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authorTomás Touceda <chiiph@leap.se>2014-06-06 14:11:24 -0300
committerTomás Touceda <chiiph@leap.se>2014-06-06 14:11:24 -0300
commit03b4ed07d1c55770d9c5b0ed8e7d42dd08f80272 (patch)
tree1c57c8e0076e5be7b2d34ca085a4a04a0e681c4f /scripts
parent5cb40a959af827e4eadde1c047664c4f4c0ae01d (diff)
parent4dc1cd8662a34893051d2f520b73a0bd0774215c (diff)
Merge branch 'release-0.5.2'0.5.2
Diffstat (limited to 'scripts')
-rwxr-xr-xscripts/build_debian_package.sh2
-rw-r--r--scripts/db_access/client_side_db.py25
l---------[-rw-r--r--]scripts/profiling/backends_cpu_usage/movingaverage.py210
-rw-r--r--scripts/profiling/movingaverage.py209
4 files changed, 224 insertions, 222 deletions
diff --git a/scripts/build_debian_package.sh b/scripts/build_debian_package.sh
index cc62c3ac..1ec9b00a 100755
--- a/scripts/build_debian_package.sh
+++ b/scripts/build_debian_package.sh
@@ -26,7 +26,7 @@ export GIT_DIR=${workdir}/soledad/.git
export GIT_WORK_TREE=${workdir}/soledad
git remote add leapcode ${SOLEDAD_MAIN_REPO}
git fetch leapcode
-git checkout debian
+git checkout -b debian leapcode/debian
git merge --no-edit ${branch}
(cd ${workdir}/soledad && debuild -uc -us)
echo "Packages generated in ${workdir}"
diff --git a/scripts/db_access/client_side_db.py b/scripts/db_access/client_side_db.py
index 2bf4ab5e..6c456c41 100644
--- a/scripts/db_access/client_side_db.py
+++ b/scripts/db_access/client_side_db.py
@@ -15,10 +15,14 @@ import srp._pysrp as srp
import binascii
import logging
+
from leap.common.config import get_path_prefix
from leap.soledad.client import Soledad
+from util import ValidateUserHandle
+
+
# create a logger
logger = logging.getLogger(__name__)
LOG_FORMAT = '%(asctime)s %(message)s'
@@ -122,26 +126,23 @@ def get_soledad_instance(username, provider, passphrase, basedir):
if __name__ == '__main__':
- class ValidateUserHandle(argparse.Action):
- def __call__(self, parser, namespace, values, option_string=None):
- m = re.compile('^([^@]+)@([^@]+\.[^@]+)$')
- res = m.match(values)
- if res == None:
- parser.error('User handle should have the form user@provider.')
- setattr(namespace, 'username', res.groups()[0])
- setattr(namespace, 'provider', res.groups()[1])
-
# parse command line
parser = argparse.ArgumentParser()
parser.add_argument(
'user@provider', action=ValidateUserHandle, help='the user handle')
parser.add_argument(
- '-b', dest='basedir', required=False, default=None, help='the user handle')
+ '-b', dest='basedir', required=False, default=None,
+ help='soledad base directory')
+ parser.add_argument(
+ '-p', dest='passphrase', required=False, default=None,
+ help='the user passphrase')
args = parser.parse_args()
# get the password
- passphrase = getpass.getpass(
- 'Password for %s@%s: ' % (args.username, args.provider))
+ passphrase = args.passphrase
+ if passphrase is None:
+ passphrase = getpass.getpass(
+ 'Password for %s@%s: ' % (args.username, args.provider))
# get the basedir
basedir = args.basedir
diff --git a/scripts/profiling/backends_cpu_usage/movingaverage.py b/scripts/profiling/backends_cpu_usage/movingaverage.py
index bac1b3e1..098b0a01 100644..120000
--- a/scripts/profiling/backends_cpu_usage/movingaverage.py
+++ b/scripts/profiling/backends_cpu_usage/movingaverage.py
@@ -1,209 +1 @@
-#!/usr/bin/env python
-#
-# Sean Reifschneider, tummy.com, ltd. <jafo@tummy.com>
-# Released into the Public Domain, 2011-02-06
-
-import itertools
-from itertools import islice
-from collections import deque
-
-
-#########################################################
-def movingaverage(data, subset_size, data_is_list = None,
- avoid_fp_drift = True):
- '''Return the moving averages of the data, with a window size of
- `subset_size`. `subset_size` must be an integer greater than 0 and
- less than the length of the input data, or a ValueError will be raised.
