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farmfunctions.py
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#!/usr/bin/env python
#
# Copyright (c) 2022, Alliance for Sustainable Energy
#
# This software is released under the BSD 3-clause license. See LICENSE file
# for more details.
#
"""
Wind farm functions
"""
import numpy as np
import pandas as pd
import sys, os, csv
import re
import shlex
from collections import OrderedDict
try:
from tkyamlgui import moretypes
except:
pass
# Load the right version of StringIO
if sys.version_info[0] < 3:
from StringIO import StringIO
import Tkinter as Tk
import tkFileDialog as filedialog
else:
from io import StringIO
import tkinter as Tk
from tkinter import filedialog as filedialog
from plotfunctions import plotRectangle
import OpenFASTutil as OpenFAST
# Load UTM library
try:
import utm
useutm = True
except:
useutm = False
# Load ruamel or pyyaml as needed
try:
import ruamel.yaml as YAML
yaml = YAML(typ='unsafe', pure=True)
#print("# Loaded ruamel.yaml")
useruamel=True
loaderkwargs = {'Loader':yaml.RoundTripLoader}
dumperkwargs = {'Dumper':yaml.RoundTripDumper, 'indent':4, 'default_flow_style':False} # 'block_seq_indent':2, 'line_break':0, 'explicit_start':True,
except:
import yaml as yaml
#print("# Loaded yaml")
useruamel=False
loaderkwargs = {}
dumperkwargs = {'default_flow_style':False }
if useruamel:
from ruamel.yaml.comments import CommentedMap
def comseq(d):
"""
Convert OrderedDict to CommentedMap
"""
if isinstance(d, OrderedDict):
cs = CommentedMap()
for k, v in d.items():
cs[k] = comseq(v)
return cs
return d
# Helper functions to get defaults from a dictionary
getdictval = lambda d, k, default: default[k] if k not in d else d[k]
def loadcsv(f, stringinput=False, reqheaders=None, optheaders=None,
**kwargs):
"""
Load the csv input, whether from string or filename
"""
# Get input f into a string
if stringinput:
s = f.lstrip()
else:
s = open(f, 'r').read().lstrip()
# Remove comments
cleanstr = re.sub(r'(?m)^ *#.*\n?', '', s)
# Check for header
firstline = cleanstr.splitlines()[0]
firstlinewords = [x.strip().lower() for x in firstline.split(',')]
if reqheaders is not None:
hasheader = all(elem in firstlinewords for elem in reqheaders)
else:
hasheader = False
## Check for header
#sniffer = csv.Sniffer()
#hasheader = sniffer.has_header(cleanstr)
#print("hasheader = %s"%repr(hasheader))
header = None
colheaders=reqheaders+optheaders
if hasheader:
header = 0
colheaders = None
# Convert string to io stream
finput = StringIO(cleanstr)
df = pd.read_csv(finput, header=header, names=colheaders, **kwargs)
# Double check headers to make sure it has the required headers
if hasheader:
# first rename the headers
renamemap = {}
for x in list(df.columns): renamemap[x] = str(x.strip().lower())
df.rename(columns=renamemap, inplace=True)
csvheaders=list(df.columns)
#csvheaders=[x.strip().lower() for x in list(df.columns)]
#print(csvheaders)
for header in reqheaders:
if header.lower() not in csvheaders:
print('ERROR: required data column %s not present in data'%(header))
return df
def dataframe2dict(df, reqheaders, optheaders, dictkeys=[]):
"""
Convert dataframe to a list of dictionaries
dictkeys are keys in optheaders which should be parsed as dictionaries
"""
listdict = []
for index, row in df.iterrows():
rowdict = OrderedDict()
for key in reqheaders:
rowdict[key] = row[key]
# Add the optional headers
for key in optheaders:
if key in list(df.columns):
rowdict[key] = parseoptions(row[key]) if key in dictkeys else row[key]
else:
rowdict[key] = None
