import numpy as np
import matplotlib.pyplot as plt
from scipy.stats.stats import pearsonr
from matplotlib.offsetbox import TextArea, VPacker, AnnotationBbox
import os

plt.rcParams.update({'font.size':11,'font.family':'arial'})
plt.rcParams['xtick.labelsize']=10
plt.rcParams['ytick.labelsize']=10

list = ['01', '02','03','04']


for date1, date2 in zip(list,list):

    fname1='Match-Imsra-Trmm-'+date1+'NOV2016-0.25.dat'
    fname2='Match-Imsra-Gsmap-'+date2+'NOV2016-0.1.dat'

    data1=np.loadtxt('./1-match-imsra-trmm-loop/'+fname1)
    data2=np.loadtxt('./2-match-imsra-gsmap-loop/'+fname2)

    lat1=data1[:,0]
    lon1=data1[:,1]
    imsra1=data1[:,2]
    trmm1=data1[:,5]

    lat2=data2[:,0]
    lon2=data2[:,1]
    imsra2=data2[:,2]
    gsmap2=data2[:,5]
#########################################################################
    plt.figure(figsize=(8,8),facecolor='grey')
    plt.subplot(211)

    plt.plot(trmm1, imsra1,'ro',markersize=5)
    plt.xlim(0,400)
    plt.ylim(0,400)
    plt.xlabel('TRMM-3B42(mm)')
    plt.ylabel('IMSRA(mm)')
    plt.title('IMSRA vs TRMM-3B42 at '+fname1[17:26],fontsize=11)

    bias=np.mean(imsra1-trmm1)
    rms=np.sqrt(np.mean((trmm1-imsra1)**2))
    std=np.std(trmm1-imsra1)
    corr1=pearsonr(trmm1,imsra1)
    print corr1
    corr1=corr1[0]

    bias=np.round(bias,2)
    rms=np.round(rms,2)
    std=np.round(std,2)
    corr=np.round(corr1,2)
#
########################################################################
    fig=plt.figure()
    ax=fig.gca()

    texts = ['Bias = '+str(bias)+'mm','Rmse = '+str(rms)+'mm','Corr =
    '+str(corr)]
     colors = ['black','black','black','black']
     Texts = []

    for t,c in zip(texts,colors):
        Texts.append(TextArea(t,textprops=dict(fontsize=10,color=c)))
    texts_vbox = VPacker(children=Texts,pad=0,sep=0)
    ann = AnnotationBbox(texts_vbox,(.55,.85),xycoords=ax.transAxes,
    box_alignment=(0,.4),bboxprops =
     dict(facecolor='white',boxstyle='square'))

    ann.set_figure(fig)
    fig.artists.append(ann)

###########################################################################
    plt.subplot(212)

    plt.plot(gsmap2, imsra2,'bo',markersize=5)
    plt.xlim(0,400)
    plt.ylim(0,400)
    plt.xlabel('GSMaP(mm)')
    plt.ylabel('IMSRA(mm)')
    plt.title('IMSRA vs GSMaP at '+fname2[18:26],fontsize=11)

    bias=np.mean(imsra2-gsmap2)
    rms=np.sqrt(np.mean((gsmap2-imsra2)**2))
    std=np.std(gsmap2-imsra2)
    corr1=pearsonr(gsmap2,imsra2)
    print corr1
    corr1=corr1[0]

    bias=np.round(bias,2)
    rms=np.round(rms,2)
    std=np.round(std,2)
    corr=np.round(corr1,2)
#
#######################################################################
    fig=plt.figure()
    ax=fig.gca()

     texts = ['Bias = '+str(bias)+'mm','Rmse = '+str(rms)+'mm','Corr =
     '+str(corr)]
     colors = ['black','black','black','black']
     Texts = []

     for t,c in zip(texts,colors):
         Texts.append(TextArea(t,textprops=dict(fontsize=10,color=c)))
      texts_vbox = VPacker(children=Texts,pad=0,sep=0)
      ann = AnnotationBbox(texts_vbox,(.55,.85),xycoords=ax.transAxes,
                    box_alignment=(0,.4),bboxprops =
     dict(facecolor='white',boxstyle='square'))

      ann.set_figure(fig)
      fig.artists.append(ann)

    # close and save file
    filename = "scatterplot-combine-daily-on-"+fname1[17:26]+".jpg"
    plt.savefig(filename,dpi=100, bbox_inches='tight')

    plt.close(fig)
    plt.ioff()