-
- `data_is_list` can be used to tune the algorithm for list or iteratable
- as an input. The default value, `None` will auto-detect this.
- The algorithm used if `data` is a list is almost twice as fast as if
- it is an iteratable.
-
- `avoid_fp_drift`, if True (the default) sums every sub-set rather than
- keeping a "rolling sum" (which may be subject to floating-point drift).
- While more correct, it is also dramatically slower for subset sizes
- much larger than 20.
-
- NOTE: You really should consider setting `avoid_fp_drift = False` unless
- you are dealing with very small numbers (say, far smaller than 0.00001)
- or require extreme accuracy at the cost of execution time. For
- `subset_size` < 20, the performance difference is very small.
- '''
- if subset_size < 1:
- raise ValueError('subset_size must be 1 or larger')
-
- if data_is_list is None:
- data_is_list = hasattr(data, '__getslice__')
-
- divisor = float(subset_size)
- if data_is_list:
- # This only works if we can re-access old elements, but is much faster.
- # In other words, it can't be just an iterable, it needs to be a list.
-
- if subset_size > len(data):
- raise ValueError('subset_size must be smaller than data set size')
-
- if avoid_fp_drift:
- for x in range(subset_size, len(data) + 1):
- yield sum(data[x - subset_size:x]) / divisor
- else:
- cur = sum(data[0:subset_size])
- yield cur / divisor
- for x in range(subset_size, len(data)):
- cur += data[x] - data[x - subset_size]
- yield cur / divisor
- else:
- # Based on the recipe at:
- # http://docs.python.org/library/collections.html#deque-recipes
- it = iter(data)
- d = deque(islice(it, subset_size))
-
- if subset_size > len(d):
- raise ValueError('subset_size must be smaller than data set size')
-
- if avoid_fp_drift:
- yield sum(d) / divisor
- for elem in it:
- d.popleft()
- d.append(elem)
- yield sum(d) / divisor
- else:
- s = sum(d)
- yield s / divisor
- for elem in it:
- s += elem - d.popleft()
- d.append(elem)
- yield s / divisor
-
-
-##########################
-if __name__ == '__main__':
- import unittest
-
- class TestMovingAverage(unittest.TestCase):
- ####################
- def test_List(self):
- try:
- list(movingaverage([1,2,3], 0))
- self.fail('Did not raise ValueError on subset_size=0')
- except ValueError:
- pass
-
- try:
- list(movingaverage([1,2,3,4,5,6], 7))
- self.fail('Did not raise ValueError on subset_size > len(data)')
- except ValueError:
- pass
-
- self.assertEqual(list(movingaverage([1,2,3,4,5,6], 1)), [1,2,3,4,5,6])
- self.assertEqual(list(movingaverage([1,2,3,4,5,6], 2)),
- [1.5,2.5,3.5,4.5,5.5])
- self.assertEqual(list(movingaverage(map(float, [1,2,3,4,5,6]), 2)),
- [1.5,2.5,3.5,4.5,5.5])
- self.assertEqual(list(movingaverage([1,2,3,4,5,6], 3)), [2,3,4,5])
- self.assertEqual(list(movingaverage([1,2,3,4,5,6], 4)), [2.5,3.5,4.5])
- self.assertEqual(list(movingaverage([1,2,3,4,5,6], 5)), [3,4])
- self.assertEqual(list(movingaverage([1,2,3,4,5,6], 6)), [3.5])
-
- self.assertEqual(list(movingaverage([40, 30, 50, 46, 39, 44],
- 3, False)), [40.0,42.0,45.0,43.0])
- self.assertEqual(list(movingaverage([40, 30, 50, 46, 39, 44],
- 3, True)), [40.0,42.0,45.0,43.0])
-
-
- ######################
- def test_XRange(self):
- try:
- list(movingaverage(xrange(1, 4), 0))
- self.fail('Did not raise ValueError on subset_size=0')
- except ValueError:
- pass
-
- try:
- list(movingaverage(xrange(1, 7), 7))
- self.fail('Did not raise ValueError on subset_size > len(data)')
- except ValueError:
- pass
-
- self.assertEqual(list(movingaverage(xrange(1, 7), 1)), [1,2,3,4,5,6])
- self.assertEqual(list(movingaverage(xrange(1, 7), 2)),
- [1.5,2.5,3.5,4.5,5.5])
- self.assertEqual(list(movingaverage(iter(map(float, xrange(1, 7))),
- 2)), [1.5,2.5,3.5,4.5,5.5])
