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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
542ed650cd51d1be301e72556d0e3aecf6a5572b | 2,711 | py | Python | tests/test_wsgisubdomain.py | xsleonard/wsgisubdomain | 394c705a9e39cf4e4e1af2551ed0ed79d2c070f3 | [
"MIT"
] | 2 | 2016-06-15T09:31:20.000Z | 2016-06-20T11:43:01.000Z | tests/test_wsgisubdomain.py | xsleonard/wsgisubdomain | 394c705a9e39cf4e4e1af2551ed0ed79d2c070f3 | [
"MIT"
] | null | null | null | tests/test_wsgisubdomain.py | xsleonard/wsgisubdomain | 394c705a9e39cf4e4e1af2551ed0ed79d2c070f3 | [
"MIT"
] | null | null | null | from unittest import TestCase
from wsgisubdomain import SubdomainDispatcher
| 37.136986 | 79 | 0.620435 | from unittest import TestCase
from wsgisubdomain import SubdomainDispatcher
class TestSubdomainDispatcher(TestCase):
def assertIs(self, a, b):
try:
super(TestSubdomainDispatcher, self).assertIs(a, b)
except AttributeError:
# python 2.6 support
self.assertTrue(a is b)
@staticmethod
def create_app(subdomain=None):
return lambda x, y: subdomain
def test_create(self):
s = SubdomainDispatcher(1)
self.assertEqual(s.create_application, 1)
self.assertTrue(hasattr(s, 'lock'))
self.assertFalse(s.instances)
def test_get_host(self):
environ = dict(HTTP_HOST='xxx.example.com', SERVER_NAME='example.com')
self.assertEqual('xxx.example.com',
SubdomainDispatcher._get_host(environ))
del environ['HTTP_HOST']
self.assertEqual('example.com', SubdomainDispatcher._get_host(environ))
def test_extract_subdomain(self):
# Test with ip address, returns None
s = SubdomainDispatcher
h = '127.0.0.1'
self.assertIs(s._extract_subdomain(h), None)
self.assertIs(s._extract_subdomain(h + ':888'), None)
# Test with no subdomain
h = 'example.com'
self.assertEqual(s._extract_subdomain(h), '')
self.assertEqual(s._extract_subdomain(h + ':888'), '')
# Test with a single subdomain
h = 'www.example.com'
self.assertEqual(s._extract_subdomain(h), 'www')
self.assertEqual(s._extract_subdomain(h + ':888'), 'www')
# Test with multiple subdomains
h = 'a.b.c.d.example.com'
self.assertEqual(s._extract_subdomain(h), 'a.b.c.d')
self.assertEqual(s._extract_subdomain(h + ':888'), 'a.b.c.d')
def test_get_application(self):
s = SubdomainDispatcher(self.create_app)
environ = dict(HTTP_HOST='xxx.example.com', SERVER_NAME='example.com')
app = s.get_application(environ)
self.assertEqual(app(0, 0), 'xxx')
environ['HTTP_HOST'] = 'example.com'
app = s.get_application(environ)
self.assertEqual(app(0, 0), '')
environ['HTTP_HOST'] = '127.0.0.1'
app = s.get_application(environ)
self.assertIs(app(0, 0), None)
def test_call(self):
s = SubdomainDispatcher(self.create_app)
environ = dict(HTTP_HOST='xxx.example.com', SERVER_NAME='example.com')
app = s(environ, None)
self.assertEqual(app, 'xxx')
environ['HTTP_HOST'] = 'example.com'
app = s(environ, None)
self.assertEqual(app, '')
environ['HTTP_HOST'] = '127.0.0.1'
app = s(environ, None)
self.assertIs(app, None)
| 2,385 | 226 | 23 |
5575aa3a08f72b6233406337d9b98d6034623d01 | 26,482 | py | Python | tests/unit_tests/test_set_dropin.py | realead/cykhash | b1a45843c3be49cd232d3c78315d2291a830284f | [
"MIT"
] | 18 | 2019-03-13T08:20:06.000Z | 2021-06-22T08:03:01.000Z | tests/unit_tests/test_set_dropin.py | realead/cykhash | b1a45843c3be49cd232d3c78315d2291a830284f | [
"MIT"
] | 6 | 2020-04-13T10:11:45.000Z | 2021-11-14T15:59:55.000Z | tests/unit_tests/test_set_dropin.py | realead/cykhash | b1a45843c3be49cd232d3c78315d2291a830284f | [
"MIT"
] | 7 | 2019-05-19T22:24:57.000Z | 2020-08-26T23:01:23.000Z | import pytest
from unittestmock import UnitTestMock
from cykhash import Int64Set, Int32Set, Float64Set, Float32Set, PyObjectSet
import cykhash
SUFFIX={Int64Set : "int64",
Int32Set : "int32",
Float64Set : "float64",
Float32Set : "float32",
PyObjectSet : "pyobject"}
@pytest.mark.parametrize(
"set_type",
[Int64Set, Int32Set, Float64Set, Float32Set, PyObjectSet]
)
@pytest.mark.parametrize(
"set_type",
[Int64Set, Int32Set, Float64Set, Float32Set, PyObjectSet]
)
@pytest.mark.parametrize(
"set_type",
[Int64Set, Int32Set, Float64Set, Float32Set, PyObjectSet]
)
@pytest.mark.parametrize(
"set_type",
[Int64Set, Int32Set, Float64Set, Float32Set, PyObjectSet]
)
@pytest.mark.parametrize(
"set_type",
[Int64Set, Int32Set, Float64Set, Float32Set, PyObjectSet]
)
@pytest.mark.parametrize(
"set_type",
[Int64Set, Int32Set, Float64Set, Float32Set, PyObjectSet]
)
@pytest.mark.parametrize(
"set_type",
[Int64Set, Int32Set, Float64Set, Float32Set, PyObjectSet]
)
@pytest.mark.parametrize(
"set_type",
[Int64Set, Int32Set, Float64Set, Float32Set, PyObjectSet]
)
@pytest.mark.parametrize(
"set_type",
[Int64Set, Int32Set, Float64Set, Float32Set, PyObjectSet]
)
| 32.099394 | 85 | 0.601088 | import pytest
from unittestmock import UnitTestMock
from cykhash import Int64Set, Int32Set, Float64Set, Float32Set, PyObjectSet
import cykhash
SUFFIX={Int64Set : "int64",
Int32Set : "int32",
Float64Set : "float64",
Float32Set : "float32",
PyObjectSet : "pyobject"}
def pick_fun(name, set_type):
return getattr(cykhash, name+"_"+SUFFIX[set_type])
@pytest.mark.parametrize(
"set_type",
[Int64Set, Int32Set, Float64Set, Float32Set, PyObjectSet]
)
class TestSetDropIn(UnitTestMock):
def test_init_from_iter(self, set_type):
s=set_type([1,2,3,1])
self.assertEqual(len(s), 3)
self.assertTrue(1 in s)
self.assertTrue(2 in s)
self.assertTrue(3 in s)
def test_clear(self, set_type):
s=set_type([1,2,3,1])
s.clear()
self.assertEqual(len(s), 0)
s.add(5)
s.update([3,4,5,6])
self.assertEqual(s, set_type([3,4,5,6]))
s.clear()
self.assertEqual(len(s), 0)
def test_str(self, set_type):
s=set_type([1,2,3,1])
ss = str(s)
self.assertTrue("1" in ss)
self.assertTrue("2" in ss)
self.assertTrue("3" in ss)
self.assertTrue(ss.startswith("{"))
self.assertTrue(ss.endswith("}"))
def test_remove_yes(self, set_type):
s=set_type([1,2])
s.remove(1)
self.assertEqual(s,set_type([2]))
s.remove(2)
self.assertEqual(s,set_type([]))
def test_remove_no(self, set_type):
s=set_type([1,2])
with pytest.raises(KeyError) as context:
s.remove(3)
self.assertEqual(3, context.value.args[0])
def test_pop_one(self, set_type):
s=set_type([1])
el=s.pop()
self.assertEqual(s,set_type([]))
self.assertEqual(el,1)
def test_pop_all(self, set_type):
s=set_type([1,2,3])
new_s={s.pop(), s.pop(), s.pop()}
self.assertEqual(s,set_type([]))
self.assertEqual(new_s,{1,2,3})
def test_pop_empty(self, set_type):
s=set_type([])
with pytest.raises(KeyError) as context:
s.pop()
self.assertEqual("pop from empty set", context.value.args[0])
def test_pyobject_same_object_pop():
a=float("3333.2")
s=PyObjectSet([a])
b=s.pop()
assert a is b
@pytest.mark.parametrize(
"set_type",
[Int64Set, Int32Set, Float64Set, Float32Set, PyObjectSet]
)
class TestIsDisjoint(UnitTestMock):
def test_aredisjoint_with_none(self, set_type):
s=set_type([1,2,3,1])
fun=pick_fun("aredisjoint", set_type)
with pytest.raises(TypeError) as context:
fun(None,s)
self.assertTrue("'NoneType' object is not iterable" in context.value.args[0])
with pytest.raises(TypeError) as context:
fun(s,None)
self.assertTrue("'NoneType' object is not iterable" in context.value.args[0])
with pytest.raises(TypeError) as context:
fun(None,None)
self.assertTrue("'NoneType' object is not iterable" in context.value.args[0])
def test_aredisjoint_with_empty(self, set_type):
empty1=set_type()
empty2=set_type()
non_empty=set_type(range(3))
aredisjoint=pick_fun("aredisjoint", set_type)
self.assertEqual(aredisjoint(empty1, non_empty), True)
self.assertEqual(aredisjoint(non_empty, empty2), True)
self.assertEqual(aredisjoint(empty1, empty2), True)
def test_aredisjoint_yes(self, set_type):
a=set_type([1,2,3,1])
b=set_type([4,55])
fun=pick_fun("aredisjoint", set_type)
self.assertEqual(fun(a,b), True)
self.assertEqual(fun(b,a), True)
def test_aredisjoint_no(self, set_type):
a=set_type([1,2,3,333,1])
b=set_type([4,55,4,5,6,7,333])
fun=pick_fun("aredisjoint", set_type)
self.assertEqual(fun(a,b), False)
self.assertEqual(fun(b,a), False)
def test_isdisjoint_yes_set(self, set_type):
a=set_type([1,2,3,1])
b=set_type([4,55])
self.assertEqual(a.isdisjoint(b), True)
self.assertEqual(b.isdisjoint(a), True)
def test_isdisjoint_no_set(self, set_type):
a=set_type([1,2,3,333,1])
b=set_type([4,55,4,5,6,7,333])
self.assertEqual(a.isdisjoint(b), False)
self.assertEqual(b.isdisjoint(a), False)
def test_isdisjoint_yes_iter(self, set_type):
a=set_type([1,2,3,1])
b=[4,55]
self.assertEqual(a.isdisjoint(b), True)
def test_isdisjoint_no_iter(self, set_type):
a=set_type([1,2,3,333,1])
b=[4,55,4,5,6,7,333]
self.assertEqual(a.isdisjoint(b), False)
@pytest.mark.parametrize(
"set_type",
[Int64Set, Int32Set, Float64Set, Float32Set, PyObjectSet]
)
class TestIsSubsetIsSuperset(UnitTestMock):
def test_with_none(self, set_type):
s=set_type([1,2,3,1])
fun=pick_fun("issubset", set_type)
with pytest.raises(TypeError) as context:
fun(None,s)
self.assertTrue("'NoneType' object is not iterable" in context.value.args[0])
with pytest.raises(TypeError) as context:
fun(s,None)
self.assertTrue("'NoneType' object is not iterable" in context.value.args[0])
with pytest.raises(TypeError) as context:
fun(None,None)
self.assertTrue("'NoneType' object is not iterable" in context.value.args[0])
def test_with_empty(self, set_type):
a=set_type([1,2,3,1])
b=set_type([])
fun=pick_fun("issubset", set_type)
self.assertEqual(fun(a,a), True)
self.assertEqual(fun(a,b), True)
self.assertEqual(fun(b,a), False)
self.assertEqual(fun(b,b), True)
def test_yes(self, set_type):
a=set_type([1,2,3,1])
b=set_type([1,3])
fun=pick_fun("issubset", set_type)
self.assertEqual(fun(a,b), True)
self.assertEqual(fun(b,a), False)
def test_no(self, set_type):
a=set_type([1,2,3,1])
b=set_type([4])
fun=pick_fun("issubset", set_type)
self.assertEqual(fun(a,b), False)
self.assertEqual(fun(b,a), False)
def test_issuperset_yes(self, set_type):
a=set_type([1,2,3,1])
b=set_type([1,3])
self.assertEqual(a.issuperset(b), True)
