def __create_col_metadata(marker, meta, colwidths):
linelist = [marker]
for colmeta, colwidth in iterators.IteratorUnion(iter(meta), iter(colwidths)):
format = '%-' + str(colwidth) + 's'
if marker == COLUMN_LABEL_MARKER:
# add quotes
colmeta = '"%s"' % colmeta
linelist.append(format % colmeta)
linelist.append('\n')
return '\t'.join(linelist)
def __calc_col_widths(table, colformat): col_widths = [] for name, type, data in iterators.IteratorUnion(iter(table.colnames), iter(table.coltypes), iter(table.coldata)): # add 2 to name length to account for quotes width = max(len(str(name)) + 2, len(type)) if colformat == 'fixed': for row in data: width = max(width, len(str(row))) col_widths.append(width) return col_widths
def ttest_table(
data1,
data2,
n,
tcrit,
regions, # general names
regions1, # normalized for classification of data1
regions2, # normalized for classification of data2
timesteps,
input_colnames,
func_iter):
COLNAMES = ['Timestep', 'Mean', 'StdDev', 'Sig', 'tval', 'pval']
COLTYPES = ['int', 'float', 'float', 'int', 'float', 'float']
def __createTable(name):
return (name, datalib.Table(name, COLNAMES, COLTYPES))
# {C-colname,table}, where colname is min, max, or mean
result = dict([
__createTable('-'.join(C_col))
for C_col in iterators.product(regions, input_colnames)
])
for C, C1, C2 in iterators.IteratorUnion(iter(regions), iter(regions1),
iter(regions2)):
for t in timesteps:
for col in input_colnames:
diffMean, stdDev, tval, pval = ttest(
(data1, C1, t, col), (data2, C2, t, col), n, func_iter)
table = result[C + '-' + col]
row = table.createRow()
row["Timestep"] = t
row["Mean"] = diffMean
row["StdDev"] = stdDev
if tval >= tcrit:
row["Sig"] = 1
else:
row["Sig"] = 0
row["tval"] = tval
row["pval"] = pval
return result
def diff_mean(ita, itb):
n = 0
sum = 0.0
n_inf = 0
inf = float('inf')
for a, b in iterators.IteratorUnion(ita, itb):
if a == inf or b == inf:
n_inf += 1
else:
n += 1
sum += a - b
if n_inf > 0:
print 'diff_mean() ignoring', n_inf, 'inf value(s)'
if n < 1:
return 0.0
else:
return sum / n
def diff_stddev(diffmean, ita, itb):
n = 0
sum = 0.0
n_inf = 0
inf = float('inf')
for a, b in iterators.IteratorUnion(ita, itb):
if a == inf or b == inf:
n_inf += 1
else:
n += 1
delta = a - b - diffmean
sum += delta**2
if n_inf > 0:
print 'diff_stddev() ignoring', n_inf, 'inf value(s)'
if n < 1:
return 0.0
else:
return sqrt(sum / (n - 1))
