def leerArchivoParametros(configs):
config = configparser.ConfigParser()
config.read(configs)
for secciones in config.sections():
for (variable, valor) in config.items(secciones):
agregarVariableGlobal(variable, valor)
var.establecerVariablesDefault()
def load_climatology_or_interannual_variation_as_npz(fy, ly, month, var, Ave_or_Std, product_n = 3): vid = Var.var_to_id(var) formal_name = Var.VAR[vid].Get_formal_name() title_name = D.Data[product_n].title_name _, strmonth = subroutine.strym(2000, month) data =subroutine.load_npz(Ave_or_Std + '_of_' + \ formal_name + '_m' + strmonth + '_' + str(fy) + '-' + str(ly) + \ '_' + title_name) return data
def save_data_as_npz(self, Timeseries, fy, ly, var, depth = 1, product_n = 3): import D import Var import subroutine vid = Var.var_to_id(var) formal_name = Var.VAR[vid].Get_formal_name() title_name = D.Data[product_n].title_name xgrid, ygrid, zgrid = D.get_grid_value(var, product_n) subroutine.save_npz(Timeseries, self.AreaName + '_Area-Averaged-' + \ formal_name + '_at_' + str(zgrid[depth - 1]) + \ 'm_Year' + str(fy) + '-' + str(ly) + '_' + title_name)
def load_data_of_npz(self, fy, ly, var, depth = 1, product_n = 3): import D import Var import subroutine vid = Var.var_to_id(var) formal_name = Var.VAR[vid].Get_formal_name() title_name = D.Data[product_n].title_name xgrid, ygrid, zgrid = D.get_grid_value(var, product_n) Timeseries = subroutine.load_npz(self.AreaName + '_Area-Averaged-' + \ formal_name + '_at_' + str(zgrid[depth - 1]) + \ 'm_Year' + str(fy) + '-' + str(ly) + '_' + title_name) months, label = subroutine.get_months_and_label(fy, ly) return Timeseries, months, label
def save_Area_Trimmed_variable_as_npz(self, year, month, var, product_n):
import Var
import D
id = Var.var_to_id(var)
VV = Var.VAR[id]
if VV.dim == '3D':
data = D.get_data(year, month, var, 0, product_n)
elif VV.dim == '2D':
data = D.get_data(year, month, var, 1, product_n)
else:
raise ValueError('your var is not valid!')
Data, _, _ = self.Get_data_of_area(data, var, product_n)
self.save_Area_Trimmed_data_as_npz(Data, year, month, VV.dir_name, VV.formal_name, product_n)
def draw_Hofmuller_Diagram(self, Timeseries, months, label, \
var = 's', Rawdata_or_Anomaly = 'Rawdata', cb_min = 33, cb_max = 37, \
fsizex = 20, my_color = 1):
import matplotlib.pyplot as plt
import quick
import numpy as np
import Var
plt.rcParams['font.size'] = 20
vid = Var.var_to_id(var)
cb_title = Var.VAR[vid].Get_cb_title()
if var == 'u' or var == 'v':
hgrid = self.hgrid[:self.Pn - 1] + 0.5
else:
hgrid = self.hgrid
if Rawdata_or_Anomaly == 'Rawdata':
clabel = cb_title
elif Rawdata_or_Anomaly == 'Anomaly':
clabel = 'Anomaly of ' + cb_title
else:
raise ValueError('your Rawdata_or_Anomaly argument is not valid!')
if self.direction == 'EW':
plta = quick.draw_with_axis(Timeseries, months, hgrid, cb_min, cb_max, \
fsizex = fsizex, fsizey = self.fsizey, clabel = clabel, my_color = my_color)
plta.ylabel('Year', fontsize = 32)
plta.xlabel('LONGITUDE', fontsize = 32)
plta.xticks(self.ticks, fontsize = 20)
plta.yticks(months, label, fontsize = 15)
plta.ylim(min(months), max(months))
for i in range(months.size):
if months[i] % 12 == 1:
plta.axhline(y = months[i], linestyle = '--')
elif self.direction == 'NS':
plta = quick.draw_with_axis(Timeseries.T, hgrid, months, cb_min, cb_max, \
fsizex = fsizex, fsizey = self.fsizey, clabel = clabel, my_color = my_color)
plta.xlabel('Year', fontsize = 32)
plta.ylabel('LATITUDE', fontsize = 32)
plta.yticks(self.ticks, fontsize = 20)
plta.xticks(months, label, fontsize = 15)
plta.xlim(min(months), max(months))
plta = quick.add_vline_at_Jan(plta, months)
else:
raise ValueError("your direction is not valid!")
