r.agropast.semiadaptive7.0.4

#!/usr/bin/python
#
############################################################################
#
# MODULE:           r.agropast.semiadaptive.tenure.py
# AUTHOR(S):        Isaac Ullah, University of Pittsburgh, Arizona State University
# PURPOSE:        Simulates agricultural and pastoral landuse and tracks yields and environmental
#                          impacts. Farming and grazing strategies and yield goals are predetermined by the
#                          researcher, and do not change (adapt) during the simulation. However, catchment sizes
#                          can be adapted over time to meet these goals. This version implments a land tenuring alogrithm.
#                          Requires r.landscape.evol.
# ACKNOWLEDGEMENTS:    National Science Foundation Grant #BCS0410269, Center for Comparative Archaeology at
#                          the University of Pittsburgh, Center for Social Dynamics and Complexity at Arizona
#                          State University
# COPYRIGHT:        (C) 2014 by Isaac Ullah, University of Pittsburgh
#            This program is free software under the GNU General Public
#            License (>=v2). Read the file COPYING that comes with GRASS
#            for details.
#
#############################################################################


#%Module
#%  description: Simulates agricultural and pastoral landuse and tracks yields and environmental impacts. Basic farming and grazing strategies and yield goals are predetermined by the researcher, and do not change (adapt) during the simulation. However, catchment sizes can be adapted over time to meet these goals. This version implments a land tenuring alogrithm. Requires r.landscape.evol. Note that some stats files will be written to current mapset, and will be appended to if you run the simulation again with the same prefix.
#%END

##################################
#Simulation Control
##################################
#%option
#% key: years
#% type: integer
#% description: Number of iterations ("years") to run
#% answer: 500
#% required: yes
#% guisection: Simulation Control
#%END
#%option
#% key: prfx
#% type: string
#% description: Prefix for all output maps
#% answer: p30f10sim
#% required: yes
#% guisection: Simulation Control
#%END

##################################
#Agent Properties
##################################
#%option
#% key: numpeople
#% type: double
#% description: Number of people in the village(s) (stays constant throughout the simulation)
#% answer: 30
#% guisection: Agent Properties
#%END
#%option
#% key: agentmem
#% type: integer
#% description: Length of the "memory" of the agent in years. The agent will use the mean surplus/defict information from this many of the most recent previous years when making a subsistence plan for the current year.
#% answer: 5
#% guisection: Agent Properties
#%END
#%option
#% key: aglabor
#% type: double
#% description: The amount of agricultural labor an average person of the village can do in a year (in "person-days")
#% answer: 300
#% guisection: Agent Properties
#%END
#%option
#% key: cerealreq
#% type: double
#% description: Amount of cereals that would be required per person per year if cereals were the ONLY food item (Kg)
#% answer: 370
#% guisection: Agent Properties
#%END
#%option
#% key: animals
#% type: double
#% description: Number of herd animals that would be needed per person per year if pastoral products were the ONLY food item
#% answer: 60
#% guisection: Agent Properties
#%END
#%option
#% key: fodderreq
#% type: double
#% description: Amount of fodder required per herd animal per year (Kg)
#% answer: 680
#% guisection: Agent Properties
#%END
#%option
#% key: a_p_ratio
#% type: double
#% description: Actual ratio of agricultural to pastoral foods in the diet (0-1, where 0 = 100% agricultural and 1 = 100% pastoral)
#% answer: 0.20
#% options: 0.0-1.0
#% guisection: Agent Properties
#%END
#%option
#% key: costsurf
#% type: string
#% gisprompt: old,cell,raster
#% description: Map of movement costs from the center of the agricultural/grazing catchments (from r.walk or r.cost).
#% guisection: Agent Properties
#%END


##################################
#Farming Options
##################################
#%option
#% key: agcatch
#% type: string
#% gisprompt: old,cell,raster
#% description: Map of the largest possible agricultural catchment map (From r.catchment or r.buffer, where catchment is a single integer value, and all else is NULL)
#% guisection: Farming
#%END
#%option
#% key: agmix
#% type: double
#% description: The wheat/barley ratio (e.g., 0.0 for all wheat, 1.0 for all barley, 0.5 for an equal mix)
#% answer: 0.25
#% options: 0.0-1.0
#% guisection: Farming
#%END
#%option
#% key: nsfieldsize
#% type: double
#% description: North-South dimension of fields in map units (Large field sizes may lead to some overshoot of catchment boundaries)
#% answer: 20
#% guisection: Farming
#%END
#%option
#% key: ewfieldsize
#% type: double
#% description: East-West dimension of fields in map units (Large field sizes may lead to some overshoot of catchment boundaries)
#% answer: 50
#% guisection: Farming
#%END
#%option
#% key: fieldlabor
#% type: double
#% description: Number of person-days required to till, sow, weed, and harvest one farm field in a year.
#% answer: 50
#% guisection: Farming
#%END
#%option
#% key: farmval
#% type: double
#% description: The landcover value for farmed fields (Should correspond to an appropriate value from your landcover rules file.)
#% answer: 5
#% guisection: Farming
#%END
#%option
#% key: farmimpact
#% type: double
#% description: The mean and standard deviation of the amount to which farming a patch decreases its fertility (in percentage points of maximum fertility, entered as: "mean,std_dev"). Fertility impact values of indvidual farm plots will be randomly chosen from a gaussian distribution that has this mean and std_dev.
#% answer: 3,2
#% multiple: yes
#% guisection: Farming
#%END
#%option
#% key: maxwheat
#% type: double
#% description: Maximum amount of wheat that can be grown (kg/ha)
#% answer: 3500
#% guisection: Farming
#%END
#%option
#% key: maxbarley
#% type: double
#% description: Maximum amount of barley that can be grown (kg/ha)
#% answer: 2500
#% guisection: Farming
#%END
#%option
#% key: tenuredrop
#% type: double
#% description: Threshold for dropping land out of Tenure (with flag -M). If the value is positive it is interpreted as a percentage below the yearly average yield of all farm cells. If the value is negative, it is interpreted as a percentage of the maximum yield of all farm cells. Value will always be interpreted as a proportion between 0 and 1.
#% answer: 0.0
#% options: -1.0-1.0
#% guisection: Farming
#%END
#%flag
#% key: i
#% description: -i Implement Land Tenure with a satisficing strategy (land is never dropped, only added if needed)
#% guisection: Farming
#%end
#%flag
#% key: m
#% description: -m Implement Land Tenure with a maximizing strategy (land is dropped if below the threshold defined by "tenuredrop")
#% guisection: Farming
#%end

##################################
#Grazing Options
##################################
#%option
#% key: grazecatch
#% type: string
#% gisprompt: old,cell,raster
#% description: Map of the largest possible grazing catchment (From r.catchment or r.buffer, where catchment is a single integer value, and all else is NULL).
#% guisection: Grazing
#%END
#%option
#% key: mingraze
#% type: double
#% description: Minimum amount of vegetation on a cell for it to be considered grazeable (in value of succession stages, matching landcover rules file)
#% answer: 2
#% guisection: Grazing
#%END
#%option
#% key: grazespatial
#% type: double
#% description: Spatial dependency of the grazing pattern in map units. This value determines how "clumped" grazing patches will be. A value close to 0 will produce a perfectly randomized grazing pattern with patch size=resolution, and larger values will produce increasingly clumped grazing patterns, with the size of the patches corresponding to the value of grazespatial (in map units).
#% answer: 50
#% guisection: Grazing
#%END
#%option
#% key: grazepatchy
#% type: double
#% description: Coefficient that determines the patchiness of the grazing catchemnt. Value must be non-zero, and usually will be <= 1.0. Values close to 0 will create a patchy grazing pattern, values close to 1 will create a "smooth" grazing pattern. Actual grazing patches will be sized to the resolution of the input landcover map.
#% answer: 1.0
#% guisection: Grazing
#%END
#%option
#% key: maxgrazeimpact
#% type: integer
#% description: Maximum impact of grazing in units of "landcover.succession". Grazing impact values of individual patches will be chosen from a gaussian distribution between 1 and this maximum value (i.e., most values will be between 1 and this max). Value must be >= 1.
#% answer: 3
#% guisection: Grazing
#%END
#%option
#% key: manurerate
#% type: double
#% description: Base rate that animal dung contributes to fertility increase on a grazed patch in units of percentage of maximum fertility regained per increment of grazing impact. Actual fertility regain values are thus calculated as "manure_rate x grazing_impact", so be aware that this variable interacts with the grazing impact settings you have chosen.