self.assertEqual(b.issuperset(a), False)
def test_issuperset_no(self, set_type):
a=set_type([1,2,3,1])
b=set_type([4])
self.assertEqual(a.issuperset(b), False)
self.assertEqual(b.issuperset(a), False)
def test_issuperset_yes_iter(self, set_type):
a=set_type([1,2,3,1])
b=[1,3]
self.assertEqual(a.issuperset(b), True)
def test_issuperset_no_iter(self, set_type):
a=set_type([1,2,3,1])
b=[4]
self.assertEqual(a.issuperset(b), False)
def test_issubset_yes_iter(self, set_type):
a=set_type([1,2])
b=[1,3,2]
self.assertEqual(a.issubset(b), True)
def test_issubset_no_iter(self, set_type):
a=set_type([1,2])
b=[1,1,3]
self.assertEqual(a.issubset(b), False)
def test_issubset_yes(self, set_type):
a=set_type([1,2])
b=set_type([1,3,2])
self.assertEqual(a.issubset(b), True)
self.assertEqual(b.issubset(a), False)
def test_issubset_no(self, set_type):
a=set_type([1,2])
b=set_type([1,1,3])
self.assertEqual(a.issubset(b), False)
self.assertEqual(b.issubset(a), False)
def test_compare_self(self, set_type):
a=set_type([1,2])
self.assertEqual(a<=a, True)
self.assertEqual(a>=a, True)
self.assertEqual(a<a, False)
self.assertEqual(a>a, False)
def test_compare_no_relation(self, set_type):
a=set_type([1,2])
b=set_type([1,3])
self.assertEqual(a<=b, False)
self.assertEqual(a>=b, False)
self.assertEqual(a<b, False)
self.assertEqual(a>b, False)
def test_compare_real_subset(self, set_type):
a=set_type([1,2,3])
b=set_type([1,3])
self.assertEqual(a<=b, False)
self.assertEqual(a>=b, True)
self.assertEqual(a<b, False)
self.assertEqual(a>b, True)
def test_compare_same(self, set_type):
a=set_type([1,3])
b=set_type([1,3])
self.assertEqual(a<=b, True)
self.assertEqual(a>=b, True)
self.assertEqual(a<b, False)
self.assertEqual(a>b, False)
def test_compare_equal_yes(self, set_type):
a=set_type([2,5,7,8,1,3])
b=set_type([1,3,7,7,7,7,7,2,5,8,8,8,8,8,8])
self.assertEqual(a==b, True)
self.assertEqual(a==b, True)
def test_compare_equal_yes(self, set_type):
a=set_type([2,5,7,8,1,3])
b=set_type([3,7,7,7,7,7,2,5,8,8,8,8,8,8])
self.assertEqual(a==b, False)
self.assertEqual(a==b, False)
@pytest.mark.parametrize(
"set_type",
[Int64Set, Int32Set, Float64Set, Float32Set, PyObjectSet]
)
class TestCopy(UnitTestMock):
def test_with_none(self, set_type):
s=set_type([1,2,3,1])
copy=pick_fun("copy", set_type)
self.assertTrue(copy(None) is None)
def test_with_empty(self, set_type):
a=set_type([])
copy=pick_fun("copy", set_type)
self.assertEqual(len(copy(a)), 0)
def test_small(self, set_type):
a=set_type([1,2,3,1])
copy=pick_fun("copy", set_type)
self.assertEqual(copy(a)==a, True)
def test_large(self, set_type):
a=set_type(range(33,10000,3))
copy=pick_fun("copy", set_type)
self.assertEqual(copy(a)==a, True)
def test_large_method(self, set_type):
a=set_type(range(33,10000,3))
self.assertEqual(a.copy()==a, True)
@pytest.mark.parametrize(
"set_type",
[Int64Set, Int32Set, Float64Set, Float32Set, PyObjectSet]
)
class TestUpdate(UnitTestMock):
def test_with_none(self, set_type):
s=set_type([1,2,3,1])
update=pick_fun("update", set_type)
with pytest.raises(TypeError) as context:
update(None,s)
self.assertTrue("'NoneType' object is not iterable" in context.value.args[0])
with pytest.raises(TypeError) as context:
update(s,None)
self.assertTrue("'NoneType' object is not iterable" in context.value.args[0])
with pytest.raises(TypeError) as context:
update(None,None)
self.assertTrue("'NoneType' object is not iterable" in context.value.args[0])
def test_some_common(self, set_type):
a=set_type([1,2,3,4])
b=set_type([2,1,2,5])
c=b.copy()
update=pick_fun("update", set_type)
update(a,b)
self.assertEqual(a, set_type([1,2,3,4,5]))
self.assertEqual(b, c)
def test_with_itself(self, set_type):
a=set_type([1,2,3,1])
b=a.copy()
update=pick_fun("update", set_type)
update(a,a)
self.assertEqual(a, b)
def test_with_disjunct(self, set_type):
a=set_type(range(50))
b=set_type(range(50,100))
update=pick_fun("update", set_type)
update(a,b)
self.assertEqual(a, set_type(range(100)))
def test_method_with_set(self, set_type):
a=set_type(range(50))
b=set_type(range(100))
a.update(b)
self.assertEqual(a, set_type(range(100)))
def test_method_with_set(self, set_type):
a=set_type(range(50))
b=set_type(range(100))
a.update(b)
self.assertEqual(a, set_type(range(100)))
def test_method_with_iterator(self, set_type):
a=set_type(range(50))
a.update(range(60))
self.assertEqual(a, set_type(range(60)))
def test_ior(self, set_type):
a=set_type(range(50))
a|=set_type(range(60))
self.assertEqual(a, set_type(range(60)))
def test_union(self, set_type):
a=set_type(range(30))
a_copy = a.copy()
b=a.union(range(30,40), set_type(range(40,50)), range(50,60))
self.assertEqual(b, set_type(range(60)))
self.assertEqual(a, a_copy)
def test_union_empty(self, set_type):
a=set_type(range(30))
a.union()
self.assertEqual(a, set_type(range(30)))
def test_or(self, set_type):
a=set_type(range(30))
b=set_type(range(30,40))
c=set_type(range(40,50))
d=a|b|c
self.assertEqual(d, set_type(range(50)))
self.assertEqual(a, set_type(range(30)))
self.assertEqual(b, set_type(range(30,40)))
self.assertEqual(c, set_type(range(40,50)))
@pytest.mark.parametrize(
"set_type",
[Int64Set, Int32Set, Float64Set, Float32Set, PyObjectSet]
)
class TestSwap(UnitTestMock):
def test_with_none(self, set_type):
s=set_type([1,2,3,1])
swap=pick_fun("swap", set_type)
with pytest.raises(TypeError) as context:
swap(None,s)
self.assertTrue("'NoneType' object is not iterable" in context.value.args[0])
with pytest.raises(TypeError) as context:
swap(s,None)
self.assertTrue("'NoneType' object is not iterable" in context.value.args[0])
with pytest.raises(TypeError) as context:
swap(None,None)
self.assertTrue("'NoneType' object is not iterable" in context.value.args[0])
def test_some_common(self, set_type):
a=set_type([1,2,3,4])
b=set_type([5,2,4])
a_copy=a.copy()
b_copy=b.copy()
swap=pick_fun("swap", set_type)
swap(a,b)
self.assertEqual(a, b_copy)
self.assertEqual(b, a_copy)
swap(a,b)
self.assertEqual(a, a_copy)
self.assertEqual(b, b_copy)
@pytest.mark.parametrize(
"set_type",
[Int64Set, Int32Set, Float64Set, Float32Set, PyObjectSet]
)
class TestIntersect(UnitTestMock):
def test_with_none(self, set_type):
s=set_type([1,2,3,1])
intersect=pick_fun("intersect", set_type)
with pytest.raises(TypeError) as context:
intersect(None,s)
self.assertTrue("'NoneType' object is not iterable" in context.value.args[0])
with pytest.raises(TypeError) as context:
intersect(s,None)
self.assertTrue("'NoneType' object is not iterable" in context.value.args[0])
with pytest.raises(TypeError) as context:
intersect(None,None)
self.assertTrue("'NoneType' object is not iterable" in context.value.args[0])
def test_small(self, set_type):
a=set_type([1,2,3,4])
b=set_type([5,2,4])
a_copy=a.copy()
b_copy=b.copy()
intersect=pick_fun("intersect", set_type)
c=intersect(a,b)
self.assertEqual(a, a_copy)
self.assertEqual(b, b_copy)
self.assertEqual(c, set_type([2,4]))
c=intersect(b,a)
self.assertEqual(a, a_copy)
self.assertEqual(b, b_copy)
self.assertEqual(c, set_type([2,4]))
def test_disjunct(self, set_type):
a=set_type([1,3,5,7,9])
b=set_type([2,2,4,6,8,10])
a_copy=a.copy()
b_copy=b.copy()
intersect=pick_fun("intersect", set_type)
c=intersect(a,b)
self.assertEqual(a, a_copy)
self.assertEqual(b, b_copy)
self.assertEqual(c, set_type())
c=intersect(b,a)
self.assertEqual(a, a_copy)
self.assertEqual(b, b_copy)
self.assertEqual(c, set_type([]))
def test_empty(self, set_type):
a=set_type([])
b=set_type([])
c=set_type([2,2,4,6,8,10])
intersect=pick_fun("intersect", set_type)
d=intersect(a,b)
self.assertEqual(len(d), 0)
d=intersect(c,b)
self.assertEqual(len(d), 0)
d=intersect(a,c)
self.assertEqual(len(d), 0)
def test_intersection_update(self, set_type):
a=set_type([1,2,3,4,5,6,7,8])
b=set_type([2,4,6,8,10,12])
b_copy = b.copy()
a.intersection_update(b)
self.assertEqual(a, set_type([2,4,6,8]))
self.assertEqual(b, b_copy)
def test_intersection_update_iter(self, set_type):
a=set_type([1,2,3,4,5,6,7,8])
a.intersection_update([2,4,6,8,10,12])
self.assertEqual(a, set_type([2,4,6,8]))
def test_empty_update(self, set_type):
a=set_type([1,2,3,4,5,6,7,8])
b=set_type([])
a.intersection_update(b)
self.assertEqual(len(a), 0)
def test_empty_update_iter(self, set_type):
a=set_type([1,2,3,4,5,6,7,8])
a.intersection_update([])
self.assertEqual(a, set_type())
def test_iadd(self, set_type):
a=set_type([1,2,3,4,5,6,7,8])
b=set_type([1,104,3])
a&=b
self.assertEqual(a, set_type([1,3]))
def test_add(self, set_type):
a=set_type([1,2,3,4,5,6,7,8])
b=set_type([1,104,3])
a_copy=a.copy()
b_copy=b.copy()
c=a&b
self.assertEqual(c, set_type([1,3]))
self.assertEqual(a, a_copy)
self.assertEqual(b, b_copy)
def test_intersection(self, set_type):
a=set_type([1,2,3,4,5,6,7,8])
a_copy=a.copy()
c=a.intersection([1,2,3,4,5,6], set_type([1,2,3,4,5]), [1,2,3])
self.assertEqual(c, set_type([1,2,3]))
self.assertEqual(a, a_copy)
@pytest.mark.parametrize(
"set_type",
[Int64Set, Int32Set, Float64Set, Float32Set, PyObjectSet]
)
class TestDifference(UnitTestMock):
def test_with_none(self, set_type):
s=set_type([1,2,3,1])
difference=pick_fun("difference", set_type)
with pytest.raises(TypeError) as context:
difference(None,s)
self.assertTrue("'NoneType' object is not iterable" in context.value.args[0])
with pytest.raises(TypeError) as context:
difference(s,None)
self.assertTrue("'NoneType' object is not iterable" in context.value.args[0])
with pytest.raises(TypeError) as context:
difference(None,None)
self.assertTrue("'NoneType' object is not iterable" in context.value.args[0])
def test_small(self, set_type):
a=set_type([1,2,3,4])
b=set_type([5,2,4])
a_copy=a.copy()
b_copy=b.copy()
difference=pick_fun("difference", set_type)
c=difference(a,b)
self.assertEqual(a, a_copy)
self.assertEqual(b, b_copy)
self.assertEqual(c, set_type([1,3]))
c=difference(b,a)
self.assertEqual(a, a_copy)
self.assertEqual(b, b_copy)
self.assertEqual(c, set_type([5]))
def test_disjunct(self, set_type):
