def __init__(self, histo=None, ff_name=None, ff_dict=forms, weighted=False,
min_npts=10, normalize=True) :
"""specify the histogram and functional form to use for initialization, or none"""
self.fits = {}
self.default_fit = None
self.ff_name = ff_name
if ff_name is not None :
self._init_ff(ff_dict[ff_name])
if histo is not None :
self.minmax = histo.minmax
self._index = ah.init_indexers(self.minmax)
self.default_contrib = histo.default_contrib
combos = histo.get_combos(units=False)
self.x_min = {}
for c in combos :
self.x_min[c] = np.mean(histo.get_edges(c, units=False)[:2])
self.default_x_min = np.mean(histo.default_edges[:2])
self._fit_functional_form(histo, weighted, min_npts, normalize)
seed(self)
else:
self.default_contrib = {}
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)