listdict.append(rowdict)
return listdict
def parseoptions(optionstr):
"""
Splits an option string into a dictionary. optionstr should be of
the form "key1:val1 key2:val2"
"""
sanitizestr = optionstr if isinstance(optionstr, str) else str(optionstr)
allopts = shlex.split(sanitizestr.replace(';',','))
optdict = OrderedDict()
for opt in allopts:
optsplit = opt.split(':')
key = optsplit[0]
if len(optsplit)>1:
val = optsplit[1]
else:
val = None
optdict[key] = val
return optdict
def button_loadcsv(self, filenameinput, csvtextbox):
"""
Button to load a CSV file
"""
# Get the filename to load
csvfile = self.inputvars[filenameinput].getval()
# Check if file exists
if not os.path.isfile(csvfile):
print("ERROR: %s does not exist"%csvfile)
return
# Load the filename and display it in the text box
csvstr = open(csvfile, 'r').read().lstrip()
self.inputvars[csvtextbox].setval(csvstr)
return
def loadcsv2textbox(self, csvtextbox, csvfile):
"""
Loads CSV file to a textbox
"""
# Check if file exists
if not os.path.isfile(csvfile):
print("ERROR: %s does not exist"%csvfile)
return
# Load the filename and display it in the text box
csvstr = open(csvfile, 'r').read().lstrip()
self.inputvars[csvtextbox].setval(csvstr)
return
def resetFarmSetup(self):
"""
Resets all variables with 'farmsetup' in outputdef to their defaults
"""
for key, var in self.inputvars.items():
outputkey = 'farmsetup'
if outputkey in var.outputdef:
var.setdefault()
return
# ----------- Functions for refinement zones ---------------
def get_turbProp(self, tdict):
# Get the default type
default_type = self.inputvars['Actuator_default_type'].getval()
default_type = None if len(default_type)==0 else default_type
default_type = default_type[0] if isinstance(default_type, list) else default_type
turbtype = default_type if 'Actuator_type' not in tdict else tdict['Actuator_type']
turbtype = turbtype[0] if isinstance(turbtype, list) else turbtype
# Get default diameter and HH
if 'Actuator_%s_rotor_diameter'%turbtype in self.inputvars:
default_turbD = self.inputvars['Actuator_%s_rotor_diameter'%turbtype].getval()
else:
default_turbD = 100.0
if 'Actuator_%s_hub_height'%turbtype in self.inputvars:
default_hh = self.inputvars['Actuator_%s_hub_height'%turbtype].getval()
else:
default_hh = 100.0
# Get the real values
turbhh = default_hh if tdict['Actuator_hub_height'] is None else tdict['Actuator_hub_height']
turbD = default_turbD if tdict['Actuator_rotor_diameter'] is None else tdict['Actuator_rotor_diameter']
return turbD, turbhh
def calc_FarmAvgProp(self):
# Go through all turbines
allturbines = self.listboxpopupwindict['listboxactuator']
alltags = allturbines.getitemlist()
keystr = lambda n, d1, d2: d2.name
#print(alltags)
acceptableTurbTypes = ['TurbineFastLine', 'TurbineFastDisk',
'UniformCtDisk' , 'JoukowskyDisk']
Nturbs = 0
AvgHH = 0.0
AvgTurbD = 0.0
AvgCenter = np.array([0.0, 0.0, 0.0])
for turb in alltags:
tdict = allturbines.dumpdict('AMR-Wind', subset=[turb], keyfunc=keystr)
if tdict['Actuator_type'] in acceptableTurbTypes:
Nturbs += 1
AvgCenter += tdict['Actuator_base_position']
turbD, turbhh = self.get_turbProp(tdict)
AvgHH += turbhh
AvgTurbD += turbD
#AvgHH += tdict['Actuator_hub_height']
#AvgTurbD += tdict['Actuator_rotor_diameter']
#print(tdict['Actuator_base_position'])
#print(tdict['Actuator_hub_height'])
else:
print('ERROR: '+tdict['Actuator_type']+' is not a recognized disk type')
if Nturbs>0:
AvgHH /= Nturbs
AvgTurbD /= Nturbs
AvgCenter /= Nturbs
else:
print("ERROR: calc_FarmAvgProp(): No turbines found to average over.")