a=set_type([1,3,5,7,9])
b=set_type([2,2,4,6,8,10])
a_copy=a.copy()
b_copy=b.copy()
difference=pick_fun("difference", set_type)
c=difference(a,b)
self.assertEqual(a, a_copy)
self.assertEqual(b, b_copy)
self.assertEqual(c, a)
c=difference(b,a)
self.assertEqual(a, a_copy)
self.assertEqual(b, b_copy)
self.assertEqual(c, b)
def test_empty(self, set_type):
a=set_type([])
b=set_type([])
c=set_type([2,2,4,6,8,10])
difference=pick_fun("difference", set_type)
d=difference(a,b)
self.assertEqual(len(d), 0)
d=difference(c,b)
self.assertEqual(c, d)
d=difference(a,c)
self.assertEqual(len(d), 0)
def test_method_update(self, set_type):
a=set_type([1,2,3,4])
b=set_type([5,2,4])
b_copy=b.copy()
a.difference_update(b)
self.assertEqual(b, b_copy)
self.assertEqual(a, set_type([1,3]))
def test_method_update2(self, set_type):
a=set_type([1,2,3,4])
b=set_type([5,2,4])
a_copy=a.copy()
b.difference_update(a)
self.assertEqual(a, a_copy)
self.assertEqual(b, set_type([5]))
def test_method_update_from_iter(self, set_type):
a=set_type([1,2,3,4])
a.difference_update([5,2,4])
self.assertEqual(a, set_type([1,3]))
def test_method_update_from_iter2(self, set_type):
a=set_type(range(1000))
a.difference_update(range(0,1000,2))
self.assertEqual(a, set_type(range(1,1000,2)))
def test_method_update_from_iter3(self, set_type):
a=set_type([1,2])
a.difference_update([1]*10000)
self.assertEqual(a, set_type([2]))
def test_sub(self, set_type):
a=set_type([0,222,3,444,5])
b=set_type([222,3,4])
a_copy=a.copy()
b_copy=b.copy()
c=a-b
self.assertEqual(a, a_copy)
self.assertEqual(b, b_copy)
self.assertEqual(c, set_type([0,444,5]))
c=b-a
self.assertEqual(a, a_copy)
self.assertEqual(b, b_copy)
self.assertEqual(c, set_type([4]))
def test_sub2(self, set_type):
a=set_type([1,2,3,4])
a_copy=a.copy()
b=a-a-a-a
self.assertEqual(a, a_copy)
self.assertEqual(b, set_type())
def test_isub(self, set_type):
a=set_type([0,222,3,444,5])
b=set_type([222,3,4])
b_copy=b.copy()
a-=b
self.assertEqual(b, b_copy)
self.assertEqual(a, set_type([0,444,5]))
def test_isub2(self, set_type):
a=set_type([1,2,3,4])
a-=a
self.assertEqual(a, set_type())
def test_difference_method(self, set_type):
a=set_type(range(10000))
a_copy=a.copy()
b=a.difference(range(5000), set_type(range(5000,10000,2)), range(1,9999,2))
self.assertEqual(b, set_type([9999]))
self.assertEqual(a, a_copy)
@pytest.mark.parametrize(
"set_type",
[Int64Set, Int32Set, Float64Set, Float32Set, PyObjectSet]
)
class TestSymmetricDifference(UnitTestMock):
def test_with_none(self, set_type):
s=set_type([1,2,3,1])
symmetric_difference=pick_fun("symmetric_difference", set_type)
with pytest.raises(TypeError) as context:
symmetric_difference(None,s)
self.assertTrue("'NoneType' object is not iterable" in context.value.args[0])
with pytest.raises(TypeError) as context:
symmetric_difference(s,None)
self.assertTrue("'NoneType' object is not iterable" in context.value.args[0])
with pytest.raises(TypeError) as context:
symmetric_difference(None,None)
self.assertTrue("'NoneType' object is not iterable" in context.value.args[0])
def test_small(self, set_type):
a=set_type([1,2,3,4])
b=set_type([5,2,4])
a_copy=a.copy()
b_copy=b.copy()
symmetric_difference=pick_fun("symmetric_difference", set_type)
c=symmetric_difference(a,b)
self.assertEqual(a, a_copy)
self.assertEqual(b, b_copy)
self.assertEqual(c, set_type([1,3,5]))
c=symmetric_difference(b,a)
self.assertEqual(a, a_copy)
self.assertEqual(b, b_copy)
self.assertEqual(c, set_type([1,3,5]))
def test_disjunct(self, set_type):
a=set_type([1,3,5,7,9])
b=set_type([2,2,4,6,8,10])
a_copy=a.copy()
b_copy=b.copy()
symmetric_difference=pick_fun("symmetric_difference", set_type)
c=symmetric_difference(a,b)
self.assertEqual(a, a_copy)
self.assertEqual(b, b_copy)
self.assertEqual(c, a|b)
c=symmetric_difference(b,a)
self.assertEqual(a, a_copy)
self.assertEqual(b, b_copy)
self.assertEqual(c, a|b)
def test_empty(self, set_type):
a=set_type([])
b=set_type([])
c=set_type([2,2,4,6,8,10])
symmetric_difference=pick_fun("symmetric_difference", set_type)
d=symmetric_difference(a,b)
self.assertEqual(len(d), 0)
d=symmetric_difference(c,b)
self.assertEqual(c, d)
d=symmetric_difference(a,c)
self.assertEqual(c, d)
def test_method_update(self, set_type):
a=set_type([1,2,3,4])
b=set_type([5,2,4])
b_copy=b.copy()
a.symmetric_difference_update(b)
self.assertEqual(b, b_copy)
self.assertEqual(a, set_type([1,3,5]))
def test_method_update2(self, set_type):
a=set_type([1,2,3,4])
b=set_type([5,2,4])
a_copy=a.copy()
b.symmetric_difference_update(a)
self.assertEqual(a, a_copy)
self.assertEqual(b, set_type([1,3,5]))
def test_method_update_from_iter(self, set_type):
a=set_type([1,2,3,4])
a.symmetric_difference_update([5,2,4])
self.assertEqual(a, set_type([1,3, 5]))
def test_method_update_from_iter2(self, set_type):
a=set_type(range(1000))
a.symmetric_difference_update(range(0,1000,2))
self.assertEqual(a, set_type(range(1,1000,2)))
def test_method_update_from_iter3(self, set_type):
a=set_type([1,2])
a.symmetric_difference_update([1]*10000)
self.assertEqual(a, set_type([2]))
def test_method_update_from_iter4(self, set_type):
a=set_type([1,2])
a.symmetric_difference_update(a)
self.assertEqual(len(a), 0)
def test_xor(self, set_type):
a=set_type([0,222,3,444,5])
b=set_type([222,3,4])
a_copy=a.copy()
b_copy=b.copy()
c=a^b
self.assertEqual(a, a_copy)
self.assertEqual(b, b_copy)
self.assertEqual(c, set_type([0,444,5,4]))
c=b^a
self.assertEqual(a, a_copy)
self.assertEqual(b, b_copy)
self.assertEqual(c, set_type([0,444,5,4]))
def test_xor2(self, set_type):
a=set_type([1,2,3,4])
a_copy=a.copy()
b=a^a^a^a
self.assertEqual(a, a_copy)
self.assertEqual(len(b), 0)
def test_xor3(self, set_type):
a=set_type([1,2,3,4])
a_copy=a.copy()
b=a^a^a^a^a
self.assertEqual(a, a_copy)
self.assertEqual(b, a)
def test_ixor(self, set_type):
a=set_type([0,222,3,444,5])
b=set_type([222,3,4])
b_copy=b.copy()
a^=b
self.assertEqual(b, b_copy)
self.assertEqual(a, set_type([0,444,5,4]))
def test_ixor2(self, set_type):
a=set_type([1,2,3,4])
a^=a
self.assertEqual(a, set_type())
def test_symmetric_method(self, set_type):
a=set_type(range(10))
a_copy=a.copy()
b=a.symmetric_difference(range(5,15), set_type(range(5,10)), range(1,16))
self.assertEqual(b, set_type([0,15]))
self.assertEqual(a, a_copy)
| 22,305 | 125 | 2,763 |
a1fba859af22a9a1b8fc1410806c26c08f872a7f | 6,478 | py | Python | lib/airflow/tests/contrib/jobs/test_dag_trigger.py | ideax-business/ai-flow | 0a7797a093beddf827f68cad7a11e0babf1b5059 | [
"Apache-2.0",
"BSD-2-Clause",
"MIT",
"ECL-2.0",
"BSD-3-Clause"
] | null | null | null | lib/airflow/tests/contrib/jobs/test_dag_trigger.py | ideax-business/ai-flow | 0a7797a093beddf827f68cad7a11e0babf1b5059 | [
"Apache-2.0",
"BSD-2-Clause",
"MIT",
"ECL-2.0",
"BSD-3-Clause"
] | null | null | null | lib/airflow/tests/contrib/jobs/test_dag_trigger.py | ideax-business/ai-flow | 0a7797a093beddf827f68cad7a11e0babf1b5059 | [
"Apache-2.0",
"BSD-2-Clause",
"MIT",
"ECL-2.0",
"BSD-3-Clause"
] | null | null | null | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
import multiprocessing
import os
import time
import unittest
import pendulum.datetime
from notification_service.base_notification import BaseEvent
from notification_service.client import NotificationClient
from notification_service.event_storage import MemoryEventStorage
from notification_service.server import NotificationServer
from notification_service.service import NotificationService
from airflow.models.serialized_dag import SerializedDagModel
from airflow.contrib.jobs.dag_trigger import DagTrigger
from airflow.contrib.jobs.scheduler_client import EventSchedulerClient, ResponseWatcher
from airflow.models import DagModel
from airflow.utils.mailbox import Mailbox
from airflow.utils.session import create_session
from airflow.events.scheduler_events import SchedulerInnerEventUtil, SchedulerInnerEventType, SCHEDULER_NAMESPACE
from tests.test_utils import db
from tests.test_utils.config import conf_vars
DAG_FOLDER = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(__file__))), 'dags')
TEST_DAG_FILE = os.path.join(DAG_FOLDER, 'test_scheduler_dags.py')
if __name__ == '__main__':
unittest.main()
| 42.064935 | 113 | 0.706082 | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
import multiprocessing
import os
import time
import unittest
import pendulum.datetime
from notification_service.base_notification import BaseEvent
from notification_service.client import NotificationClient
from notification_service.event_storage import MemoryEventStorage
from notification_service.server import NotificationServer
from notification_service.service import NotificationService
from airflow.models.serialized_dag import SerializedDagModel
from airflow.contrib.jobs.dag_trigger import DagTrigger
from airflow.contrib.jobs.scheduler_client import EventSchedulerClient, ResponseWatcher
from airflow.models import DagModel
from airflow.utils.mailbox import Mailbox
from airflow.utils.session import create_session
from airflow.events.scheduler_events import SchedulerInnerEventUtil, SchedulerInnerEventType, SCHEDULER_NAMESPACE
from tests.test_utils import db
from tests.test_utils.config import conf_vars
DAG_FOLDER = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(__file__))), 'dags')
TEST_DAG_FILE = os.path.join(DAG_FOLDER, 'test_scheduler_dags.py')
class TestDagTrigger(unittest.TestCase):
def setUp(self) -> None:
db.clear_db_dags()
db.clear_db_serialized_dags()
def test_dag_trigger_is_alive(self):
mailbox = Mailbox()
dag_trigger = DagTrigger(".", -1, [], False, mailbox)
assert not dag_trigger.is_alive()
dag_trigger.start()
time.sleep(1)
assert dag_trigger.is_alive()
dag_trigger.end()
assert not dag_trigger.is_alive()
def test_dag_trigger(self):
mailbox = Mailbox()
dag_trigger = DagTrigger(".", -1, [], False, mailbox)
dag_trigger.start()
type(self)._add_dag_needing_dagrun()
message = mailbox.get_message()
message = SchedulerInnerEventUtil.to_inner_event(message)
