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simposio2.py
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simposio2.py
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# -*- coding: UTF-8 -*-
'''
'''
import os
import pandas as pd
import numpy as np
import matplotlib.pylab as pl
import espec
import matplotlib as mpl
mpl.rc('font',family='Times New Roman')
pl.close('all')
pathname_hp = os.environ['HOME'] + '/Dropbox/projects/reserva/dados/ADCP_Reef_Reserva/20151221/adcp/'
pathname_dn = 'C:/Users/douglas.MENTAWAII/Dropbox/nemes/dados/ADCP_Reef_Reserva/20150720_1.65_8s/'
arqs = ['ADCP_REEF_21_12_2015.wad',
'ADCP_20_07_2015.wad',
'DADOS_21a22_11_2013.wad',
'ADCP_REEF_01-07-2015.wad',
'ADCP_REEF_29-07-2015.wad',
'ADCP_05_08_2015.wad',
'ADCP_REEF_10_09_2015.wad',
'ADCP_REEF_16_09_2015.wad',
'ADCP_REEF_14_12_2015.wad',
'ADCP_REEF_13_01_2016.wad',
'ADCP_REEF_28_01_2016.wad',
'ADCP_REEF_14_12_2015.wad']
dd = pd.read_table(pathname_hp + arqs[0],sep='\s*',header=None, usecols=(0,2,5,6,7), names=['ens','pr','vx','vy','vz'])
#dd = pd.read_table(pathname_dn + arqs[0],sep='\s*',header=None, usecols=(0,2,5,6,7), names=['ens','pr','vx','vy','vz'])
#freq de amostragem
fs = 2
nfft = 2048 / 2
#numero de amostras
a=np.unique(dd.ens)[:-2]
#hora de coleta
hora = ['0745','0819','0853','0927','1001','1035','1109','1143','1218','1252','1326','1400','1434','1508','1542','1617','1651','1725','1759','1833','1907'][:-2]
#hora = ['1305','1335','1405','1435','1505','1535','1605','1635','1705','1735','1805','1835','1905','1935','2005','2035','2105','2135','2205','2235','2305','2335','0005','0035','0105','0135','0205','0235','0305','0335','0405','0435','0505','0535','0605','0635','0705','0735','0805','0835','0905','0935','1005','1035','1105','1135','1205'][2:-4]
#hora = ['0735','0750','0805','0820','0835','0850','0905','0920','0935','0950','1005','1020','1035','1050','1105','1120','1135','1150','1205','1220','1235','1250','1305','1220','1235','1250','1305','1320','1335','1350','1405','1420','1435','1450','1505','1520','1535','1550','1605','1620','1635','1650','1705','1720','1735','1750'][1:-3] #46
dd1 = {}
epr = {}
evx = {}
evy = {}
evz = {}
#separa os dados e calcula os espectros
for i in range(len(a)):
dd1[hora[i]] = dd.ix[pl.find(dd.ens == a[i]),:]
epr[hora[i]] = espec.espec1(dd1[hora[i]].pr,nfft,fs)
evx[hora[i]] = espec.espec1(dd1[hora[i]].vx,nfft,fs)
evy[hora[i]] = espec.espec1(dd1[hora[i]].vy,nfft,fs)
evz[hora[i]] = espec.espec1(dd1[hora[i]].vz,nfft,fs)
dd1[hora[i]]['date'] = pd.date_range('2015-12-21 ' + hora[i][0:2] + ':' + hora[i][2:],periods=len(dd1[hora[i]]), freq='500ms')
