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CSPP_VIIRS_SST_blobPlots.py
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CSPP_VIIRS_SST_blobPlots.py
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import numpy as np
from os import path,readlink
import string
from numpy import ma
from glob import glob
import re
import matplotlib
import matplotlib.cm as cm
from matplotlib.colors import ListedColormap
from matplotlib.figure import Figure
matplotlib.use('Agg')
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
# This must come *after* the backend is specified.
import matplotlib.pyplot as ppl
from mpl_toolkits.basemap import Basemap,addcyclic,shiftgrid
import adl_blob
from VIIRS import ViirsData as ViirsData
# every module should have a LOG object
# e.g. LOG.warning('my dog has fleas')
import logging
logging.basicConfig()
dpi=200
### Moderate and Imager resolution trim table arrays. These are
### bool arrays, and the trim pixels are set to True.
trimObj = ViirsData.ViirsTrimTable()
modTrimMask = trimObj.createModTrimArray(nscans=48,trimType=bool)
xmlDir = '/home/geoffc/SSEC/sshfs/rh5b/RH5B/CSPP/EDR/common/ADL/xml/VIIRS'
#oldBlobPath = '/home/geoffc/SSEC/sshfs/rh5b/WORK/sample_data/viirs/edr/input/VIIRS_DB_ADL4.1/blobs/single/ANC'
#newBlobPath = '/home/geoffc/SSEC/sshfs/rh5b/WORK/Work_Area/VIIRS_DB_ADL4.1'
def get_blob_asc_dict(xmlDir,blobDir,collShortNames):
blob_asc_Files = {}
blobDir = path.expanduser(blobDir)
if path.isdir(blobDir):
pass
else :
blobDir = path.dirname(blobDir)
for shortName in collShortNames :
#print '%s --> ' % (shortName)
fileGlob = path.join(path.expanduser(blobDir),'*.%s'%(shortName))
blobFiles = glob(fileGlob)
if blobFiles != []:
blobFiles.sort()
blobDict = {}
print '%s --> ' % (shortName)
for files in blobFiles :
blobFile = path.basename(files)
ascFile = string.replace(blobFile,shortName,'asc')
ascFullPath = path.join(blobDir,'%s'%(ascFile))
ascFileObj = open(ascFullPath,"r")
for line in ascFileObj:
if re.search("N_Granule_ID", line):
granID = string.split(line,'"')[3]
ascFileObj.close()
blobDict[granID] = [blobFile,ascFile]
blob_asc_Files[shortName] = blobDict
return blob_asc_Files
class SSTclass():
def __init__(self,xmlDir,blobPath):
self.xmlDir = xmlDir
self.blobPath = blobPath
self.collShortNames = [
'VIIRS-SST-EDR',
]
self.xmlName = {}
self.xmlName['VIIRS-SST-EDR'] = 'VIIRS_SST_EDR.xml'
self.plotDescr = {}
self.plotDescr['VIIRS-SST-EDR'] = ['Skin Sea Surface Temperature (K)','Bulk Sea Surface Temperature (K)']
self.plotLims = {}
#self.plotLims['VIIRS-SST-EDR'] = [250., 290.]
self.plotLims['VIIRS-SST-EDR'] = [None,None]
self.dataName = {}
self.dataName['VIIRS-SST-EDR'] = ['skinSST','bulkSST']
self.dataScales = {}
self.dataScales['VIIRS-SST-EDR'] = ['Skin_Scale','Bulk_Scale']
self.dataOffsets = {}
self.dataOffsets['VIIRS-SST-EDR'] = ['Skin_Offset','Bulk_Offset']
dataFlags =['sstFlags0','sstFlags1','sstFlags2','sstFlags3']
self.blob_dict = get_blob_asc_dict(self.xmlDir,self.blobPath,self.collShortNames)
def plot_SST_granules(self,pngDir=None,endian=adl_blob.LITTLE_ENDIAN):
if pngDir is None :
pngDir = path.abspath(path.curdir)
xmlDir = self.xmlDir
blobPath = self.blobPath
xmlName = self.xmlName
#plotDescr = self.plotDescr
plotLims = self.plotLims
blobDict = self.blob_dict
collShortNames = blobDict.keys()
for shortName in collShortNames :
dataName = self.dataName[shortName]
for granID in blobDict[shortName].keys() :
print '%s --> %s ' % (shortName, granID)
xmlFile = path.join(xmlDir,xmlName[shortName])
print '%s' % (xmlFile)
blobFile = blobDict[shortName][granID][0]
blobFile = path.join(blobPath,'%s'%(blobFile))
print '%s' % (blobFile)
blobObj = adl_blob.map(xmlFile,blobFile,endian=endian)
blobArrObj = blobObj.as_arrays()
for dataName,dataScales,dataOffsets,plotDescr in zip(self.dataName[shortName],self.dataScales[shortName],self.dataOffsets[shortName],self.plotDescr[shortName]):
print dataName,dataScales,dataOffsets
data = getattr(blobArrObj,dataName)
if (data.dtype.kind == 'O') :
data = data.astype(np.float)
scale = getattr(blobArrObj,dataScales)
offset = getattr(blobArrObj,dataOffsets)
print "Scale, Offset = %f %f" % (scale,offset)
data = data * scale + offset
pixelTrimValue = trimObj.sdrTypeFill['ONGROUND_PT_FILL'][data.dtype.name]
print "pixelTrimValue is %r" % (pixelTrimValue)
