def ba_multiyear_ratios(years,
ba_template,
ind_template,
ind_names,
histo_outfile=None,
ratio_outfile=None,
bins=10,
minmaxyears=None):
"""computes multiyear histograms and stores in a netcdf file."""
# open netcdf files
bafiles = []
indfiles = []
for y in years:
bafiles.append(nc.Dataset(ba_template % y))
indfiles.append(nc.Dataset(ind_template % y))
# compute min/max
if minmaxyears is None:
minmax = oi.multifile_minmax(indfiles, ind_names)
else:
minmax = oi.multifile_minmax(ind_template,
ind_names,
years=minmaxyears)
if not ('__iter__' in dir(bins)):
bins = [bins] * len(ind_names)
minmax = zip(minmax[0], minmax[1], bins)
# compute histogram
ratios, halfdeg_counts, histo = ba_ratio_histograms(
bafiles, indfiles, ind_names, minmax)
# write output
if histo_outfile is not None:
ah.save_sparse_histos(histo, histo_outfile)
if ratio_outfile is not None:
write_raw_ratio_file(ratio_outfile, ratios, halfdeg_counts, ind_names,
minmax)
# close netcdf files
for i_files in range(len(years)):
bafiles[i_files].close()
indfiles[i_files].close()
return ratios, histo
def ba_multiyear_ratios(years, ba_template, ind_template, ind_names,
histo_outfile=None, ratio_outfile=None, bins=10, minmaxyears=None) :
"""computes multiyear histograms and stores in a netcdf file."""
# open netcdf files
bafiles = [ ]
indfiles = [ ]
for y in years :
bafiles.append(nc.Dataset(ba_template % y))
indfiles.append(nc.Dataset(ind_template % y))
# compute min/max
if minmaxyears is None :
minmax = oi.multifile_minmax(indfiles, ind_names)
else :
minmax = oi.multifile_minmax(ind_template, ind_names, years=minmaxyears)
if not ('__iter__' in dir(bins)) :
bins = [ bins ] * len(ind_names)
minmax = zip(minmax[0], minmax[1], bins)
# compute histogram
ratios, halfdeg_counts, histo = ba_ratio_histograms(bafiles, indfiles, ind_names, minmax)
# write output
if histo_outfile is not None :
ah.save_sparse_histos(histo, histo_outfile)
if ratio_outfile is not None:
write_raw_ratio_file(ratio_outfile, ratios, halfdeg_counts, ind_names, minmax)
# close netcdf files
for i_files in range(len(years)) :
bafiles[i_files].close()
indfiles[i_files].close()
return ratios, histo
def sparse_multiyear_histogram(years, csv_template, bahistfile,
count_threshold=50, bins=25, out_template=None) :
"""computes and optionally saves sparse histograms of MODIS BA counts"""
# open the ba histogram file
bahist = nc.Dataset(bahistfile)
counts = bahist.variables['burned_total']
# read all csv files and concatenate
file_list = []
for y in years :
file_list.append(pd.read_csv(csv_template % y))
compare = pd.concat(file_list)
compare = compare[ np.logical_and(compare.icol(0)>=10,compare.icol(0)<364) ]
# get min/max/bin from multiyear histogram file
mmb, binsizes = read_multiyear_minmax(bahist,counts.dimensions)
# create an indexer
index = ah.init_indexers(mmb)
# strip out geometry
dim_bins = [m[2] for m in mmb]
# create sparse histograms
shisto_forest = ah.SparseKeyedHistogram(minmax=mmb, threshold=count_threshold,
bins=bins, default_minmax=(1,count_threshold,count_threshold-1))
shisto_not_forest = ah.SparseKeyedHistogram(minmax=mmb, threshold=count_threshold,
bins=bins, default_minmax=(1,count_threshold,count_threshold-1))
shisto_total = ah.SparseKeyedHistogram(minmax=mmb, threshold=count_threshold,
bins=bins, default_minmax=(1,count_threshold,count_threshold-1))
# loop through all bins with nonzero data
i_nonzero = np.where( counts[:]>0 )
for i_bin in zip(*i_nonzero) :
total = select_data(compare, counts.dimensions, i_bin, index, dim_bins)
forest = total[ total.ix[:,1].isin(FOREST_LC) ]
not_forest = total [ total.ix[:,1].isin(NONFOREST_LC) ]
shisto_forest.put_combo(i_bin, forest['BA Count'], units=False)
shisto_not_forest.put_combo(i_bin, not_forest["BA Count"], units=False)
shisto_total.put_combo(i_bin, total['BA Count'], units=False)
# save file if filename template specified
if out_template is not None :
ah.save_sparse_histos(shisto_total, out_template%'total')
ah.save_sparse_histos(shisto_forest, out_template%'forest')
ah.save_sparse_histos(shisto_not_forest, out_template%'not_forest')
bahist.close()
return (shisto_total, shisto_forest, shisto_not_forest)
def sparse_multiyear_histogram(years,
csv_template,
bahistfile,
count_threshold=50,
bins=25,
out_template=None):
"""computes and optionally saves sparse histograms of MODIS BA counts"""
# open the ba histogram file
bahist = nc.Dataset(bahistfile)
counts = bahist.variables['burned_total']
# read all csv files and concatenate
file_list = []
for y in years:
file_list.append(pd.read_csv(csv_template % y))
compare = pd.concat(file_list)
compare = compare[np.logical_and(
compare.icol(0) >= 10,
compare.icol(0) < 364)]
# get min/max/bin from multiyear histogram file
mmb, binsizes = read_multiyear_minmax(bahist, counts.dimensions)
# create an indexer
index = ah.init_indexers(mmb)
# strip out geometry
dim_bins = [m[2] for m in mmb]
# create sparse histograms
shisto_forest = ah.SparseKeyedHistogram(
minmax=mmb,
threshold=count_threshold,
bins=bins,
default_minmax=(1, count_threshold, count_threshold - 1))
shisto_not_forest = ah.SparseKeyedHistogram(
minmax=mmb,
threshold=count_threshold,
bins=bins,
default_minmax=(1, count_threshold, count_threshold - 1))
shisto_total = ah.SparseKeyedHistogram(
minmax=mmb,
threshold=count_threshold,
bins=bins,
default_minmax=(1, count_threshold, count_threshold - 1))
# loop through all bins with nonzero data
i_nonzero = np.where(counts[:] > 0)
for i_bin in zip(*i_nonzero):
total = select_data(compare, counts.dimensions, i_bin, index, dim_bins)
forest = total[total.ix[:, 1].isin(FOREST_LC)]
not_forest = total[total.ix[:, 1].isin(NONFOREST_LC)]
shisto_forest.put_combo(i_bin, forest['BA Count'], units=False)
shisto_not_forest.put_combo(i_bin, not_forest["BA Count"], units=False)
shisto_total.put_combo(i_bin, total['BA Count'], units=False)
# save file if filename template specified
if out_template is not None:
ah.save_sparse_histos(shisto_total, out_template % 'total')
ah.save_sparse_histos(shisto_forest, out_template % 'forest')
ah.save_sparse_histos(shisto_not_forest, out_template % 'not_forest')
bahist.close()
return (shisto_total, shisto_forest, shisto_not_forest)