return AvgCenter, AvgTurbD, AvgHH
def refine_calcZone(zonename, zonedict, zonecenter, sx, cx, vx, scale):
refinedict = {}
# Get the distances
upstream = scale*zonedict['upstream']
downstream = scale*zonedict['downstream']
lateral = scale*zonedict['lateral']
below = scale*zonedict['below']
above = scale*zonedict['above']
# Calculate the corner
corner = zonecenter - below*vx - upstream*sx - lateral*cx
axis1 = (upstream+downstream)*sx
axis2 = 2.0*lateral*cx
axis3 = (below+above)*vx
# Edit the parameters of the refinement window
refinedict['tagging_name'] = zonename
refinedict['tagging_shapes'] = zonename
refinedict['tagging_type'] = 'GeometryRefinement'
refinedict['tagging_level'] = zonedict['level']
refinedict['tagging_geom_type'] = 'box'
refinedict['tagging_geom_origin'] = list(corner)
refinedict['tagging_geom_xaxis'] = list(axis1)
refinedict['tagging_geom_yaxis'] = list(axis2)
refinedict['tagging_geom_zaxis'] = list(axis3)
return refinedict
def refine_createZoneForTurbine(self, turbname, turbinedict, zonedict,
defaultopt):
# Get the wind direction
winddir = self.inputvars['ABL_winddir'].getval()
# Get the turbine properties
base_position = np.array(turbinedict['Actuator_base_position'])
turbD, turbhh = self.get_turbProp(turbinedict)
turbyaw = winddir if turbinedict['Actuator_yaw'] is None else turbinedict['Actuator_yaw']
# Get the zone options
units = getdictval(zonedict['options'], 'units', defaultopt).lower()
orient = getdictval(zonedict['options'], 'orientation', defaultopt).lower()
# Set scale and orientation axes
scale = turbD if units=='diameter' else 1.0
if orient == 'x':
streamwise = np.array([1.0, 0.0, 0.0])
crossstream = np.array([0.0, 1.0, 0.0])
vert = np.array([0.0, 0.0, 1.0])
elif orient == 'y':
streamwise = np.array([0.0, 1.0, 0.0])
crossstream = np.array([1.0, 0.0, 0.0])
vert = np.array([0.0, 0.0, 1.0])
elif orient == 'nacdir':
streamwise, crossstream, vert = self.convert_winddir_to_xy(turbyaw)
elif isFloat(orient):
streamwise, crossstream, vert = self.convert_winddir_to_xy(float(orient))
else: # Use the wind direction
streamwise, crossstream, vert = self.convert_winddir_to_xy(winddir)
# Get the name
zonename = '%s_level_%i_zone'%(turbname, zonedict['level'])
zonecenter = base_position + turbhh*vert
refinedict = refine_calcZone(zonename, zonedict, zonecenter,
streamwise, crossstream, vert, scale)
return refinedict
def refine_createZoneForFarm(self, zonedict, autofarmcenter, AvgTurbD, AvgHH,
defaultopt):
# Get the wind direction
winddir = self.inputvars['ABL_winddir'].getval()
# Get the zone options
units = getdictval(zonedict['options'], 'units', defaultopt).lower()
orient = getdictval(zonedict['options'], 'orientation', defaultopt).lower()
# Set scale and orientation axes
scale = AvgTurbD if units=='diameter' else 1.0
if orient == 'x':
streamwise = np.array([1.0, 0.0, 0.0])
crossstream = np.array([0.0, 1.0, 0.0])
vert = np.array([0.0, 0.0, 1.0])
elif orient == 'y':
streamwise = np.array([0.0, 1.0, 0.0])
crossstream = np.array([1.0, 0.0, 0.0])
vert = np.array([0.0, 0.0, 1.0])
elif orient == 'nacdir':
print("Zone orientation nacdir not possible for farm zone.")
print("Using wind direction instead")
streamwise, crossstream, vert = self.convert_winddir_to_xy(winddir)
elif isFloat(orient):
streamwise, crossstream, vert = self.convert_winddir_to_xy(float(orient))
else: # Use the wind direction
streamwise, crossstream, vert = self.convert_winddir_to_xy(winddir)
# Set the farm center
center=getdictval(zonedict['options'], 'center', defaultopt).lower()
if center == 'specified':
defaultctr = {'centerx':0.0, 'centery':0.0, 'centerz':0.0}
# Use a specified center location
centerx = float(getdictval(zonedict['options'], 'centerx', defaultctr))
centery = float(getdictval(zonedict['options'], 'centery', defaultctr))
centerz = float(getdictval(zonedict['options'], 'centerz', defaultctr))
zonecenter = np.array([centerx, centery, centerz])
if 'name' in zonedict['options']:
zonename = zonedict['options']['name']
else:
zonename = 'Farm_level_%i_center_%.0f_%.0f_%.0f'%(zonedict['level'], centerx, centery, centerz)
else:
# Use the farm center
if self.inputvars['turbines_autocalccenter'].getval() == True:
usecenter = autofarmcenter
centerz = usecenter[2] + AvgHH
