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read_hdf.py
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read_hdf.py
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#!/usr/bin/env python3
from pyhdf import SD
import numpy as np
import numpy.ma as ma
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
from matplotlib.colors import LinearSegmentedColormap
from matplotlib import ticker
from glob import glob
from mpl_toolkits.basemap import Basemap
import os, h5py, time
import multiprocessing
from multiprocessing import Pool, TimeoutError
import csv, sys
import pandas as pd
path_to_data = './Data/NASA'
path_to_param = './speices_parameters.csv'
path_to_loc = './Data/Species'
# get temperature data
def get_temperature(filename):
hdf = SD.SD(filename)
#datasets_dic = hdf.datasets()
#for idx,sds in enumerate(datasets_dic.keys()):
# print (idx,sds)
data_day = hdf.select('LST_Day_CMG')
data_night = hdf.select('LST_Night_CMG')
temp_day=np.array(data_day[:,:],np.float)
temp_night=np.array(data_night[:,:],np.float)
temp_day[np.where(temp_day==0)]=np.nan
temp_night[np.where(temp_night==0)]=np.nan
temp_day = temp_day *0.02 - 273.15
temp_night = temp_night *0.02 - 273.15
temp_day = np.flipud(temp_day)
temp_day = temp_day[0:-1:2,0:-1:2]
temp_dimension = temp_day.shape
temp_night = np.flipud(temp_night)
temp_night = temp_night[0:-1:2,0:-1:2]
lat = np.linspace(-90, 90, temp_dimension[0])
lon = np.linspace(-180, 180, temp_dimension[1])
#generate lon and lat mesh for temperature
temp_lon, temp_lat = np.meshgrid(lon, lat)
return temp_lat, temp_lon, temp_day, temp_night
# get precipitation data
def get_precipitation(filename):
with h5py.File(filename, mode='r') as f:
name = '/Grid/precipitation'
data = f[name][:]
units = f[name].attrs['units']
_FillValue = f[name].attrs['_FillValue']
data[data == _FillValue] = np.nan
data = np.ma.masked_where(np.isnan(data), data)
# print (units)
# print (list(f['Grid'].keys()))
# Get the geolocation data
prec_lat = f['/Grid/lat'][:]
prec_lon = f['/Grid/lon'][:]
precipitation = data.T * 30. * 24. # convert rate to precipitation
return prec_lat, prec_lon, precipitation
def get_ti(td,tn,dv):
shp = td.shape
ti = np.zeros(shp,dtype=np.float)
for j in range(shp[0]):
for i in range(shp[1]):
hi_temp = td[j,i]
low_temp = tn[j,i]
dt = hi_temp - low_temp
if dt > 0:
iq = min( 1, 12 * max(0, hi_temp - dv[0]) ** 2 / dt / ((dv[1] - dv[0]) * 24))
else:
iq = np.nan
ih = min( 1 - ( max(0, hi_temp - dv[2]) / (dv[3] - dv[2])) , 1)
ti[j,i] = iq * ih
return ti
def get_mi(prec,sm):
shp = prec.shape
mi = np.zeros(shp,dtype=np.float)
for j in range(shp[0]):
for i in range(shp[1]):
m = prec[j,i]
if m < sm[0]:
mi_index = 0
elif m < sm[1]:
mi_index = (m - sm[0])/(sm[1] - sm[0])
elif m < sm[2]:
mi_index = 1.
elif m < sm[3]:
mi_index = (m - sm[3])/(sm[2] - sm[3])
else:
mi_index = 0.
mi[j,i] = mi_index
return mi
def main():
df_sp = pd.read_csv(path_to_param,encoding="iso-8859-1")
scientific_name = df_sp['scientific_name']
nick_name = df_sp['nick_name']
nspe = len(nick_name)
spe_dv = np.zeros((nspe,4))
spe_sm = np.zeros((nspe,4))
for i in range(4):
spe_dv[:,i] = df_sp['dv_%d' % i]
spe_sm[:,i] = df_sp['sm_%d' % i]
file_speloc = []
for i in range(nspe):
name = '/' + scientific_name[i].replace("_"," ").capitalize() + '.csv'
file_speloc.append(path_to_loc + name)
file_temp = glob(path_to_data + '/MOD*2017*')
file_temp.sort()
file_prec = glob(path_to_data + '/3B*2017*')
file_prec.sort()
nmonth = len(file_temp)
lon = np.linspace(-180, 180, 3600)
lat = np.linspace(-90, 90, 1800)
lon, lat = np.meshgrid(lon, lat)
time_tag1 = time.clock()
for month in range(nmonth):
# month +=
temp_lat, temp_lon, temp_day, temp_night = get_temperature(file_temp[month])
prec_lat, prec_lon, prec_raw = get_precipitation(file_prec[month])
TI = np.zeros(temp_day.shape,dtype=np.float)
MI = np.zeros(temp_day.shape,dtype=np.float)
prec_raw.mask = ma.nomask
precipitation = np.array(prec_raw)
precipitation[precipitation==np.nan] = 0.