# plot(dd_adv.index,dd_adv.vz)
# plot(dd1[hora[0]].date,dd1[hora[0]].vz)
# plot(dd1[hora[1]].date,dd1[hora[1]].vz)
# plot(dd1[hora[2]].date,dd1[hora[2]].vz)
# ...
#teste
# dd1['date'] = pd.date_range('2015-12-21 07:30',periods=len(dd), freq='500ms')
# dd = dd.set_index('date')
pl.figure()
cont = 0
for i in range(0,len(a),1):
cont += 2
pl.subplot(1,4,1)
pl.semilogy(epr[hora[i]][:,0],cont+epr[hora[i]][:,1])
pl.xlim(0,0.2), pl.ylim(0,45)
pl.xlabel('Frequency (Hz)')
pl.ylabel('Spectral Density (m2/Hz')
pl.title('Pressure', fontname="Times New Roman Bold")
# pl.grid(axis)
pl.subplot(1,4,2)
pl.semilogy(evx[hora[i]][:,0],cont+evx[hora[i]][:,1])
pl.xlim(0,0.2), pl.ylim(0,45)
pl.xlabel('Frequency (Hz)')
pl.title('Longshore Velocity Spectra Vx')
# pl.grid(axis)
pl.subplot(1,4,3)
pl.semilogy(evy[hora[i]][:,0],cont+evy[hora[i]][:,1])
pl.xlabel('Frequency (Hz)')
pl.xlim(0,0.2), pl.ylim(0,45)
pl.title('Crosshore Velocity Spectra Vy')
# pl.grid(axis)
pl.subplot(1,4,4)
pl.semilogy(evz[hora[i]][:,0],cont+evz[hora[i]][:,1])
pl.xlabel('Frequency (Hz)')
pl.xlim(0,0.2), pl.ylim(0,45)
pl.title('Up Velocity Spectra Vz')
# pl.grid(axis)
# pl.figure()
# pl.plot(evy[hora[0]][:,0],0+evy[hora[0]][:,1])
# pl.plot(evy[hora[1]][:,0],2+evy[hora[1]][:,1])
# pl.plot(evy[hora[2]][:,0],4+evy[hora[2]][:,1])
pl.show()
# #from mpl_toolkits.mplot3d import Axes3D
# pl.close('all')
# #pathname = 'C:/Users/douglas.MENTAWAII/Dropbox/nemes/dados/ADCP_Reef_Reserva/20150720/'
# #pathname = os.environ['HOME'] + '/Dropbox/nemes/dados/ADCP_Reef_Reserva/20150720/'
# pathname = 'C:/Users/douglas.MENTAWAII/Documents/Dr NEMES/NEMES/NEMES/TESE/DADOS/CAMPO Praia da Reserva/DADOS/Perfil Praial/2015/30 28-01/ADCP/'
# #carrega arquivo .sen
# sen = np.loadtxt(pathname + 'ADCP_REEF_28_01_2016.sen')
# data = [dt.datetime(int(sen[i,2]),int(sen[i,0]),int(sen[i,1]),int(sen[i,3]),int(sen[i,4])) for i in range(len(sen))]
# h = 3.54 #profundidade
# nfft = 2048 #para 1024 pontos - 16 gl #numero de dados para a fft (p/ nlin=1312: 32gl;nfft=82, 16gl;nfft=164, 8gl;nfft=328)