else:
usecenter = self.inputvars['turbines_farmcenter'].getval()
centerz = AvgHH
zonecenter = np.array([usecenter[0], usecenter[1], centerz])
# Get the name
zonename = 'Farm_level_%i_zone'%(zonedict['level'])
refinedict = refine_calcZone(zonename, zonedict, zonecenter,
streamwise, crossstream, vert, scale)
return refinedict
def refine_createAllZones(self):
"""
Create all of the refinement zones
"""
# Default dictionary for optional inputs
defaultopt = {'orientation':'winddir', # winddir/nacdir/x/y/float
'units':'diameter', # diameter/meter
'center':'turbine', # turbine/farm
'applyto':'', # Act only on specific turbs
}
# Get the csv input
csvstring = self.inputvars['refine_csvtextbox'].getval()
reqheaders = ['level', 'upstream', 'downstream',
'lateral', 'below', 'above']
optheaders = ['options']
#print(csvstring)
df = loadcsv(csvstring, stringinput=True,
reqheaders=reqheaders, optheaders=optheaders)
alldf = dataframe2dict(df, reqheaders, optheaders, dictkeys=optheaders)
#for zone in alldf: print(zone['options'])
# See if any zones are farm-centered
allcenters = [getdictval(z['options'], 'center', defaultopt).lower() for z in alldf]
#print(allcenters)
if 'farm' in allcenters:
#print("Calculating farm center")
AvgCenter, AvgTurbD, AvgHH = calc_FarmAvgProp(self)
#print("AvgCenter = "+repr(AvgCenter))
#print("AvgTurbD = "+repr(AvgTurbD))
#print("AvgHH = "+repr(AvgHH))
else:
AvgHH = 0.0
AvgTurbD = 0.0
AvgCenter = np.array([0.0, 0.0, 0.0])
# Get all turbine properties
allturbines = self.listboxpopupwindict['listboxactuator']
alltags = allturbines.getitemlist()
keystr = lambda n, d1, d2: d2.name
# Get the wind direction
self.ABL_calculateWDirWS()
# Delete all old zones (if necessary)
if self.inputvars['refine_deleteprev'].getval():
alltagging = self.listboxpopupwindict['listboxtagging']
alltagging.deleteall()
# Go through all rows and create zones
for zone in alldf:
center=getdictval(zone['options'], 'center', defaultopt).lower()
filterstr=getdictval(zone['options'], 'applyto', defaultopt)
if center=='turbine':
# Apply to specific turbines
if len(filterstr)>0:
applyturbs = [x for x in alltags if bool(re.search(filterstr, x))]
else:
applyturbs = alltags
for turb in applyturbs:
tdict = allturbines.dumpdict('AMR-Wind',
subset=[turb], keyfunc=keystr)
refinedict = refine_createZoneForTurbine(self, turb, tdict,
zone, defaultopt)
#print(refinedict)
if refinedict is not None:
self.add_tagging(refinedict)
else:
# Apply to the farm center
refinedict = refine_createZoneForFarm(self, zone, AvgCenter,
AvgTurbD, AvgHH, defaultopt)
#print(refinedict)
if refinedict is not None:
self.add_tagging(refinedict)
# Automatically set the max_level value
self.autoMaxLevel()
return
# ----------- Functions for wind farm turbines -------------
def convertLatLong(x, y, useutm, coordsys, stoponerror=True):
"""
Convert lat/long to utm x/y if necessary
"""
turbx = x
turby = y
if coordsys=='latlong':
if useutm:
utmxy = utm.from_latlon(x, y)
turbx = utmxy[0]
turby = utmxy[1]
else:
print("ERROR: UTM conversion not available ")
if stoponerror: sys.exit(1)
return turbx, turby
def getTurbAvgCenter(self, turbdf, updatewidget=False, convertlatlong=True):
"""
Calculate the farm center based on turbine locations
"""
AvgCenter = np.array([0.0, 0.0])
latcol = 0 # which coordinate is latitude
longcol = 1 # which coordinate is longitude
# Get the average center
if self.inputvars['turbines_autocalccenter'].getval():
for turb in turbdf:
AvgCenter += np.array([turb['x'], turb['y']])
AvgCenter = AvgCenter/len(turbdf)
else:
AvgCenter = self.inputvars['turbines_farmcenter'].getval()
# Convert from lat/long if necessary
coordsys = self.inputvars['turbines_coordsys'].getval()
if coordsys=='latlong' and convertlatlong:
# Convert AvgCenter to lat/long
if useutm:
utmxy = utm.from_latlon(AvgCenter[latcol],
AvgCenter[longcol])
AvgCenter = [utmxy[0], utmxy[1]]
#utm.from_latlon(row['ylat'], row['xlong'])
else:
print("ERROR: UTM conversion not available ")
if updatewidget:
self.inputvars['turbines_farmcenter'].setval(AvgCenter,
forcechange=True)
return AvgCenter
def turbines_getAllTurbineTypes(self):
"""
Get a list of all turbine types from csv input
"""
reqheaders = ['name', 'x', 'y', 'type', 'yaw', 'hubheight']