#% answer: 0.2
#% options: 0.0-100.0
#% guisection: Grazing
#%END
#%option
#% key: fodder_rules
#% type: string
#% gisprompt: string
#% description: Path to foddering rules file for calculation of fodder gained by grazing
#% answer: /home/mdlpd/Dropbox/Scripts_Working_Dir/rules/fodder_rules.txt
#% guisection: Grazing
#%END
#%flag
#% key: f
#% description: -f Do not graze in unused portions of the agricultural catchment (i.e., do not graze on "fallowed" fields, and thus no "manuring" of those fields will occur)
#% guisection: Grazing
#%end
#%flag
#% key: g
#% description: -g Do not allow "stubble grazing" on harvested fields (and thus no "manuring" of fields)
#% guisection: Grazing
#%end

##################################
#Landcover Dynamics Options
##################################
#%option
#% key: fertilrate
#% type: double
#% description: Comma separated list of the mean and standard deviation of the natural fertility recovery rate (percentage by which soil fertility increases per year if not farmed, entered as: "mean,stdev".) Fertility recovery values of individual landscape patches will be randomly chosen from a gaussian distribution that has this mean and std_dev.
#% answer: 2,0.5
#% multiple: yes
#% guisection: Landcover Dynamics
#%END
#%option
#% key: inlcov
#% type: string
#% gisprompt: old,cell,raster
#% description: Input landcover map (Coded according to landcover rules file below)
#% guisection: Landcover Dynamics
#%END
#%option
#% key: infert
#% type: string
#% gisprompt: old,cell,raster
#% description: Input fertility map (Coded according to percentage retained fertility, with 100 being full fertility)
#% guisection: Landcover Dynamics
#%END
#%option
#% key: maxlcov
#% type: string
#% gisprompt: old,cell,raster
#% description: Maximum value of landcover for the landscape (Can be a single numerical value or a cover map of different maximum values. Shouldn't exceed maximum value in rules file')
#% answer: 50
#% guisection: Landcover Dynamics
#%END
#%option
#% key: maxfert
#% type: string
#% gisprompt: old,cell,raster
#% description: Maximum value of fertility for the landscape (Can be a single numerical value or a cover map of different maximum values. Shouldn't exceed 100)
#% answer: 100
#% guisection: Landcover Dynamics
#%END
#%option
#% key: lc_rules
#% type: string
#% gisprompt: string
#% description: Path to reclass rules file for landcover map
#% answer: /home/mdlpd/Dropbox/Scripts_Working_Dir/rules/luse_reclass_rules.txt
#% guisection: Landcover Dynamics
#%END
#%option
#% key: cfact_rules
#% type: string
#% gisprompt: string
#% description: Path to recode rules file for c-factor map
#% answer: /home/mdlpd/Dropbox/Scripts_Working_Dir/rules/cfactor_recode_rules.txt
#% guisection: Landcover Dynamics
#%END
#%flag
#% key: c
#% description: -c Keep C-factor (and rainfall excess) maps for each year
#% guisection: Landcover Dynamics
#%end

##################################
#Landscape Evolution Options
##################################
#%option
#% key: elev
#% type: string
#% gisprompt: old,cell,raster
#% description: Input elevation map (DEM of surface)
#% guisection: Landscape Evolution
#%end
#%option
#% key: initbdrk
#% type: string
#% gisprompt: old,cell,raster
#% description: Bedrock elevations map (DEM of bedrock)
#% answer:
#% guisection: Landscape Evolution
#%end
#%option
#% key: k
#% type: double
#% gisprompt: old,cell,raster
#% description: Soil erodability index (K factor) map or constant
#% answer: 0.42
#% guisection: Landscape Evolution
#%end
#%option
#% key: sdensity
#% type: double
#% gisprompt: old,cell,raster
#% description: Soil density map or constant [T/m3] for conversion from mass to volume
#% answer: 1.2184
#% guisection: Landscape Evolution
#%end
#%option
#% key: kt
#% type: double
#% description: Stream transport efficiency variable (0.001 for a soft substrate, 0.0001 for a normal substrate, 0.00001 for a hard substrate, 0.000001 for a very hard substrate)
#% answer: 0.0001
#% options: 0.001,0.0001,0.00001,0.000001
#% guisection: Landscape Evolution
#%end
#%option
#% key: loadexp
#% type: double
#% description: Stream transport type variable (1.5 for mainly bedload transport, 2.5 for mainly suspended load transport)
#% answer: 1.5
#% options: 1.5,2.5
#% guisection: Landscape Evolution
#%end
#%option
#% key: manningn
#% type: string
#% gisprompt: old,cell,raster
#% description: Map or constant of the value of Manning's "N" value for channelized flow.
#% answer: 0.05
#% required : no
#% guisection: Landscape Evolution
#%end
#%option
#% key: kappa
#% type: double
#% description: Hillslope diffusion (Kappa) rate map or constant [m/kyr]
#% answer: 1
#% guisection: Landscape Evolution
#%end
#%option
#% key: rain
#% type: string
#% gisprompt: old,cell,raster
#% description: Precip totals for the average storm [mm] (or path to climate file of comma separated values of "rain,R,storms,stormlength", with a new line for each year of the simulation)
#% answer: 20.61
#% guisection: Landscape Evolution
#%end
#%option
#% key: r
#% type: string
#% description: Rainfall (R factor) constant (AVERAGE FOR WHOLE MAP AREA) (or path to climate file of comma separated values of "rain,R,storms,stormlength", with a new line for each year of the simulation)
#% answer: 4.54
#% guisection: Landscape Evolution
#%end
#%option
#% key: storms
#% type: string
#% description: Number of storms per year (integer) (or path to climate file of comma separated values of "rain,R,storms,stormlength", with a new line for each year of the simulation)
#% answer: 25
#% guisection: Landscape Evolution
#%end
#%option
#% key: stormlength
#% type: string
#% description: Average length of the storm [h] (or path to climate file of comma separated values of "rain,R,storms,stormlength", with a new line for each year of the simulation)
#% answer: 24.0
#% guisection: Landscape Evolution
#%end
#%option
#% key: speed
#% type: double
#% description: Average velocity of flowing water in the drainage [m/s]
#% answer: 1.4
#% guisection: Landscape Evolution
#%end
#%option
#% key: cutoff1
#% type: double
#% description: Flow accumulation breakpoint value for shift from diffusion to overland flow
#% answer: 0
#% guisection: Landscape Evolution
#%end
#%option
#% key: cutoff2
#% type: double
#% description: Flow accumulation breakpoint value for shift from overland flow to rill/gully flow (if value is the same as cutoff1, no sheetwash procesess will be modeled)
#% answer: 100
#% guisection: Landscape Evolution
#%end
#%option
#% key: cutoff3
#% type: double
#% description: Flow accumulation breakpoint value for shift from rill/gully flow to stream flow (if value is the same as cutoff2, no rill procesess will be modeled)
#% answer: 100
#% guisection: Landscape Evolution
#%end
#%option
#% key: smoothing
#% type: string
#% description: Amount of additional smoothing (answer "no" unless you notice large spikes in the erdep rate map)
#% answer: no
#% options: no,low,high
#% guisection: Landscape Evolution
#%end
#%flag
#% key: 1
#% description: -1 Calculate streams as 1D difference instead of 2D divergence
#% guisection: Landscape Evolution
#%end
#%flag
#% key: k
#% description: -k Keep ALL temporary maps (overides flags -drst). This will make A LOT of maps!
#% guisection: Landscape Evolution
#%end
#%flag
#% key: d
#% description: -d Don't output yearly soil depth maps
#% guisection: Landscape Evolution
#%end
#%flag
#% key: r
#% description: -r Don't output yearly maps of the erosion/deposition rates ("ED_rate" map, in vertical meters)
#% guisection: Landscape Evolution
#%end
#%flag
#% key: s
#% description: -s Keep all slope maps
#% guisection: Landscape Evolution
#%end
#%flag
#% key: t
#% description: -t Keep yearly maps of the Transport Capacity at each cell ("Qs" maps)
#% guisection: Landscape Evolution
#%end
#%flag
#% key: e
#% description: -e Keep yearly maps of the Excess Transport Capacity (divergence) at each cell ("DeltaQs" maps)
#% guisection: Landscape Evolution
#%end

import sys
import os
import tempfile
import random
import numpy
import grass.script as grass

#main block of code starts here
def main():
    grass.message("Setting up Simulation........")