assert message.dag_id == "test"
dag_trigger.end()
def test_dag_trigger_parse_dag(self):
mailbox = Mailbox()
dag_trigger = DagTrigger(TEST_DAG_FILE, -1, [], False, mailbox)
dag_trigger.start()
message = mailbox.get_message()
message = SchedulerInnerEventUtil.to_inner_event(message)
# only one dag is executable
assert "test_task_start_date_scheduling" == message.dag_id
assert DagModel.get_dagmodel(dag_id="test_task_start_date_scheduling") is not None
assert DagModel.get_dagmodel(dag_id="test_start_date_scheduling") is not None
assert SerializedDagModel.get(dag_id="test_task_start_date_scheduling") is not None
assert SerializedDagModel.get(dag_id="test_start_date_scheduling") is not None
dag_trigger.end()
def test_user_trigger_parse_dag(self):
port = 50101
notification_server_uri = 'localhost:{}'.format(port)
storage = MemoryEventStorage()
server = NotificationServer(NotificationService(storage), port)
server.run()
mailbox = Mailbox()
dag_trigger = DagTrigger(TEST_DAG_FILE, -1, [], False, mailbox, 5, notification_server_uri)
dag_trigger.start()
message = mailbox.get_message()
message = SchedulerInnerEventUtil.to_inner_event(message)
# only one dag is executable
assert "test_task_start_date_scheduling" == message.dag_id
sc = EventSchedulerClient(notification_server_uri=notification_server_uri, namespace='a')
sc.trigger_parse_dag(TEST_DAG_FILE)
dag_trigger.end()
server.stop()
def test_file_processor_manager_kill(self):
mailbox = Mailbox()
dag_trigger = DagTrigger(".", -1, [], False, mailbox)
dag_trigger.start()
dag_file_processor_manager_process = dag_trigger._dag_file_processor_agent._process
dag_file_processor_manager_process.kill()
dag_file_processor_manager_process.join(1)
assert not dag_file_processor_manager_process.is_alive()
cnt = 0
while cnt < 100 and not dag_file_processor_manager_process.is_alive():
dag_file_processor_manager_process = dag_trigger._dag_file_processor_agent._process
if not dag_file_processor_manager_process.is_alive():
time.sleep(0.1)
cnt = cnt + 1
continue
else:
break
assert dag_file_processor_manager_process.is_alive()
dag_trigger.end()
def _send_request_and_receive_response(self, notification_server_uri, file_path):
key = '{}_{}'.format(file_path, time.time_ns())
client = NotificationClient(server_uri=notification_server_uri,
default_namespace=SCHEDULER_NAMESPACE)
event = BaseEvent(key=key,
event_type=SchedulerInnerEventType.PARSE_DAG_REQUEST.value,
value=file_path)
watcher: ResponseWatcher = ResponseWatcher()
client.start_listen_event(key=key,
event_type=SchedulerInnerEventType.PARSE_DAG_RESPONSE.value,
watcher=watcher)
client.send_event(event)
res: BaseEvent = watcher.get_result()
self.assertEquals(event.key, res.key)
self.assertEquals(event.value, file_path)
client.stop_listen_events()
@staticmethod
def _add_dag_needing_dagrun():
with create_session() as session:
orm_dag = DagModel(dag_id="test")
orm_dag.is_paused = False
orm_dag.is_active = True
orm_dag.next_dagrun_create_after = pendulum.now()
session.merge(orm_dag)
session.commit()
if __name__ == '__main__':
unittest.main()
| 4,286 | 253 | 23 |
db967d18b0150b9b46f807e4175b670ec1cd835a | 1,063 | py | Python | tests/test_model_registry.py | murthyn/composer | 2a04cf387dd8558556500f7ef2bc6d3d131043d5 | [
"Apache-2.0"
] | null | null | null | tests/test_model_registry.py | murthyn/composer | 2a04cf387dd8558556500f7ef2bc6d3d131043d5 | [
"Apache-2.0"
] | null | null | null | tests/test_model_registry.py | murthyn/composer | 2a04cf387dd8558556500f7ef2bc6d3d131043d5 | [
"Apache-2.0"
] | null | null | null | # Copyright 2021 MosaicML. All Rights Reserved.
import pytest
from composer.models import ModelHparams
from composer.trainer.trainer_hparams import model_registry
@pytest.mark.parametrize("model_name", model_registry.keys())
| 26.575 | 61 | 0.686736 | # Copyright 2021 MosaicML. All Rights Reserved.
import pytest
from composer.models import ModelHparams
from composer.trainer.trainer_hparams import model_registry
@pytest.mark.parametrize("model_name", model_registry.keys())
def test_model_registry(model_name, request):
if model_name in ['timm']:
pytest.importorskip("timm")
if model_name in ['unet']:
pytest.importorskip("monai")
# create the model hparams object
model_hparams = model_registry[model_name]()
requires_num_classes = set([
"deeplabv3",
"resnet9_cifar10",
"resnet56_cifar10",
"efficientnetb0",
"resnet",
"mnist_classifier",
])
if model_name in requires_num_classes:
model_hparams.num_classes = 10
if model_name == "resnet":
model_hparams.model_name = 'resnet50'
if model_name == "deeplabv3":
model_hparams.is_backbone_pretrained = False
if model_name == "timm":
model_hparams.model_name = "resnet18"
assert isinstance(model_hparams, ModelHparams)
| 812 | 0 | 22 |
f0d8e904d213c2024920577bec459677043c4496 | 1,490 | py | Python | hardhat/recipes/poppler.py | stangelandcl/hardhat | 1ad0c5dec16728c0243023acb9594f435ef18f9c | [
"MIT"
] | null | null | null | hardhat/recipes/poppler.py | stangelandcl/hardhat | 1ad0c5dec16728c0243023acb9594f435ef18f9c | [
"MIT"
] | null | null | null | hardhat/recipes/poppler.py | stangelandcl/hardhat | 1ad0c5dec16728c0243023acb9594f435ef18f9c | [
"MIT"
] | null | null | null | from .base import GnuRecipe
| 32.391304 | 75 | 0.502013 | from .base import GnuRecipe
class Extra:
pass
class PopplerRecipe(GnuRecipe):
def __init__(self, *args, **kwargs):
super(PopplerRecipe, self).__init__(*args, **kwargs)
self.sha256 = '420abaab63caed9e1ee28964a0ba216d' \
'1979506726164bc99ad5ade289192a1b'
self.name = 'poppler'
self.version = '0.41.0'
self.depends = ['cairo',
'curl',
'fontconfig',
'gtk2',
'libjpeg-turbo',
'libpng',
'libtiff',
'qt5',
'pkgconfig']
self.url = 'http://poppler.freedesktop.org/poppler-$version.tar.xz'
e = Extra()
e.name = 'poppler-data'
e.version = '0.4.7'
e.url = 'http://poppler.freedesktop.org/' \
'poppler-data-%s.tar.gz' % e.version
e.sha256 = 'e752b0d88a7aba54574152143e7bf764' \
'36a7ef51977c55d6bd9a48dccde3a7de'
self.extra_downloads = [e]
self.configure_args += [
'--disable-static',
'--enable-cmyk',
'--enable-build-type=release',
'--enable-xpdf-headers',
'--with-testdatadir=$PWD/testfiles'
]
def extract(self):
super(PopplerRecipe, self).extract()
for e in self.extra_downloads:
self.extract_into(e.filename, self.extract_dir)
| 1,351 | 10 | 99 |
6d2e3d350112bf5ba43f5eeb7294864da2a45653 | 1,290 | py | Python | src/lab2rgb.py | ytyaru/Python.ColorSpace.Converter.20210606081641 | 7cd88aa12eb23ac26785ad1bff3a3e8034947c65 | [
"CC0-1.0"
] | null | null | null | src/lab2rgb.py | ytyaru/Python.ColorSpace.Converter.20210606081641 | 7cd88aa12eb23ac26785ad1bff3a3e8034947c65 | [
"CC0-1.0"
] | null | null | null | src/lab2rgb.py | ytyaru/Python.ColorSpace.Converter.20210606081641 | 7cd88aa12eb23ac26785ad1bff3a3e8034947c65 | [
"CC0-1.0"
] | null | null | null | #!/usr/bin/env python3
# coding: utf8
from labconstants import LabConstants
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser(description='色空間を変換する。Lab->RGB', formatter_class=argparse.RawTextHelpFormatter)
parser.add_argument('lab', help="色。カンマ区切りで渡す。\n 0.0 <= l <= 100.0\n -86.18463 <= a <= 98.25422\n -107.863686 <= b <= 94.48248")
args = parser.parse_args()
lab = args.lab.split(',')
lab[0] = float(lab[0])
lab[1] = float(lab[1])
lab[2] = float(lab[2])
r, g, b = lab2rgb(lab)
print('%d,%d,%d' % (r, g, b))
| 33.076923 | 134 | 0.589147 | #!/usr/bin/env python3
# coding: utf8
from labconstants import LabConstants
def lab2rgb(lab):
import math
l, a, b = lab
y = (l + 16) / 116
x = y if math.isnan(a) else y + a / 500
z = y if math.isnan(b) else y - b / 200
y = LabConstants.Yn * lab_xyz(y)
x = LabConstants.Xn * lab_xyz(x)
z = LabConstants.Zn * lab_xyz(z)
r = xyz_rgb(3.2404542 * x - 1.5371385 * y - 0.4985314 * z) # D65 -> sRGB
g = xyz_rgb(-0.9692660 * x + 1.8760108 * y + 0.0415560 * z)
b_ = xyz_rgb(0.0556434 * x - 0.2040259 * y + 1.0572252 * z)
return (r, g, b_)
def xyz_rgb(r):
return 255 * (12.92 * r if r <= 0.00304 else 1.055 * pow(r, 1 / 2.4) - 0.055)
def lab_xyz(t):
return t * t * t if t > LabConstants.t1 else LabConstants.t2 * (t - LabConstants.t0)
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser(description='色空間を変換する。Lab->RGB', formatter_class=argparse.RawTextHelpFormatter)
parser.add_argument('lab', help="色。カンマ区切りで渡す。\n 0.0 <= l <= 100.0\n -86.18463 <= a <= 98.25422\n -107.863686 <= b <= 94.48248")
args = parser.parse_args()
lab = args.lab.split(',')
lab[0] = float(lab[0])
lab[1] = float(lab[1])
lab[2] = float(lab[2])
r, g, b = lab2rgb(lab)
print('%d,%d,%d' % (r, g, b))
| 642 | 0 | 68 |
0300076d66cbcef4d8deb5912fdfdb704586b6be | 23,969 | py | Python | app/model.py | Princessgladys/googleresourcefinder | 7715276b3c588f7c457de04944559052c8170f7e | [
"Apache-2.0"
] | null | null | null | app/model.py | Princessgladys/googleresourcefinder | 7715276b3c588f7c457de04944559052c8170f7e | [
"Apache-2.0"
] | null | null | null | app/model.py | Princessgladys/googleresourcefinder | 7715276b3c588f7c457de04944559052c8170f7e | [
"Apache-2.0"
] | null | null | null | # Copyright 2009-2010 by Ka-Ping Yee
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""The Resource Mapper data model. All entities and fields are add-only and
are never deleted or overwritten. To represent modifications to a country's
data, create a new Version under the appropriate Country. To represent
modifications to a subject's attributes, add a new Report for that Subject.
This project uses the following naming conventions in variables and properties:
"key":
An App Engine entity key.
"name":
A unique identifier for an entity, used as the entity's key_name.
Fields ending in "_name" contain the key_names of other entities.
Names should be C-style identifiers (thus usable as HTML element IDs,
form element names, and CSS classes) and are usually not displayed in
the UI directly. For each name, there should be a corresponding
Message entity that provides the localized text.