optheaders = ['options']
# Get the csv input
csvstring = self.inputvars['turbines_csvtextbox'].getval()
df = loadcsv(csvstring, stringinput=True,
reqheaders=reqheaders, optheaders=optheaders)
alldf = dataframe2dict(df, reqheaders, optheaders, dictkeys=optheaders)
# Get the turbine list
allturbtypes = self.listboxpopupwindict['listboxturbinetype'].getitemlist()
# build the list of turbine types
includedturbtypes = []
for turb in alldf:
turbtype = turb['type'].strip()
if turbtype not in allturbtypes:
print("ERROR: %s is not in list all turbine types:%s"%(turbtype,
allturbtypes))
continue
if (turbtype not in includedturbtypes):
includedturbtypes.append(turbtype)
return includedturbtypes
def isInt(s):
try:
int(s)
return True
except:
return False
def isFloat(s):
try:
float(s)
return True
except:
return False
def convertString(s):
if isInt(s): return int(s)
if isFloat(s): return float(s)
else: return s
def extractkeystartingwith(d, key, removeprefix=False):
"""
Extract all keys from dict that start with key
"""
returndict = OrderedDict()
for k, v in d.items():
if k.startswith(key):
newkey = k.replace(key, '', 1) if removeprefix else k
returndict[newkey] = convertString(v)
return returndict
def turbines_createAllTurbines(self):
"""
Create all of the turbines from csv input
"""
# Default dictionary for optional inputs
defaultopt = {'copyfast':False, # True/False
}
reqheaders = ['name', 'x', 'y', 'type', 'yaw', 'hubheight']
optheaders = ['options']
# Get the csv input
csvstring = self.inputvars['turbines_csvtextbox'].getval()
#print(csvstring)
df = loadcsv(csvstring, stringinput=True,
reqheaders=reqheaders, optheaders=optheaders)
alldf = dataframe2dict(df, reqheaders, optheaders, dictkeys=optheaders)
#for zone in alldf: print(zone['options'])
# Get all turbine properties
allturbines = self.listboxpopupwindict['listboxactuator']
alltags = allturbines.getitemlist()
keystr = lambda n, d1, d2: d2.name
# Calculate the farm center
AvgCenter = getTurbAvgCenter(self, alldf)
#print("AvgCenter = "+repr(AvgCenter))
createnewdomain = self.inputvars['turbines_createnewdomain'].getval()
# Set prob_lo/prob_hi/n_cell if necessary
if createnewdomain:
# Get the farm domain size
domainsize = self.inputvars['turbines_domainsize'].getval()
if domainsize is None:
# WARNING
print("ERROR: Farm domain size is not valid!")
return
if self.inputvars['turbines_freespace'].getval():
groundoffset = -0.5*domainsize[2]
else:
groundoffset = 0.0
corner1 = [AvgCenter[0] - 0.5*domainsize[0],
AvgCenter[1] - 0.5*domainsize[1],
0.0+groundoffset]
corner2 = [AvgCenter[0] + 0.5*domainsize[0],
AvgCenter[1] + 0.5*domainsize[1],
domainsize[2]+groundoffset]
self.inputvars['prob_lo'].setval(corner1)
self.inputvars['prob_hi'].setval(corner2)
# Set the mesh size (if necessary)
backgrounddx = self.inputvars['turbines_backgroundmeshsize'].getval()
if backgrounddx is not None:
Nx = int(round(domainsize[0]/backgrounddx))
Ny = int(round(domainsize[1]/backgrounddx))
Nz = int(round(domainsize[2]/backgrounddx))
self.inputvars['n_cell'].setval([Nx, Ny, Nz])
# Make sure to add turbines to simulation
source_terms = self.inputvars['ICNS_source_terms'].getval()
if source_terms is None: source_terms = []
if 'ActuatorForcing' not in source_terms:
source_terms.append('ActuatorForcing')
self.inputvars['ICNS_source_terms'].setval(source_terms)
#print(source_terms)
# Make sure to add Actuator to icns.physics
physicsterms = self.inputvars['physics'].getval()
if physicsterms is None: physicsterms = []
if 'Actuator' not in physicsterms:
physicsterms.append('Actuator')
self.inputvars['physics'].setval(physicsterms)
# Delete all old turbines (if necessary)
if self.inputvars['turbines_deleteprev'].getval():
allturbines.deleteall()
# Add all turbines
# Get the turbine list
allturbinemodels = self.listboxpopupwindict['listboxturbinetype']
allturbtypes = allturbinemodels.getitemlist()
# Get the wind direction
self.ABL_calculateWDirWS()
winddir = self.inputvars['ABL_winddir'].getval()
coordsys = self.inputvars['turbines_coordsys'].getval()
for turb in alldf:
turbtype = turb['type'].strip()
# Check the turbine type
if turbtype not in allturbtypes:
print("ERROR: turbine type %s not found for turbine %s"%(turbtype,turb['name']))