    #setting up Land Use variables for use later on
    agcatch = options['agcatch']
    nsfieldsize = options['nsfieldsize']
    ewfieldsize = options['ewfieldsize']
    grazecatch = options['grazecatch']
    grazespatial = options['grazespatial']
    grazepatchy = options['grazepatchy']
    maxgrazeimpact = options['maxgrazeimpact']
    manurerate = options['manurerate']
    inlcov = options['inlcov']
    years = int(options['years'])
    farmval = options['farmval']
    maxlcov = options['maxlcov']
    prfx = options['prfx']
    lc_rules = options['lc_rules']
    cfact_rules = options['cfact_rules']
    fodder_rules = options['fodder_rules']
    infert = options['infert']
    maxfert = options['maxfert']
    maxwheat = options['maxwheat']
    maxbarley = options['maxbarley']
    mingraze = options['mingraze']
    tenuredrop = options['tenuredrop']
    costsurf = options['costsurf']
    agmix = options['agmix']
    numpeople = float(options['numpeople'])
    agentmem = int(options['agentmem'])
    animals = float(options['animals'])
    agratio = 1 - float(options['a_p_ratio'])
    pratio =  float(options['a_p_ratio'])
    indcerreq = float(options['cerealreq'])
    indfodreq = float(options['fodderreq'])
    aglabor = float(options['aglabor'])
    fieldlabor = float(options['fieldlabor'])
    cereal_pers = numpeople * agratio
    fodder_anim = animals * numpeople * pratio
    cerealreq = indcerreq * cereal_pers
    fodderreq = indfodreq * fodder_anim
    totlabor = numpeople * aglabor
    maxfields = int(round(totlabor / fieldlabor))
    #these are various rates with min and max values entered in the gui that we need to parse
    farmimpact = map(float, options['farmimpact'].split(','))
    fertilrate = map(float, options['fertilrate'].split(','))
    #Setting up Landscape Evol variables to write the r.landscape.evol command later
    elev = options["elev"]
    initbdrk = options["initbdrk"]
    k = options["k"]
    sdensity = options["sdensity"]
    kappa = options["kappa"]
    manningn = options["manningn"]
    cutoff1 = options["cutoff1"]
    cutoff2 = options["cutoff2"]
    cutoff3 = options["cutoff3"]
    speed = options["speed"]
    kt = options["kt"]
    loadexp = options["loadexp"]
    smoothing = options["smoothing"]
    #these values could be read in from a climate file, so check that, and act accordingly
    rain2 = []
    try:
        rain1 = float(options['rain'])
        for year in range(int(years)):
            rain2.append(rain1)
    except:
        with open(options['rain'], 'rU') as f:
            for line in f:
                rain2.append(line.split(",")[0])
        #check for text header and remove if present
        try:
            float(rain2[0])
        except:
            del rain2[0]
        #throw a fatal error if there aren't enough values in the column
        if len(rain2) != int(years):
            grass.fatal("Number of rows of rainfall data in your climate file\n do not match the number of iterations you wish to run.\n Please ensure that these numbers match and try again")
            sys.exit(1)
    R2 = []
    try:
        R1 = float(options['r'])
        for year in range(int(years)):
            R2.append(R1)
    except:
        with open(options['r'], 'rU') as f:
            for line in f:
                R2.append(line.split(",")[1])
        #check for text header and remove if present
        try:
            float(R2[0])
        except:
            del R2[0]
        #throw a fatal error if there aren't enough values in the column
        if len(R2) != int(years):
            grass.fatal("Number of rows of R-Factor data in your climate file\n do not match the number of iterations you wish to run.\n Please ensure that these numbers match and try again")
            sys.exit(1)
    storms2 = []
    try:
        storms1 = float(options['storms'])
        for year in range(int(years)):
            storms2.append(storms1)
    except:
        with open(options['storms'], 'rU') as f:
            for line in f:
                storms2.append(line.split(",")[2])
        #check for text header and remove if present
        try:
            float(storms2[0])
        except:
            del storms2[0]
        #throw a fatal error if there aren't enough values in the column
        if len(storms2) != int(years):
            grass.fatal("Number of rows of storm frequency data in your climate file\n do not match the number of iterations you wish to run.\n Please ensure that these numbers match and try again")
            sys.exit(1)
    stormlength2 = []
    try:
        stormlength1 = float(options['stormlength'])
        for year in range(int(years)):
            stormlength2.append(stormlength1)
    except:
        with open(options['stormlength'], 'rU') as f:
            for line in f:
                stormlength2.append(line.split(",")[3])
        #check for text header and remove if present
        try:
            float(stormlength2[0])
        except:
            del stormlength2[0]
        #throw a fatal error if there aren't enough values in the column
        if len(stormlength2) != int(years):
            grass.fatal("Number of rows of storm length data in your climate file\n do not match the number of iterations you wish to run.\n Please ensure that these numbers match and try again")
            sys.exit(1)
    #get the process id to tag any temporary maps we make for easy clean up in the loop
    pid = os.getpid()
    #we need to separate out flags used by this script, and those meant to be sent to r.landscape.evol. We will do this by popping them out of the default "flags" dictionary, and making a new dictionary called "use_flags"
    use_flags = {}
    use_flags.update({'g': flags.pop('g'), 'f': flags.pop('f'), 'c': flags.pop('c'), 'i': flags.pop('i'), 'm': flags.pop('m')})
    #check the two tenure flags, and throw a fatal error if both are set.
    if use_flags['i'] is True and use_flags['m'] is True:
        grass.fatal("You have set both the i and the m flags, and this is not allowed. Please uncheck one and try again.")
    #now assemble the flag string for r.landscape.evol'
    levol_flags = []
    for flag in flags:
        if flags[flag] is True:
            levol_flags.append(flag)
    #check if maxlcov is a map or a number, and grab the actual max value for the stats file
    try:
        maxval = int(float(maxlcov))

    except:
        maxlcovdict = grass.parse_command('r.univar', flags = 'ge', map = maxlcov)
        maxval = int(float(maxlcovdict['max']))
    #check if maxfert is a map or a number, and grab the actual max value for the stats file
    try:
        maxfertval = int(float(maxfert))
    except:
        maxfertdict = grass.parse_command('r.univar', flags = 'ge', map = maxfert)
        maxfertval = int(float(maxfertdict['max']))
    #set up the stats files names
    env = grass.gisenv()
    statsdir = os.path.join(env['GISDBASE'], env['LOCATION_NAME'], env['MAPSET'])
    textout = statsdir + os.sep + prfx + '_landcover_temporal_matrix.txt'
    textout2 = statsdir + os.sep + prfx + '_fertility_temporal_matrix.txt'
    textout3 = statsdir + os.sep + prfx + '_yields_stats.txt'
    textout4 = statsdir + os.sep + prfx + '_landcover_and_fertility_stats.txt'
    statsout = statsdir + os.sep + prfx + '_erdep_stats.txt'
    # Make color rules for landcover, cfactor, and soil fertilty maps
    lccolors = tempfile.NamedTemporaryFile()
    lccolors.write('0 grey\n10 red\n20 orange\n30 brown\n40 yellow\n%s green' % maxval)
    lccolors.flush()
    cfcolors = tempfile.NamedTemporaryFile()
    cfcolors.write('0.1 grey\n0.05 red\n0.03 orange\n0.01 brown\n0.008 yellow\n0.005 green')
    cfcolors.flush()
    fertcolors = tempfile.NamedTemporaryFile()
    fertcolors.write('0 white\n20 grey\n40 yellow\n60 orange\n80 brown\n100 black')
    fertcolors.flush()
    #Figure out the number of cells per hectare and how many square meters per cell to use as conversion factors for yields
    region = grass.region()
    cellperhectare = 10000 / (float(region['nsres']) * float(region['ewres']))
    #sqmeterpercell = (float(region['nsres']) * float(region['ewres']))
    #do same for farm field size
    fieldsperhectare = 10000 / (float(nsfieldsize) * float(ewfieldsize))
    #find conversion from field size to cell size
    #cellsperfield = (float(nsfieldsize) * float(ewfieldsize)) / (float(region['nsres']) * float(region['ewres']))
    #find out the maximum amount of cereals per field according to the proper mix
    #maxyield = (((1-float(agmix))*float(maxwheat))+(float(agmix)*float(maxbarley)))/fieldsperhectare
    #find out number of digits in 'years' for zero padding
    digits = len(str(abs(years)))
    #set up the agent memory
    farmingmemory = []
    farmyieldmemory = []
    grazingmemory = []
    grazeyieldmemory = []
    grass.message('Simulation will run for %s iterations.\n\n............................STARTING SIMULATION...............................' % years)
    #Set up loop
    for year in range(int(years)):
        now = str(year + 1).zfill(digits)
        then = str(year).zfill(digits)
        #grab the current climate vars from the lists
        rain = rain2[year]
        r = R2[year]
        storms = storms2[year]
        stormlength = stormlength2[year]
        #figure out total precip (in meters) for the year for use in the veg growth and farm yields formulae
        precip = 0.001 * (float(rain) * float(storms))
        grass.message('_____________________________\nSIMULATION YEAR: %s\n--------------------------' % now)
        #make some map names
        fields = "%s_Year_%s_Farming_Impacts_Map" % (prfx, now)
        outlcov = "%s_Year_%s_Landcover_Map" % (prfx, now)
        outfert = "%s_Year_%s_Soil_Fertilty_Map" % (prfx, now)
        outcfact = "%s_Year_%s_Cfactor_Map" % (prfx, now)
        grazeimpacts = "%s_Year_%s_Gazing_Impacts_Map" % (prfx, now)
        outxs = "%s_Year_%s_Rainfall_Excess_Map" % (prfx, now)
        #check if this is year one, use the starting landcover and soilfertily and calculate soildepths
        if (year + 1) == 1:
            oldlcov = inlcov
            oldfert = infert
            oldsdepth = "%s_Year_%s_Soil_Depth_Map" % (prfx, then)
            grass.mapcalc("${sdepth}=(${elev}-${bdrk})", quiet ="True", sdepth = oldsdepth, elev = elev, bdrk = initbdrk)
        else:
            oldlcov = "%s_Year_%s_Landcover_Map" % (prfx, then)
            oldfert = "%s_Year_%s_Soil_Fertilty_Map" % (prfx, then)
            oldsdepth = "%s_Year_%s_Soil_Depth_Map" % (prfx, then)
        #GENERATE FARM IMPACTS
        #create some temp map names
        tempfields = "%stemporary_fields_map" % pid
        tempimpacta = "%stemporary_farming_fertility_impact" % pid
        #temporarily change region resolution to align to farm field size
        grass.use_temp_region()
        grass.run_command('g.region', quiet = 'True',nsres = nsfieldsize, ewres = ewfieldsize)
        #generate the yields
        grass.message("Calculating potential farming yields.....")
