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 = []