continue
modelparams = allturbinemodels.dumpdict('AMR-Wind',
subset=[turbtype],
keyfunc=lambda n, d1, d2: d2.name)
model_zHH = modelparams['Actuator_hub_height']
# Set the turbine xy
turbx, turby = convertLatLong(turb['x'], turb['y'], useutm, coordsys)
# ==== Set the turbine dictionary ====
turbdict = self.get_default_actuatordict()
turbdict['Actuator_name'] = turb['name']
# Set the hub-height
try: # Hub-height specified in CSV
hubheight = float(turb['hubheight'])
turbdict['Actuator_hub_height'] = hubheight
except: # Hub-height left alone
hubheight = model_zHH
turbdict['Actuator_base_position'] = [turbx, turby, hubheight - model_zHH]
# Set the yaw
try:
turbyaw = float(turb['yaw'])
except:
turbyaw = winddir
turbdict['Actuator_yaw'] = turbyaw
# Get all of the turbine model defaults
turbdict = self.turbinemodels_applyturbinemodel(turbdict,
turbtype,
docopy=True,
updatefast=True)
# Set any AMR-Wind options
AMRoptions = extractkeystartingwith(turb['options'], 'AMRparam_', removeprefix=True)
def tryeval(s) :
try:
x = eval(s)
except:
x = s
return x
if bool(AMRoptions):
for key, val in AMRoptions.items():
if isinstance(val, str): val = val.replace(';',',')
turbdict[key] = tryeval(val)
print("Setting "+key+" to "+repr(turbdict[key]))
# Process options if using OpenFAST model
if turbdict['Actuator_type'] in ['TurbineFastLine', 'TurbineFastDisk']:
fstfile = turbdict['Actuator_openfast_input_file']
options = turb['options']
#print("turbine fst file: "+fstfile)
#print("turbine options: "+repr(options))
# Make any edits to the FST file
FSToptions = extractkeystartingwith(options, 'FSTparam_', removeprefix=True)
if bool(FSToptions):
print(FSToptions)
OpenFAST.editFASTfile(fstfile, FSToptions)
# Make any edits to AeroDyn
ADoptions = extractkeystartingwith(options, 'ADparam_', removeprefix=True)
if bool(ADoptions):
print(ADoptions)
ADfile = OpenFAST.getFileFromFST(fstfile,'AeroFile')
OpenFAST.editFASTfile(ADfile, ADoptions)
# Make any edits to ServoDyn
SDoptions = extractkeystartingwith(options, 'SDparam_', removeprefix=True)
if bool(SDoptions):
print(SDoptions)
SDfile = OpenFAST.getFileFromFST(fstfile,'ServoFile')
OpenFAST.editFASTfile(SDfile, SDoptions)
# Make any edits to ElastoDyn
EDoptions = extractkeystartingwith(options, 'EDparam_', removeprefix=True)
if bool(EDoptions):
print(EDoptions)
EDfile = OpenFAST.getFileFromFST(fstfile,'EDFile')
OpenFAST.editFASTfile(EDfile, EDoptions)
# Make any edits to HydroDyn
HDoptions = extractkeystartingwith(options, 'HDparam_', removeprefix=True)
if bool(HDoptions):
print(HDoptions)
HDfile = OpenFAST.getFileFromFST(fstfile,'HydroFile')
OpenFAST.editFASTfile(HDfile, HDoptions)
# Make any edits to MoorDyn
MDoptions = extractkeystartingwith(options, 'MDparam_', removeprefix=True)
if bool(MDoptions):
print(MDoptions)
MDfile = OpenFAST.getFileFromFST(fstfile,'MooringFile')
OpenFAST.editFASTfile(MDfile, MDoptions)
# Make any edits to DISCON
DISCONoptions = extractkeystartingwith(options, 'DISCONparam_', removeprefix=True)
if bool(DISCONoptions):
print(DISCONoptions)
SDfile = OpenFAST.getFileFromFST(fstfile,'ServoFile')
DISCONfile = OpenFAST.getFileFromFST(SDfile, 'DLL_InFile')
OpenFAST.editDISCONfile(DISCONfile, DISCONoptions)
# Add the turbine to the list
self.add_turbine(turbdict, verbose=False)
return
def turbines_previewAllTurbines(self, ax=None):
"""
Plot all of the turbines from the csv input
"""
reqheaders = ['name', 'x', 'y', 'type', 'yaw', 'hubheight']
optheaders = ['options']
# Get the csv input
csvstring = self.inputvars['turbines_csvtextbox'].getval()
#print(csvstring)
df = loadcsv(csvstring, stringinput=True,
reqheaders=reqheaders, optheaders=optheaders)
alldf = dataframe2dict(df, reqheaders, optheaders, dictkeys=optheaders)
#for turb in alldf: print("%10s %f %f %10s"%(turb['name'], turb['x'], turb['y'], turb['type']))
createnewdomain = self.inputvars['turbines_createnewdomain'].getval()
# Set prob_lo/prob_hi/n_cell if necessary
if createnewdomain:
# Calculate the farm center
AvgCenter = getTurbAvgCenter(self, alldf)
#print("AvgCenter = "+repr(AvgCenter))
# Get the farm domain size
domainsize = self.inputvars['turbines_domainsize'].getval()
if domainsize is None:
# WARNING
print("ERROR: Farm domain size is not valid!")
