Stations

Global

Regional

Climate

Temperature

Precipitation

# -*- coding: utf-8 -*-

import matplotlib
matplotlib.use('Agg') # Must be before importing matplotlib.pyplot or pylab!
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap,maskoceans
from netCDF4 import Dataset,num2date
from scipy.io import netcdf
import numpy as N
import datetime
from matplotlib.offsetbox import AnchoredText

params = {'legend.fontsize': 8,'font.family': 'serif'}
plt.rcParams.update(params)

plt.style.use('seaborn-talk')

dat = N.genfromtxt('../data/stations_list_CLIMAT_data.txt',skip_header=1,usecols=(2,3),delimiter=';',dtype=float)
ort = N.genfromtxt('../data/stations_list_CLIMAT_data.txt',skip_header=1,usecols=(5),delimiter=';',dtype=str)

print (dat.shape)

no = dat.shape[0]

for box in ['global','med']:

    plt.figure(figsize=(10,6))
    ax = plt.subplot(111)

    if(box=='global'):

         m = Basemap(projection='cyl',llcrnrlat=-90,urcrnrlat=90,llcrnrlon=-180,urcrnrlon=180,resolution='l')
         m.drawcoastlines(color='gray',linewidth=0.5,zorder=20)
         m.shadedrelief()

    if(box=='med'):

         m = Basemap(projection='cyl',llcrnrlat=20,urcrnrlat=45,llcrnrlon=0,urcrnrlon=60,resolution='h')
         m.drawcoastlines(color='k',linewidth=0.5,zorder=20)
         m.drawcountries(color='k',linewidth=0.5,zorder=20)
         m.shadedrelief()

    for o in range(no):

        y,x = m(dat[o,0],dat[o,1])

        if(box=='global'):m.scatter(x,y,s=3,c='r',ec='k',lw=0.5,zorder=30)
        if(box=='med'):m.scatter(x,y,s=5,c='r',ec='k',lw=0.5,zorder=30)

    at = AnchoredText('WMO-Stations',prop=dict(size=12),frameon=True,loc='lower left')
    at.patch.set_boxstyle("round,pad=0.,rounding_size=0.2")
    at.zorder = 40
    ax.add_artist(at)

    plt.savefig('./img/stations_%s.png'%box,dpi=240,transparent=False,bbox_inches='tight',pad_inches=0)

#40250;          Rwaished;                                      32.50;     38.20;    683; Jordan                                         
#40255;          Irbed;                                         32.55;     35.85;    616; Jordan                                         
#40265;          Mafraq;                                        32.37;     36.25;    683; Jordan                                         
#40270;          Amman Airp.;                                   31.97;     35.99;    779; Jordan                                         
#40296;          Ghor Safi;                                     31.03;     35.47;   -350; Jordan                                         
#40310;          Ma'an Airport;                                 30.17;     35.78;   1069; Jordan 

mon = ['Jan','Feb','Mrz','Apr','Mai','Jun','Jul','Aug','Sep','Okt','Nov','Dez']
nm = len(mon)

ids = ['40310','40296','40265']

for ip in ['air_temperature_mean','precipitation_total']:

    plt.figure(figsize=(10,6))
    ax = plt.subplot(111)

    for id in ids:

        dat = N.genfromtxt('../data/%s/%s.txt'%(ip,id),names=True,delimiter=';',dtype=None)

        jo = dat['Jahr']

        nj = dat.shape[0]

        tmp = N.zeros((nj,nm),float)

        for j in range(nj):
            for m in range(nm):

                tmp[j,m] = dat[mon[m]][j]

        plt.plot(jo,N.mean(tmp,1),label=id)

    plt.xlim(1991,2020)
    plt.legend(loc=2,shadow=True)

    plt.savefig('./img/%s.png'%ip,dpi=240,transparent=False,bbox_inches='tight',pad_inches=0)