# fs = 2 #freq de amostragem
# nlin = 2048 #comprimento da serie temporal a ser processada
# gl = (nlin/nfft) * 2
# t = np.linspace(0,nlin*1./fs,nlin) #vetor de tempo
# #numero de testes habilitados (parametros para consistencia)
# #ntb = 9 #brutos
# #ntp = 3 #processado
# #npa = 19 #numero de parametros a serem calculados
# #lista os arquivo .wad
# lista = []
# for f in os.listdir(pathname):
# if f.endswith('.wad'):
# lista.append(f)
# #lista = np.sort(lista)[1:-1] #retira o primeiro arquivo que tem todos os .wad (eh o primeiro do sort)
# lista = np.sort(lista)[:]
# #retira o primeiro arquivo que tem todas as medicoes
# #e os dados que o adcp estava fora da agua
# ini = 0 #inicio das medicoes boas
# fim = len(lista) #ultima medicao boa
# lista=np.sort(lista)[ini:fim]
# data = np.array(data)[ini:fim]
# sen = sen[ini:fim,:]
# #ONDE ESTÃO SENDO RETIRADA A MARÉ DOS DADOS DE NIVEL (ONDAS)? FILTRO?
# #matonda: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
# # data, hs,h10,hmax,tmed,thmax,hm0,tp,dp,sigma1p,sigma2p,hm01,tp1,dp1,hm02,tp2,dp2,gam,gam1,gam2
# matonda = [] #matriz com parametros de onda
# matondap = [] #pressao - hs, tp dp
# fase_nnx = [] #valor do espec de fase para a fp
# fase_nny = [] #valor do espec de fase para a fp
# fase_nxny = [] #valor do espec de fase para a fp
# coer_nnx = [] #valor do espec de coerencia para a fp
# coer_nny = [] #valor do espec de coerencia para a fp
# coer_nxny = [] #valor do espec de coerencia para a fp
# #concatena todos os valores de vz (para hist)
# eta = np.array([])
# #concatena os espectros para fazer evolucao espectral
# evolspec = []
# dc = -1
# #loop para processar os dados
# for arq in lista:
# dc += 1
# print arq
# dd = np.loadtxt(pathname + arq)
# pr, vx, vy, vz = dd[:,[2,5,6,7]].T
# #eta = np.concatenate((eta,vz),axis=1)
# #processamento no dominio do tempo
# hs,h10,hmax,tmed,thmax = proconda.ondat(t,vz,h)
# #processamento no dominio da frequencia
# #hm0_pr, tp_pr, dp_pr, sigma1, sigma2, sigma1p, sigma2p, freq, df, k, sn_pr, snx,sny, snn, snnx, snny, snxny, snxnx, snyny, a1, b1, a2, b2, dire1_pr, dire2 = proconda.ondaf(
# #pr,vx,vy,h,nfft,fs) ##pressao
# hm0, tp, dp, sigma1, sigma2, sigma1p, sigma2p, freq, df, k, sn, snx,sny, snn, snnx, snny, snxny, snxnx, snyny, a1, b1, a2, b2, dire1, dire2 = proconda.ondaf(vz,vx,vy,h,nfft,fs)
# #calcula os espectros de pr, vz, vx e vy
# apr = espec.espec1(pr,nfft,fs)
# avz = espec.espec1(vz,nfft,fs)
# avx = espec.espec1(vx,nfft,fs)
# avy = espec.espec1(vy,nfft,fs)
# #cria matriz para fazer evolucao espectral
# evolspec.append(avy[:,1])
# #corrige a declinacao magnetica
# dp = dp - 23
# dire1 = dire1 - 23
# dire2 = dire2 - 23
# #calcula o espectro de fase (fase e coerencia)
# #acha o indice da fp
# indfp = pl.find(sn[:,0]==sn[sn[:,1]==max(sn[0:4,1]),0])
# #stop
# fase_nnx.append(np.real(snnx[indfp,4])[0]) #fase de heave e dspx
# fase_nny.append(np.real(snny[indfp,4])[0]) #fase de heave e dspx
# fase_nxny.append(np.real(snxny[indfp,4])[0]) #fase de heave e dspx
# #indfp = 1
# coer_nnx.append(np.real(snnx[indfp,5])[0]) #coerencia de heave e dspx
# coer_nny.append(np.real(snny[indfp,5])[0]) #coerencia de heave e dspx
# coer_nxny.append(np.real(snxny[indfp,5])[0]) #coerencia de heave e dspx
# pl.figure()
# pl.plot(apr[:,0],apr[:,1],'k')
# pl.title(str(data[dc]))
# pl.legend(['pr'],loc=2)
# pl.xlim(0,0.3)
# pl.twinx()
# pl.plot(avx[:,0],avx[:,1],'b')
# pl.plot(avy[:,0],avy[:,1],'r')
# pl.plot(avz[:,0],avz[:,1],'g')
# pl.legend(['vx','vy','vz'],loc=1)
# pl.xlim(0,0.3)
# pl.figure()
