normal
    standard_deviation_estimator: median_density_single_chain
    ntries_sample_limit: 2000

  # This parameter determines the length of the chains
  chain_length_factor: 8.0

 # -----------------------------------------------------------------------------
 # Configuration section for synthetic seismogram engine
 #
 # Configures where to look for GF stores.
 # -----------------------------------------------------------------------------
 engine_config: !grond.EngineConfig

  # Whether to use GF store directories listed in ~/.pyrocko/config.pf
  gf_stores_from_pyrocko_config: true

  # List of directories with GF stores
  gf_store_superdirs: ['gf_stores']
--- a/src/data/init/example_regional_cmt.gronf
+++ b/src/data/init/example_regional_cmt.gronf
@ -0,0 +1,296 @@
 %YAML 1.1
 # Example: Inversion of centroid moment tensor from regional waveform observations.
 --- !grond.Config

 # All file paths referenced below are treated relative to the location of this
 # configuration file, here we may give a common prefix. E.g. setting it to '..'
 # if the configuration file is in the sub-directory '${project_root}/config'
 # allows us to give the paths below relative to '${project_root}'.
 path_prefix: '..'

 # Path, where to store output (run directories). The placeholder
 # '${problem_name}' will be expanded to a name configured below in
 # problem_config.name_template and will typically include a config identifier
 # and the event name.
 rundir_template: 'runs/${problem_name}.grun'

 # If given, restrict to processing of listed events
 #event_names:
 #- 'gfz2018pmjk'


 # -----------------------------------------------------------------------------
 # Configuration section for dataset (input data)
 #
 # The placeholder '${event_name}' will be expanded to the current event. This
 # enables us to use the same configuration for multiple events. The available 
 # events are detected by looking into possible expansions of
 # dataset_config.events_path
 # -----------------------------------------------------------------------------

 dataset_config: !grond.DatasetConfig

  # List of files with station coordinates.
  stations_stationxml_paths: 
  - 'data/events/${event_name}/waveforms/stations.geofon.xml'
  - 'data/events/${event_name}/waveforms/stations.iris.xml'

  # File with hypocenter information and possibly reference solution
  events_path: 'data/events/${event_name}/event.txt'

  # List of directories with raw waveform data
  waveform_paths: ['data/events/${event_name}/waveforms/raw']

  # List of stations/components to be excluded according to their STA, NET.STA,
  # NET.STA.LOC, or NET.STA.LOC.CHA codes
  blacklist: ['GE.UGM', 'GE.PLAI']

  # List of files with additional exclusion lists (one entry per line, same 
  # format as above)
  blacklist_paths:
  - 'data/events/${event_name}/waveforms/blacklist.txt'

  # List of files with instrument response information (can be the same as in
  # stations_stationxml_paths above)
  responses_stationxml_paths:
  - 'data/events/${event_name}/waveforms/stations.geofon.xml'
  - 'data/events/${event_name}/waveforms/stations.iris.xml'


 # -----------------------------------------------------------------------------
 # Configuration section for the observational data fitting
 #
 # This defines the objective function to be minimized in the optimisation. It
 # can be composed of one or more contributions, each represented by a
 # !grond.*TargetGroup section.
 # -----------------------------------------------------------------------------

 target_groups:
 - !grond.WaveformTargetGroup

  # Normalisation family (see the Grond documentation for how it works).
  # Use distinct normalisation families when mixing misfit contributors with
  # different magnitude scaling, like e.g. cross-correlation based misfit and 
  # time-domain Lx norm.
  normalisation_family: 'td'

  # Just a name used to identify targets from this group. Use dot-separated path
  # notation to group related contributors
  path: 'td.love'

  # Epicentral distance range of stations to be considered in meter
  distance_min: 1e3
  distance_max: 900e3

  # Names of components to be included. Available: N=north, E=east, Z=vertical
  # (up), R=radial (away), T=transverse (right)
  channels: ['T']

  # How to weight contributions from this group in the global misfit
  weight: 1.0

  # subsection on how to fit the traces
  misfit_config: !grond.WaveformMisfitConfig

    # Frequency band [Hz] of acausal filter (flat part of frequency taper)
    fmin: 0.01
    fmax: 0.05

    # Factor defining fall-off of frequency taper (zero at fmin/ffactor and
    # fmax*ffactor)
    ffactor: 1.5

    # Time window to include in the data fitting. Times can be defined offset
    # to given phase arrivals. E.g. '{stored:P}-600' would mean 600 s
    # before arrival of the phase named 'P'. The phase must be defined in the
    # travel time tables in the GF store.
    tmin: '{stored:any_P}'
    tmax: '{vel_surface:2.5}'
    # tfade: 120.0

