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#!/usr/bin/env python
"""
Do stuff with csv files.
"""
import optparse
import defapp
import csv
import operator
import os.path
from collections import defaultdict as defdict
from itertools import izip
o = optparse.make_option
opts = [
o('-c', '--column', action='append', dest='col', type='int',
help='The column(s) on which to operate.'),
o(None, '--write-to-file', action='store_true', dest='write_to_file',
help='Write the output of operation xyz on file abc.csv to xyz_abc.csv.'),
o('-u', '--upper-bound', action='store_true', dest='upper_bound',
help="The linear regression is modified to represent an upper bound."),
# o(None, '--degree', action='store', type='float', dest='thruthiness',
# help='Not quite absolut truth.'),
]
defaults = {
'col' : [],
'write_to_file' : False,
'upper_bound' : False,
}
def pair_iter(it):
it = iter(it)
while True:
yield (it.next(), it.next())
def make_vector_op(op):
def vector_op(a, b, defvalue=0):
if len(a) > len(b):
shorter = b
longer = a
else:
shorter = a
longer = b
c = list(longer)
for i in xrange(len(shorter)):
c[i] = op(longer[i], shorter[i])
for i in xrange(len(shorter), len(longer)):
c[i] = op(longer[i], defvalue)
return c
return vector_op
def make_scalar_op(op):
def scalar_op(scalar, a):
return [op(x, scalar) for x in a]
return scalar_op
row_add = make_vector_op(operator.add)
row_min = make_vector_op(min)
row_max = make_vector_op(max)
def row_reduce(row_op, fixup=lambda key, rows, res: res):
def _reduce(order, by_key):
for key in order:
if key in by_key:
rows = by_key[key]
res = reduce(row_op, rows)
del by_key[key]
yield fixup(key, rows, res)
return _reduce
row_mul = make_scalar_op(operator.mul)
row_div = make_scalar_op(operator.div)
def transpose(rows):
rows = list(rows)
if rows:
r = len(rows)
c = max([len(x) for x in rows])
def at(x, y):
try:
return rows[x][y]
except IndexError:
return 0
for i in xrange(c):
yield [at(j, i) for j in xrange(r) ]
def reorder_columns(rows, xchg_pairs):
for r in rows:
for (x,y) in xchg_pairs:
r[x], r[y] = r[y], r[x]
yield r
def select_columns(rows, cols):
for r in rows:
yield [r[x] for x in cols]
def numpy_lstsq(x, y):
from numpy import ones, array
from numpy.linalg import lstsq
A = ones((len(y), 2), dtype=float)
A[:,0] = array(x)
b = array(y)
return lstsq(A, b)[0]
def max_delta(c0, c1, x, y):
return max([abs(c0 + c1 * float(x) - float(y)) for (x,y) in izip(x, y)])
def least_squares(rows, xy_pairs, upper_bound):
cols = []
for (x, y) in xy_pairs:
cols += [x, y]
rows = select_columns(rows, cols)
cols = transpose(rows)
for ((x, y), (xval, yval)) in izip(xy_pairs, pair_iter(cols)):
c1, c0 = numpy_lstsq(xval, yval)
if upper_bound:
c0 += max_delta(c0, c1, xval, yval)
yield [x + 1, y + 1, c0, c1]
def select_by_key(rows, col, cast=None):
by_key = defdict(list)
order = []
for r in rows:
key = r[col]
if cast:
by_key[key] += [[cast(x) for x in r]]
else:
by_key[key] += [r]
order += [key]
return (order, by_key)
class CsvApp(defapp.App):
def __init__(self):
defapp.App.__init__(self, opts, defaults)
# fixup human-friendly offsets
if not self.options.col:
self.options.col = [1]
self.options.col = [x - 1 for x in self.options.col]
self.options.col_pairs = list(pair_iter(self.options.col))
def transform(self, make_iterator, ordered=True):
"""
Read a file, pass the rows in the file to an iterator factory, and
write out the output of the iterator. The iterator performs the
desired transformation.
"""
files = list(self.args)
del files[0]
for fn in files:
try:
# read in content
rows = csv.reader(open(fn, 'r'))
# set up transformation
if ordered:
(order, by_key) = select_by_key(rows, self.options.col[0],
float)
rows = make_iterator(order, by_key)
else:
rows = make_iterator(rows)
# write out
outfile = self.outfile()
if self.options.write_to_file:
(dir, file) = os.path.split(fn)
fn = os.path.join(dir, self.args[0] + '_' + file)
outfile = open(fn, 'w')
csv.writer(outfile).writerows(rows)
if self.options.write_to_file:
outfile.close()
except IOError, ex:
self.err("%s:%s" % (fn, str(ex)))
except IndexError, ex:
self.err("%s: Sorry, index out of range." % fn)
def do_avg(self, _):
def fixup_avg(key, rows, res):
res = row_div(len(rows), res)
res[self.options.col[0]] = key
return res
self.transform(row_reduce(row_add, fixup_avg))
def do_max(self, _):
self.transform(row_reduce(row_max))
def do_min(self, _):
self.transform(row_reduce(row_min))
def do_transpose(self, _):
self.transform(transpose, ordered=False)
def do_reorder(self, _):
self.transform(lambda rows: reorder_columns(
rows, self.options.col_pairs), ordered=False)
def do_select(self, _):
self.transform(lambda rows: select_columns(
rows, self.options.col), ordered=False)
def do_lstsqrs(self, _):
self.transform(
lambda rows: least_squares(rows, self.options.col_pairs,
self.options.upper_bound),
ordered=False)
if __name__ == '__main__':
CsvApp().launch()
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