def compute(temperature, pressure, humidity, windspeed, winddirn, sunrise,
sunset, date, time, paramFile):
pwrModel, params = parameters.read(paramFile)
radiation = []
for dataIndex in range(0, len(temperature)):
leap = tm.isLeap(date)
day = tm.dayInYear(date, leap)
hour = tm.toDecHours(time)
midday = (float(tm.toDecHours(sunrise)) +
float(tm.toDecHours(sunset))) / 2.0
nominalRad = solarData.nominalRadiation(day, hour, midday, leap)
# print(leap,day,hour,midday,nominalRad)
temp = float(temperature[dataIndex]) + 459.67
press = float(pressure[dataIndex])
humid = float(humidity[dataIndex]) / 100.0
speed = float(windspeed[dataIndex])
dirn = float(winddirn[dataIndex])
# print(temp,press,humid,speed,dirn)
if pwrModel == "linear":
model = computeNormRad.linear(temp, press, humid, speed, dirn,
params)
else:
model = computeNormRad.quadratic(temp, press, humid, speed, dirn,
params)
radiation.append(max(0.0, nominalRad * (1.0 - model)))
return radiation
def __init__(self, cfile='default.cfg'):
if cfile == 'default.cfg':
configuration_file = os.path.join(os.path.dirname(__file__), cfile)
else:
configuration_file = cfile
atmosphere, zm, D, pixels, ngs_array, lgs_array, lgs_height = parameters.read(configuration_file)
self.Zernike = zernike.Zernike()
self.pixdiam = pixels #must be integer
self.cn2 = atmosphere['cn2']
self.h_profile = atmosphere['h_profile']
self.pupil_diameter = D
self.dr0 = atmosphere['dr0']
self.large_scale = atmosphere['L0']
self.nz1 = zm
self.nz2 = zm
self.ngspos = ngs_array
self.lgspos = lgs_array
self.hlgs = lgs_height
# self.lgscoefmatrix = np.zeros((12, self.nz1-4, self.nz2-4)) #make as function of number of lgs
# self.ngscoefmatrix = np.zeros((2,2,2))
# self.propervectors = np.zeros((12, self.nz1-4, self.nz2-4))
# self.propervalues = np.zeros((12, self.nz1-4))
self.rho, self.phi, self.mask = polar2(self.pixdiam/2., fourpixels=True, length=self.pixdiam)
self.zernikes = np.zeros((self.nz1-2, self.pixdiam, self.pixdiam))
# self.lgsvmatrices = np.zeros((15, self.nz1-4, 2*self.pixdiam, 2*self.pixdiam)) #make as function of number of lgs
self.ngsvmatrices = np.zeros((2, 2, 2*self.pixdiam, 2*self.pixdiam))
# self.lgsnewzernikes = np.zeros((15, self.nz1-4, self.pixdiam, self.pixdiam))
self.ngsnewzernikes = np.zeros((2, 2, self.pixdiam, self.pixdiam))
if self.nz1 > 980:
self.zcoef_mask = compmask(self.nz1, self.nz2).T
else:
self.zcoef_mask = zcoef_mask
import parameters as prms
import flagger as flg
import calibrator as clb
import casatasks as cts
# apply the calibrations on the target
params = prms.read('parameters.yaml')
target = params['general']['target']
gaintables = params['calibration']['gaintables']
'''
print('Applying the calibration tables to the target...')
msfile = '../blcal_test/CGCG032-017_cen1K_split.ms'
uvran = '0.5~100klambda'
phasecal = '0745+101'
cts.applycal(msfile, field=target, uvrange=uvran, gaintable=gaintables, gainfield=['','',phasecal], interp=['','','linear'], calwt=[False], parang=False)
# rflag the data
print('Mildly Rflagging the target at cutoff of 10 sigma...')
flg.rflagger(msfile, params, field=target, tcut=10, fcut=10, instance='postcal')
flg.extend(msfile, params, field=target, grow=80, instance='postcal')
# split the calibrated target data
out_cal_file = '../blcal_test/RGG5_yarrp_cal.ms'#params['general']['targetcalfile']
print('Splitting the target...')
cts.mstransform(msfile, field=target, spw='0', chanaverage=False, datacolumn='corrected', outputvis=out_cal_file)
print('Doing uvsub...')
target = params['general']['target']
targetcalfile = '../blcal_test/RGG5_cen1k_yarrp_cal.ms' #params['general']['targetcalfile']
import data
import lstm
import parameters
import plot
%matplotlib inline
%load_ext autoreload
%autoreload 2
# Initialization of seeds
set_random_seed(2)
seed(2)
# load json and create model
params = parameters.read()
raw = data.read(params)
print('Original dataset num samples:', raw.shape)
adjusted = parameters.adjust(raw, params)
X_train, Y_train, X_test, Y_test = data.prepare(adjusted, params)
# Build the model and train it.
params['lstm_batch_size'] = 1
model = lstm.build(params)
# load weights into new model
model.load_weights("20180116_0438.h5")
print("Loaded weights from disk")
print('Actual:', params['y_scaler'].inverse_transform(Y_test[31]))
full_cost = 0
full_iterations = 0
for i in range(0, n):
ga = genetic(param_path, 'graph.txt', 2020)
ga.init_pop()
ga.sort()
iterations, chrome = ga.evolve()
# print 'total iterations:', iterations, 'chrome:', chrome, 'cost:', chrome.cost,
full_cost += chrome.cost
full_iterations += iterations
print 'Average cost:', full_cost / n, "Iterations:", full_iterations / n
params = parameters.read(param_path)
print "Crossover:", params["CrossoverType"].upper(), "\nSelection:", params["SelectionType"].upper(), "\nMutation:", \
params["MutateType"].upper()
test(TravelingSalesman_GA)
print '\n'
# print "Testing PMX with Tournament"
# test(TravelingSalesman_PMX_Tourn)
# print '\n'
#
# print "Testing PMX with Top Down"
# test(TravelingSalesman_PMX_TopDown)
# print '\n'
#
# print "Testing Cycle CX with Tournament"
# test(TravelingSalesman_CX_Tourn)
def __init__(self, param_path):
self.params = parameters.read(param_path)
self.chars = list(lowercase) # Should be overwritten by subclass
self.population = []
def __init__(self, param_path):
self.params = parameters.read(param_path)
self.genes = [] # Should be overwritten by subclass
self.population = []
