__all__ = [ 'LAtLonSgn', 'read_profile', 'read_stnsz','read_eta_staids','read_hgtsmklmh','hst_3d','variaveis_3d' ] def LAtLonSgn(latlon): card = latlon[-1] if card == "S" or card == "W": card_R = float(latlon[0:-1]) * -1 elif card == "N" or card == "W": card_R = float(latlon[0:-1]) return card_R def read_profile(*args,**kwargs): import array import pandas as pd import numpy as np import matplotlib.pyplot as plt import os ifile = args[0] lm = args[1] nsta = args[2] nfct = args[3] lcl1ml = args[4] lcl1sl = args[5] # data = array.array('f') # f = open(ifile, 'rb') # lrecpr = int(8+9+lcl1ml*lm+lcl1sl) # data.fromfile(f, lrecpr*nsta*nfct) f.close() # data_new = np.zeros((nfct, nsta, lrecpr)) # reci=0 recf= lrecpr-1 for nh in range(0,nfct): for nst in range(0,nsta): l=0 for k in range(reci,recf+1): data_new[nh][nst][l]=data[k] l=l+1 reci = recf + 1 recf = reci+ lrecpr -1 return(data_new) def read_ntnsz(*args,**kwargs): ifile = args[0] with open(ifile, encoding="utf-8") as f: r_info = f.readlines() result = (' '.join(r_info[0].split( ))).split(' ') return(int(result[0]),int(result[1]),int(result[2])) def read_eta_staids(*args,**kwargs): import pandas as pd import numpy as np ifile = args[0] nsta = args[1] std = np.array((nsta, 7)) with open(ifile) as f: eta_staids = f.readlines() lista = [] for j in range(0,nsta): std_line = (' '.join(eta_staids[j].split( ))).split(' ') line=[int(std_line[0])-1,std_line[1],std_line[2],std_line[3] ,std_line[5][:-1],std_line[6][:-1]] lista.append(line) df_eta_staids = pd.DataFrame(lista) df_eta_staids.columns = ["sta_idx","lat","lon","sigla","cidade","estado"] return(eta_staids,df_eta_staids) def read_hgtsmklmh(*args,**kwargs): import numpy as np import pandas as pd ifile = args[0] nsta = args[1] eta_staids = args[2] stn_hgtsmklmh = np.empty((nsta, 3)) with open(ifile) as f: hgtsmklmh = f.readlines() # for j in range(0,nsta): var2 = [] std = (' '.join(eta_staids[j].split( ))).split(' ') std_lat = LAtLonSgn(std[1]) std_lon = LAtLonSgn(std[2]) stn_hgtsmklmh_line = (' '.join(hgtsmklmh[j+1].split( ))).split(' ') stn_hgtsmklmh[j][0] = stn_hgtsmklmh_line[1] stn_hgtsmklmh[j][1] = stn_hgtsmklmh_line[2] stn_hgtsmklmh[j][2] = stn_hgtsmklmh_line[3] df_hgtsmklmh = pd.DataFrame(stn_hgtsmklmh) df_hgtsmklmh.columns = ["hgt","sm","lmh"] return(df_hgtsmklmh) def hst_3d(*args,**kwargs): import numpy as np import pandas as pd array1 = args[0] sta = args[1] var = args[2] nfct = args[3] Mod_3dvar = np.zeros((int(array1[0][sta][11]),nfct)) #print(Mod_3dvar.shape) indx_ini = 17 + var * int(array1[0][sta][11]) indx_end = indx_ini+int(array1[0][sta][11]) #print(indx_ini,indx_end) for j in range(0,nfct): k = 0 for i in range(indx_ini,indx_end): Mod_3dvar[k][j] = array1[j][sta][i] k = k + 1 #print(Mod_levels_T) return(Mod_3dvar) def hst_2d(*args,**kwargs): import numpy as np import pandas as pd array1 = args[0] sta = args[1] var = args[2] nfct = args[3] sname = args[4] Mod_2dvar = np.zeros((nfct,2)) #print(Mod_2dvar.shape) offset = 17 + 15 * int(array1[0][sta][11]) indx = offset + var #print(indx) for j in range(0,nfct): Mod_2dvar[j][0] = int(j) Mod_2dvar[j][1] = array1[j][sta][indx] df_Mod_2dvar = pd.DataFrame(Mod_2dvar) df_Mod_2dvar.columns = ["fct",sname] return(Mod_2dvar,df_Mod_2dvar) def variaveis_3d(): import pandas as pd lista = [] lista.append([ 0, "pres", "Pressure (Pa)"]) lista.append([ 1, "temp", "Temperature (K)"]) lista.append([ 2, "uvel", "Zonal Wind (m/s)"]) lista.append([ 3, "vvel", "Meridional Wind (m/s)"]) lista.append([ 4, "umes", "Specific Humidity (g/g)"]) lista.append([ 5, "omega", "Omega (Pa/s)"]) lista.append([ 6, "cwm", "Cloud Water/Ice"]) lista.append([ 7, "tcucn", "Cumulus Precipitation"]) lista.append([ 8, "train", "Total Precipitation"]) lista.append([ 9, "rswtt", "SW RADIATIVE TEMPERATURE TENDENCIES"]) lista.append([10, "rlwtt", "LW RADIATIVE TEMPERATURE TENDENCIES"]) lista.append([11, "ccr", "Cloud Cover Ratio"]) lista.append([12, "q2", "TURBULENT KINETIC ENERGY"]) df_variaveis_3d = pd.DataFrame(lista) df_variaveis_3d.columns = ["var_idx","sname","lname"] return(df_variaveis_3d) def variaveis_2d(): import pandas as pd lista = [] lista.append([0,"slp", "Pressão (Pa)"]) lista.append([1,"pds", ""]) lista.append([2,"tsf", ""]) lista.append([3,"tlmin", ""]) lista.append([4,"tlmax", ""]) lista.append([5,"acprec", ""]) lista.append([6,"prec", ""]) lista.append([7,"cuprec", ""]) lista.append([8,"sfclhx", ""]) lista.append([9,"potflx", ""]) lista.append([10,"sfcshx", ""]) lista.append([11,"subshx", ""]) lista.append([12,"snopcx", ""]) lista.append([13,"aswin", ""]) lista.append([14,"aswout", ""]) lista.append([15,"alwin", ""]) lista.append([16,"alwout", ""]) lista.append([17,"alwtoa", ""]) lista.append([18,"aswtoa", ""]) lista.append([19,"acsnow", ""]) lista.append([20,"smstot", ""]) lista.append([21,"sno", ""]) lista.append([22,"acsnom", ""]) lista.append([23,"ssroff", ""]) lista.append([24,"bgroff", ""]) lista.append([25,"soiltb", ""]) lista.append([26,"z0", ""]) lista.append([27,"ut", ""]) lista.append([28,"vt", ""]) lista.append([29,"th10", ""]) lista.append([30,"q10", ""]) lista.append([31,"tshltr", ""]) lista.append([32,"qshltr", ""]) lista.append([33,"sfcexc", ""]) lista.append([34,"vegfrac", ""]) lista.append([35,"cmc", ""]) lista.append([36,"smc1", ""]) lista.append([37,"smc2", ""]) lista.append([38,"smc3", ""]) lista.append([39,"smc4", ""]) lista.append([40,"smc5", ""]) lista.append([41,"smc6", ""]) lista.append([42,"smc7", ""]) lista.append([43,"smc8", ""]) lista.append([44,"stc1", ""]) lista.append([45,"stc2", ""]) lista.append([46,"stc3", ""]) lista.append([47,"stc4", ""]) lista.append([48,"stc5", ""]) lista.append([49,"stc6", ""]) lista.append([50,"stc7", ""]) lista.append([51,"stc8", ""]) lista.append([52,"sm", ""]) lista.append([53,"cfracl", ""]) lista.append([54,"cfracm", ""]) lista.append([55,"cfrach", ""]) lista.append([56,"sr", ""]) lista.append([57,"hbot", ""]) df_variaveis_2d = pd.DataFrame(lista) df_variaveis_2d.columns = ["var_idx","sname","lname"] return(df_variaveis_2d)