# Apply the moderate pixel trim, so that we can properly mask them out at plot time.
data = ma.array(data,mask=modTrimMask,fill_value=trimObj.sdrTypeFill['ONBOARD_PT_FILL'][data.dtype.name])
data = data.filled()
vmin,vmax = plotLims[shortName]
plotTitle = '%s : %s' % (shortName,granID)
cbTitle = plotDescr
# Create figure with default size, and create canvas to draw on
scale=1.5
fig = Figure(figsize=(scale*8,scale*3))
canvas = FigureCanvas(fig)
# Create main axes instance, leaving room for colorbar at bottom,
# and also get the Bbox of the axes instance
ax_rect = [0.05, 0.18, 0.9, 0.75 ] # [left,bottom,width,height]
ax = fig.add_axes(ax_rect)
# Granule axis title
ax_title = ppl.setp(ax,title=plotTitle)
ppl.setp(ax_title,fontsize=12)
ppl.setp(ax_title,family="sans-serif")
# Plot the data
im = ax.imshow(ma.masked_less(data,-800.),axes=ax,interpolation='nearest',vmin=vmin,vmax=vmax)
ppl.setp(ax.get_xticklabels(), visible=False)
ppl.setp(ax.get_yticklabels(), visible=False)
ppl.setp(ax.get_xticklines(),visible=False)
ppl.setp(ax.get_yticklines(),visible=False)
# add a colorbar axis
cax_rect = [0.05 , 0.05, 0.9 , 0.10 ] # [left,bottom,width,height]
cax = fig.add_axes(cax_rect,frameon=False) # setup colorbar axes
# Plot the colorbar.
cb = fig.colorbar(im, cax=cax, orientation='horizontal')
ppl.setp(cax.get_xticklabels(),fontsize=9)
# Colourbar title
cax_title = ppl.setp(cax,title=cbTitle)
ppl.setp(cax_title,fontsize=10)
# Redraw the figure
canvas.draw()
# Save the figure to a png file...
pngFile = path.join(pngDir,'%s_%s_%s.png' % (shortName,dataName,granID))
canvas.print_figure(pngFile,dpi=100)
ppl.close('all')
#def plot_SST_granules_HDF5:
#from os import path
#import numpy as np
#from numpy import ma
#from matplotlib import pyplot as ppl
#from glob import glob
#import tables as pytables
#import ViirsData
#trimObj = ViirsData.ViirsTrimTable()
#trimFillValue = trimObj.sdrTypeFill['ONBOARD_PT_FILL']['float32']
#modTrimMask = trimObj.createModTrimArray(nscans=4*48,trimType=bool)
#naFillValue_uint16 = trimObj.sdrTypeFill['NA_FILL']['uint16']
#naFillValue_float32 = trimObj.sdrTypeFill['NA_FILL']['float32']
#SSTfiles = glob('/home/geoffc/SSEC/sshfs/jpss-cloud/data/rayg/viirs_gtm_test/VSSTO*.h5')
#SSTfiles.sort()
#for files in SSTfiles :
#print "Opening %s ..." % (files)
#SSTobj = pytables.openFile(files)
#skinSST = SSTobj.getNode('/All_Data/VIIRS-SST-EDR_All/SkinSST')[:,:]
#skinSSTfactors = SSTobj.getNode('/All_Data/VIIRS-SST-EDR_All/SkinSSTFactors')[:]
#bulkSST = SSTobj.getNode('/All_Data/VIIRS-SST-EDR_All/BulkSST')[:,:]
#bulkSSTfactors = SSTobj.getNode('/All_Data/VIIRS-SST-EDR_All/BulkSSTFactors')[:]
#SSTobj.close()
#naFillMask_skin = ma.masked_equal(skinSST,naFillValue_uint16).mask
#skinSST = skinSST * skinSSTfactors[0] + skinSSTfactors[1]
#skinSST = ma.array(skinSST,mask=modTrimMask,fill_value=trimFillValue)
#skinSST = skinSST.filled()
#skinSST = ma.array(skinSST,mask=naFillMask_skin,fill_value=naFillValue_float32)
#skinSST = skinSST.filled()
#f = ppl.figure();
#ppl.imshow(ma.masked_less(skinSST,-800.),interpolation='nearest');
#ppl.colorbar(orientation='horizontal');
#fileName = path.basename(files)
#f.savefig('/home/geoffc/SSEC/ADL/CSPP_granTest/PEATE/SST/'+fileName+'_skin.png',dpi=200)
#ppl.close('all')
#naFillMask_bulk = ma.masked_equal(bulkSST,naFillValue_uint16).mask
#bulkSST = bulkSST * bulkSSTfactors[0] + bulkSSTfactors[1]
#bulkSST = ma.array(bulkSST,mask=modTrimMask,fill_value=trimFillValue)
#bulkSST = bulkSST.filled()
#bulkSST = ma.array(bulkSST,mask=naFillMask_bulk,fill_value=naFillValue_float32)
#bulkSST = bulkSST.filled()
#f = ppl.figure();
#ppl.imshow(ma.masked_less(bulkSST,-800.),interpolation='nearest');
#ppl.colorbar(orientation='horizontal');
#fileName = path.basename(files)
#f.savefig('/home/geoffc/SSEC/ADL/CSPP_granTest/PEATE/SST/'+fileName+'_bulk.png',dpi=200)
#ppl.close('all')