print ('\nMonth: %2d' % (month + 1))
for spe in range(6):
# spe += 6
print ('%d: ' % (spe+1) + scientific_name[spe].replace("_"," ").capitalize())
time_tag2 = time.clock()
dv = spe_dv[spe,:]
sm = spe_sm[spe,:]
nblock = 12
interval = int(1800/nblock)
pool = Pool(processes=4)
print ('** Start to calculate EI.')
result_ti = []
result_mi = []
for b in range(nblock):
td_b = temp_day[b*interval:(b+1)*interval,:]
tn_b = temp_night[b*interval:(b+1)*interval,:]
result_ti.append(pool.apply_async(get_ti, (td_b,tn_b,dv)))
mi_b = precipitation[b*interval:(b+1)*interval,:]
result_mi.append(pool.apply_async(get_mi, (mi_b,sm)))
for b in range(nblock):
TI[b*interval:(b+1)*interval,:] = result_ti[b].get()
MI[b*interval:(b+1)*interval,:] = result_mi[b].get()
# clean up
pool.close()
pool.join()
GI = MI * TI * 100
print ('** Start to plot data.')
fig = plt.figure(figsize=(20,10))
m = Basemap(projection='cyl', resolution='l',
llcrnrlat=-90, urcrnrlat=90,
llcrnrlon=-180, urcrnrlon=180)
m.drawcoastlines(linewidth=0.5,zorder=2)
GI[GI==0] = -1
gi_plot = plt.contourf(lon, lat, GI,
cmap="Greens",
vmax=100,vmin=0,
levels=np.linspace(0,100,101),
extend='neither',
zorder=1)
cb = m.colorbar(gi_plot)
cb.set_label(r'Ecoclimatic Index (%)', fontsize = 20)
tick_locator = ticker.MaxNLocator(nbins=5)
cb.locator = tick_locator
cb.update_ticks()
if spe != 6:
df_sp = pd.read_csv(file_speloc[spe],
skiprows = 2,
encoding="iso-8859-1")
spe_lon = df_sp["Longitude"]
spe_lat = df_sp["Latitude"]
lon_ = [];lat_ = []
for x, y in zip(spe_lon,spe_lat):
try:
xx, yy = m(float(x),float(y))
lon_.append(xx);lat_.append(yy)
except:
pass
m.scatter(lon_, lat_, marker = "o" ,
s=50, c="r", zorder=3,
edgecolors = "k",
label = "Species occurrence")
plt.legend(fontsize=20)
plt.savefig(scientific_name[spe] + "_%2.2d.png" % (1 + month),bbox_inches = "tight")
plt.close()
print('Spent Time: %4.1fs (total: %6.1fs)' % (time.clock() - time_tag2, time.clock() - time_tag1))
sys.exit()
if 0:
fig = plt.figure(figsize=(20,20))
ax_temp = plt.subplot2grid((12, 6), (6, 0), rowspan=1, colspan=3)
ax_temp.set_title("Temperature", fontsize = 20)
ax_prec = plt.subplot2grid((12, 6), (5, 0), rowspan=1, colspan=3)
plt.title("Precipitation", fontsize = 20)
ax_ti = plt.subplot2grid((12, 6), (4, 0), rowspan=1, colspan=3)
plt.title("TI", fontsize = 20)
ax_mi = plt.subplot2grid((12, 6), (3, 0), rowspan=1, colspan=3)
plt.title("MI", fontsize = 20)
ax_gi = plt.subplot2grid((12, 6), (2, 0), rowspan=1, colspan=3)
plt.title("GI", fontsize = 20)
ax3 = plt.subplot2grid((12, 6), (7, 5), rowspan=1, colspan=1)
ax4 = plt.subplot2grid((12, 6), (8, 5), rowspan=1, colspan=1)
ax5 = plt.subplot2grid((12, 6), (9, 5), rowspan=1, colspan=1)
ax6 = plt.subplot2grid((12, 6), (10, 5), rowspan=1, colspan=1)