        #Calculate the wheat yield map (kg/cell)
        tempwheatreturn = "%stemporary_wheat_yields_map" % pid
        grass.mapcalc("${tempwheatreturn}=eval(x=if(${precip} > 0, (0.51*log(${precip}))+1.03, 0), y=if(${sfertil} > 0, (0.28*log(${sfertil}))+0.87, 0), z=if(${sdepth} > 0, (0.19*log(${sdepth}))+1, 0), a=if(x <= 0 || z <= 0, 0, ((((x*y*z)/3)*${maxwheat})/${fieldsperhectare})), if(a < 0, 0, a))", quiet = "True", tempwheatreturn = tempwheatreturn, precip = precip, sfertil = oldfert, sdepth = oldsdepth, maxwheat = maxwheat, fieldsperhectare = fieldsperhectare)
        #Calculate barley yield map (kg/cell)
        tempbarleyreturn = "%stemporary_barley_yields_map" % pid
        grass.mapcalc("${tempbarleyreturn}=eval(x=if(${precip} > 0, (0.48*log(${precip}))+1.51, 0), y=if(${sfertil} > 0, (0.34*log(${sfertil}))+1.09, 0), z=if(${sdepth} > 0, (0.18*log(${sdepth}))+0.98, 0), a=if(x <= 0 || z <= 0, 0, ((((x*y*z)/3)*${maxbarley})/${fieldsperhectare})), if(a < 0, 0, a))", quiet = "True", tempbarleyreturn = tempbarleyreturn, precip = precip, sfertil = oldfert, sdepth = oldsdepth, maxbarley = maxbarley, fieldsperhectare = fieldsperhectare)
        #Create the desired cereal mix
        tempcerealreturn = "%stemporary_cereal_yields_map" %pid
        grass.mapcalc("${tempcerealreturn}=if(isnull(${agcatch}), null(),  (((1-${agmix})*${tempwheatreturn})+(${agmix}*${tempbarleyreturn})) )", quiet = "True", tempcerealreturn = tempcerealreturn, agmix = agmix, tempwheatreturn = tempwheatreturn, tempbarleyreturn = tempbarleyreturn, agcatch = agcatch)
        grass.message("Figuring out the farming plan for this year...")
        #gather some stats from yields maps in order to make an estimate of number of farm plots...
        cerealstats2 = grass.parse_command('r.univar', flags = 'ge', map = tempcerealreturn)
        #"Fuzz up" the agent's memory of past yields and shortfalls. We do this by padding the actual values of these things to a randomly generated percentage that is drawn from a gaussian probability distribution with mu of the mean value and sigma of 0.0333. This means that the absolute max/min pad can only be up to +- %10 of the mean value (eg. at the 3-sigma level of a gaussian distribution with sigma of 0.0333), and that pad values closer to 0% will be more likely than pad values close to +- 10%. This more closely models how good people are at "educated guesses" of central tendencies (i.e., it's how people "guesstimate" the "average" value). This also ensures some variation from year to year, regardless of the "optimum" solution.
        if len(farmyieldmemory) is 0: #if it's the first year, then just use the fuzzed average potential yield from all cells in agcatch, and make the padded amount 1
            fuzzyyieldmemory = random.gauss(float(cerealstats2["mean"]), (float(cerealstats2['mean']) * 0.0333))
            fuzzydeficitmemory = -1
        else:
            if agentmem > (year -1):
                slicer = year - 1
            else:
                slicer = agentmem
            grass.debug("slicer: %s" % slicer)
            fuzzyyieldmemory = random.gauss(numpy.mean(farmyieldmemory[slicer:]), (numpy.mean(farmyieldmemory[slicer:]) * 0.0333))
            fuzzydeficitmemory = random.gauss(numpy.mean(farmingmemory[slicer:]), (numpy.mean(farmingmemory[slicer:]) * 0.0333))
        #Figure out how many fields the agent thinks it needs based on current average yield
        numfields = int(round(float(cerealreq) / fuzzyyieldmemory))
        grass.debug("total fields should be %s" % numfields)
        #discover how many cells to add or subtract based on agent memory of surplus or deficit
        fieldpad = int(round(fuzzydeficitmemory / fuzzyyieldmemory))
        grass.debug("fieldpad (sign reversed) %s" % fieldpad)
        #add or remove the number of extra cells calculated from agent memory surplus or deficit
        numfields = numfields - fieldpad
        grass.debug("did numfields change? %s" % numfields)
        #Then, make sure we don't exceed our catchment size or labor capacity (and reduce number of fields if necessary)
        if numfields > int(round((float(cerealstats2['cells']) - float(cerealstats2["null_cells"])))):
            numfields = int(round((float(cerealstats2['cells']) - float(cerealstats2["null_cells"]))))
        if numfields > maxfields:
            numfields = maxfields
        grass.debug("did numfields hit the max and be curtailed? %s" % numfields)
        #check for tenure, and do the appropriate type of tenure if asked
        if use_flags['m'] is True:
            grass.message("Land Tenure is ON, with MAXIMIZING strategy")
            #check for first year, and zero out tenure if so
            if (year + 1) == 1:
                grass.run_command('r.random', quiet = 'True', input = agcatch, npoints = numfields, raster = tempfields)
                grass.message('First year, so all farm fields randomly assigned')
                tenuredcells = 0
                droppedcells = 0
                newcells = 0
            else:
                #make some map names
                oldfields = "%s_Year_%s_Farming_Impacts_Map" % (prfx, then)
                tenuredfields = "%s_Year_%s_Tenured_Fields_Map" % (prfx, now)
                tempagcatch = "%stemporary_agricultural_catchment_map" % pid
                grass.message('Performing yearly land tenure evaluation')
                #grab stats from old fields
                oldfarmstats = grass.parse_command('r.univar', flags = 'ge', map = oldfields)
                #check the comparison metric and drop underperforming fields compared to average yield...