- self.assertEqual(list(movingaverage(xrange(1, 7), 3)), [2,3,4,5])
- self.assertEqual(list(movingaverage(xrange(1, 7), 4)), [2.5,3.5,4.5])
- self.assertEqual(list(movingaverage(xrange(1, 7), 5)), [3,4])
- self.assertEqual(list(movingaverage(xrange(1, 7), 6)), [3.5])
-
-
- ###########################
- def test_ListRolling(self):
- try:
- list(movingaverage([1,2,3], 0, avoid_fp_drift = False))
- self.fail('Did not raise ValueError on subset_size=0')
- except ValueError:
- pass
-
- try:
- list(movingaverage([1,2,3,4,5,6], 7, avoid_fp_drift = False))
- self.fail('Did not raise ValueError on subset_size > len(data)')
- except ValueError:
- pass
-
- self.assertEqual(list(movingaverage([1,2,3,4,5,6], 1,
- avoid_fp_drift = False)), [1,2,3,4,5,6])
- self.assertEqual(list(movingaverage([1,2,3,4,5,6], 2,
- avoid_fp_drift = False)),
- [1.5,2.5,3.5,4.5,5.5])
- self.assertEqual(list(movingaverage(map(float, [1,2,3,4,5,6]), 2,
- avoid_fp_drift = False)), [1.5,2.5,3.5,4.5,5.5])
- self.assertEqual(list(movingaverage([1,2,3,4,5,6], 3,
- avoid_fp_drift = False)), [2,3,4,5])
- self.assertEqual(list(movingaverage([1,2,3,4,5,6], 4,
- avoid_fp_drift = False)), [2.5,3.5,4.5])
- self.assertEqual(list(movingaverage([1,2,3,4,5,6], 5,
- avoid_fp_drift = False)), [3,4])
- self.assertEqual(list(movingaverage([1,2,3,4,5,6], 6,
- avoid_fp_drift = False)), [3.5])
-
- self.assertEqual(list(movingaverage([40, 30, 50, 46, 39, 44],
- 3, False, avoid_fp_drift = False)), [40.0,42.0,45.0,43.0])
- self.assertEqual(list(movingaverage([40, 30, 50, 46, 39, 44],
- 3, True, avoid_fp_drift = False)), [40.0,42.0,45.0,43.0])
-
-
- #############################
- def test_XRangeRolling(self):
- try:
- list(movingaverage(xrange(1, 4), 0, avoid_fp_drift = False))
- self.fail('Did not raise ValueError on subset_size=0')
- except ValueError:
- pass
-
- try:
- list(movingaverage(xrange(1, 7), 7, avoid_fp_drift = False))
- self.fail('Did not raise ValueError on subset_size > len(data)')
- except ValueError:
- pass
-
- self.assertEqual(list(movingaverage(xrange(1, 7), 1,
- avoid_fp_drift = False)), [1,2,3,4,5,6])
- self.assertEqual(list(movingaverage(xrange(1, 7), 2,
- avoid_fp_drift = False)), [1.5,2.5,3.5,4.5,5.5])
- self.assertEqual(list(movingaverage(iter(map(float, xrange(1, 7))),
- 2, avoid_fp_drift = False)), [1.5,2.5,3.5,4.5,5.5])
- self.assertEqual(list(movingaverage(xrange(1, 7), 3,
- avoid_fp_drift = False)), [2,3,4,5])
- self.assertEqual(list(movingaverage(xrange(1, 7), 4,
- avoid_fp_drift = False)), [2.5,3.5,4.5])
- self.assertEqual(list(movingaverage(xrange(1, 7), 5,
- avoid_fp_drift = False)), [3,4])
- self.assertEqual(list(movingaverage(xrange(1, 7), 6,
- avoid_fp_drift = False)), [3.5])
-
-
- ######################################################################
- suite = unittest.TestLoader().loadTestsFromTestCase(TestMovingAverage)
- unittest.TextTestRunner(verbosity = 2).run(suite)
-
+../movingaverage.py \ No newline at end of file
diff --git a/scripts/profiling/movingaverage.py b/scripts/profiling/movingaverage.py
new file mode 100644
index 00000000..bac1b3e1
--- /dev/null
+++ b/scripts/profiling/movingaverage.py
@@ -0,0 +1,209 @@
+#!/usr/bin/env python
+#
+# Sean Reifschneider, tummy.com, ltd. <jafo@tummy.com>
+# Released into the Public Domain, 2011-02-06
+
+import itertools
+from itertools import islice
+from collections import deque
+
+
+#########################################################
+def movingaverage(data, subset_size, data_is_list = None,
+ avoid_fp_drift = True):
+ '''Return the moving averages of the data, with a window size of
+ `subset_size`. `subset_size` must be an integer greater than 0 and
+ less than the length of the input data, or a ValueError will be raised.