ax7 = plt.subplot2grid((12, 6), (11, 5), rowspan=1, colspan=1)
temp_plot = ax_temp.contourf(lon, lat, (temp_day+temp_night)/2,cmap="jet")
prec_plot = ax_prec.contourf(lon, lat, precipitation,cmap="jet")
ti_max, ti_min = 1 , 0
ti_nl = 101
color_ti = 'jet'
orientation = 'vertical'
cm = plt.cm.get_cmap(color_ti,ti_nl)
TI[TI==0] = -1
ti_plot = ax_ti.contourf(lon, lat, TI, levels=np.linspace(ti_min,ti_max,ti_nl),extend='neither',cmap=cm,vmax=ti_max,vmin=ti_min)
mi_max, mi_min = 1 , 0
mi_nl = 101
color_mi = 'jet'
orientation = 'vertical'
cm = plt.cm.get_cmap(color_mi,mi_nl)
MI[MI==0] = -1
mi_plot = ax_mi.contourf(lon, lat, MI, levels=np.linspace(mi_min,mi_max,mi_nl),extend='neither',cmap=cm,vmax=mi_max,vmin=mi_min)
gi_max, gi_min = 100 , 0
gi_nl = 101
color_gi = 'jet'
orientation = 'vertical'
cm = plt.cm.get_cmap(color_gi,gi_nl)
GI[GI==0] = -1
gi_plot = ax_gi.contourf(lon, lat, GI, levels=np.linspace(gi_min,gi_max,gi_nl),extend='neither',cmap=cm,vmax=gi_max,vmin=gi_min)
cb = fig.colorbar(temp_plot, cax=ax3,orientation=orientation)
cb.set_label(r'Temperature')
tick_locator = ticker.MaxNLocator(nbins=5)
cb.locator = tick_locator
cb.update_ticks()
cb = fig.colorbar(prec_plot, cax=ax4,orientation=orientation)
cb.set_label(r'Precipitation')
tick_locator = ticker.MaxNLocator(nbins=5)
cb.locator = tick_locator
cb.update_ticks()
cb = fig.colorbar(ti_plot, cax=ax5,orientation=orientation)
cb.set_label(r'TI')
tick_locator = ticker.MaxNLocator(nbins=5)
cb.locator = tick_locator
cb.update_ticks()
cb = fig.colorbar(mi_plot, cax=ax6,orientation=orientation)
cb.set_label(r'MI')
tick_locator = ticker.MaxNLocator(nbins=5)
cb.locator = tick_locator
cb.update_ticks()
cb = fig.colorbar(gi_plot, cax=ax7,orientation=orientation)
cb.set_label(r'GI')
tick_locator = ticker.MaxNLocator(nbins=5)
cb.locator = tick_locator
cb.update_ticks()
ax1_left, ax1_bot, ax1_width, ax1_height = 0.05, 0.75, 0.35, 0.18
ax_temp.set_position( [ax1_left, ax1_bot , ax1_width, ax1_height])
ax_prec.set_position([ax1_left, ax1_bot-0.23, ax1_width, ax1_height])
ax_ti.set_position([ax1_left+0.45, ax1_bot , ax1_width, ax1_height])
ax_mi.set_position([ax1_left+0.45, ax1_bot-0.23, ax1_width, ax1_height])
ax_gi.set_position([ax1_left+0.45, ax1_bot-0.46, ax1_width, ax1_height])
lg_left, lg_bot, lg_width, lg_height = 0.42, 0.75, 0.02, 0.18
ax3.set_position([lg_left , lg_bot , lg_width,lg_height])
ax4.set_position([lg_left , lg_bot-0.23 , lg_width,lg_height])
ax5.set_position([lg_left+0.45 , lg_bot , lg_width,lg_height])
ax6.set_position([lg_left+0.45 , lg_bot-0.23 , lg_width,lg_height])
ax7.set_position([lg_left+0.45 , lg_bot-0.46 , lg_width,lg_height])
plt.savefig("fireants_pres.png")
if __name__ == '__main__':
main()