                if tenuredrop >= 0:
                    grass.mapcalc("${tenuredfields}=if(isnull(${oldfields}), null(), if(${tempcerealreturn} >= (${meanyield}*${tenuredrop}), 1, null()))", quiet = "True", tenuredfields=tenuredfields, oldfields = oldfields, tempcerealreturn = tempcerealreturn, meanyield = oldfarmstats['mean'], tenuredrop = tenuredrop)
                else: #or compared to the maximum yeild.
                    tenuredrop = tenuredrop * -1
                    grass.mapcalc("${tenuredfields}=if(isnull(${oldfields}), null(), if(${tempcerealreturn} >= (${maxyield}*${tenuredrop}), 1, null()))", quiet = "True", tenuredfields=tenuredfields, oldfields = oldfields, tempcerealreturn = tempcerealreturn, maxyield = oldfarmstats['max'], tenuredrop = tenuredrop)
                #temporarily update the agricultural catchment by withholding tenured cells. All other cells in the catchment will be fair game to be chosen for the new fields.
                grass.mapcalc("${tempagcatch}=if(isnull(${agcatch}), null(), if(isnull(${tenuredfields}), ${agcatch}, null() ))", quiet = "True", tempagcatch = tempagcatch, agcatch = agcatch, tenuredfields = tenuredfields)
                #pull stats out to see what we did (how many old fields kept, how many dropped, and how many new ones we need this year
                tenuredstats = grass.parse_command('r.univar', flags = 'ge', map = tenuredfields)
                tenuredcells = int(float(tenuredstats['cells']) - float(tenuredstats['null_cells']))
                droppedcells = int(float(oldfarmstats['cells']) - float(oldfarmstats['null_cells'])) - tenuredcells
                if numfields-tenuredcells <= 0:
                    newcells = 0
                    tempfields = tenuredfields
                else:
                    newcells = numfields-tenuredcells
                    #Now run r.random to get the required number of additional fields
                    tempfields1 = "%stemporary_extra_fields_map" % pid
                    grass.run_command('r.random', quiet = 'True', input = tempagcatch, npoints = newcells, raster = tempfields1)
                    #patch the new fields into the old fields
                    grass.run_command('r.patch', quiet = "True", input = "%s,%s" % (tempfields1,tenuredfields), output = tempfields)
                grass.message("Keeping %s fields in tenure list, dropping %s underperforming fields, adding %s new fields" % (tenuredcells, droppedcells,newcells))
        elif use_flags['i'] is True:
            grass.message("Land Tenure is ON, with SATSFICING strategy")
            #check for first year, and zero out tenure if so
            if (year + 1) == 1:
                grass.run_command('r.random', quiet = 'True', input = agcatch, npoints = numfields, raster = tempfields)
                grass.message('First year, so all farm fields randomly assigned')
                tenuredcells = 0
                droppedcells = 0
                newcells = 0
            else:
                #make some map names
                oldfields = "%s_Year_%s_Farming_Impacts_Map" % (prfx, then)
                tenuredfields = "%s_Year_%s_Tenured_Fields_Map" % (prfx, now)
                tempcomparefields = "%stemporary_Old_Fields_yield_map" % pid
                tempagcatch = "%stemporary_agricultural_catchment_map" % pid
                grass.message('Performing yearly land tenure evaluation')
                #make a map of the current potential yields from the old fields
                grass.mapcalc("${tempcomparefields}=if(isnull(${oldfields}), null(), ${tempcerealreturn} )", quiet = "True", tempcomparefields = tempcomparefields, oldfields = oldfields, tempcerealreturn = tempcerealreturn)
                #grab stats from the old fields
                oldfarmstats = grass.parse_command('r.univar', flags = 'ge', map = tempcomparefields)
                #make map of tenured fields for this year
                grass.mapcalc("${tenuredfields}=if(isnull(${oldfields}), null(), 1)", quiet = "True", tenuredfields=tenuredfields, oldfields = oldfields)
                #temporarily update the agricultural catchment by withholding tenured cells. All other cells in the catchment will be fair game to be chosen for the new fields.
                grass.mapcalc("${tempagcatch}=if(isnull(${agcatch}), null(), if(isnull(${tenuredfields}), ${agcatch}, null() ))", quiet = "True", tempagcatch = tempagcatch, agcatch = agcatch, tenuredfields = tenuredfields)
                #pull stats out to see what we did (how many old fields kept, how many dropped, and how many new ones we need this year
                tenuredstats = grass.parse_command('r.univar', flags = 'ge', map = tenuredfields)
                tenuredcells = int(float(tenuredstats['cells']) - float(tenuredstats['null_cells']))
                droppedcells = 0
                if numfields-tenuredcells <= 0:
                    newcells = 0
                    tempfields = tenuredfields
                else:
                    newcells = numfields-tenuredcells
                    #Now run r.random to get the required number of additional fields
                    tempfields1 = "%stemporary_extra_fields_map" % pid
                    grass.run_command('r.random', quiet = 'True', input = tempagcatch, npoints = newcells, raster = tempfields1)
                    #patch the new fields into the old fields
                    grass.run_command('r.patch', quiet = "True", input = "%s,%s" % (tempfields1,tenuredfields), output = tempfields)
                grass.message("Adding %s new fields" % (newcells))
        else:
            grass.message("Land Teure is OFF")
            tenuredcells = 0
            droppedcells = 0
            newcells = 0
            #Now run r.random to get the required number of fields
            grass.run_command('r.random', quiet = 'True', input = agcatch, npoints = numfields, raster = tempfields)
        #use r.surf.gaussian to cacluate fertily impacts in the farmed areas
        grass.run_command('r.surf.gauss', quiet = "True", output = tempimpacta, mean = farmimpact[0], sigma = farmimpact[1])
        grass.mapcalc("${fields}=if(isnull(${tempfields}), null(), ${tempimpacta})", quiet = "True", fields = fields, tempfields = tempfields, tempimpacta = tempimpacta)
        #grab some yieled stats while region is still aligned to desired field size
        #first make a temporary "zone" map for the farmed areas in which to run r.univar
        tempfarmzone = "%stemporary_farming_zones_map" % pid
        grass.mapcalc("${tempfarmzone}=if(isnull(${fields}), null(), ${tempcerealreturn})", quiet = "True", tempfarmzone = tempfarmzone, fields = fields, tempcerealreturn = tempcerealreturn)
        #now use this zone map to grab some stats about the actual yields for this year's farmed fields
        cerealstats = grass.parse_command('r.univar', flags = 'ge', percentile = '90', map = tempfarmzone)
        #calculate some useful stats
        cerealdif = float(cerealstats['sum']) - float(cerealreq)
        numfarmcells = int(float(cerealstats['cells']) - float(cerealstats['null_cells']))
        areafarmed = numfarmcells * float(nsfieldsize) * float(ewfieldsize)
        #update agent mempory with the farming surplus or deficit from this year
        farmingmemory.append(cerealdif)
        farmyieldmemory.append(float(cerealstats['mean']))
         #find out the percentage of agcatch that was farmed this year.
        basepercent = 100 * (numfarmcells / (float(cerealstats2['cells']) - float(cerealstats2["null_cells"]) ) )
        if basepercent > 100:
            agpercent = 100.00
        else:
            agpercent = basepercent
        #reset region to original resolution
        grass.del_temp_region()
        grass.message('We farmed %s fields, using %.2f percent of agcatch...' % (numfarmcells,agpercent))
        #GENERATE GRAZING IMPACTS
        grass.message("Calculating potential grazing yields")
        #generate basic impact values
        tempimpactg = "%stemporary_grazing_impact" % pid
        grass.run_command("r.random.surface", quiet = "True", output = tempimpactg, distance = grazespatial, exponent = grazepatchy, high = maxgrazeimpact)
        #Calculate temporary grazing yield map in kg/ha
        tempgrazereturnha = "%stemporary_hectares_grazing_returns_map" % pid
        tempgrazereturn = "%stemporary_grazing_returns_map" % pid
        grass.run_command('r.recode', quiet = 'True', flags = 'da', input = oldlcov, output = tempgrazereturnha, rules = fodder_rules)
        #convert to kg / cell, and adjust to impacts
        grass.mapcalc("${tempgrazereturn}=(${tempgrazereturnha}/${cellperhectare}) * ${tempimpactg}", quiet = "True", tempgrazereturn = tempgrazereturn, tempgrazereturnha = tempgrazereturnha, cellperhectare = cellperhectare, tempimpactg = tempimpactg)
        grass.message('Figuring out grazing plans for this year....')
