setup.py

   1 import warnings
   2 warnings.filterwarnings("ignore")
   3 import scipy.io as sio
   4 import numpy as np
   5 from scipy import spatial
   6 import matplotlib.pyplot as plt
   7 import saveload as sl
   8 from utils import Dict
   9 from interpolation import sort_dates, nearest_scipy, neighbor_indices_ball, neighbor_indices_pixel, neighbor_indices_ellipse
  10 import os, sys, time, itertools
  11
  12 def process_detections(data,fxlon,fxlat,time_num):
  13         """
  14         Process detections to obtain upper and lower bounds
  15
  16         :param data: data obtained from JPSSD
  17         :param fxlon: longitude coordinates of the fire mesh (from wrfout)
  18         :param fxlat: latitude coordinates of the fire mesh (from wrfout)
  19         :param time_num: numerical value of the starting and ending time
  20         :return result: upper and lower bounds with some parameters
  21
  22         Developed in Python 2.7.15 :: Anaconda 4.5.10, on MACINTOSH.
  23         Angel Farguell (angel.farguell@gmail.com), 2019-04-01
  24         """
  25
  26         # process satellite settings
  27         maxsize=400 # Max size of the fire mesh
  28         ut=1 # Upper bound technique, ut=1: Center of the pixel -- ut=2: Ellipse inscribed in the pixel
  29         lt=1 # Lower bound technique, lt=1: Center of the pixel -- lt=2: Ellipse inscribed in the pixel (very slow)
  30         mt=2 # Mask technique, mt=1: Ball -- mt=2: Pixel -- mt=3: Ellipse
  31         dist=8 # If mt=1 (ball neighbours), radius of the balls is R=sqrt(2*dist^2)
  32         mm=5 # If mt=3 (ellipse neighbours), larger ellipses constant: (x/a)^2+(x/b)^2<=mm
  33         confl=70. # Minimum confidence level for the pixels
  34         confa=False # Histogram plot of the confidence level distribution
  35         confm=True # Store confidence of each fire detection
  36         burn=False # Using or not the burned scar product
  37
  38         print 'mesh shape %s %s' % fxlon.shape
  39         coarsening=np.int(1+np.max(fxlon.shape)/maxsize)
  40         print 'maximum size is %s, coarsening %s' % (maxsize, coarsening)
  41         fxlon = fxlon[0::coarsening,0::coarsening]
  42         fxlat = fxlat[0::coarsening,0::coarsening]
  43         print 'coarsened  %s %s' % fxlon.shape
  44
  45         bounds=[fxlon.min(),fxlon.max(),fxlat.min(),fxlat.max()]
  46         vfxlon=np.reshape(fxlon,np.prod(fxlon.shape))
  47         vfxlat=np.reshape(fxlat,np.prod(fxlat.shape))
  48         vfgrid=np.column_stack((vfxlon,vfxlat))
  49         print 'Setting up interpolation'
  50         stree=spatial.cKDTree(vfgrid)
  51         vfind=np.array(list(itertools.product(np.array(range(0,fxlon.shape[0])),np.array(range(0,fxlon.shape[1])))))
  52         itree=spatial.cKDTree(vfind)
  53
  54         # Sort dictionary by time_num into an array of tuples (key, dictionary of values)
  55         print 'Sort the granules by dates'
  56         sdata=sort_dates(data)
  57         tt=[ dd[1]['time_num'] for dd in sdata ]  # array of times
  58         print 'Sorted?'