return plta
def draw_line_with_data_map(self, plt, year, month, var, depth, cb_min, cb_max, product_n = 3, xlim = [40, 110], ylim = [ - 20, 30], \ fsizex = 10, fsizey = 6, div = 20.0, interval = 10, color = 'red', linetype = '--'): import D import Var import quick import subroutine xgrid, ygrid, zgrid = D.get_grid_value(var, product_n) data = D.get_data(year, month, var, depth, product_n) stryear, strmonth = subroutine.strym(year, month) vid = Var.var_to_id(var) formal_name = Var.VAR[vid].Get_formal_name() title_name = D.Data[product_n].title_name clabel = stryear + '/' + strmonth + ' ' + formal_name + ' ' + ' at ' + str(zgrid[depth - 1]) + 'm '+ title_name plta = quick.draw_with_axis_and_map(data, ygrid, xgrid, cb_min, cb_max, xlim = xlim, ylim = ylim, \ interval = interval, clabel = clabel, fsizex = fsizex, fsizey = fsizey) plta = self.draw_line(plta, xlim, ylim, interval = interval, color = color, linetype = linetype) return plta
def save_data_as_npz(self, Timeseries, fy, ly, var = 's', depthn = 1, product_n = 3, Rawdata_or_Anomaly = 'Rawdata', fy_of_Anomalydata = 1990, ly_of_Anomalydata = 2011):
import D
import subroutine
import Var
title_name = D.Data[product_n].title_name
xgrid, ygrid, zgrid = D.get_grid_value('s', product_n)
vid = Var.var_to_id(var)
formal_name = Var.VAR[vid].Get_formal_name()
if Rawdata_or_Anomaly == 'Rawdata':
fname_RA = formal_name
elif Rawdata_or_Anomaly == 'Anomaly':
fname_RA = '[Anomaly_of_' + formal_name + str(fy_of_Anomalydata) + '-' + str(ly_of_Anomalydata) + ']'
else:
raise ValueError('your Rawdata_or_Anomaly argument is not valid!')
subroutine.save_npz(Timeseries, self.AreaName + '_of_' + fname_RA + '_at_' + str(zgrid[depthn - 1]) + \
'm_Year' + str(fy) + '-' + str(ly) + '_' + title_name)
def masuda_data_read(year,month,var,depth):
Mt=60*60*24*30 # 1ヶ月の秒数
Threshold=-1e+30 # Threshold of Missing value
data_dir=subroutine.dat_dir()+'ESTOCver03/binary/each_data/'
ID = Var.var_to_id(var)
dt=np.dtype([("data","<"+str(nx*ny)+"f")])
stryear,strmonth=subroutine.strym(year,month)
fd=open(data_dir+"/"+stryear+strmonth+"/"+var+".out","r")
dim = Var.VAR[ID].Get_dim()
if dim == '3D':
count=ik
else:
count=1
if count == 1 and depth != 1:
raise ValueError('if you choose 2D data, depth must be 1!')
chunk=np.fromfile(fd,dtype=dt,count=count)
if depth != 0:
data=chunk[depth-1]['data']
data=np.reshape(data,(ny,nx))
else:
data=np.ones((ny,nx,ik))
for k in range(0,ik):
data[:,:,k]=np.reshape(chunk[k]['data'],(ny,nx))
data[np.where(data<=Threshold)]=np.nan # 欠損値のところはnanに。
# 単位に関してはすでに修正済み。
# taux,tauy:N/m^2
# u,v,w,sff:m/s
# q:cal/(m^2 s)
if var=='sff':# 単位をm/Monthに
data=data*Mt
elif var=='ustar' or var=='vstar' or var=='wstar':
data=data / 100.0 # 単位をm/sに
elif var=='eq': # 淡水フラックスと同じ次元で扱えるように
data=data*-Mt*0.01
return data
# Caso especial cuando los atomos vienen en formato de numero atomico.
def transformarNumeroASimbolo(coords):
nuevo = coords
for line in nuevo:
#si es numero
line.append(line[0])
line[0] = var.atomic_number[line[0] - 1]
nuevo = [coords, nuevo]
return nuevo
# Cola a enviar
queue = sys.argv[2]
# Inicializacion de variables globales
var.init()
# Verificacion y elminacion de archivos innecesarios (otras pruebas)
#Impresora.crearArchivos()
# Lectura de archivo de configuracion
Lector.leerArchivoParametros(sys.argv[1])
#exit(1)
#print (list(var.Big_variable))
# Inicializaicon de variables locales
convergenciaObtenida = 0
calculosterminados = 1
hashtotal = var.formulaQuimicaAHash()
generation = 0
MinEnergyEver = 0
reset = False
def load_Area_Trimmed_variable_of_npz(self, year, month, var, product_n): import Var id = Var.var_to_id(var) VV = Var.VAR[id] Data = self.load_Area_Trimmed_data_of_npz(year, month, VV.dir_name, VV.formal_name, product_n) return Data