        #Do we graze on the stubble of agricultural fields? If so, how much fodder to we think we will get?
        if use_flags['g'] is False:
            #temporarily set region to match the field size again
            grass.use_temp_region()
            grass.run_command('g.region', quiet = 'True',nsres = nsfieldsize, ewres = ewfieldsize)
            #set up a map with the right values of stubble fodder in it, and get them to the right units (fodder per farm field)
            stubfod1 = "%stemporary_stubblefodder_1" % pid
            stubfod2 = "%stemporary_stubblefodder_2" % pid
            stubfod3 = "%stemporary_stubblefodder_3" % pid
            #make map of the basic landcover value for fields (grass stubbles)
            grass.mapcalc("${stubfod1}=if(${tempfarmzone}, ${farmval}, null())", quiet = "True", stubfod1 = stubfod1, farmval = farmval, tempfarmzone = tempfarmzone)
            #turn that map into baseline grazing yields/ha for that landcover value
            grass.run_command('r.recode', quiet = 'True', flags = 'da', input = stubfod1, output = stubfod2, rules = fodder_rules)
            #Match the variability in stubble yields to that in cereal returns, and convert to yields per field
            grass.mapcalc("${stubfod3}=(${stubfod2} / ${fieldsperhectare}) * (${tempcerealreturn}/${maxcereals})", quiet = "True", stubfod3 = stubfod3, stubfod2 = stubfod2, fieldsperhectare = fieldsperhectare, tempcerealreturn = tempcerealreturn, maxcereals = cerealstats['max'])
            stubblestats = grass.parse_command('r.univar', flags = 'ge', percentile = '90', map = stubfod3)
            #Since we grazed stubble, let's' estimate how much stubbles we got by padding the mean value to a randomly generated percentage that is drawn from a gaussian probability distribution with mu of the mean value and sigma of 0.0333. This means that the absolute max/min pad can only be up to +- %10 of the mean value (eg. at the 3-sigma level of a gaussian distribution with sigma of 0.0333), and that pad values closer to 0% will be more likely than pad values close to +- 10%. This more closely models how good people are at "educated guesses" of central tendencies (i.e., it's how people "guesstimate" the "average" value). This also ensures some variation from year to year, regardless of the "optimum" solution. Once we've done that, do we think there's still some remaining fodder needs to be grazed from wild patches? If so, how much?
            if (float(fodderreq) - ( float(stubblestats['mean']) * numfarmcells )) < 0:
                remainingfodder = 0
            else:
                remainingfodder = float(fodderreq) - ( random.gauss(float(stubblestats['mean']), (float(stubblestats['mean']) * 0.0333)) * numfarmcells )
            #reset region
            grass.del_temp_region()
        else:
            stubblestats = {"mean": '0', "sum": "0", "cells": "0", "stddev": "0", "min": "0", "first_quartile": "0", "median": "0", "third_quartile": "0", "max": "0"}
            remainingfodder = float(fodderreq)
        #Do we graze on the "fallowed" portions of the agricultural catchment?
        tempgrazecatch = "%stemporary_grazing_catchment_map" % pid
        if use_flags['f'] is True:
            grass.mapcalc("${tempgrazecatch}=if(isnull(${grazecatch}), null(), if(isnull(${agcatch}), ${tempgrazereturn}, null()))", quiet = "True", tempgrazecatch = tempgrazecatch, grazecatch = grazecatch, agcatch = agcatch, tempgrazereturn = tempgrazereturn)
        else:
            grass.mapcalc("${tempgrazecatch}=if(isnull(${grazecatch}), null(), if(isnull(${fields}), ${tempgrazereturn}, null()))", quiet = "True", tempgrazecatch = tempgrazecatch, grazecatch = grazecatch, fields = fields, tempgrazereturn = tempgrazereturn)
        #Now that we know where we are allowed to graze, how much of the grazing catchment does the agent think it needs to meet its remaining fodder requirements? First grab some general stats from the grazing catchment.
        fodderstats = grass.parse_command('r.univar', flags = 'ge', percentile = '90', map = tempgrazecatch)
        #"Fuzz up" the agent's memory of past yields and shortfalls. We do this by padding the actual values of these things to a randomly generated percentage that is drawn from a gaussian probability distribution with mu of the mean value and sigma of 0.0333. This means that the absolute max/min pad can only be up to +- %10 of the mean value (eg. at the 3-sigma level of a gaussian distribution with sigma of 0.0333), and that pad values closer to 0% will be more likely than pad values close to +- 10%. This more closely models how good people are at "educated guesses" of central tendencies (i.e., it's how people "guesstimate" the "average" value). This also ensures some variation from year to year, regardless of the "optimum" solution.
        if len(grazeyieldmemory) is 0: #if it's the first year, then just use the fuzzed average potential yield from all cells in agcatch, and make the padded amount 1
            fuzzygyieldmemory = random.gauss(float(fodderstats['mean']), (float(fodderstats['mean']) * 0.0333))
            fuzzygdeficitmemory = -1
        else:
            fuzzygyieldmemory = random.gauss(numpy.mean(grazeyieldmemory[slicer:]), (numpy.mean(grazeyieldmemory[slicer:]) * 0.0333))
            fuzzygdeficitmemory = random.gauss(numpy.mean(grazingmemory[slicer:]), (numpy.mean(grazingmemory[slicer:]) * 0.0333))
        #Figure out how many grazing patches the agent thinks it needs based on current average patch yield
        numfoddercells = int(round(float(remainingfodder) / fuzzygyieldmemory))
        grass.debug("total graze patches should be %s" % numfoddercells)
        #discover how many cells to add or subtract based on agent memory of surplus or deficit
        fodderpad = int(round(fuzzygdeficitmemory / fuzzygyieldmemory))
        grass.debug("fodderpad (sign reversed) %s" % fodderpad)
        #add or remove the number of extra cells calculated from agent memory surplus or deficit
        numfoddercells = numfoddercells - fodderpad
        grass.debug("did numfoddercells change? %s" % numfoddercells)
        #check if we need more cells than are in the maximum grazing catchment, and clip to that catchment if so
        totgrazecells = int(float(fodderstats['cells'])) - int(float(fodderstats['null_cells']))
        grass.debug("did we have to clip numfoddercells to the catchment size? %s" % numfoddercells)
        #make the actual grazing impacts map
        if numfoddercells > totgrazecells:
            celltarget = totgrazecells
            grass.mapcalc("${grazeimpacts}=if(isnull(${tempgrazecatch}), null(), if(${oldlcov} <= ${mingraze}, null(), ${tempimpactg}))", quiet = "True", grazeimpacts = grazeimpacts, tempimpactg = tempimpactg, tempgrazecatch = tempgrazecatch, oldlcov = oldlcov, mingraze = mingraze)
        elif numfoddercells == 0:
            grass.mapcalc("${grazeimpacts}=null()", quiet = "True", grazeimpacts = grazeimpacts)
        else:
            celltarget = numfoddercells
            #now clip the cost surface to the grazable area (including cells with vegetation too low to graze on), and iterate through it to figure out the actual grazing area to be used this year
            tempgrazecost = "%stemporary_grazing_cost_map" % pid
            grass.mapcalc("${tempgrazecost}=if(isnull(${tempgrazecatch}), null(),if(${oldlcov} <= ${mingraze}, null(), ${costsurf}))", quiet = "True", tempgrazecatch = tempgrazecatch, tempgrazecost = tempgrazecost, costsurf = costsurf, oldlcov = oldlcov, mingraze = mingraze)
            catchstat = [float(x) for x in grass.read_command("r.stats", quiet = "True", flags = "1n", input = tempgrazecost, separator="=", nsteps = "10000").splitlines()]
            target = 0
            cutoff = []
            for x in sorted(catchstat):
                target = target +  1
                cutoff.append(x)
                if target >= celltarget:
                    break
            try:
                #make the actual grazing impacts map
                grass.mapcalc("${grazeimpacts}=if(${tempgrazecost} > ${cutoff}, null(), ${tempimpactg})", quiet = "True", grazeimpacts = grazeimpacts, tempimpactg = tempimpactg, tempgrazecost = tempgrazecost, cutoff = cutoff[-1])
            except:
                grass.fatal("Uh oh! Somethng wierd happened when figuring out this year\'s grazing catchment! Check your numbers and try again! Sorry!")