"title":
A non-localizable, UI-displayable text label for an entity.
"""
import datetime
from google.appengine.ext import db
MAX_DATE = datetime.datetime(datetime.MAXYEAR, 1, 1)
class Subdomain(db.Model):
"""A separate grouping of Subjects and SubjectTypes. Top-level entity,
with no parent. Key name: unique subdomain name. In the UI, each
subdomain appears to be an independent instance of the application. The
permitted actions of Accounts and key_names of Subjects and SubjectTypes
are namespaced by prefixing them with the subdomain name and a colon. All
other entities are shared across all subdomains."""
pass # No properties for now; only the key_name is significant.
def filter_by_prefix(query, key_name_prefix, root_kind=None):
"""Filters a query for key_names that have the given prefix. If root_kind
is specified, filters the query for children of any entities that are of
that kind with the given prefix; otherwise, the results are assumed to be
top-level entities of the kind being queried."""
root_kind = root_kind or query._model_class.__name__
min_key = db.Key.from_path(root_kind, key_name_prefix)
max_key = db.Key.from_path(root_kind, key_name_prefix + u'\uffff')
return query.filter('__key__ >=', min_key).filter('__key__ <=', max_key)
def value_or_none(value):
"""Converts any false value other than 0 or 0.0 to None."""
if value or value == 0:
return value
return None
def get_name(name_or_entity):
"""If given an entity, returns its name (without subdomain); if given a
string, returns the string itself."""
if isinstance(name_or_entity, (Subject, SubjectType)):
return name_or_entity.name
elif isinstance(name_or_entity, db.Model):
return name_or_entity.key().name()
else:
return name_or_entity
class SubdomainMixin:
"""A mix-in class providing common methods for entities whose key names
begin with a subdomain and a colon."""
@classmethod
def get(cls, subdomain, name):
"""Gets an entity by its subdomain and name. This method overrides
the default get() method, which takes a db.Key."""
return cls.get_by_key_name(subdomain + ':' + name)
@classmethod
def all_in_subdomain(cls, subdomain):
"""Gets a query for all entities with the given subdomain."""
root_kind = getattr(cls, 'ROOT_KIND', None)
return filter_by_prefix(cls.all(), subdomain + ':', root_kind)
def get_subdomain(self):
"""Gets the entity's subdomain."""
return self.key().name().split(':', 1)[0]
subdomain = property(get_subdomain)
def get_name(self):
"""Gets the entity's name (without the subdomain)."""
return self.key().name().split(':', 1)[1]
name = property(get_name)
class Subject(SubdomainMixin, db.Expando):
"""A thing whose attributes are tracked by this application. Top-level
entity, has no parent. Key name: subdomain + ':' + subject name.
A subject name is a globally unique ID that starts with a domain name and
a slash. In the 'haiti' subdomain, Subjects are health facilities with a
government or internationally established health facility ID."""
timestamp = db.DateTimeProperty(auto_now_add=True) # creation time
type = db.StringProperty(required=True) # key_name of a SubjectType,
# without the subdomain prefix
author = db.UserProperty() # who created this Subject
# additional properties for the current value of each attribute
# (named by Attribute's key_name). This denormalization is for read speed.
# Consider an attribute named 'foo'. We will store 6 values here:
# foo__ various types, the attribute value
# foo__observed datetime, timestamp when the value was valid
# foo__author users.User, the user who provided the change
# foo__author_nickname string, source of the change
# foo__author_affiliation string, affiliation of the source
# foo__comment string, a comment about the change
# These properties will exist with the following invariants:
# 1. If subject.foo__ is not present, that means attribute "foo" has never
# existed on this subject at any point in the past.
# 2. If subject.foo__ is None, that means some user actually set the
# attribute to "(unspecified)".
# 3. All six fields are always written together at the same time, and are
# never removed. (Hence either all are present or none are present.)
@staticmethod
def create(subdomain, subject_type_or_type_name, subject_name, author):
"""Creates a Subject with a given subdomain, type, name, and author."""
return Subject(key_name='%s:%s' % (subdomain, subject_name),
type=get_name(subject_type_or_type_name), author=author)
@staticmethod
def generate_name(host, subject_type_or_type_name):
"""Makes a new unique subject_name for an original subject (originally
created in this repository, not cloned from an external repository)."""
id = UniqueId.create_id()
return '%s/%s.%d' % (host, get_name(subject_type_or_type_name), id)
@staticmethod
@classmethod
def has_value(self, attribute_name):
"""Returns the value of the Attribute with the given key_name,
or default if it does not exist."""
return hasattr(self, '%s__' % attribute_name)
def get_value(self, attribute_name, default=None):
"""Returns the value of the Attribute with the given key_name,
or default if it does not exist."""
return getattr(self, '%s__' % attribute_name, default)
def get_observed(self, attribute_name, default=None):
"""Returns the timestamp when the Attribute with the given key_name
was valid, or default if it does not exist."""
return getattr(self, '%s__observed' % attribute_name, default)
def get_author(self, attribute_name, default=None):
"""Returns the author of the Attribute value with the given key_name,
or default if it does not exist."""
return getattr(self, '%s__author' % attribute_name, default)
def get_author_nickname(self, attribute_name, default=None):
"""Returns the author nickname of the Attribute value with the given
key_name, or default if it does not exist."""
return getattr(self, '%s__author_nickname' % attribute_name, default)
def get_author_affiliation(self, attribute_name, default=None):
"""Returns the affiliation of the author of the Attribute value
with the given key_name, or default if it does not exist."""
return getattr(self, '%s__author_affiliation' % attribute_name, default)
def get_comment(self, attribute_name, default=None):
"""Returns the author's comment about the Attribute value with the
given key_name, or default if it does not exist."""
return getattr(self, '%s__comment' % attribute_name, default)
def set_attribute(self, name, value, observed, author, author_nickname,
author_affiliation, comment):
"""Sets the value for the Attribute with the given key_name."""
setattr(self, '%s__' % name, value_or_none(value))
setattr(self, '%s__observed' % name, value_or_none(observed))
setattr(self, '%s__author' % name, value_or_none(author))
setattr(self, '%s__author_nickname' % name,
value_or_none(author_nickname))
setattr(self, '%s__author_affiliation' % name,
value_or_none(author_affiliation))
setattr(self, '%s__comment' % name, value_or_none(comment))
class MinimalSubject(SubdomainMixin, db.Expando):
"""Minimal version of Subject that loads fast from the datastore and
contains just the information needed to populate the initial list and map.
Parent: Subject. Key name: same as its parent Subject. Wouldn't be
necessary if we could select columns from the datastore."""
type = db.StringProperty(required=True) # key_name of a SubjectType,
# without the subdomain prefix
# More properties for the current values of ONLY the most critically
# important attributes of Subject (named by Attribute's key_name).
# An attribute named foo will be stored as 'foo__' to match Subject.
ROOT_KIND = 'Subject' # filter_by_prefix uses this to filter keys properly
@staticmethod
@staticmethod
def get_by_subject(subject):
"""Gets the MinimalSubject entity for the given Subject."""
return MinimalSubject.all().ancestor(subject).get()
@staticmethod
def has_value(self, attribute_name):
"""Returns the value of the Attribute with the given key_name,
or default if it does not exist."""
return hasattr(self, '%s__' % attribute_name)
def get_value(self, attribute_name, default=None):
"""Returns the value of the Attribute with the given key_name,
or default if it does not exist."""
return getattr(self, '%s__' % attribute_name, default)
def set_attribute(self, name, value):
"""Sets the value for the Attribute with the given key_name."""
setattr(self, '%s__' % name, value_or_none(value))
class UniqueId(db.Model):
"""This entity is used just to generate unique numeric IDs."""
@staticmethod
def create_id():
"""Gets a numeric ID that is guaranteed to be different from any ID
previously returned by this static method."""
unique_id = UniqueId()
unique_id.put()
return unique_id.key().id()
class SubjectType(SubdomainMixin, db.Model):
"""A type of Subject, e.g. hospital, warehouse, charity, camp. Top-level
entity, has no parent. Key name: subdomain + ':' + type name. A type name
is an identifier used as the value of Subject.type."""
timestamp = db.DateTimeProperty(auto_now_add=True)
attribute_names = db.StringListProperty() # key_names of Attribute entities
minimal_attribute_names = db.StringListProperty() # key_names of
# Attribute entities whose values should be copied into MinimalSubject
@staticmethod
class Attribute(db.Model):
"""An attribute of a subject, e.g. services available, # of
patients. Top-level entity, has no parent. Key name: name of a property
in a Report, and also the name of the Message providing the
UI-displayable attribute name."""
timestamp = db.DateTimeProperty(auto_now_add=True)
type = db.StringProperty(required=True, choices=[
'str', # value is a single-line string (Python unicode)
'text', # value is a string, shown as long text (Python unicode)
'contact', # value is a 3-line string (name, phone, e-mail address)
'date', # value is a date (Python datetime with time 00:00:00)
'int', # value is an integer (64-bit long)
'float', # value is a float (Python float, i.e. double)
'bool', # value is a boolean
'choice', # value is a string (one of the elements in 'values')
'multi', # value is a list of strings (which are elements of 'values')
'geopt', # value is a db.GeoPt with latitude and longitude
])
edit_action = db.StringProperty() # What Account action can edit?
values = db.StringListProperty() # allowed value names for choice or multi
class Report(db.Expando):
"""A report on the attributes and resources of a Subject.
Parent: Subject. A Report may represent a partial update of the
attributes of the subject."""
arrived = db.DateTimeProperty(auto_now_add=True) # date we received report
source = db.StringProperty() # a URL, the source of the report
author = db.UserProperty() # author of the report
observed = db.DateTimeProperty() # date that report contents were valid
# additional properties for each Attribute (named by Attribute's key_name)
# Consider an attribute named 'foo'. We will store 2 values here:
# foo__ various, the attribute value
# foo__comment db.StringProperty, a comment from user making the change
# These properties will exist with the following invariants:
# 1. If report.foo__ is not present, that means attribute "foo" should
# not change its value from the previous report.
# 2. If report.foo__ is None, that means some user actually set the
# attribute to "(unspecified)".
# 3. Both fields are always written together at the same time, and
# are never removed. (Hence either all are present or none are present.)
def get_value(self, attribute_name, default=None):
"""Returns the value of the Attribute with the given key_name,
or default if it does not exist."""
return getattr(self, '%s__' % attribute_name, default)
def get_comment(self, attribute_name, default=None):
"""Returns the author's comment about the Attribute value with the
given key_name, or default if it does not exist."""
return getattr(self, '%s__comment' % attribute_name, default)
def set_attribute(self, name, value, comment):
"""Sets the value for the Attribute with the given key_name."""
setattr(self, '%s__' % name, value_or_none(value))
setattr(self, '%s__comment' % name, value_or_none(comment))
class Account(db.Model):
"""User account. Top-level entity, has no parent. Users without Account
entities can use the application; for such users, their permissions are
determined by the special Account object with key_name='default'. Users
get their own Account entities when editing a Subject, requesting
permissions, being granted permissions, or subscribing to alerts."""
timestamp = db.DateTimeProperty(auto_now_add=True) # creation time
description = db.StringProperty() # full name or description
email = db.StringProperty() # e-mail address of the account
user_id = db.StringProperty() # users.User.id() of the account
nickname = db.StringProperty() # nickname for display in the UI; may
# differ from users.User.nickname()
affiliation = db.StringProperty() # company or organization, etc.
token = db.StringProperty() # secret token for looking up an Account with
# no e-mail address (so we can have Account
# entities for nonexistent Google Accounts)
actions = db.StringListProperty() # actions allowed for this user (items
# have the form subdomain + ':' + verb;
# '*' is a wildcard subdomain or verb)
requested_actions = db.StringListProperty() # permissions requested but
# not yet granted
locale = db.StringProperty() # user chosen locale
# default frequency for updates
default_frequency = db.StringProperty(default='instant')
# preferred format to receive e-mail in
email_format = db.StringProperty(choices=['plain', 'html'], default='plain')
# For explanation of default settings for the next alert times, see
# mail_alerts.py's send_digests function.
# next time to send a daily update
next_daily_alert = db.DateTimeProperty(default=MAX_DATE)
# next time to send a weekly update to the user
next_weekly_alert = db.DateTimeProperty(default=MAX_DATE)
# next time to send a monthly update to the user
next_monthly_alert = db.DateTimeProperty(default=MAX_DATE)
class Message(db.Expando):
"""Internationalized strings for value identifiers. Top-level entity,
has no parent."""