  59         stt=sorted(tt)
  60         print tt==stt
  61
  62         # Max and min time_num
  63         maxt=time_num[1]
  64         mint=time_num[0]
  65         # time_scale_num = time_num
  66         time_scale_num=[mint-0.5*(maxt-mint),maxt+2*(maxt-mint)]
  67
  68         # Creating the resulting arrays
  69         DD=np.prod(fxlon.shape)
  70         U=np.empty(DD)
  71         U[:]=time_scale_num[1]
  72         L=np.empty(DD)
  73         L[:]=time_scale_num[0]
  74         T=np.empty(DD)
  75         T[:]=time_scale_num[1]
  76         if confm:
  77                 C=np.zeros(DD)
  78
  79         # Confidence analysis
  80         confanalysis=Dict({'f7': np.array([]),'f8': np.array([]), 'f9': np.array([])})
  81
  82         # For granules in order increasing in time
  83         GG=len(sdata)
  84         for gran in range(GG):
  85                 t_init = time.time()
  86                 print 'Loading data of granule %d/%d' % (gran+1,GG)
  87                 print 'Granule name: %s' % sdata[gran][0]
  88                 # Load granule lon, lat, fire arrays and time number
  89                 slon=sdata[gran][1]['lon']
  90                 slat=sdata[gran][1]['lat']
  91                 ti=sdata[gran][1]['time_num']
  92                 fire=sdata[gran][1]['fire']
  93                 print 'Interpolation to fire grid'
  94                 sys.stdout.flush()
  95                 # Interpolate all the granule coordinates in bounds in the wrfout fire mesh
  96                 # gg: mask in the granule of g-points = pixel coordinates inside the fire mesh
  97                 # ff: the closed points in fire mesh indexed by g-points
  98                 (ff,gg)=nearest_scipy(slon,slat,stree,bounds) ## indices to flattened granule array
  99                 vfire=np.reshape(fire,np.prod(fire.shape)) ## flaten the fire detection array
 100                 gfire=vfire[gg]   # the part withing the fire mesh bounds
 101                 fi=gfire >= 7  # where fire detected - low, nominal or high confidence (all the fire data in the granule)
 102                 ffi=ff[fi] # indices in the fire mesh where the fire detections are
 103                 nofi=np.logical_or(gfire == 3, gfire == 5) # where no fire detected
 104                 unkn=np.logical_not(np.logical_or(fi,nofi)) # where unknown
 105                 print 'fire detected    %s' % fi.sum()
 106                 print 'no fire detected %s' % nofi.sum()
 107                 print 'unknown          %s' % unkn.sum()
 108                 if fi.any():   # at fire points
 109                         rfire=gfire[gfire>=7]
 110                         conf=sdata[gran][1]['conf_fire'] # confidence of the fire detections
 111                         confanalysis.f7=np.concatenate((confanalysis.f7,conf[rfire==7]))
 112                         confanalysis.f8=np.concatenate((confanalysis.f8,conf[rfire==8]))
 113                         confanalysis.f9=np.concatenate((confanalysis.f9,conf[rfire==9]))
 114                         flc=conf>confl # fire large confidence indexes
 115                         if ut>1 or mt>1:
 116                                 # taking lon, lat, scan and track of the fire detections which fire large confidence indexes
 117                                 lon=sdata[gran][1]['lon_fire'][flc]
 118                                 lat=sdata[gran][1]['lat_fire'][flc]
 119                                 scan=sdata[gran][1]['scan_fire'][flc]
 120                                 track=sdata[gran][1]['track_fire'][flc]
 121
 122                         # Set upper bounds
 123                         if ut==1:
 124                                 # indices with high confidence
 125                                 iu=ffi[flc]
 126                                 if confm:
 127                                         C[iu]=conf[flc]
 128                         elif ut==2:
 129                                 # creating the indices for all the pixel neighbours of the upper bound
 130                                 iu=neighbor_indices_ellipse(vfxlon,vfxlat,lon,lat,scan,track)
 131                         else:
 132                                 print 'ERROR: invalid ut option.'
 133                                 sys.exit()
 134                         U[iu][U[iu] > ti]=ti # set U to granule time where fire detected and not detected before
 135
 136                         # Set mask
 137                         if mt==1:
 138                                 # creating the indices for all the pixel neighbours of the upper bound indices
 139                                 kk=neighbor_indices_ball(itree,ffi[flc],fxlon.shape,dist)
 140                                 im=sorted(np.unique([x[0]+x[1]*fxlon.shape[0] for x in vfind[kk]]))
 141                         elif mt==2:
 142                                 # creating the indices for all the pixel neighbours of the upper bound indices
 143                                 im=neighbor_indices_pixel(vfxlon,vfxlat,lon,lat,scan,track)
 144                         elif mt==3:
 145                                 # creating the indices for all the pixel neighbours of the upper bound indices
 146                                 im=neighbor_indices_ellipse(vfxlon,vfxlat,lon,lat,scan,track,mm)
 147                         else:
 148                                 print 'ERROR: invalid mt option.'
