! Input file to set up a parameter estimation run (using 'estimate')

! --- FILE NAMES ---

FILENAME = lef
! FILENAME.param is the file of parameter values and info
!  (unless PARAM_FILE is specified below)
! FILENAME.param-spatial is the file of spatially-varying parameters
!  (first line must contain a single int: # of locations)
!  (if PARAM_FILE is specified, then instead use PARAM_FILE-spatial for
!  spatially-varying parameters)
! FILENAME.clim is the file of climate data for each time step
! FILENAME.dat is the file of measured data (one column per data type)
! FILENAME.spd (steps per day) contains one line per location; each line
!  begins with year and julian day of 1st point, followed by the number
!  of steps in each day, terminated by -1
! FILENAME.valid contains fraction of valid data points for each time
!  step (one col. per data type)

PARAM_FILE = none
! If specified (not 'none'), use given file for parameter values and
!  info, rather than FILENAME.param

OUTPUT_NAME = lef-estimated
! OUTPUT_NAME will store user-readable info about the run
! OUTPUT_NAME.param and OUTPUT_NAME.param-spatial will store best
!  parameter set found
! OUTPUT_NAME.hist<n> will store history of run - i.e. every accepted
!  parameter set - for each location n


! --- DATA TYPES TO INCLUDE IN OPTIMIZATION ---

! 1 = include in optimization; 0 = don't include
OPT_NEE = 1
OPT_EVAPOTRANSPIRATION = 0
OPT_SOIL_WETNESS = 0


! --- VARIABLES CONTROLLING OPTIMIZATION ---

LOC = -1
! Location to run at (-1 means run at all locations)

NUM_RUNS = 1
! Number of separate runs to perform

RANDOM_START = 0
! If 0, start chains with param. values = guess
! If 1, start randomly between min and max

NUM_AT_ONCE = 10000
! To determine convergence: run for NUM_AT_ONCE iterations, then check
!  accept % - if not close to A_STAR, run for another NUM_AT_ONCE
!  iterations

NUM_CHAINS = 10
! Start by running NUM_CHAINS to convergence, then choosing the best of
!  these as a starting point for the optimization
!  (doing multiple starts helps prevent getting stuck in local optima)

NUM_SPINUPS = 125000
! Once temperatures have converged, number of additional iterations
!  (spin-ups) to run before we start recording (to allow posteriors to
!  stabilize)

ITER = 375000
! Number of Metropolis iterations once we've converged and finished
!  NUM_SPINUPS

ADD_FRACTION = 0.5
! Initial pDelta, as fraction of parameter range

VALID_FRAC = 0.5
! Fraction of data points which must be valid to use data from a given
!  time step

SCALE_FACTOR = 1.0
! Amount to multiply log likelihood difference by
!  (anything but 1 goes against theory, unless used in conjunction with
!  UNAGGED_WEIGHT)

PARAM_WEIGHT = 0.0
! Relative weight of param. vs. data error

OPT_INDICES_EXT = none
! If not 'none', this gives the extension of the optimization indices
!  file; the full filename is FILENAME.OPT_INDICES_EXT
! File giving, for each location, start and end indices for optimization
!  (one line per location, with each line containing two integers: start
!  & end; if no file specified, use all points)
!  (1-indexing; end = -1 means go to end)

COMPARE_INDICES_EXT = none
! If not 'none', this gives the extension of the comparison indices
!  file; the full filename is FILENAME.COMPARE_INDICES_EXT
! File giving, for each location, start and end indices for model-data
!  comparisons (one line per location, with each line containing two
!  integers: start & end; if no file specified, use all points)
!  (1-indexing; end = -1 means go to end)

AGGREGATION_EXT = none
! If not 'none', this gives the extension of the file containing number
!  of time steps per model/data aggregation - i.e. aggregations that are
!  performed prior to doing model-data comparisons in the optimization
! The full filename is FILENAME.AGGREGATION_EXT
! Each line contains aggregation info for one location, and each line
!  is terminated with a -1
!  Each value (i) on a given line is an integer giving the number of
!  time steps in aggregation i in that time step
!  (e.g. to run model-data comparison on a yearly aggregation, each
!  value would be number of steps in year i)
!  (if no file specified, will perform no aggregation)
! Note that VALID_FRAC is ignored for aggregated data (assumed equal to
!  0)

UNAGGED_WEIGHT = 0.0
! If aggregation is done, relative weight of unaggregated points in
!  optimization (should probably be 0 or 1; 0 means only use aggregated
!  points in optimization)