# Formerly namespace, renamed to work around bug in GAE 1.3.5 (b/2811890);
# Can change back after 1.3.6, which contains the fix
ns = db.StringProperty(required=True, choices=[
'english', # name is an English string
'attribute_name', # name is an Attribute's key_name
'attribute_value', # name is a value name in a choice or multi attribute
'subject_type' # name is a SubjectType's key_name (including subdomain)
])
name = db.StringProperty()
# additional properties for each language (named by locale code)
class Dump(db.Model):
"""A record of the data received from a data source in its native format,
before it was converted and loaded into the datastore. Top-level entity,
has no parent."""
timestamp = db.DateTimeProperty(auto_now_add=True)
base = db.SelfReference() # if present, this dump is a clone of base
source = db.StringProperty() # URL identifying the source
data = db.BlobProperty() # received raw data
# TODO(kpy): Clean up the inconsistent use of the term "subject_name".
# In Subscription, subject_name is the entire Subject key including the
# subdomain; elsewhere it is just the part after the subdomain.
class Subscription(db.Model):
"""A subscription by a user to receive notification when details for a
facility change. Top-level entity, has no parent.
Key name: follows the format subject_name:user_email"""
user_email = db.StringProperty(required=True) # user to alert
subject_name = db.StringProperty(required=True) # key_name of subject
frequency = db.StringProperty(required=True, choices=[
'instant', # send an alert whenever the facility is updated
'daily', # send during a daily digest e-mail
'weekly', # send during a weekly digest e-mail
'monthly' # send during a monthly digest e-mail on the 1st of the month
]) # frequency of updates for this subject
@staticmethod
def get(subject_name, user_email):
"""Gets a Subscription entity by its subject_name and e-mail."""
return Subscription.get_by_key_name(subject_name + ':' + user_email)
@staticmethod
def get_by_subject(subject_name):
"""Gets a query for all PendingAlert with the given subject name."""
return filter_by_prefix(Subscription.all(), subject_name + ':')
class PendingAlert(MinimalSubject):
"""A pending notification for a user; waiting to be sent on a daily/weekly/
monthly basis, pending the frequency of the particular alert. Top-level
entity, has no parent.
Key name: follows the format frequency:user_email:subject_name"""
user_email = db.StringProperty(required=True) # user to alert
subject_name = db.StringProperty(required=True) # key_name of subject
timestamp = db.DateTimeProperty() # creation time of the pending alert
frequency = db.StringProperty(required=True, choices=[
'instant', # send an alert whenever the subject is updated
'daily', # send during a daily digest e-mail
'weekly', # send during a weekly digest e-mail
'monthly' # send during a monthly digest e-mail on the 1st of the month
]) # frequency of updates for this subject
@staticmethod
def get(frequency, user_email, subject_name):
"""Gets a PendingAlert entity by its frequency, e-mail, and
subject name."""
return PendingAlert.get_by_key_name(frequency + ':' + user_email +
':' + subject_name)
@staticmethod
def get_by_frequency(frequency, user_email):
"""Gets a query for all PendingAlert with the given frequency and
associated user e-mail."""
return filter_by_prefix(PendingAlert.all(), frequency + ':' +
user_email + ':')
class MailUpdateText(db.Expando):
"""A map from attribute names and values to alternate values accepted
in the mail editing system. They are strings that users can type into
e-mails to refer to attribute names or values. Key name: follows the
format namespace:name.
This table should at all times contain the following special cases for
general-use attribute values: true, false, and none. Accepted values are:
true: ['Yes', 'y', 'true']
false: ['No', 'n', 'false']
none: ['*none']
by default. In total, the number of entities in the table should be equal
to the number of attribute entities, plus the unique values across all
multi and choice attributes, plus the 3 general values defined above."""
# name is an attribute name or attribute value; see below
name = db.StringProperty(required=True)
ns = db.StringProperty(required=True, choices=[
'attribute_name', # name is an attribute's key_name
'attribute_value' # name is a value name in a choice or multi attribute
])
# Expando values should be initialized on a per-language basis as a list
# of accepted input strings for this particular map, in that language.
# Use the same naming format as in the Message table [en for English,
# fr for French, etc.]. The first value in each list should match the
# corresponding Message, to make mail editing behavior resilient to
# changes in translations. For comparison purposes, each item in the
# list will be treated as case-insensitive. Spaces should be used in
# favor of underscores.
@classmethod
def get(cls, ns, name):
"""Gets an entity by its namespace and name."""
key_name = '%s:%s' % (ns, name)
return cls.get_by_key_name(key_name)
@classmethod
def create(cls, ns, name, **kwargs):
"""Creates an entity with the specified namespace and name."""
key_name = '%s:%s' % (ns, name)
return cls(key_name=key_name, ns=ns, name=name, **kwargs)
@classmethod
def all_in_namespace(cls, ns):
"""Gets a query for all entities with the given namespace."""
return filter_by_prefix(cls.all(), ns + ':')
| 49.217659 | 80 | 0.683925 | # Copyright 2009-2010 by Ka-Ping Yee
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""The Resource Mapper data model. All entities and fields are add-only and
are never deleted or overwritten. To represent modifications to a country's
data, create a new Version under the appropriate Country. To represent
modifications to a subject's attributes, add a new Report for that Subject.
This project uses the following naming conventions in variables and properties:
"key":
An App Engine entity key.
"name":
A unique identifier for an entity, used as the entity's key_name.
Fields ending in "_name" contain the key_names of other entities.
Names should be C-style identifiers (thus usable as HTML element IDs,
form element names, and CSS classes) and are usually not displayed in
the UI directly. For each name, there should be a corresponding
Message entity that provides the localized text.
"title":
A non-localizable, UI-displayable text label for an entity.
"""
import datetime
from google.appengine.ext import db
MAX_DATE = datetime.datetime(datetime.MAXYEAR, 1, 1)
class Subdomain(db.Model):
"""A separate grouping of Subjects and SubjectTypes. Top-level entity,
with no parent. Key name: unique subdomain name. In the UI, each
subdomain appears to be an independent instance of the application. The
permitted actions of Accounts and key_names of Subjects and SubjectTypes
are namespaced by prefixing them with the subdomain name and a colon. All
other entities are shared across all subdomains."""
pass # No properties for now; only the key_name is significant.
def filter_by_prefix(query, key_name_prefix, root_kind=None):
"""Filters a query for key_names that have the given prefix. If root_kind
is specified, filters the query for children of any entities that are of
that kind with the given prefix; otherwise, the results are assumed to be
top-level entities of the kind being queried."""
root_kind = root_kind or query._model_class.__name__
min_key = db.Key.from_path(root_kind, key_name_prefix)
max_key = db.Key.from_path(root_kind, key_name_prefix + u'\uffff')
return query.filter('__key__ >=', min_key).filter('__key__ <=', max_key)
def value_or_none(value):
"""Converts any false value other than 0 or 0.0 to None."""
if value or value == 0:
return value
return None
def get_name(name_or_entity):
"""If given an entity, returns its name (without subdomain); if given a
string, returns the string itself."""
if isinstance(name_or_entity, (Subject, SubjectType)):
return name_or_entity.name
elif isinstance(name_or_entity, db.Model):
return name_or_entity.key().name()
else:
return name_or_entity
class SubdomainMixin:
"""A mix-in class providing common methods for entities whose key names
begin with a subdomain and a colon."""
@classmethod
def get(cls, subdomain, name):
"""Gets an entity by its subdomain and name. This method overrides
the default get() method, which takes a db.Key."""
return cls.get_by_key_name(subdomain + ':' + name)
@classmethod
def all_in_subdomain(cls, subdomain):
"""Gets a query for all entities with the given subdomain."""
root_kind = getattr(cls, 'ROOT_KIND', None)
return filter_by_prefix(cls.all(), subdomain + ':', root_kind)
def get_subdomain(self):
"""Gets the entity's subdomain."""
return self.key().name().split(':', 1)[0]
subdomain = property(get_subdomain)
def get_name(self):
"""Gets the entity's name (without the subdomain)."""
return self.key().name().split(':', 1)[1]
name = property(get_name)
class Subject(SubdomainMixin, db.Expando):
"""A thing whose attributes are tracked by this application. Top-level
entity, has no parent. Key name: subdomain + ':' + subject name.
A subject name is a globally unique ID that starts with a domain name and
a slash. In the 'haiti' subdomain, Subjects are health facilities with a
government or internationally established health facility ID."""
timestamp = db.DateTimeProperty(auto_now_add=True) # creation time
type = db.StringProperty(required=True) # key_name of a SubjectType,
# without the subdomain prefix
author = db.UserProperty() # who created this Subject
# additional properties for the current value of each attribute
# (named by Attribute's key_name). This denormalization is for read speed.
# Consider an attribute named 'foo'. We will store 6 values here:
# foo__ various types, the attribute value
# foo__observed datetime, timestamp when the value was valid
# foo__author users.User, the user who provided the change
# foo__author_nickname string, source of the change
# foo__author_affiliation string, affiliation of the source
# foo__comment string, a comment about the change
# These properties will exist with the following invariants:
# 1. If subject.foo__ is not present, that means attribute "foo" has never
# existed on this subject at any point in the past.
# 2. If subject.foo__ is None, that means some user actually set the
# attribute to "(unspecified)".
# 3. All six fields are always written together at the same time, and are
# never removed. (Hence either all are present or none are present.)
@staticmethod
def create(subdomain, subject_type_or_type_name, subject_name, author):
"""Creates a Subject with a given subdomain, type, name, and author."""
return Subject(key_name='%s:%s' % (subdomain, subject_name),
type=get_name(subject_type_or_type_name), author=author)
@staticmethod
def generate_name(host, subject_type_or_type_name):
"""Makes a new unique subject_name for an original subject (originally
created in this repository, not cloned from an external repository)."""
id = UniqueId.create_id()
return '%s/%s.%d' % (host, get_name(subject_type_or_type_name), id)
@staticmethod
def get_stored_name(attribute_name):
return '%s__' % attribute_name
@classmethod
def delete_complete(cls, subject):
if subject:
minimal_subject = MinimalSubject.get_by_subject(subject)
db.delete([subject, minimal_subject])
def has_value(self, attribute_name):
"""Returns the value of the Attribute with the given key_name,
or default if it does not exist."""
return hasattr(self, '%s__' % attribute_name)
def get_value(self, attribute_name, default=None):
"""Returns the value of the Attribute with the given key_name,
or default if it does not exist."""
return getattr(self, '%s__' % attribute_name, default)
def get_observed(self, attribute_name, default=None):
"""Returns the timestamp when the Attribute with the given key_name
was valid, or default if it does not exist."""
return getattr(self, '%s__observed' % attribute_name, default)
def get_author(self, attribute_name, default=None):
"""Returns the author of the Attribute value with the given key_name,
or default if it does not exist."""
return getattr(self, '%s__author' % attribute_name, default)
def get_author_nickname(self, attribute_name, default=None):
"""Returns the author nickname of the Attribute value with the given
key_name, or default if it does not exist."""
return getattr(self, '%s__author_nickname' % attribute_name, default)
def get_author_affiliation(self, attribute_name, default=None):
"""Returns the affiliation of the author of the Attribute value
with the given key_name, or default if it does not exist."""
return getattr(self, '%s__author_affiliation' % attribute_name, default)
def get_comment(self, attribute_name, default=None):
"""Returns the author's comment about the Attribute value with the
given key_name, or default if it does not exist."""
return getattr(self, '%s__comment' % attribute_name, default)
def set_attribute(self, name, value, observed, author, author_nickname,
author_affiliation, comment):
"""Sets the value for the Attribute with the given key_name."""
setattr(self, '%s__' % name, value_or_none(value))
setattr(self, '%s__observed' % name, value_or_none(observed))
setattr(self, '%s__author' % name, value_or_none(author))
setattr(self, '%s__author_nickname' % name,
value_or_none(author_nickname))
setattr(self, '%s__author_affiliation' % name,
value_or_none(author_affiliation))
setattr(self, '%s__comment' % name, value_or_none(comment))
class MinimalSubject(SubdomainMixin, db.Expando):
"""Minimal version of Subject that loads fast from the datastore and
contains just the information needed to populate the initial list and map.
