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ViirsSDR_RadianceToBrightnessTemp_drv.py
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ViirsSDR_RadianceToBrightnessTemp_drv.py
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#!/usr/bin/env python
# encoding: utf-8
"""
ViirsSDR_RadianceToBrightnessTemp_drv.py
This is a driver script for ViirsSDR_RadianceToBrightnessTemp.py
Created by Geoff Cureton on 2014-11-11.
Copyright (c) 2014 University of Wisconsin SSEC. All rights reserved.
"""
file_Date = '$Date$'
file_Revision = '$Revision$'
file_Author = '$Author$'
file_HeadURL = '$HeadURL$'
file_Id = '$Id$'
__author__ = 'G.P. Cureton <geoff.cureton@ssec.wisc.edu>'
__version__ = '$Id$'
__docformat__ = 'Epytext'
import os, sys
from os import path, uname, mkdir
from glob import glob
import string, logging, traceback
from time import time
from datetime import datetime
import numpy as np
from numpy import ma as ma
import scipy as scipy
import matplotlib
import matplotlib.cm as cm
from matplotlib.colors import Colormap,normalize,LinearSegmentedColormap,\
ListedColormap,LogNorm
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
import h5py
# every module should have a LOG object
# e.g. LOG.warning('my dog has fleas')
import logging
LOG = logging.getLogger(__file__)
# Set up the logging
console_logFormat = '%(asctime)s : (%(levelname)s):%(filename)s:%(funcName)s:%(lineno)d: %(message)s'
dateFormat = '%Y-%m-%d %H:%M:%S'
levels = [logging.ERROR, logging.WARN, logging.INFO, logging.DEBUG]
#logging.basicConfig(level = levels[3],
logging.basicConfig(level = logging.DEBUG,
format = console_logFormat,
datefmt = dateFormat)
import ViirsSDR_RadianceToBrightnessTemp
def SDR_histogram_generate(data_1,data_2, plotProd='BrightnessTemperature',
histBins=20,histMin=None,histMax=None):
# Construct fill masks to cover whatever isn't covered by
# the bow-tie pixels.
LOG.debug("Performing mask of float types")
fill_mask_1 = ma.masked_less(data_1,-800.).mask
fill_mask_2 = ma.masked_less(data_2,-800.).mask
LOG.debug("data_1 is {}".format(data_1))
LOG.debug("data_2 is {}".format(data_2))
# Construct the total masks
totalMask_1 = fill_mask_1
totalMask_2 = fill_mask_1
totalMask = np.ravel(totalMask_1 + totalMask_2)
LOG.debug("totalMask_1.sum() = {}".format(totalMask_1.sum()))
LOG.debug("totalMask_2.sum() = {}".format(totalMask_2.sum()))
LOG.debug("totalMask.sum() = {}".format(totalMask.sum()))
# Flatten the datasets
data_1 = np.ravel(data_1)
data_2 = np.ravel(data_2)
LOG.debug("ravelled data_1.shape is {}".format(data_1.shape))
LOG.debug("ravelled data_2.shape is {}".format(data_2.shape))
# Mask the SDR so we only have the radiometric values
data_1 = ma.masked_array(data_1,mask=totalMask)
data_2 = ma.masked_array(data_2,mask=totalMask)
LOG.debug("data_1.mask.sum() = {}".format(data_1.mask.sum()))
LOG.debug("data_2.mask.sum() = {}".format(data_2.mask.sum()))
## Compress the datasets
data_1 = ma.compressed(data_1)
data_2 = ma.compressed(data_2)
LOG.debug("compressed data_1.shape is {}".format(data_1.shape))
LOG.debug("compressed data_2.shape is {}".format(data_2.shape))
LOG.debug("data_1 is {}".format(data_1))
LOG.debug("data_2 is {}".format(data_2))
## Generate the histogram for this granule
LOG.info("Creating histogram...")
vmin = np.min(data_1) if (histMin == None) else histMin
vmax = np.max(data_1) if (histMax == None) else histMax
LOG.debug("vmin is {}".format(vmin))
LOG.debug("vmax is {}".format(vmax))
histRange = np.array([[vmin,vmax],[vmin,vmax]])
H, xedges, yedges = np.histogram2d(data_2,data_1,
bins=histBins,range=histRange,normed=False)
return H, xedges, yedges
def histogramPlot(xedges, yedges,histogram,
vmin=0.001,vmax=1.,histMin=None,histMax=None,scale=None,
axis_label_1=None,axis_label_2=None,plot_title=r'',pngDpi=300,
cmap=None, pngName='SDR_hist.png'):
'''
Plots a 2D histogram defined by xedges, yedges, and histogram.
