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plot_trmm.py
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'''
Need transform HDF4 to HDF5 using
h4toh5 tool
Raul Valenzuela
December 2015
'''
import matplotlib.pyplot as plt
import numpy as np
import read_trmm
from mpl_toolkits.basemap import Basemap, cm, shiftgrid
from datetime import datetime
def product(prod, datef):
print datef
if prod == '1B01':
lons, lats, dates, data = read_trmm.retrieve_1B01(datef)
elif prod == '1C21':
# read_trmm.print_dataset_1C21(base_dir,datef)
lons, lats, dates, data = read_trmm.retrieve_1C21(datef)
elif prod == '2A12':
lons, lats, dates, data = read_trmm.retrieve_2A12(datef)
elif prod == '2A25':
print datef
lons, lats, dates, data = read_trmm.retrieve_2A25(datef)
''' create figure and axes instances '''
fig = plt.figure(figsize=(8, 5))
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])
''' create eq distance cylindrival Basemap instance '''
m = Basemap(projection='cyl',
llcrnrlat=-40, urcrnrlat=-20,
llcrnrlon=-90, urcrnrlon=-60,
# llcrnrlat=-50, urcrnrlat=-10,
# llcrnrlon=-100, urcrnrlon=-50,
# llcrnrlat=-90,urcrnrlat=90,\
# llcrnrlon=-180,urcrnrlon=180,\
resolution='l')
''' draw lines '''
m.drawcoastlines()
m.drawcountries()
''' draw parallels '''
parallels = np.arange(-90., 90, 10.)
m.drawparallels(parallels, labels=[1, 0, 0, 0], fontsize=10)
''' draw meridians '''
meridians = np.arange(0., 360., 10.)
m.drawmeridians(meridians, labels=[0, 0, 0, 1], fontsize=10)
''' colormesh '''
if prod == '1B01':
# print lats
cmap = get_IRcolors()
m.pcolormesh(lons, lats, data - 273.15, vmin=-70, vmax=30, latlon=True, cmap=cmap)
else:
vmin = 0
vmax = 50
data[data < 10.] == np.nan
# m.pcolormesh(lons, lats, data, latlon=True,cmap='gray_r',vmin=vmin, vmax=vmax)
m.pcolormesh(lons, lats, data, latlon=True, cmap='nipy_spectral', vmin=vmin, vmax=vmax)
m.colorbar()
plt.suptitle('IR-channel date: ' + dates[0].strftime('%Y-%b-%d') \
+ '\nTime: ' + dates[0].strftime('%H:%M') + '-' \
+ dates[1].strftime('%H:%M') + ' UTC')
def fuse(product,datef,accum=False):
from scipy.spatial import cKDTree
data_list = list()
lons_list = list()
lats_list = list()
for n,f in enumerate(datef):
if product.keys()[0] == '1B01':
out = read_trmm.retrieve_1B01(f)
elif product.keys()[0] == '1C21':
out = read_trmm.retrieve_1C21(f)
elif product.keys()[0] == '2A25':
out = read_trmm.retrieve_2A25(f, product['2A25'])
lons, lats, dates, data = out
if n == 0:
beg = dates[0]
if n == len(datef)-1:
end = dates[1]
data_list.extend(data.flatten())
lons_list.extend(lons.flatten())
lats_list.extend(lats.flatten())
''' interpolation using nearest neighbor '''
coords = zip(lons_list, lats_list)
tree = cKDTree(coords)
cols = 700
rows = 500
lon_target = np.linspace(-90,-60,num=cols)
lat_target = np.linspace(-20,-40,num=rows)
long, latg = np.meshgrid(lon_target,lat_target)
loni = long.flatten()
lati = latg.flatten()
coordsi = zip(loni,lati)
dist, idx = tree.query(coordsi, k=8, eps=0, p=1,
distance_upper_bound=5)
ig = np.empty((rows*cols,))
data_np = np.array(data_list)
for n in range(idx.shape[0]):
if np.min(dist[n])>0.2:
ig[n] = -9999
else:
if product.keys()[0] == '1B01':
ig[n] = np.nanmin(data_np[idx[n]])
elif product.keys()[0] == '1C21':
ig[n] = np.nanmax(data_np[idx[n]])
elif product.keys()[0] == '2A25':
ig[n] = np.nanmax(data_np[idx[n]])
mig = np.ma.masked_values(ig,-9999)
mig = mig.reshape((rows,cols))
if product.keys()[0] == '2A25' and accum is True:
return mig, beg, end, latg, long
plot_map(product, mig,
datef, beg, end, latg, long)
def daily_accum2A25(datef):
out = fuse({'2A25':'rainrate'}, datef, accum=True)
grid, beg, end, latg, long = out
delta = (end-beg).seconds/3600. #hours
gridAccum = grid * delta
plot_map({'2A25':'accum'},gridAccum,
datef, beg, end, latg, long)
def plot_map(product, mig, datef, beg, end, latg, long):
''' create eq distance cylindrival Basemap instance '''
fig, ax = plt.subplots()
m = Basemap(projection='cyl',
llcrnrlat=-45, urcrnrlat=-26,
llcrnrlon=-75, urcrnrlon=-69,
# llcrnrlat=-90,urcrnrlat=90,\
# llcrnrlon=-180,urcrnrlon=180,\
resolution='i')
''' draw lines '''
m.drawcoastlines()
m.drawcountries(color=(0.5,0.5,0.5))
''' draw parallels '''
parallels = np.arange(-90., 90, 10.)
m.drawparallels(parallels, labels=[1, 0, 0, 0], fontsize=10)
''' draw meridians '''
meridians = np.arange(0., 360., 10.)
m.drawmeridians(meridians, labels=[0, 0, 0, 1], fontsize=10)
if product.keys()[0] == '1B01':
cmap = get_IRcolors()
datagrid = mig - 273.15
vmin = -70
vmax = 30
titletxt = 'IR composite'
if product.keys()[0] == '1C21':
cmap = 'nipy_spectral'
datagrid = mig
vmin = -20
vmax = 50
titletxt = 'Radar reflectivity'
elif product.keys()[0] == '2A25':
cmap = 'nipy_spectral'
datagrid = mig
if product['2A25'] == 'dBZnearSurf':
vmin, vmax, titletxt = [-10, 50, 'Relfectivity near surface (dBZ) ']
elif product['2A25'] == 'rainrate':
vmin, vmax, titletxt = [0, 20, 'Rainfall rate (mm/hr) ']
elif product['2A25'] == 'accum':
vmin, vmax, titletxt = [0, 250, 'Rainfall accumulation ']
''' plot '''
m.pcolormesh(long, latg, datagrid, vmin=vmin, vmax=vmax,
latlon=True, cmap=cmap)
fmt = '%H:%M UTC %d-%b-%Y'
plt.suptitle(titletxt + ' (n={})'.format(len(datef)) \
+ '\nBeg: ' \
+ beg.strftime(fmt) \
+ '\nEnd: ' \
+ end.strftime(fmt))
m.colorbar()
def get_IRcolors():
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
colors2 = plt.cm.binary(np.linspace(0.5, 1., 128))
colors1 = plt.cm.rainbow_r(np.linspace(0, 1, 128))
colors = np.vstack((colors1, colors2))
mymap = mcolors.LinearSegmentedColormap.from_list('my_colormap', colors)
return mymap