return
else:
corner1 = self.inputvars['prob_lo'].getval()
corner2 = self.inputvars['prob_hi'].getval()
domainsize = np.array(corner2) - np.array(corner1)
AvgCenter = (np.array(corner2) + np.array(corner1))*0.5
# Set the corner points
corner1 = [AvgCenter[0] - 0.5*domainsize[0],
AvgCenter[1] - 0.5*domainsize[1],
0.0]
corner2 = [AvgCenter[0] + 0.5*domainsize[0],
AvgCenter[1] + 0.5*domainsize[1],
domainsize[2]]
# Clear and resize figure
if ax is None: ax=self.setupfigax()
# Do the domain plot first
ix = 0; xstr='X'
iy = 1; ystr='Y'
x1, y1, x2, y2 = plotRectangle(ax, corner1, corner2, ix, iy,
color='gray', ec='k', alpha=0.25)
# Plot the turbines
coordsys = self.inputvars['turbines_coordsys'].getval()
addturbinename = self.inputvars['turbines_plotnames'].getval()
for turb in alldf:
turbx, turby = convertLatLong(turb['x'], turb['y'], useutm, coordsys)
# plot the point
ax.plot(turbx, turby, marker='s', color='k', markersize=8)
if addturbinename:
ax.text(turbx+50, turby+50, turb['name'],
color='r', ha='right', va='top', fontdict={'fontsize':8})
# --------------------------------
# Set some plot formatting parameters
if coordsys=='latlong' or coordsys=='utm':
# this this for different hemispheres
xstr, ystr = 'EASTING', 'NORTHING'
ax.set_xlim([AvgCenter[0]-domainsize[0]*0.55,
AvgCenter[0]+domainsize[0]*0.55])
ax.set_ylim([AvgCenter[1]-domainsize[1]*0.55,
AvgCenter[1]+domainsize[1]*0.55])
ax.set_aspect('equal')
ax.set_xlabel('%s [m]'%xstr)
ax.set_ylabel('%s [m]'%ystr)
ax.set_title(r'Wind Farm Preview')
self.figcanvas.draw()
return
# ----------- Functions for sample plane creation ----------
def sampling_createDictForTurbine(self, turbname, tdict, pdict, defaultopt):
"""
Creates a sampling dictionary for turbine-oriented probes
"""
# Get the wind direction
winddir = self.inputvars['ABL_winddir'].getval()
# Get the turbine properties
base_position = np.array(tdict['Actuator_base_position'])
turbD, turbhh = self.get_turbProp(tdict)
turbyaw = winddir if tdict['Actuator_yaw'] is None else tdict['Actuator_yaw']
# Get the zone options
units = getdictval(pdict['options'], 'units', defaultopt).lower()
orient = getdictval(pdict['options'], 'orientation', defaultopt).lower()
usedx = getdictval(pdict['options'], 'usedx', defaultopt)
outputto= getdictval(pdict['options'], 'outputto', defaultopt)
outputfreq = getdictval(pdict['options'], 'outputfreq', defaultopt)
outputvars = getdictval(pdict['options'], 'outputvars', defaultopt)
outputderived = getdictval(pdict['options'], 'outputderived', defaultopt)
if outputvars is not None:
outputvars = outputvars.split(',')
#print('outputvars = '+repr(outputvars))
# Set scale and orientation axes
scale = turbD if units=='diameter' else 1.0
if orient == 'x':
streamwise = np.array([1.0, 0.0, 0.0])
crossstream = np.array([0.0, 1.0, 0.0])
vert = np.array([0.0, 0.0, 1.0])
elif orient == 'y':
streamwise = np.array([0.0, 1.0, 0.0])
crossstream = np.array([1.0, 0.0, 0.0])
vert = np.array([0.0, 0.0, 1.0])
elif orient == 'nacdir':
streamwise, crossstream, vert = self.convert_winddir_to_xy(turbyaw)
elif isFloat(orient):
streamwise, crossstream, vert = self.convert_winddir_to_xy(float(orient))
else: # Use the wind direction
streamwise, crossstream, vert = self.convert_winddir_to_xy(winddir)