+
+ `data_is_list` can be used to tune the algorithm for list or iteratable
+ as an input. The default value, `None` will auto-detect this.
+ The algorithm used if `data` is a list is almost twice as fast as if
+ it is an iteratable.
+
+ `avoid_fp_drift`, if True (the default) sums every sub-set rather than
+ keeping a "rolling sum" (which may be subject to floating-point drift).
+ While more correct, it is also dramatically slower for subset sizes
+ much larger than 20.
+
+ NOTE: You really should consider setting `avoid_fp_drift = False` unless
+ you are dealing with very small numbers (say, far smaller than 0.00001)
+ or require extreme accuracy at the cost of execution time. For
+ `subset_size` < 20, the performance difference is very small.
+ '''
+ if subset_size < 1:
+ raise ValueError('subset_size must be 1 or larger')
+
+ if data_is_list is None:
+ data_is_list = hasattr(data, '__getslice__')
+
+ divisor = float(subset_size)
+ if data_is_list:
+ # This only works if we can re-access old elements, but is much faster.
+ # In other words, it can't be just an iterable, it needs to be a list.
+
+ if subset_size > len(data):
+ raise ValueError('subset_size must be smaller than data set size')
+
+ if avoid_fp_drift:
+ for x in range(subset_size, len(data) + 1):
+ yield sum(data[x - subset_size:x]) / divisor
+ else:
+ cur = sum(data[0:subset_size])
+ yield cur / divisor
+ for x in range(subset_size, len(data)):
+ cur += data[x] - data[x - subset_size]
+ yield cur / divisor
+ else:
+ # Based on the recipe at:
+ # http://docs.python.org/library/collections.html#deque-recipes
+ it = iter(data)
+ d = deque(islice(it, subset_size))
+
+ if subset_size > len(d):
+ raise ValueError('subset_size must be smaller than data set size')
+
+ if avoid_fp_drift:
+ yield sum(d) / divisor
+ for elem in it:
+ d.popleft()
+ d.append(elem)
+ yield sum(d) / divisor
+ else:
+ s = sum(d)
+ yield s / divisor
+ for elem in it:
+ s += elem - d.popleft()
+ d.append(elem)
+ yield s / divisor
+
+
+##########################
+if __name__ == '__main__':
+ import unittest
+
+ class TestMovingAverage(unittest.TestCase):
+ ####################
+ def test_List(self):
+ try:
+ list(movingaverage([1,2,3], 0))
+ self.fail('Did not raise ValueError on subset_size=0')
+ except ValueError:
+ pass
+
+ try:
+ list(movingaverage([1,2,3,4,5,6], 7))
+ self.fail('Did not raise ValueError on subset_size > len(data)')
+ except ValueError:
+ pass
+
+ self.assertEqual(list(movingaverage([1,2,3,4,5,6], 1)), [1,2,3,4,5,6])
+ self.assertEqual(list(movingaverage([1,2,3,4,5,6], 2)),
+ [1.5,2.5,3.5,4.5,5.5])
+ self.assertEqual(list(movingaverage(map(float, [1,2,3,4,5,6]), 2)),
+ [1.5,2.5,3.5,4.5,5.5])
+ self.assertEqual(list(movingaverage([1,2,3,4,5,6], 3)), [2,3,4,5])
+ self.assertEqual(list(movingaverage([1,2,3,4,5,6], 4)), [2.5,3.5,4.5])
+ self.assertEqual(list(movingaverage([1,2,3,4,5,6], 5)), [3,4])
+ self.assertEqual(list(movingaverage([1,2,3,4,5,6], 6)), [3.5])
+
+ self.assertEqual(list(movingaverage([40, 30, 50, 46, 39, 44],
+ 3, False)), [40.0,42.0,45.0,43.0])
+ self.assertEqual(list(movingaverage([40, 30, 50, 46, 39, 44],
+ 3, True)), [40.0,42.0,45.0,43.0])
+
+
+ ######################
+ def test_XRange(self):
+ try:
+ list(movingaverage(xrange(1, 4), 0))
+ self.fail('Did not raise ValueError on subset_size=0')