Parent: Subject. Key name: same as its parent Subject. Wouldn't be
necessary if we could select columns from the datastore."""
type = db.StringProperty(required=True) # key_name of a SubjectType,
# without the subdomain prefix
# More properties for the current values of ONLY the most critically
# important attributes of Subject (named by Attribute's key_name).
# An attribute named foo will be stored as 'foo__' to match Subject.
ROOT_KIND = 'Subject' # filter_by_prefix uses this to filter keys properly
@staticmethod
def create(subject):
return MinimalSubject(
subject, key_name=subject.key().name(), type=subject.type)
@staticmethod
def get_by_subject(subject):
"""Gets the MinimalSubject entity for the given Subject."""
return MinimalSubject.all().ancestor(subject).get()
@staticmethod
def get_stored_name(attribute_name):
return '%s__' % attribute_name
def has_value(self, attribute_name):
"""Returns the value of the Attribute with the given key_name,
or default if it does not exist."""
return hasattr(self, '%s__' % attribute_name)
def get_value(self, attribute_name, default=None):
"""Returns the value of the Attribute with the given key_name,
or default if it does not exist."""
return getattr(self, '%s__' % attribute_name, default)
def set_attribute(self, name, value):
"""Sets the value for the Attribute with the given key_name."""
setattr(self, '%s__' % name, value_or_none(value))
class UniqueId(db.Model):
"""This entity is used just to generate unique numeric IDs."""
@staticmethod
def create_id():
"""Gets a numeric ID that is guaranteed to be different from any ID
previously returned by this static method."""
unique_id = UniqueId()
unique_id.put()
return unique_id.key().id()
class SubjectType(SubdomainMixin, db.Model):
"""A type of Subject, e.g. hospital, warehouse, charity, camp. Top-level
entity, has no parent. Key name: subdomain + ':' + type name. A type name
is an identifier used as the value of Subject.type."""
timestamp = db.DateTimeProperty(auto_now_add=True)
attribute_names = db.StringListProperty() # key_names of Attribute entities
minimal_attribute_names = db.StringListProperty() # key_names of
# Attribute entities whose values should be copied into MinimalSubject
@staticmethod
def create(subdomain, subject_type_name):
return SubjectType(key_name=subdomain + ':' + subject_type_name)
class Attribute(db.Model):
"""An attribute of a subject, e.g. services available, # of
patients. Top-level entity, has no parent. Key name: name of a property
in a Report, and also the name of the Message providing the
UI-displayable attribute name."""
timestamp = db.DateTimeProperty(auto_now_add=True)
type = db.StringProperty(required=True, choices=[
'str', # value is a single-line string (Python unicode)
'text', # value is a string, shown as long text (Python unicode)
'contact', # value is a 3-line string (name, phone, e-mail address)
'date', # value is a date (Python datetime with time 00:00:00)
'int', # value is an integer (64-bit long)
'float', # value is a float (Python float, i.e. double)
'bool', # value is a boolean
'choice', # value is a string (one of the elements in 'values')
'multi', # value is a list of strings (which are elements of 'values')
'geopt', # value is a db.GeoPt with latitude and longitude
])
edit_action = db.StringProperty() # What Account action can edit?
values = db.StringListProperty() # allowed value names for choice or multi
class Report(db.Expando):
"""A report on the attributes and resources of a Subject.
Parent: Subject. A Report may represent a partial update of the
attributes of the subject."""
arrived = db.DateTimeProperty(auto_now_add=True) # date we received report
source = db.StringProperty() # a URL, the source of the report
author = db.UserProperty() # author of the report
observed = db.DateTimeProperty() # date that report contents were valid
# additional properties for each Attribute (named by Attribute's key_name)
# Consider an attribute named 'foo'. We will store 2 values here:
# foo__ various, the attribute value
# foo__comment db.StringProperty, a comment from user making the change
# These properties will exist with the following invariants:
# 1. If report.foo__ is not present, that means attribute "foo" should
# not change its value from the previous report.
# 2. If report.foo__ is None, that means some user actually set the
# attribute to "(unspecified)".
# 3. Both fields are always written together at the same time, and
# are never removed. (Hence either all are present or none are present.)
def get_value(self, attribute_name, default=None):
"""Returns the value of the Attribute with the given key_name,
or default if it does not exist."""
return getattr(self, '%s__' % attribute_name, default)
def get_comment(self, attribute_name, default=None):
"""Returns the author's comment about the Attribute value with the
given key_name, or default if it does not exist."""
return getattr(self, '%s__comment' % attribute_name, default)
def set_attribute(self, name, value, comment):
"""Sets the value for the Attribute with the given key_name."""
setattr(self, '%s__' % name, value_or_none(value))
setattr(self, '%s__comment' % name, value_or_none(comment))
class Account(db.Model):
"""User account. Top-level entity, has no parent. Users without Account
entities can use the application; for such users, their permissions are
determined by the special Account object with key_name='default'. Users
get their own Account entities when editing a Subject, requesting
permissions, being granted permissions, or subscribing to alerts."""
timestamp = db.DateTimeProperty(auto_now_add=True) # creation time
description = db.StringProperty() # full name or description
email = db.StringProperty() # e-mail address of the account
user_id = db.StringProperty() # users.User.id() of the account
nickname = db.StringProperty() # nickname for display in the UI; may
# differ from users.User.nickname()
affiliation = db.StringProperty() # company or organization, etc.
token = db.StringProperty() # secret token for looking up an Account with
# no e-mail address (so we can have Account
# entities for nonexistent Google Accounts)
actions = db.StringListProperty() # actions allowed for this user (items
# have the form subdomain + ':' + verb;
# '*' is a wildcard subdomain or verb)
requested_actions = db.StringListProperty() # permissions requested but
# not yet granted
locale = db.StringProperty() # user chosen locale
# default frequency for updates
default_frequency = db.StringProperty(default='instant')
# preferred format to receive e-mail in
email_format = db.StringProperty(choices=['plain', 'html'], default='plain')
# For explanation of default settings for the next alert times, see
# mail_alerts.py's send_digests function.
# next time to send a daily update
next_daily_alert = db.DateTimeProperty(default=MAX_DATE)
# next time to send a weekly update to the user
next_weekly_alert = db.DateTimeProperty(default=MAX_DATE)
# next time to send a monthly update to the user
next_monthly_alert = db.DateTimeProperty(default=MAX_DATE)
class Message(db.Expando):
"""Internationalized strings for value identifiers. Top-level entity,
has no parent."""
# Formerly namespace, renamed to work around bug in GAE 1.3.5 (b/2811890);
# Can change back after 1.3.6, which contains the fix
ns = db.StringProperty(required=True, choices=[
'english', # name is an English string
'attribute_name', # name is an Attribute's key_name
'attribute_value', # name is a value name in a choice or multi attribute
'subject_type' # name is a SubjectType's key_name (including subdomain)
])
name = db.StringProperty()
# additional properties for each language (named by locale code)
class Dump(db.Model):
"""A record of the data received from a data source in its native format,
before it was converted and loaded into the datastore. Top-level entity,
has no parent."""
timestamp = db.DateTimeProperty(auto_now_add=True)
base = db.SelfReference() # if present, this dump is a clone of base
source = db.StringProperty() # URL identifying the source
data = db.BlobProperty() # received raw data
# TODO(kpy): Clean up the inconsistent use of the term "subject_name".
# In Subscription, subject_name is the entire Subject key including the
# subdomain; elsewhere it is just the part after the subdomain.
class Subscription(db.Model):
"""A subscription by a user to receive notification when details for a
facility change. Top-level entity, has no parent.
Key name: follows the format subject_name:user_email"""
user_email = db.StringProperty(required=True) # user to alert
subject_name = db.StringProperty(required=True) # key_name of subject
frequency = db.StringProperty(required=True, choices=[
'instant', # send an alert whenever the facility is updated
'daily', # send during a daily digest e-mail
'weekly', # send during a weekly digest e-mail
'monthly' # send during a monthly digest e-mail on the 1st of the month
]) # frequency of updates for this subject
@staticmethod
def get(subject_name, user_email):
"""Gets a Subscription entity by its subject_name and e-mail."""
return Subscription.get_by_key_name(subject_name + ':' + user_email)
@staticmethod
def get_by_subject(subject_name):
"""Gets a query for all PendingAlert with the given subject name."""
return filter_by_prefix(Subscription.all(), subject_name + ':')
class PendingAlert(MinimalSubject):
"""A pending notification for a user; waiting to be sent on a daily/weekly/
monthly basis, pending the frequency of the particular alert. Top-level
entity, has no parent.
Key name: follows the format frequency:user_email:subject_name"""
user_email = db.StringProperty(required=True) # user to alert
subject_name = db.StringProperty(required=True) # key_name of subject
timestamp = db.DateTimeProperty() # creation time of the pending alert
frequency = db.StringProperty(required=True, choices=[
'instant', # send an alert whenever the subject is updated
'daily', # send during a daily digest e-mail
'weekly', # send during a weekly digest e-mail
'monthly' # send during a monthly digest e-mail on the 1st of the month
]) # frequency of updates for this subject
@staticmethod
def get(frequency, user_email, subject_name):
"""Gets a PendingAlert entity by its frequency, e-mail, and
subject name."""
return PendingAlert.get_by_key_name(frequency + ':' + user_email +
':' + subject_name)
@staticmethod
def get_by_frequency(frequency, user_email):
"""Gets a query for all PendingAlert with the given frequency and
associated user e-mail."""
return filter_by_prefix(PendingAlert.all(), frequency + ':' +
user_email + ':')
class MailUpdateText(db.Expando):
"""A map from attribute names and values to alternate values accepted
in the mail editing system. They are strings that users can type into
e-mails to refer to attribute names or values. Key name: follows the
format namespace:name.
This table should at all times contain the following special cases for
general-use attribute values: true, false, and none. Accepted values are:
true: ['Yes', 'y', 'true']
false: ['No', 'n', 'false']
none: ['*none']
by default. In total, the number of entities in the table should be equal
to the number of attribute entities, plus the unique values across all
multi and choice attributes, plus the 3 general values defined above."""
# name is an attribute name or attribute value; see below
name = db.StringProperty(required=True)
ns = db.StringProperty(required=True, choices=[
'attribute_name', # name is an attribute's key_name
'attribute_value' # name is a value name in a choice or multi attribute
])
# Expando values should be initialized on a per-language basis as a list
# of accepted input strings for this particular map, in that language.
# Use the same naming format as in the Message table [en for English,
# fr for French, etc.]. The first value in each list should match the
# corresponding Message, to make mail editing behavior resilient to
# changes in translations. For comparison purposes, each item in the
# list will be treated as case-insensitive. Spaces should be used in
# favor of underscores.
@classmethod
def get(cls, ns, name):
"""Gets an entity by its namespace and name."""
key_name = '%s:%s' % (ns, name)
return cls.get_by_key_name(key_name)
@classmethod
def create(cls, ns, name, **kwargs):
"""Creates an entity with the specified namespace and name."""
key_name = '%s:%s' % (ns, name)
return cls(key_name=key_name, ns=ns, name=name, **kwargs)
@classmethod
def all_in_namespace(cls, ns):
"""Gets a query for all entities with the given namespace."""
return filter_by_prefix(cls.all(), ns + ':')
| 455 | 0 | 130 |
d9b6dacb47bb3bb1903c75fd6a311e2b090af877 | 12,452 | py | Python | rllib/policy/torch_policy.py | brechtmann/ray | 0c76ebd676f794847ea990aecced22b88717d09e | [
"Apache-2.0"
] | null | null | null | rllib/policy/torch_policy.py | brechtmann/ray | 0c76ebd676f794847ea990aecced22b88717d09e | [
"Apache-2.0"
] | null | null | null | rllib/policy/torch_policy.py | brechtmann/ray | 0c76ebd676f794847ea990aecced22b88717d09e | [
"Apache-2.0"
] | null | null | null | import numpy as np
import time
from ray.rllib.policy.policy import Policy, LEARNER_STATS_KEY, ACTION_PROB, \
ACTION_LOGP
from ray.rllib.policy.sample_batch import SampleBatch
from ray.rllib.utils.annotations import override, DeveloperAPI
from ray.rllib.utils.framework import try_import_torch
from ray.rllib.utils.schedules import ConstantSchedule, PiecewiseSchedule
from ray.rllib.utils.torch_ops import convert_to_non_torch_type
from ray.rllib.utils.tracking_dict import UsageTrackingDict
torch, _ = try_import_torch()
class TorchPolicy(Policy):
"""Template for a PyTorch policy and loss to use with RLlib.