# pl.plot(snxny[:,0],snxny[:,5])
# pl.title(str(data[dc]))
# pl.xlim(0,0.3)
# # #plota serie
# # pl.figure()
# # pl.plot(t,vz)
# # pl.title(str(data[dc]) + ' - Hs=%.1f' %hm0 + ' m ; Tp=%.1f ' %tp + 's; Dp=%i' %dp + ' gr')
# # pl.plot([t[0],t[-1]],[0,0],'--r',linewidth=3)
# # pl.axis('tight'), pl.grid()
# # pl.ylim(-1.2,1.2)
# # pl.ylabel('Vel. Z (m/s)')
# # pl.xlabel('Tempo (s)')
# # pl.savefig('fig/vz_serie_' + data[dc].strftime('%Y%m%d%H%M'))
# #
# # #plota o espectro
# # pl.figure()
# # pl.subplot(211)
# # pl.plot(sn[:,0],sn[:,1])
# # #pl.plot(sn_pr[:,0],sn_pr[:,1],label='pr')
# # pl.title(str(data[dc]) + ' - Hs=%.1f' %hm0 + ' m ; Tp=%.1f ' %tp + ' s; Dp=%i' %dp + ' gr')
# # pl.grid()
# # pl.legend()
# # pl.ylabel(r'$(m/s)^{2}/Hz$',size=14)
# # pl.subplot(212)
# # pl.plot(sn[:,0],dire1)
# # pl.yticks(np.arange(0,360+45,45))
# # pl.ylim(0,360)
# # #pl.plot(sn_pr[:,0],dire1_pr)
# # pl.grid()
# # pl.xlabel('Freq. (Hz)')
# # pl.ylabel('Dir (graus)')
# # pl.savefig('figure_ADCP_05_08_2015/vz_espec_' + data[dc].strftime('%Y%m%d%H%M'))
# #
# #
# # #plota o espectro de cada variavel (pr, vz, vx e vy)
# # pl.figure()
# # pl.subplot(221)
# # pl.plot(apr[:,0],apr[:,1])
# # pl.title('Pressao')
# # pl.grid()
# # pl.subplot(222)
# # pl.plot(avz[:,0],avz[:,1])
# # pl.title('Vz')
# # pl.grid()
# # pl.subplot(223)
# # pl.plot(avx[:,0],avx[:,1])
# # pl.title('Vx')
# # pl.grid()
# # pl.subplot(224)
# # pl.plot(avy[:,0],avy[:,1])
# # pl.title('Vy')
# # pl.grid()
# #
# # pl.savefig('figure_ADCP_05_08_2015/espectros4_' + data[dc].strftime('%Y%m%d%H%M'))
# #
# #
# #processamento no dominio da frequencia particionado (sea e swell)
# hm01, tp1, dp1, hm02, tp2, dp2 = proconda.ondap(hm0,tp,dp,sn,dire1,df)
# #calculo do parametro gamma - LIOc
# gam = jonswap.gamma(tp)
# gam1 = jonswap.gamma(tp1)
# gam2 = jonswap.gamma(tp2)
# #espectro de jonswap
# s_js = jonswap.spec(hm0,tp,freq,gam)
# s_js2 = jonswap.spec(hm02,tp2,freq,gam2)
# #concatena os dados
# matonda.append([int(data[dc].strftime('%Y%m%d%H%M')),hs,h10,hmax,tmed,thmax,hm0,tp,dp,sigma1p,sigma2p,hm01,tp1,dp1,hm02,tp2,dp2,gam,gam1,gam2])
# # matondap.append([hm0_pr,tp_pr,dp_pr])
# matonda = np.array(matonda)
# #matondap = np.array(matondap)
# #data = np.array(data)
# #eta = np.array(eta).reshape(np.size(eta),1)
# evolspec = np.array(evolspec).T
# pl.figure()
# pl.subplot(211)
# pl.plot(data,sen[:,14],'b')
# pl.grid()
# pl.twinx()
# #pl.plot(data,matonda[:,8],'r')
# pl.plot(data,coer_nnx,'r')
# pl.plot(data,coer_nny,'g')
# pl.plot(data,coer_nxny,'y')
# #pl.ylim(0,360)
# pl.subplot(212)
# pl.contour(np.arange(0,len(lista)),apr[:,0],evolspec,30)
# pl.xticks(range(0,len(lista),5),data[0:-1:5])
# pl.ylim(0,0.5)
# pl.grid()
# pl.show()
# '''
# pl.figure()
# h = pl.hist(eta,100)
# pl.axis('tight')
# pl.xlim(-1,1)
# pl.grid()
# pl.xlabel(r'$m/s$',fontsize=14)
# pl.ylabel(r'$Nu\'mero\ de\ ocorre\^ncias$',fontsize=14)
# pl.plot([0,0],[0,np.max(h[0])],'r--',linewidth=3)
# pl.figure()
# pl.plot(vz)
# pl.figure()
# pl.plot(sn[:,0],sn[:,1])
# pl.figure()
# pl.subplot(311)
# pl.plot(data,matonda[:,6],label='vz')
# #pl.plot(data,matondap[:,0],label='pr')
# pl.legend()
# pl.ylabel('Hm0 (m)')
# pl.subplot(312)
# pl.plot(data,matonda[:,7])
# #pl.plot(data,matondap[:,1])
# pl.ylabel('Tp (s)')
# pl.subplot(313)
# #pl.plot(data,matondap[:,2])
# pl.plot(data,matonda[:,8])
# pl.ylabel('Dp (graus)')
# pl.figure()
# pl.title('Espectros de Fase')
# pl.plot(data,fase_nnx,label='nnx')
# pl.plot(data,fase_nny,label='nny')
# pl.plot(data,fase_nxny,label='nxny')
# pl.ylabel('graus')
# pl.show()
# '''