'''
figWidth = 5. # inches
figHeight = 4.2 # inches
# Create figure with default size, and create canvas to draw on
fig = Figure(figsize=((figWidth,figHeight)))
canvas = FigureCanvas(fig)
ax_len = 0.80
ax_x_len = ax_y_len = ax_len
x0,y0 = 0.07,0.10
x1,y1 = x0+ax_len,y0+ax_len
cax_x_pad = 0.0
cax_x_len = 0.05
cax_y_len = ax_len
ax_rect = [x0, y0, ax_len , ax_len ] # [left,bottom,width,height]
cax_rect = [x1+cax_x_pad , y0, cax_x_len , cax_y_len ] # [left,bottom,width,height]
LOG.info("ax_rect = {}".format(ax_rect))
LOG.info("cax_rect = {}".format(cax_rect))
timeString = 'Creation date: %s' %(datetime.strftime(datetime.utcnow(),"%Y-%m-%d %H:%M:%S Z"))
fig.text(0.98, 0.01, timeString,fontsize=5, color='gray',ha='right',va='bottom',alpha=0.9)
# the histogram axis ranges
histMin = np.min(xedges) if (histMin == None) else histMin
histMax = np.max(xedges) if (histMax == None) else histMax
LOG.info("_histogramPlot Histogram range: {}".format([histMin, histMax]))
# the histogram count range
LOG.info("_histogramPlot Counts range: {}".format([vmin, vmax]))
# Create main axes instance, leaving room for colorbar at bottom,
# and also get the Bbox of the axes instance
ax = fig.add_axes(ax_rect)
Nbins = len(xedges) - 1
parity = np.linspace(histMin,histMax,Nbins)
parLine = ax.plot(parity,parity,'--')
ppl.setp(parLine,color='gray')
# Set the display ranges of the x and y axes...
ax.set_xlim(histMin,histMax)
ax.set_ylim(histMin,histMax)
ppl.setp(ax.get_xticklabels(),fontsize=6)
ppl.setp(ax.get_yticklabels(),fontsize=6)
ppl.setp(ax,xlabel=axis_label_1)
ppl.setp(ax,ylabel=axis_label_2)
ax_title = ppl.setp(ax,title=plot_title)
# The extent values just change the axis limits, they do NOT
# alter which part of the array gets plotted.
extent = [yedges[0], yedges[-1], xedges[0], xedges[-1]]
LOG.info("xedges.shape = {}".format(xedges.shape))
LOG.info("yedges.shape = {}".format(yedges.shape))
H = histogram.astype(np.float)
H /= np.max(H)
LOG.info("Histogram.shape = {}".format(H.shape))
LOG.info("Histogram min,max = {},{}".format(np.min(H),np.max(H)))
LOG.info("Scaled Histogram min,max = {},{}".format(np.min(H),np.max(H)))
cs = ax.imshow(H[:,:], extent=extent, vmin=vmin, vmax=vmax,
interpolation='nearest',origin='lower',
norm=LogNorm(vmin=vmin, vmax=vmax),cmap=cmap)
# add a colorbar.
cax = fig.add_axes(cax_rect,frameon=False) # setup colorbar axes
t = [0.001,0.01,0.1,1.]
cb = fig.colorbar(cs, cax=cax, ticks=t, format='$%.3f$', orientation='vertical')
ppl.setp(cax.get_yticklabels(),fontsize=6)
cax_title = ppl.setp(cax,title="counts/counts$_{max}$")
ppl.setp(cax_title,fontsize=5)
# Redraw the figure
canvas.draw()
# save image
LOG.info("Creating the image file {}".format((pngName)))
canvas.print_figure(pngName,dpi=pngDpi)
def histogram2DPlot(xedges, yedges,histogram,
vmin=0.001,vmax=1.,histMin=None,histMax=None,scale=None,
axis_label_1=None,axis_label_2=None,plot_title=r'',pngDpi=300,
cmap=None, pngName='SDR_hist.png'):
'''
Plots a 2D histogram defined by xedges, yedges, and histogram.
