add utility to aggregate and split weighted schedulability experiments

1 files changed, 185 insertions, 0 deletions

diff --git a/wsched.py b/wsched.py
new file mode 100755
index 0000000..8214bf0
--- /dev/null
+++ b/wsched.py
@@ -0,0 +1,185 @@
+#!/usr/bin/env python
+
+import defapp
+from optparse import make_option as o
+from os.path  import splitext, basename
+
+import sys
+
+from util import load_csv_file
+
+def split_rows(rows, key_col):
+    by_key_col = {}
+    key_order  = []
+
+    # hash each row according by its key
+    for r in rows:
+        key = r[key_col]
+        if not key in by_key_col:
+            key_order.append(key)
+            by_key_col[key] = []
+        by_key_col[key].append(r)
+
+    return (by_key_col, key_order)
+
+
+def group(rows, key_col):
+    (by, order) = split_rows(rows, key_col)
+    for k in order:
+        yield (k, by[k])
+
+def aggregate_column(rows, key_col, aggregate_fun):
+    by_key, order = split_rows(rows, key_col)
+    for key in order:
+        yield aggregate_fun(by_key[key])
+
+
+def weighted_schedulability(data, key_col, ucap_col):
+    def wsched(rows):
+        # Every column except for the key_col and the ucap_col
+        # is presumed to be schedulability values.
+        key   = rows[0][key_col]
+        cols  = len(rows[0])
+        accum = [0.0 for _ in xrange(cols)]
+        norm  = 0.0
+        umax  = 0.0
+        for r in rows:
+            ucap = r[ucap_col]
+            umax = max(ucap, umax)
+            norm += ucap
+            for i, val in enumerate(r):
+                if i != key_col and i != ucap_col:
+                    accum[i] += ucap * val
+        return [(accum[i] / norm) if i != key_col and i != ucap_col
+                else (key if i == key_col else umax) for i in xrange(cols)]
+
+    return aggregate_column(data, key_col, wsched)
+
+
+def print_row(row, out, fmt):
+    out.write(", ".join(fmt % d for d in row))
+    out.write("\n")
+
+def filter_wsched(fname, key, ucap, out=sys.stdout, fmt="%10.3f"):
+    data = load_csv_file(fname, dtype=float)
+    for row in weighted_schedulability(data, key, ucap):
+        print_row(row, out, fmt)
+    out.flush()
+
+def filter_comments(fname, is_comment=lambda line: line and line[0] == '#',
+                    out=sys.stdout):
+    f = open(fname, 'r')
+    for line in f:
+        if is_comment(line):
+            out.write(line)
+    out.flush()
+    f.close()
+
+options = [
+    # output options
+    o('-o', '--output-prefix', action='store', dest='prefix'),
+    o('-k', '--key-column', action='store', type='int', dest='keycol'),
+    o('-u', '--ucap-column', action='store', type='int', dest='ucol'),
+    o(None, '--no-comments', action='store_true', dest='no_comments'),
+    o('-w', '--col-width', action='store', type='int', dest='colwidth'),
+    o(None, '--split', action='store_true', dest='want_split'),
+    ]
+
+defaults = {
+    'prefix'      : None,
+    'keycol'      : 0,
+    'ucol'        : 1,
+    'no_comments' : False,
+    'colwidth'    : 10,
+    'precision'   :  3,
+    'indent'      :  2,
+    'key_fmt'     : "_key=%d",
+    'want_split'  : False,
+    }
+
+
+class WschedFilter(defapp.App):
+    def __init__(self):
+        defapp.App.__init__(self, options, defaults, no_std_opts=True)
+        self.out = sys.stdout
+
+
+    def filter_wsched(self, fname, out=sys.stdout):
+        data = load_csv_file(fname, dtype=float)
+        for row in weighted_schedulability(
+            data, self.options.keycol, self.options.ucol):
+            out.write(" " * self.options.indent)
+            out.write(", ".join(self.fmt % d for d in row))
+            out.write("\n")
+        out.flush()
+
+    def target_name(self, fname, extra=None):
+        bname = basename(fname)
+        if extra:
+            bname, ext = splitext(bname)
+            bname = "%s%s%s" % (bname, extra, ext)
+        return self.options.prefix + bname
+
+    def filter_file(self, fin, fout=None):
+        out = open(fout, 'w') if fout else sys.stdout
+        if not self.options.no_comments:
+            filter_comments(fin, out=out)
+        self.filter_wsched(fin, out=out)
+        if fout:
+            out.close()
+
+    def write_split(self, fin, fout, rows):
+        out = open(fout, 'w')
+        if not self.options.no_comments:
+            filter_comments(fin, out=out)
+        for r in rows:
+            print_row(r, out, self.fmt)
+        out.close()
+
+    def split_file(self, fname):
+        data = load_csv_file(fname, dtype=float)
+        for (key, rows) in group(data, self.options.keycol):
+            fout = self.target_name(fname, self.options.key_fmt % key)
+            print '\t->', fout
+            self.write_split(fname, fout, rows)
+
+    def wsched(self, _):
+        if self.options.prefix:
+            for i, datafile in enumerate(self.args):
+                fout = self.target_name(datafile)
+                print "[%d/%d] %s -> %s" % \
+                    (i + 1, len(self.args), datafile, fout)
+                try:
+                    self.filter_file(datafile, fout)
+                except IOError as ioe:
+                    sys.stderr.write("[!!] ")
+                    sys.stderr.write(str(ioe))
+                    sys.stderr.write("\n")
+        else:
+            for fname in self.args:
+                self.filter_file(fname)
+
+    def split(self, _):
+        if self.options.prefix:
+            for i, datafile in enumerate(self.args):
+                print "[%d/%d] Splitting %s..." % \
+                    (i + 1, len(self.args), datafile)
+                try:
+                    self.split_file(datafile)
+                except IOError as ioe:
+                    sys.stderr.write("[!!] ")
+                    sys.stderr.write(str(ioe))
+                    sys.stderr.write("\n")
+        else:
+            print "split: requires output prefix (-o)."
+
+    def default(self, _):
+        self.fmt = "%%%d.%df" % (self.options.colwidth, self.options.precision)
+        if self.options.want_split:
+            self.split(_)
+        else:
+            self.wsched(_)
+
+if __name__ == '__main__':
+    WschedFilter().launch()
+