# Turbine hub center
hubcenter = base_position + turbhh*vert
# # Get the distances
# upstream = scale*pdict['upstream']
# downstream = scale*pdict['downstream']
# lateral = scale*pdict['lateral']
# below = scale*pdict['below']
# above = scale*pdict['above']
# Get the name and probe type
probename = '%s_%s'%(turbname, pdict['name'])
probetype = pdict['type'].lower().strip()
sampledict = {}
# Set the output postprocessing object
if outputto is None:
sampledict['sampling_outputto'] = self.getPostProSamplingDefault()
else:
self.addPostProSamplingObject(outputto,
output_freq=outputfreq,
fields=outputvars,
derived_fields=outputderived)
sampledict['sampling_outputto'] = [outputto]
# --- Create centerline sampling probes ---
if probetype == 'centerline':
# Calculate the start, end, and number of points
upstream = scale*float(pdict['upstream'])
downstream = scale*float(pdict['downstream'])
clstart = hubcenter - upstream*streamwise
clend = hubcenter + downstream*streamwise
# Calculate the grid points
if usedx is None:
N1 = int(pdict['n1'])
else:
N1 = int(round((upstream+downstream)/(scale*float(usedx))))+1
# Set up the sampling dict
sampledict['sampling_name'] = probename
sampledict['sampling_type'] = 'LineSampler'
sampledict['sampling_l_num_points'] = N1
sampledict['sampling_l_start'] = clstart
sampledict['sampling_l_end'] = clend
# --- Create rotorplane sampling plane ---
elif probetype == 'rotorplane':
# Calculate the geometry
upstream = scale*float(pdict['upstream'])
#print('below = ['+repr(pdict['below'].strip())+']')
pdictbelow = repr(pdict['below']).replace("'",'').strip()
pdictabove = repr(pdict['above']).replace("'",'').strip()
pdictlateral = repr(pdict['lateral']).replace("'",'').strip()
below = 0.5*turbD if len(pdictbelow)<1 else scale*float(pdictbelow)
above = 0.5*turbD if len(pdictabove)<1 else scale*float(pdictabove)
lateral = 0.5*turbD if len(pdictlateral)<1 else scale*float(pdictlateral)
origin = hubcenter - upstream*streamwise
origin = origin - lateral*crossstream - below*vert
# Calculate dimensions
L1 = 2.0*lateral
L2 = above + below
# Calculate the grid points
if usedx is None:
N1 = int(pdict['n1'])
N2 = int(pdict['n2'])
else:
N1 = int(round((L1)/(scale*float(usedx))))+1
N2 = int(round((L2)/(scale*float(usedx))))+1
# Set up the sampling dict
sampledict['sampling_name'] = probename
sampledict['sampling_type'] = 'PlaneSampler'
sampledict['sampling_p_num_points'] = [N1, N2]
sampledict['sampling_p_origin'] = origin
sampledict['sampling_p_axis1'] = crossstream*L1
sampledict['sampling_p_axis2'] = vert*L2
# Calculate offsets
noffsets = int(getdictval(pdict['options'], 'noffsets', defaultopt))
if noffsets>0:
downstream = scale*float(pdict['downstream'])
offsetvec = np.linspace(0, upstream+downstream, noffsets+1)
offsetstr = ' '.join([repr(x) for x in offsetvec])
sampledict['sampling_p_normal'] = streamwise
sampledict['sampling_p_offset_vector'] = streamwise
sampledict['sampling_p_offsets'] = offsetstr
# --- Create hub-height sampling planes ---
elif probetype == 'hubheight':
# Calculate the geometry
upstream = scale*float(pdict['upstream'])
downstream = scale*float(pdict['downstream'])
lateral = scale*float(pdict['lateral'])
below = scale*float(pdict['below'])
origin = hubcenter - upstream*streamwise - below*vert
origin = origin - lateral*crossstream