'''
figWidth = 5. # inches
figHeight = 4.2 # inches
# Create figure with default size, and create canvas to draw on
fig = Figure(figsize=((figWidth,figHeight)))
canvas = FigureCanvas(fig)
ax_len = 0.80
ax_x_len = ax_y_len = ax_len
x0,y0 = 0.07,0.10
x1,y1 = x0+ax_len,y0+ax_len
cax_x_pad = 0.0
cax_x_len = 0.05
cax_y_len = ax_len
ax_rect = [x0, y0, ax_len , ax_len ] # [left,bottom,width,height]
cax_rect = [x1+cax_x_pad , y0, cax_x_len , cax_y_len ] # [left,bottom,width,height]
LOG.info("ax_rect = {}".format(ax_rect))
LOG.info("cax_rect = {}".format(cax_rect))
timeString = 'Creation date: %s' %(datetime.strftime(datetime.utcnow(),"%Y-%m-%d %H:%M:%S Z"))
fig.text(0.98, 0.01, timeString,fontsize=5, color='gray',ha='right',va='bottom',alpha=0.9)
# the histogram axis ranges
histMin = np.min(xedges) if (histMin == None) else histMin
histMax = np.max(xedges) if (histMax == None) else histMax
LOG.info("_histogramPlot Histogram range: {}".format([histMin, histMax]))
# the histogram count range
LOG.info("_histogramPlot Counts range: {}".format([vmin, vmax]))
# Create main axes instance, leaving room for colorbar at bottom,
# and also get the Bbox of the axes instance
ax = fig.add_axes(ax_rect)
Nbins = len(xedges) - 1
parity = np.linspace(histMin,histMax,Nbins)
parLine = ax.plot(parity,parity,'--')
ppl.setp(parLine,color='gray')
# Set the display ranges of the x and y axes...
ax.set_xlim(histMin,histMax)
ax.set_ylim(histMin,histMax)
ppl.setp(ax.get_xticklabels(),fontsize=6)
ppl.setp(ax.get_yticklabels(),fontsize=6)
ppl.setp(ax,xlabel=axis_label_1)
ppl.setp(ax,ylabel=axis_label_2)
ax_title = ppl.setp(ax,title=plot_title)
# The extent values just change the axis limits, they do NOT
# alter which part of the array gets plotted.
extent = [yedges[0], yedges[-1], xedges[0], xedges[-1]]
LOG.info("xedges.shape = {}".format(xedges.shape))
LOG.info("yedges.shape = {}".format(yedges.shape))
H = histogram.astype(np.float)
H /= np.max(H)
LOG.info("Histogram.shape = {}".format(H.shape))
LOG.info("Histogram min,max = {},{}".format(np.min(H),np.max(H)))
LOG.info("Scaled Histogram min,max = {},{}".format(np.min(H),np.max(H)))
cs = ax.imshow(H[:,:], extent=extent, vmin=vmin, vmax=vmax,
interpolation='nearest',origin='lower',
norm=LogNorm(vmin=vmin, vmax=vmax),cmap=cmap)
# add a colorbar.
cax = fig.add_axes(cax_rect,frameon=False) # setup colorbar axes
t = [0.001,0.01,0.1,1.]
cb = fig.colorbar(cs, cax=cax, ticks=t, format='$%.3f$', orientation='vertical')
ppl.setp(cax.get_yticklabels(),fontsize=6)
cax_title = ppl.setp(cax,title="counts/counts$_{max}$")
ppl.setp(cax_title,fontsize=5)
# Redraw the figure
canvas.draw()
# save image
LOG.info("Creating the image file {}".format((pngName)))
canvas.print_figure(pngName,dpi=pngDpi)
def calc_bTemps(lut_file,xml_file=None):
BT_prefixes = ['SVI04','SVI05','SVM12','SVM13','SVM14','SVM15','SVM16']
bandIndices = np.arange(7)
chan_dict = {}
for prefix,bandIdx in zip(BT_prefixes,bandIndices):
sdr_file = glob('{}*.h5'.format(prefix))[0]