    # How to fit the data (available: 'time_domain', 'frequency_domain',
    # 'log_frequency_domain', 'absolute', 'envelope', 'cc_max_norm')
    domain: 'time_domain'
    tautoshift_max: 4.0
    autoshift_penalty_max: 0.05
    
    # For L1 norm: 1, L2 norm: 2, etc.
    norm_exponent: 1

  # How to interpolate the Green's functions (available choices:
  # 'nearest_neighbor', 'multilinear'). Choices other than 'nearest_neighbor'
  # may require dense GF stores to avoid aliasing artifacts in the forward 
  # modelling.
  interpolation: 'nearest_neighbor'

  # Name of the GF Store to use
  store_id: 'crust2_j3'


 - !grond.WaveformTargetGroup

  # Normalisation family (see the Grond documentation for how it works).
  # Use distinct normalisation families when mixing misfit contributors with
  # different magnitude scaling, like e.g. cross-correlation based misfit and 
  # time-domain Lx norm.
  normalisation_family: 'td'

  # Just a name used to identfy targets from this group. Use dot-separated path
  # notation to group related contributors
  path: 'td.rayleigh'

  # Epicentral distance range of stations to be considered in meter
  distance_min: 1e3
  distance_max: 900e3

  # Names of components to be included. Available: N=north, E=east, Z=vertical
  # (up), R=radial (away), T=transverse (right)
  channels: ['R', 'Z']

  # How to weight contributions from this group in the global misfit
  weight: 1.0

  # subsection on how to fit the traces
  misfit_config: !grond.WaveformMisfitConfig

    # Frequency band [Hz] of acausal filter (flat part of frequency taper)
    fmin: 0.01
    fmax: 0.05

    # Factor defining fall-off of frequency taper (zero at fmin/ffactor and
    # fmax*ffactor)
    ffactor: 1.5

    # Time window to include in the data fitting. Times can be defined offset
    # to given phase arrivals. E.g. '{stored:P}-600' would mean 600 s
    # before arrival of the phase named 'P'. The phase must be defined in the
    # travel time tables in the GF store.
    tmin: '{stored:any_P}'
    tmax: '{vel_surface:2.5}'
    # tfade: 120.0

    # How to fit the data (available: 'time_domain', 'frequency_domain',
    # 'log_frequency_domain', 'absolute', 'envelope', 'cc_max_norm')
    domain: 'time_domain'
    tautoshift_max: 4.0
    autoshift_penalty_max: 0.05
    
    # For L1 norm: 1, L2 norm: 2, etc.
    norm_exponent: 1

  # How to interpolate the Green's functions (available choices:
  # 'nearest_neighbor', 'multilinear'). Choices other than 'nearest_neighbor'
  # may require dense GF stores to avoid aliasing artifacts in the forward 
  # modelling.
  interpolation: 'nearest_neighbor'

  # Name of the GF Store to use
  store_id: 'crust2_j3'


 # -----------------------------------------------------------------------------
 # Definition of the problem to be solved
 #
 # In this section the source model to be fitted is chosen, the parameter space
 # defined, and how to combine the misfit contributions defined in the
 # target_groups section above.
 # 
 # The marker !grond.CMTProblemConfig selects a centroid moment tensor source
 # model. 
 # -----------------------------------------------------------------------------
 problem_config: !grond.CMTProblemConfig

  # Name used to identify the output
  name_template: 'cmt_${event_name}'

  # Definition of model parameter space to be searched in the optimisation
  ranges:
    # Time relative to hypocenter origin time [s]
    time: '-10 .. 10 | add'

    # Centroid location with respect to hypocenter origin [m]
    north_shift: '-15e3 .. 15e3'
    east_shift: '-15e3 .. 15e3'
    depth: '5e3 .. 30e3'

    # Range of magnitudes to allow
    magnitude: '5.7 .. 6.2'

    # Relative moment tensor component ranges (don't touch)
    rmnn: '-1.41421 .. 1.41421'
    rmee: '-1.41421 .. 1.41421'
    rmdd: '-1.41421 .. 1.41421'
    rmne: '-1 .. 1'
    rmnd: '-1 .. 1'
    rmed: '-1 .. 1'

    # Source duration range [s]
    duration: '1. .. 5.'

  # Clearance distance around stations (no models with origin closer than this
  # distance to any station are produced by the sampler)
  distance_min: 1e3

  # Type of moment tensor to restrict to (choices: 'full', 'deviatoric', 'dc')
  mt_type: 'deviatoric'