def getDefsList( dirForFound, cbFindCond, cbGetFileList, typeInFile, ofile ): # Создаем список найденных файлов fileList = cbGetFileList( dirForFound, typeInFile ) # читаем файл и выбираем из него директивы f = io.open( ofile+'.h', 'w', encoding='utf-8') f.write(u'#-*- coding: utf-8 -*-\n') definedList = list('') # сюда складываем все for at in fileList: # содержимое string = IOOperations.getFileContent( at ) # удаляем закомменченное strPure = PreProc.delCom( string ) # Дробим на строки stringSplit = strPure.split('\n') for item in stringSplit: if cbFindCond( item ): f.write(item+'\n') newItem = item.split()[1] definedList.append( newItem ) f.close() # сохраняем результаты поиска f = io.open( ofile+'.py', 'w', encoding='utf-8') f.write(u'#-*- coding: utf-8 -*-\n') f.write( ofile.split('/')[-1]+'List'+u' = [\n' ) for at in definedList: f.write(u'\''+at+u'\','+u'\n') f.write(u']\n') f.close()
ice_obs_file = '/glade/p/work/mickelso/PyAvg-IceDiag-obs/gx1v6_grid.nc' reg_file = '/glade/p/work/mickelso/PyAvg-IceDiag-obs/REGION_MASK.nc' year0 = 1 year1 = 10 ncl_location = '/glade/scratch/mickelso/pyAverager_trunk/trunk/pyaverager' #### End user modify #### pyAveSpecifier = specification.create_specifier(in_directory=in_dir, out_directory=out_dir, prefix=pref, suffix=suffix, date_pattern=date_pattern, hist_type=htype, avg_list=average, weighted=wght, split=spl, split_files=split_fn, split_orig_size=split_size, ncformat=ncfrmt, serial=serial, ice_obs_file=ice_obs_file, reg_file=reg_file, year0=year0, year1=year1, clobber=clobber, ncl_location=ncl_location) PreProc.run_pre_proc(pyAveSpecifier) PyAverager.run_pyAverager(pyAveSpecifier)
reg_file ='/glade/p/work/mickelso/PyAvg-IceDiag-obs/REGION_MASK.nc' year0 = 1 year1 = 10 ncl_location = '/glade/scratch/mickelso/pyAverager_trunk/trunk/pyaverager' #### End user modify #### pyAveSpecifier = specification.create_specifier(in_directory=in_dir, out_directory=out_dir, prefix=pref, suffix=suffix, date_pattern=date_pattern, hist_type=htype, avg_list=average, weighted=wght, split=spl, split_files=split_fn, split_orig_size=split_size, ncformat=ncfrmt, serial=serial, ice_obs_file=ice_obs_file, reg_file=reg_file, year0=year0, year1=year1, clobber=clobber, ncl_location=ncl_location) PreProc.run_pre_proc(pyAveSpecifier) PyAverager.run_pyAverager(pyAveSpecifier)
import IOOperations dirForFound = ["../headers/"] # поис и среди исходных файлов for dir in dirForFound: files = os.listdir( dir ) # И обрабатываем его fileList = list() for p in files : if p.find('.inc') != -1: fileList.append( dirForFound[0]+p ) import PreProc PreProc.getMacroFile( fileList ) import mFu # Обрабатываем файл с кодом ifile = '../src/_v1_IRQ.asm' # читаем string = IOOperations.getFileContent( ifile ) commFree = PreProc.delCom( string ) commFreeList = commFree.split('\n') # замена в файлах i = 0 f = io.open('mFu.asm', 'w', encoding='utf-8') f.write(u'#-*- coding: utf-8 -*-\n') for item in commFreeList:
# Preprocessing # ============================================================================= # Import data data_source = 'git' market = 'AEX' stocks = get_data(data_source, market) # ONLY FOR NOW, SHOULD BE CHANGED!! df = stocks['PHIA'] # Preprocessing data split_datapoint = 5000 smoothing_window_size = 1000 pp_data = PreProc(df) pp_data.splitdata(split_datapoint) pp_data.normalize_smooth(smoothing_window_size, EMA=0.0, gamma=0.1) # ============================================================================= # Define and apply LSTM # ============================================================================= # Define hyperparameters D = 1 # Dimensionality of the data. Since our data is 1-D this would be 1 num_unrollings = 50 # Number of time steps you look into the future. batch_size = 500 # Number of samples in a batch num_nodes = [200, 200, 150] # Number of hidden nodes in each layer of the deep LSTM stack we're using n_layers = len(num_nodes) # number of layers dropout = 0.2 # Dropout amount