+ except ValueError:
+ pass
+
+ try:
+ list(movingaverage(xrange(1, 7), 7))
+ self.fail('Did not raise ValueError on subset_size > len(data)')
+ except ValueError:
+ pass
+
+ self.assertEqual(list(movingaverage(xrange(1, 7), 1)), [1,2,3,4,5,6])
+ self.assertEqual(list(movingaverage(xrange(1, 7), 2)),
+ [1.5,2.5,3.5,4.5,5.5])
+ self.assertEqual(list(movingaverage(iter(map(float, xrange(1, 7))),
+ 2)), [1.5,2.5,3.5,4.5,5.5])
+ self.assertEqual(list(movingaverage(xrange(1, 7), 3)), [2,3,4,5])
+ self.assertEqual(list(movingaverage(xrange(1, 7), 4)), [2.5,3.5,4.5])
+ self.assertEqual(list(movingaverage(xrange(1, 7), 5)), [3,4])
+ self.assertEqual(list(movingaverage(xrange(1, 7), 6)), [3.5])
+
+
+ ###########################
+ def test_ListRolling(self):
+ try:
+ list(movingaverage([1,2,3], 0, avoid_fp_drift = False))
+ self.fail('Did not raise ValueError on subset_size=0')
+ except ValueError:
+ pass
+
+ try:
+ list(movingaverage([1,2,3,4,5,6], 7, avoid_fp_drift = False))
+ self.fail('Did not raise ValueError on subset_size > len(data)')
+ except ValueError:
+ pass
+
+ self.assertEqual(list(movingaverage([1,2,3,4,5,6], 1,
+ avoid_fp_drift = False)), [1,2,3,4,5,6])
+ self.assertEqual(list(movingaverage([1,2,3,4,5,6], 2,
+ avoid_fp_drift = False)),
+ [1.5,2.5,3.5,4.5,5.5])
+ self.assertEqual(list(movingaverage(map(float, [1,2,3,4,5,6]), 2,
+ avoid_fp_drift = False)), [1.5,2.5,3.5,4.5,5.5])
+ self.assertEqual(list(movingaverage([1,2,3,4,5,6], 3,
+ avoid_fp_drift = False)), [2,3,4,5])
+ self.assertEqual(list(movingaverage([1,2,3,4,5,6], 4,
+ avoid_fp_drift = False)), [2.5,3.5,4.5])
+ self.assertEqual(list(movingaverage([1,2,3,4,5,6], 5,
+ avoid_fp_drift = False)), [3,4])
+ self.assertEqual(list(movingaverage([1,2,3,4,5,6], 6,
+ avoid_fp_drift = False)), [3.5])
+
+ self.assertEqual(list(movingaverage([40, 30, 50, 46, 39, 44],
+ 3, False, avoid_fp_drift = False)), [40.0,42.0,45.0,43.0])
+ self.assertEqual(list(movingaverage([40, 30, 50, 46, 39, 44],
+ 3, True, avoid_fp_drift = False)), [40.0,42.0,45.0,43.0])
+
+
+ #############################
+ def test_XRangeRolling(self):
+ try:
+ list(movingaverage(xrange(1, 4), 0, avoid_fp_drift = False))
+ self.fail('Did not raise ValueError on subset_size=0')
+ except ValueError:
+ pass
+
+ try:
+ list(movingaverage(xrange(1, 7), 7, avoid_fp_drift = False))
+ self.fail('Did not raise ValueError on subset_size > len(data)')
+ except ValueError:
+ pass
+
+ self.assertEqual(list(movingaverage(xrange(1, 7), 1,
+ avoid_fp_drift = False)), [1,2,3,4,5,6])
+ self.assertEqual(list(movingaverage(xrange(1, 7), 2,
+ avoid_fp_drift = False)), [1.5,2.5,3.5,4.5,5.5])
+ self.assertEqual(list(movingaverage(iter(map(float, xrange(1, 7))),
+ 2, avoid_fp_drift = False)), [1.5,2.5,3.5,4.5,5.5])
+ self.assertEqual(list(movingaverage(xrange(1, 7), 3,
+ avoid_fp_drift = False)), [2,3,4,5])
+ self.assertEqual(list(movingaverage(xrange(1, 7), 4,
+ avoid_fp_drift = False)), [2.5,3.5,4.5])
+ self.assertEqual(list(movingaverage(xrange(1, 7), 5,
+ avoid_fp_drift = False)), [3,4])
+ self.assertEqual(list(movingaverage(xrange(1, 7), 6,
+ avoid_fp_drift = False)), [3.5])
+
+
+ ######################################################################
+ suite = unittest.TestLoader().loadTestsFromTestCase(TestMovingAverage)
+ unittest.TextTestRunner(verbosity = 2).run(suite)
+