chan_dict[prefix] = {'sdr_file':sdr_file,'radiance':None,'bTemp':None,
'bandIdx':bandIdx,'bTemp_new':None}
# Initialise the ViirsRadToBtemp object with the LUT file...
#lut_file = 'ViirsSDR_EBBT_1.5.0.48/proSdrViirsCalLtoEBBTLUT_le.bin'
rad2Bt = ViirsSDR_RadianceToBrightnessTemp.ViirsRadToBtemp(
lut_file,xml_file=xml_file)
# Open the HDF5 file, and read the Radiance and RadianceFactors data.
for prefix in BT_prefixes:
# Open the SDR file
sdr_file = chan_dict[prefix]['sdr_file']
sdrObj = h5py.File(sdr_file,'r')
# Read in the datasets
band_key = '{}{}'.format(prefix[2],lstrip(prefix[3:],'0'))
group_name = '/All_Data/VIIRS-{}-SDR_All'.format(band_key)
print "Reading band {} datasets".format(group_name)
radiance_str = '{}/Radiance'.format(group_name)
bTemp_str = '{}/BrightnessTemperature'.format(group_name)
radiance = sdrObj[radiance_str].value
brightnessTemperature = sdrObj[bTemp_str].value
try:
radiance_str = '{}/RadianceFactors'.format(group_name)
bTemp_str = '{}/BrightnessTemperatureFactors'.format(group_name)
radianceFactors = sdrObj[radiance_str].value
brightnessTemperatureFactors = sdrObj[bTemp_str].value
# Determine where the bow-tie deleted pixels are, so we can restore
# them with the type appropriate values after unscaling the
# radiance...
fill_mask = ma.masked_inside(radiance,65528,65535).mask
except KeyError:
radianceFactors = np.array([1.,0.])
brightnessTemperatureFactors = np.array([1.,0.])
fill_mask = ma.masked_inside(radiance,-999.2,-999.9).mask
# Close the SDR file
sdrObj.close()
# Unscale the radiance, and restore the correct bow-tie fill value
# for the float datatype...
radiance = radiance*radianceFactors[0] + radianceFactors[1]
radiance = ma.array(radiance,mask=fill_mask,fill_value=-999.3)
radiance = radiance.filled().astype('float32')
chan_dict[prefix]['radiance'] = radiance
# Unscale the brightness temperature, and restore the correct bow-tie fill value
# for the float datatype...
brightnessTemperature = brightnessTemperature*brightnessTemperatureFactors[0] + brightnessTemperatureFactors[1]
brightnessTemperature = ma.array(brightnessTemperature,mask=fill_mask,fill_value=-999.3)
brightnessTemperature = brightnessTemperature.filled().astype('float32')
chan_dict[prefix]['bTemp'] = brightnessTemperature
# Convert the radiance to brightness temperature. The band indicies
# are [0,1,..,6], corresponding to [I04,I05,M12,M13,M14,M15,M16].
# Since we are doing M15, the band index is 5.
bandIdx = chan_dict[prefix]['bandIdx']
bTemp = rad2Bt.convertToBtemp(bandIdx,radiance)
bTemp = ma.array(bTemp,mask=fill_mask,fill_value=-999.3)
bTemp = bTemp.filled().astype('float32')
chan_dict[prefix]['bTemp_new'] = bTemp
return chan_dict
def plot_bTemps(chan_dict,chan_str,vmin=None,vmax=None):
bTemp = ma.masked_less(chan_dict[chan_str]['bTemp'],-800.)
f = ppl.figure();
ppl.imshow(bTemp,interpolation='nearest',vmin=vmin,vmax=vmax);
ppl.colorbar(orientation='horizontal');
ppl.title('IDPS {} Brightness Temperature (K)'.format(chan_str));
ppl.show(block=False)
bTemp_new = ma.masked_less(chan_dict[chan_str]['bTemp_new'],-800.)
f = ppl.figure();
ppl.imshow(bTemp_new,interpolation='nearest',vmin=vmin,vmax=vmax);
ppl.colorbar(orientation='horizontal');
ppl.title('Converted IDPS {} Brightness Temperature (K)'.format(chan_str));
ppl.show(block=False)
def plot_bTemp_diffs(chan_dict,chan_str,eps=1.):
bTemp = ma.masked_less(chan_dict[chan_str]['bTemp'],-800.)
bTemp_new = ma.masked_less(chan_dict[chan_str]['bTemp_new'],-800.)
bTemp_diff = bTemp - bTemp_new
#bTemp_diff = 100.*(bTemp - bTemp_new)/bTemp
BT_diff_min = np.min(bTemp_diff)
BT_diff_max = np.max(bTemp_diff)
BT_diff_min_idx = np.where(bTemp_diff.data==BT_diff_min)
BT_diff_max_idx = np.where(bTemp_diff.data==BT_diff_max)
print "Minimum BT difference = {} @ {}".format(BT_diff_min,BT_diff_min_idx)
print "Maximum BT difference = {} @ {}".format(BT_diff_max,BT_diff_max_idx)
f = ppl.figure();
ppl.imshow(bTemp_diff,interpolation='nearest',
vmin=-1.*eps,vmax=eps,cmap='RdBu_r')
ppl.colorbar(orientation='horizontal');
ppl.title('(Original - Converted) IDPS {} Brightness Temperature (K)'.format(chan_str));
ppl.show(block=False)