Downloads, crops and plot the TRMM Rainfall anomalies image for the last 30 days¶

%matplotlib inline

import os, sys
from datetime import datetime, timedelta
from StringIO import StringIO
import requests
from PIL import Image
import numpy as np
from matplotlib import pyplot as plt
from mpl_toolkits.basemap import Basemap

def plot_trmm_from_images(img_arr, cbar_arr, paths, date):
    fig = plt.figure(figsize=(9.1,4.9))
    ax = fig.add_axes([0.03,0.05,0.95,0.9])

    ### date formatted 
    fdate = date.strftime('%Y-%B-%d')

    curr_map = Basemap(projection='cyl', llcrnrlon=ll_lon, llcrnrlat=ll_lat, urcrnrlon=ur_lon, urcrnrlat=ur_lat, \
                       resolution='h', ax=ax, area_thresh=10.)

    curr_map.imshow(img_arr,interpolation='bilinear', origin='upper')

    curr_map.drawcoastlines(linewidth=1.,color='k')
    #draw parallels and meridians.
    delat = 10.
    circles = np.arange(ll_lat,ur_lat + delat, delat)
    curr_map.drawparallels(circles, labels=[1,0,0,0], fontsize=14, linewidth=0.8, color='k')
    delon = 10.
    meridians = np.arange(ll_lon,ur_lon, delon)
    curr_map.drawmeridians(meridians, labels=[0,0,0,1], fontsize=14, linewidth=0.8, color='k')
    plt.title("TRMM rainfall anomalies for last 30 days ending %s" % (fdate))

    ### now load the colorbar image
    ax2 = fig.add_axes([0.61,-0.08,0.32,0.32], frameon=False)
    ### take the last 60 pixels (image originally 673 x 138)
    ax2.imshow(cbar_arr[-60:,:,:])
    plt.setp(ax2,xticks=[])
    plt.setp(ax2,yticks=[])

    for p in paths: 
        plt.savefig(os.path.join(p,'TRMM_last30days_anoms_{}'.format(fdate)), dpi=200)

dpaths = [os.path.join(os.environ['HOME'],'drives/auck_projects/ICU14101/Working/TRMM_KMLS/real_time'),\
          os.path.join(os.environ['HOME'], 'operational/ICU/TRMM_KMLS/SP')]

proxies = {}
# proxies['http'] = 'http://proxy:port'

date = datetime.now() - timedelta(days=1)

nlat = 400
nlon = 1440

### create lat and lon vectors
lon = np.arange(0.125, 0.125 + nlon * 0.25, 0.25) 
lat = np.arange(49.875, 49.875 - nlat * 0.25, -0.25)

### set the domain we want [lonW, lonE, latS, latN]
ll_lon, ur_lon, ll_lat, ur_lat = [135., 240., -50., 10.]

base_url = "http://trmm.gsfc.nasa.gov/trmm_rain/Events/"

r = requests.get(base_url + 'tafd_latest_google_earth_anomaly.png', proxies=proxies)
img = Image.open(StringIO(r.content))
img_arr = np.array(img)

r = requests.get(base_url + 'tafd_anomaly_google_wedge.png', proxies=proxies)
cbar = Image.open(StringIO(r.content))
cbar_arr = np.array(cbar)

ilon = np.where( (lon >= ll_lon) & (lon <= ur_lon) )[0]
ilat = np.where( (lat >= ll_lat) & (lat <= ur_lat) )[0]

img_arr = np.take(np.take(img_arr,ilat,axis=0),ilon,axis=1)

img_arr[:,:,-1] = 255 # get rid of the transparency