 149                                 sys.exit()
 150                         T[im]=ti # update mask T
 151
 152                 # Set mask from burned scar data
 153                 if burn:
 154                         if 'burned' in sdata[gran][1].keys():
 155                                 # if burned scar exists, set the mask in the burned scar pixels
 156                                 burned=sdata[gran][1]['burned']
 157                                 bm=ff[np.reshape(burned,np.prod(burned.shape))[gg]]
 158                                 T[bm]=ti
 159
 160                 if nofi.any(): # set L at no-fire points and not masked
 161                         if lt==1:
 162                                 # indices of clear ground
 163                                 jj=np.logical_and(nofi,ti<T[ff])
 164                                 il=ff[jj]
 165                         elif lt==2:
 166                                 # taking lon, lat, scan and track of the ground detections
 167                                 lon=sdata[gran][1]['lon_nofire']
 168                                 lat=sdata[gran][1]['lat_nofire']
 169                                 scan=sdata[gran][1]['scan_nofire']
 170                                 track=sdata[gran][1]['track_nofire']
 171                                 # creating the indices for all the pixel neighbours of lower bound indices
 172                                 nofi=neighbor_indices_pixel(vfxlon,vfxlat,lon,lat,scan,track)
 173                                 il=np.logical_and(nofi,ti<T)
 174                         else:
 175                                 print 'ERROR: invalid lt option.'
 176                                 sys.exit()
 177                         L[il]=ti # set L to granule time where fire detected
 178                         print 'L set at %s points' % jj.sum()
 179                 t_final = time.time()
 180                 print 'elapsed time: %ss.' % str(t_final-t_init)
 181
 182         print "L<U: %s" % (L<U).sum()
 183         print "L=U: %s" % (L==U).sum()
 184         print "L>U: %s" % (L>U).sum()
 185         print "average U-L %s" % ((U-L).sum()/np.prod(U.shape))
 186         print np.histogram((U-L)/(24*3600))
 187
 188         print 'Confidence analysis'
 189         if confa:
 190                 plt.subplot(1,3,1)
 191                 plt.hist(x=confanalysis.f7,bins='auto',color='#ff0000',alpha=0.7, rwidth=0.85)
 192                 plt.xlabel('Confidence')
 193                 plt.ylabel('Frequency')
 194                 plt.title('Fire label 7: %d' % len(confanalysis.f7))
 195                 plt.subplot(1,3,2)
 196                 plt.hist(x=confanalysis.f8,bins='auto',color='#00ff00',alpha=0.7, rwidth=0.85)
 197                 plt.xlabel('Confidence')
 198                 plt.ylabel('Frequency')
 199                 plt.title('Fire label 8: %d' % len(confanalysis.f8))
 200                 plt.subplot(1,3,3)
 201                 plt.hist(x=confanalysis.f9,bins='auto',color='#0000ff',alpha=0.7, rwidth=0.85)
 202                 plt.xlabel('Confidence')
 203                 plt.ylabel('Frequency')
 204                 plt.title('Fire label 9: %d' % len(confanalysis.f9))
 205                 plt.show()
 206
 207         print 'Saving results'
 208         # Result
 209         U=np.transpose(np.reshape(U-time_scale_num[0],fxlon.shape))
 210         L=np.transpose(np.reshape(L-time_scale_num[0],fxlon.shape))
 211         T=np.transpose(np.reshape(T-time_scale_num[0],fxlon.shape))
 212
 213         print 'U L R are shifted so that zero there is time_scale_num[0] = %s' % time_scale_num[0]
 214
 215         if confm:
 216                 C=np.transpose(np.reshape(C,fxlon.shape))
 217                 result = {'U':U, 'L':L, 'T':T, 'fxlon': fxlon, 'fxlat': fxlat,
 218                 'time_num':time_num, 'time_scale_num' : time_scale_num,
 219                 'time_num_granules' : tt, 'C':C}
 220         else:
 221                 result = {'U':U, 'L':L, 'T':T, 'fxlon': fxlon, 'fxlat': fxlat,
 222                 'time_num':time_num, 'time_scale_num' : time_scale_num,
 223                 'time_num_granules' : tt}
 224
 225         sio.savemat('result.mat', mdict=result)
 226
 227         print 'To visualize, run in Matlab the script plot_results.m'
 228         print 'Multigrid using in fire_interpolation the script jpss_mg.m'
 229
 230         result['fxlon'] = np.transpose(result['fxlon'])
 231         result['fxlat'] = np.transpose(result['fxlat'])
 232
 233         return result
 234
 235 if __name__ == "__main__":
 236
 237         sat_file = 'data'
 238
 239         if os.path.isfile(sat_file) and os.access(sat_file,os.R_OK):
 240                 print 'Loading the data...'
 241                 data,fxlon,fxlat,time_num=sl.load('data')
 242         else:
 243                 print 'Error: file %s not exist or not readable' % sat_file
 244                 sys.exit(1)
 245
 246         process_detections(data,fxlon,fxlat,time_num)
 247