                sys.exit(1)
        #now get some grazing yields stats
        #Check if stubble grazing got us everything we wanted, and act appropriately
        if numfoddercells == 0:
            grazestats = {"mean": '0', "sum": "0", "cells": "0", "stddev": "0", "min": "0", "first_quartile": "0", "median": "0", "third_quartile": "0", "max": "0"}
            totalfodder = float(stubblestats['sum'])
            grazepercent = 0
            fodderdif = totalfodder - float(fodderreq)
            areagrazed = 0
        else:
            #first make a temporary "zone" map for the grazed areas in which to run r.univar
            tempgrazezone = "%stemporary_grazing_zones_map" % pid
            grass.mapcalc("${tempgrazezone}=if(isnull(${grazeimpacts}), null(), ${tempgrazereturn})", quiet = "True", tempgrazezone = tempgrazezone, grazeimpacts = grazeimpacts, tempgrazereturn = tempgrazereturn)
            #now grab the univar stats with this zone file
            grazestats = grass.parse_command('r.univar', flags = 'ge', percentile = '90', map = tempgrazezone)
            numgrazecells = (float(grazestats['cells']) - float(grazestats["null_cells"]) )
            totalfodder = float(grazestats['sum']) + float(stubblestats['sum'])
            grazepercent = 100 * (numgrazecells / totgrazecells)
            fodderdif =  totalfodder - float(fodderreq)
            areagrazed = numgrazecells * (float(region['nsres']) * float(region['ewres']))
        #update agent mempory with the grazing surplus or deficit from this year
        grazingmemory.append(fodderdif)
        grazeyieldmemory.append(float(grazestats['mean']))
        grass.message('We got %.2f kg of fodder from stubbles, and so we grazed %.2f percent of grazecatch this year.' % (float(stubblestats['sum']),grazepercent))
        #figure out how many animals and people were fed
        animfed = (totalfodder / indfodreq)
        peoplefed = ((float(cerealstats['sum']) / indcerreq) + (animfed / animals))
        grass.message('We fed %i herd animals, and supported %i people this year...' %(animfed, peoplefed))
        #write the yield stats to the stats file
        grass.message('Writing some farming and grazing stats from this year....')
        f = open(textout3, 'a')
        if os.path.getsize(textout3) == 0:
            f.write("Farming and Grazing Yields Stats\n\nVariables used in the model:\ncell resolution (grazing patch size),%s\nagcatch,%s\nnsfieldsize,%s\newfieldsize,%s\ngrazecatch,%s\ngrazespatial,%s\ngrazepatchy,%s\nmaxgrazeimpact,%s\nmanurerate,%s\ninlcov,%s\nyears,%s\nfarmval,%s\nmaxfert,%s\nmaxwheat,%s\nmaxbarley,%s\nagmix,%s\nagentmem,%s\nnumpeople,%s\nanimals,%s\ncalculated agricultural ratio,%s\ncalculated pastoral ratio,%s\ncalculated cereal required per person,%s\ncalculated fodder required per animal,%s\ncalculated total cereal required,%s\ncalculated total number of animals required,%s\ncalculated total fodder required,%s\n\nFarming stats in Kg wheat and/or barley seeds per farmplot.\nGrazing stats in Kg of digestable matter per grazing plot. Note that this may also include stubble grazing if enabled.\n\n,,Agricultural Yields,,,,,,,,,,,,,Grazing Yields,,,,,,,,,,,,,,,Additional Stats\nYear,People Fed,Percent of Agricultural Catchment Used,Number of Farm Fields,Number of Tenured Fields,Number of Dropped Fields,Number of New Fields,Total Farmed Area (m2),Per Field Harves Median,Per Field Harvest Mean,Per Field Harvest Standard Deviation,Total Cereals Harvested,Total Cereals Required,Cereal Surplus/Deficit,,Herd Animals Fed,Percent of Grazing Catchment Used,Total Grazed Area (m2),Wild Grazing Patch Median,Wild Grazing Patch Mean,Wild Grazing Patch Standard Deviation,Total Wild Fodder,Field Stubbles Median,Field Stubbles Mean,Field Stubbles Standard Deviation,Total Stubble Fodder,Total Fodder Consumed,Total Amount of Fodder Required,Fodder Surplus/Deficit,,,Minimum Cereals,First Quartile Cereals,Third Quartile Cereals,Maximum Cereals,,Minimum Wild Fodder,First Quartile Wild Fodder,Third Quartile Wild Fodder,Maximum Wild Fodder,,Minimum Stubble Fodder,Stubble Quartile Stubble Fodder,Third Quartile Stubble Fodder,Maximum Stubble Fodder" % (region['nsres'],agcatch,nsfieldsize,ewfieldsize,grazecatch,grazespatial,grazepatchy,maxgrazeimpact,manurerate,inlcov,years,farmval,maxfert,maxwheat,maxbarley,agmix,agentmem,numpeople,animals,agratio,pratio,indcerreq,fodder_anim,indfodreq,cerealreq,fodderreq))
        f.write('\n%s' % now + ',' + str(peoplefed) + ',' + str(agpercent) + ',' + str(numfarmcells) + ',' + str(tenuredcells) + ',' + str(droppedcells) + ',' + str(newcells) + ',' + str(areafarmed) + ',' + cerealstats['median'] + ',' + cerealstats['mean'] + ',' + cerealstats['stddev'] + ',' + cerealstats['sum'] + ',' + str(cerealreq) + ',' + str(cerealdif) + ',,' + str(animfed) + ',' + str(grazepercent) + ',' + str(areagrazed) + ',' + grazestats['median'] + ',' + grazestats['mean'] + ',' + grazestats['stddev'] + ',' + grazestats['sum'] + ',' + stubblestats['median'] + ',' + stubblestats['mean'] + ',' + stubblestats['stddev'] + ',' + stubblestats['sum'] + ',' + str(totalfodder) + ',' + str(fodderreq) + ',' + str(fodderdif) + ',,,' + cerealstats['min'] + ',' + cerealstats['first_quartile'] + ',' + cerealstats['first_quartile'] + ',' + cerealstats['max'] + ',,' + grazestats['min'] + ',' + grazestats['first_quartile'] + ',' + grazestats['third_quartile'] + ',' + grazestats['max'] + ',,' + stubblestats['min'] + ',' + stubblestats['first_quartile'] + ',' + stubblestats['third_quartile'] + ',' + stubblestats['max'])
        #UPDATE LANDCOVER AND SOIL FERTILITY
        grass.message('Updating landcover and soil fertility with new impacts')
        #update fertility
        tempfertil = "%stemporary_fertility_regain_map" % pid
        #use r.surf.gaussian to cacluate fertily regain map
        grass.run_command('r.surf.gauss', quiet = "True", output = tempfertil, mean = fertilrate[0], sigma = fertilrate[1])
        #figure out what happened to fertility (see if stubble-grazing is enabled, and make sure to add some manure where grazing occured, scaled to the degree of graing that happened)
        if use_flags['g'] is False:
            grass.mapcalc("${outfert}=eval(a=if(isnull(${grazeimpacts}) && isnull(${fields}), ${tempfertil}, ${tempfertil} + (${manurerate} * ${tempimpactg})), b=if(isnull(${fields}), ${oldfert}, ${oldfert} - ${fields}), c=if(b <= ${maxfert} - a, b + a, ${maxfert}), if(c < 0, 0, c))", quiet = "True", outfert = outfert, oldfert = oldfert, fields = fields, tempimpactg = tempimpactg, grazeimpacts = grazeimpacts, manurerate = manurerate, maxfert = maxfert, tempfertil = tempfertil)
        else:
            grass.mapcalc("${outfert}=eval(a=if(isnull(${grazeimpacts}), ${tempfertil}, ${tempfertil} + (${manurerate} * ${tempimpactg})), b=if(isnull(${fields}), ${oldfert}, ${oldfert} - ${fields}), if(b <= ${maxfert} - a, b + a, ${maxfert}))", quiet = "True", outfert = outfert, oldfert = oldfert, fields = fields, tempimpactg = tempimpactg, grazeimpacts = grazeimpacts, manurerate = manurerate, maxfert = maxfert, tempfertil = tempfertil)
        grass.run_command('r.colors', quiet = "True", map = outfert, rules = fertcolors.name)
        #update landcover
        # calculating rate of regrowth based on current soil fertility, spil depths, and precipitation. Recoding fertility (0 to 100%), depth (0 to >= 1m), and precip (0 to >= 1000mm) with a power regression curve from 0 to 1, then taking the mean of the two as the regrowth rate
        growthrate = "%stemporary_vegetation_regrowth_map" % pid
        grass.mapcalc('${growthrate}=eval(x=if(${sdepth} <= 1.0, ( -0.000118528 * (exp((100*${sdepth}),2.0))) + (0.0215056 * (100*${sdepth})) + 0.0237987, 1), y=if(${precip} <= 1.0, ( -0.000118528 * (exp((100*${precip}),2.0))) + (0.0215056 * (100*${precip})) + 0.0237987, 1), z=(-0.000118528 * (exp(${outfert},2.0))) + (0.0215056 * ${outfert}) + 0.0237987, a=if(x <= 0 || z <= 0, 0, (x+y+z)/3), if(a < 0, 0, a) )', quiet = "True", growthrate = growthrate,  sdepth = oldsdepth, outfert = outfert, precip = precip)
        #Calculate this year's landcover impacts and regrowth
        grass.mapcalc("${outlcov}=eval(a=if(${oldlcov} - ${grazeimpacts} + ${growthrate} >= 0, ${oldlcov} - ${grazeimpacts} + ${growthrate}, 0) , b=if(isnull(${fields}), a, ${farmval}), if(${oldlcov} < (${maxlcov} - ${growthrate}) && isnull(b), ${oldlcov} + ${growthrate}, if(isnull(b), ${maxlcov}, b) ))", quiet = "True", outlcov = outlcov, oldlcov = oldlcov, maxlcov = maxlcov, growthrate = growthrate, fields = fields, farmval = farmval, grazeimpacts = grazeimpacts)
        #Make a rainfall excess map to send to r.landcape.evol. This is a logarithmic regression (R^2=0.99.) for the data pairs: 0,90;3,85;8,70;13,60;19,45;38,30;50,20. These are the same succession cutoffs that are used in the c-factor coding.
        grass.mapcalc("${outxs}=193.522 - (42.3272 * log(${lcov} + 10.9718))", quiet = "True", outxs = outxs, lcov = outlcov)
        #if rules set exists, create reclassed landcover labels map
        try:
            temp_reclass = "%stemporary_reclassed_landcover" %pid
            grass.run_command('r.reclass', quiet = "True",  input = outlcov,  output = temp_reclass,  rules = lc_rules)
            grass.mapcalc('${out}=${input}', quiet = "True", overwrite = "True", out = outlcov, input = temp_reclass)
        except:
            grass.warning("No landcover labling rules found at path \"%s\"\nOutput landcover map will not have text labels in queries" % lc_rules)
        grass.run_command('r.colors',  quiet = "True",  map = outlcov, rules = lccolors.name)
        #collect and write landcover and fertiltiy temporal matrices
        grass.message('Collecting some landcover and fertility stats from this year....')
        f = open(textout, 'a')
        if os.path.getsize(textout) == 0:
            f.write("Temporal Matrix of Landcover\n\nYear," + ",".join(str(i) for i in range(maxval + 1)) + "\n")
        statdict = grass.parse_command('r.stats', quiet = "True",  flags = 'ani', input = outlcov, separator = '=', nv ='*')
        f.write("%s," % now)
        for key in range(maxval + 1):
            try:
                f.write(statdict[str(key)] + "," )
            except:
                f.write("0,")
        f.write("\n")
        f.close()
        f = open(textout2, 'a')
        if os.path.getsize(textout2) == 0:
            f.write("Temporal Matrix of Soil Fertility\n\nYear," + ",".join(str(i) for i in range(maxfertval + 1)) + "\n")
        statdict = grass.parse_command('r.stats', quiet = "True",  flags = 'ani', input = outfert, separator = '=', nv ='*')
        f.write("%s," % now)
        for key in range(maxfertval + 1):
            try:
                f.write(statdict[str(key)] + "," )
            except:
                f.write("0,")
        f.write("\n")
        f.close()
        #collect and write univariate stats
        #grass.run_command('r.mask', quiet = "True", raster = grazecatch)
        grass.mapcalc("MASK=if(isnull(${grazecatch}), null(), 1)", quiet = "True", overwrite = "True", grazecatch = grazecatch)
        lcovstats = grass.parse_command('r.univar', flags = 'ge', percentile = '90', map = outlcov)
        grass.run_command('g.remove', quiet = "True", flags = "f", type = "rast", name = "MASK")
        #grass.run_command('r.mask', quiet = "True", raster = agcatch)
        grass.mapcalc("MASK=if(isnull(${agcatch}), null(), 1)", quiet = "True", overwrite = "True", agcatch = agcatch)
        fertstats = grass.parse_command('r.univar', flags = 'ge', percentile = '90', map = outfert)
        grass.run_command('g.remove', quiet = "True", flags = "f", type = "rast", name = "MASK")
        f = open(textout4, 'a')
        if os.path.getsize(textout4) == 0:
            f.write("Landcover and Soil Fertility Stats\nNote that these stats are collected within the grazing catchment (landcover) and agricultural catchment (fertility) ONLY. Rest of the map is ignored.\n\n,,Basic Stats,,,,Extended Stats\nYear,,Mean Landcover,Standard Deviation Landcover,Mean Soil Fertility,Standard Deviation Soil Fertility,,Minimum Landcover,First Quartile Landcover,Median Landcover,Third Quartile Landcover,Maximum Landcover,,Minimum Soil Fertility,First Quartile Soil Fertility,Median Soil Fertility,Third Quartile Soil Fertility,Maximum Soil Fertility")
        f.write('\n%s' % now + ',,' + lcovstats['mean'] + ',' + lcovstats['stddev'] + ',' + fertstats['mean'] + ',' + fertstats['stddev'] + ',,' + lcovstats['max'] + ',' + lcovstats['third_quartile'] + ',' + lcovstats['median'] + ',' + lcovstats['first_quartile'] + ',' + lcovstats['min'] + ',,' + fertstats['min'] + ',' + fertstats['first_quartile'] + ',' + fertstats['median'] + ',' + fertstats['third_quartile'] + ',' + fertstats['max'])
        #creating c-factor map
        grass.message('Creating C-factor map for r.landscape.evol')
        try:
            grass.run_command('r.recode', quiet = True, input = outlcov, output = outcfact, rules = cfact_rules)
        except:
            grass.fatal("NO CFACTOR RECLASS RULES WERE FOUND AT PATH \"%s\"\nPLEASE ENSURE THAT THE CFACTOR RECODE RULES EXIST AND ARE WRITTEN PROPERLY, AND THEN TRY AGAIN" % cfact_rules)
            sys.exit(1)
        grass.run_command('r.colors',  quiet = True, map = outcfact, rules = cfcolors.name)
        #Run r.landscape.evol with this years' cfactor map
        grass.message('Running landscape evolution for this year....')
        #set the prefix for r.landscape.evol output files
        prefix = prfx + "_Year_%s_" % now
        #check if this is year one, and use the starting dem if so
        if (year + 1) == 1:
            inelev = elev
        else:
            inelev = "%s_Year_%s_Elevation_Map" % (prfx, then)
        try:
            grass.run_command('r.landscape.evol7.py', quiet = "True", number = 1, prefx = prefix, c = outcfact, elev = inelev, initbdrk = initbdrk, outdem = "Elevation_Map", outsoil = "Soil_Depth_Map", r = r, k = k, sdensity = sdensity, kappa = kappa, manningn = manningn, flowcontrib = outxs, cutoff1 = cutoff1, cutoff2 = cutoff2, cutoff3 = cutoff3, rain = rain, storms = storms, stormlength = stormlength, speed = speed, kt = kt, loadexp = loadexp, smoothing = smoothing, statsout = statsout, flags = ''.join(levol_flags))
        except:
            grass.fatal("Something is wrong with the values you sent to r.landscape.evol. Did you forget something? Check the values and try again...\nSimulation terminated with an error at time step %s" % now)
            sys.exit(1)
        #delete C-factor map, unless asked to save it
        if use_flags['c'] is False:
            grass.run_command("g.remove", quiet = "True", flags = 'f', type = "rast", name = "%s,%s" %  (outcfact,outxs))
        else:
            pass
        #clean up temporary maps
        grass.run_command('g.remove', quiet = "True", flags = 'f', type = "rast", pattern = '%s*' % pid)
        grass.message('Completed year %s of the simulation' % now)
    lccolors.close()
    cfcolors.close()
    fertcolors.close()
    return(grass.message(".........................SIMULATION COMPLETE...........................\nCheck in the current mapset for farming/grazing yields, landcover, fertility, and erosion/depostion stats files from this run."))


#Here is where the code in "main" actually gets executed. This way of programming is neccessary for the way g.parser needs to run.
if __name__ == "__main__":
    options, flags = grass.parser()
    main()
    exit(0)