This is similar to TFPolicy, but for PyTorch.
Attributes:
observation_space (gym.Space): observation space of the policy.
action_space (gym.Space): action space of the policy.
config (dict): config of the policy.
model (TorchModel): Torch model instance.
dist_class (type): Torch action distribution class.
"""
def __init__(self, observation_space, action_space, config, model, loss,
action_distribution_class):
"""Build a policy from policy and loss torch modules.
Note that model will be placed on GPU device if CUDA_VISIBLE_DEVICES
is set. Only single GPU is supported for now.
Arguments:
observation_space (gym.Space): observation space of the policy.
action_space (gym.Space): action space of the policy.
config (dict): The Policy config dict.
model (nn.Module): PyTorch policy module. Given observations as
input, this module must return a list of outputs where the
first item is action logits, and the rest can be any value.
loss (func): Function that takes (policy, model, dist_class,
train_batch) and returns a single scalar loss.
action_distribution_class (ActionDistribution): Class for action
distribution.
"""
self.framework = "torch"
super().__init__(observation_space, action_space, config)
self.device = (torch.device("cuda")
if torch.cuda.is_available() else torch.device("cpu"))
self.model = model.to(self.device)
self.unwrapped_model = model # used to support DistributedDataParallel
self._loss = loss
self._optimizer = self.optimizer()
self.dist_class = action_distribution_class
# If set, means we are using distributed allreduce during learning.
self.distributed_world_size = None
@override(Policy)
@override(Policy)
@override(Policy)
@override(Policy)
@override(Policy)
@override(Policy)
@override(Policy)
@override(Policy)
@override(Policy)
@override(Policy)
def extra_grad_process(self):
"""Allow subclass to do extra processing on gradients and
return processing info."""
return {}
def extra_action_out(self,
input_dict,
state_batches,
model,
action_dist=None):
"""Returns dict of extra info to include in experience batch.
Arguments:
input_dict (dict): Dict of model input tensors.
state_batches (list): List of state tensors.
model (TorchModelV2): Reference to the model.
action_dist (Distribution): Torch Distribution object to get
log-probs (e.g. for already sampled actions).
"""
return {}
def extra_grad_info(self, train_batch):
"""Return dict of extra grad info."""
return {}
def optimizer(self):
"""Custom PyTorch optimizer to use."""
if hasattr(self, "config"):
return torch.optim.Adam(
self.model.parameters(), lr=self.config["lr"])
else:
return torch.optim.Adam(self.model.parameters())
@override(Policy)
def export_model(self, export_dir):
"""TODO: implement for torch.
"""
raise NotImplementedError
@override(Policy)
def export_checkpoint(self, export_dir):
"""TODO: implement for torch.
"""
raise NotImplementedError
@DeveloperAPI
class LearningRateSchedule:
"""Mixin for TFPolicy that adds a learning rate schedule."""
@DeveloperAPI
@override(Policy)
@override(TorchPolicy)
@DeveloperAPI
class EntropyCoeffSchedule:
"""Mixin for TorchPolicy that adds entropy coeff decay."""
@DeveloperAPI
@override(Policy)
| 37.963415 | 79 | 0.608336 | import numpy as np
import time
from ray.rllib.policy.policy import Policy, LEARNER_STATS_KEY, ACTION_PROB, \
ACTION_LOGP
from ray.rllib.policy.sample_batch import SampleBatch
from ray.rllib.utils.annotations import override, DeveloperAPI
from ray.rllib.utils.framework import try_import_torch
from ray.rllib.utils.schedules import ConstantSchedule, PiecewiseSchedule
from ray.rllib.utils.torch_ops import convert_to_non_torch_type
from ray.rllib.utils.tracking_dict import UsageTrackingDict
torch, _ = try_import_torch()
class TorchPolicy(Policy):
"""Template for a PyTorch policy and loss to use with RLlib.
This is similar to TFPolicy, but for PyTorch.
Attributes:
observation_space (gym.Space): observation space of the policy.
action_space (gym.Space): action space of the policy.
config (dict): config of the policy.
model (TorchModel): Torch model instance.
dist_class (type): Torch action distribution class.
"""
def __init__(self, observation_space, action_space, config, model, loss,
action_distribution_class):
"""Build a policy from policy and loss torch modules.
Note that model will be placed on GPU device if CUDA_VISIBLE_DEVICES
is set. Only single GPU is supported for now.
Arguments:
observation_space (gym.Space): observation space of the policy.
action_space (gym.Space): action space of the policy.
config (dict): The Policy config dict.
model (nn.Module): PyTorch policy module. Given observations as
input, this module must return a list of outputs where the
first item is action logits, and the rest can be any value.
loss (func): Function that takes (policy, model, dist_class,
train_batch) and returns a single scalar loss.
action_distribution_class (ActionDistribution): Class for action
distribution.
"""
self.framework = "torch"
super().__init__(observation_space, action_space, config)
self.device = (torch.device("cuda")
if torch.cuda.is_available() else torch.device("cpu"))
self.model = model.to(self.device)
self.unwrapped_model = model # used to support DistributedDataParallel
self._loss = loss
self._optimizer = self.optimizer()
self.dist_class = action_distribution_class
# If set, means we are using distributed allreduce during learning.
self.distributed_world_size = None
@override(Policy)
def compute_actions(self,
obs_batch,
state_batches=None,
prev_action_batch=None,
prev_reward_batch=None,
info_batch=None,
episodes=None,
explore=None,
timestep=None,
**kwargs):
explore = explore if explore is not None else self.config["explore"]
with torch.no_grad():
input_dict = self._lazy_tensor_dict({
SampleBatch.CUR_OBS: obs_batch,
})
if prev_action_batch:
input_dict[SampleBatch.PREV_ACTIONS] = prev_action_batch
if prev_reward_batch:
input_dict[SampleBatch.PREV_REWARDS] = prev_reward_batch
state_batches = [self._convert_to_tensor(s) for s in state_batches]
model_out = self.model(input_dict, state_batches,
self._convert_to_tensor([1]))
logits, state = model_out
action_dist = None
actions, logp = \
self.exploration.get_exploration_action(
logits, self.dist_class, self.model,
timestep if timestep is not None else
self.global_timestep, explore)
input_dict[SampleBatch.ACTIONS] = actions
extra_action_out = self.extra_action_out(input_dict, state_batches,
self.model, action_dist)
if logp is not None:
logp = convert_to_non_torch_type(logp)
extra_action_out.update({
ACTION_PROB: np.exp(logp),
ACTION_LOGP: logp
})
return convert_to_non_torch_type(
(actions, state, extra_action_out))
@override(Policy)
def compute_log_likelihoods(self,
actions,
obs_batch,
state_batches=None,
prev_action_batch=None,
prev_reward_batch=None):
with torch.no_grad():
input_dict = self._lazy_tensor_dict({
SampleBatch.CUR_OBS: obs_batch,
SampleBatch.ACTIONS: actions
})
if prev_action_batch:
input_dict[SampleBatch.PREV_ACTIONS] = prev_action_batch
if prev_reward_batch:
input_dict[SampleBatch.PREV_REWARDS] = prev_reward_batch
parameters, _ = self.model(input_dict, state_batches, [1])
action_dist = self.dist_class(parameters, self.model)
log_likelihoods = action_dist.logp(input_dict[SampleBatch.ACTIONS])
return log_likelihoods
@override(Policy)
def learn_on_batch(self, postprocessed_batch):
train_batch = self._lazy_tensor_dict(postprocessed_batch)
loss_out = self._loss(self, self.model, self.dist_class, train_batch)
self._optimizer.zero_grad()
loss_out.backward()
info = {}
info.update(self.extra_grad_process())
if self.distributed_world_size:
grads = []
for p in self.model.parameters():
if p.grad is not None:
grads.append(p.grad)
start = time.time()
if torch.cuda.is_available():
# Sadly, allreduce_coalesced does not work with CUDA yet.
for g in grads:
torch.distributed.all_reduce(
g, op=torch.distributed.ReduceOp.SUM)
else:
torch.distributed.all_reduce_coalesced(
grads, op=torch.distributed.ReduceOp.SUM)
for p in self.model.parameters():
if p.grad is not None:
p.grad /= self.distributed_world_size
info["allreduce_latency"] = time.time() - start
self._optimizer.step()
info.update(self.extra_grad_info(train_batch))
return {LEARNER_STATS_KEY: info}
@override(Policy)
def compute_gradients(self, postprocessed_batch):
train_batch = self._lazy_tensor_dict(postprocessed_batch)
loss_out = self._loss(self, self.model, self.dist_class, train_batch)
self._optimizer.zero_grad()
loss_out.backward()
grad_process_info = self.extra_grad_process()
# Note that return values are just references;
# calling zero_grad will modify the values
grads = []
for p in self.model.parameters():
if p.grad is not None:
grads.append(p.grad.data.cpu().numpy())
else:
grads.append(None)
grad_info = self.extra_grad_info(train_batch)
grad_info.update(grad_process_info)
return grads, {LEARNER_STATS_KEY: grad_info}
@override(Policy)
def apply_gradients(self, gradients):
for g, p in zip(gradients, self.model.parameters()):
if g is not None:
p.grad = torch.from_numpy(g).to(self.device)
self._optimizer.step()
@override(Policy)
def get_weights(self):
return {k: v.cpu() for k, v in self.model.state_dict().items()}
@override(Policy)
def set_weights(self, weights):
self.model.load_state_dict(weights)
@override(Policy)
def is_recurrent(self):
return len(self.model.get_initial_state()) > 0
@override(Policy)
def num_state_tensors(self):
return len(self.model.get_initial_state())
@override(Policy)
def get_initial_state(self):
return [s.numpy() for s in self.model.get_initial_state()]
def extra_grad_process(self):
"""Allow subclass to do extra processing on gradients and
return processing info."""
return {}
def extra_action_out(self,
input_dict,
state_batches,
model,
action_dist=None):
"""Returns dict of extra info to include in experience batch.
Arguments:
input_dict (dict): Dict of model input tensors.
state_batches (list): List of state tensors.
model (TorchModelV2): Reference to the model.
action_dist (Distribution): Torch Distribution object to get
log-probs (e.g. for already sampled actions).
"""
return {}
def extra_grad_info(self, train_batch):
"""Return dict of extra grad info."""
return {}
def optimizer(self):
"""Custom PyTorch optimizer to use."""
if hasattr(self, "config"):
return torch.optim.Adam(
self.model.parameters(), lr=self.config["lr"])
else:
return torch.optim.Adam(self.model.parameters())
def _lazy_tensor_dict(self, postprocessed_batch):
train_batch = UsageTrackingDict(postprocessed_batch)
train_batch.set_get_interceptor(self._convert_to_tensor)
return train_batch
def _convert_to_tensor(self, arr):
if torch.is_tensor(arr):
return arr.to(self.device)
tensor = torch.from_numpy(np.asarray(arr))
if tensor.dtype == torch.double:
tensor = tensor.float()
return tensor.to(self.device)
@override(Policy)
def export_model(self, export_dir):
"""TODO: implement for torch.
"""
raise NotImplementedError
@override(Policy)
def export_checkpoint(self, export_dir):
"""TODO: implement for torch.
"""
raise NotImplementedError
@DeveloperAPI
class LearningRateSchedule:
"""Mixin for TFPolicy that adds a learning rate schedule."""
@DeveloperAPI
def __init__(self, lr, lr_schedule):
self.cur_lr = lr
if lr_schedule is None:
self.lr_schedule = ConstantSchedule(lr, framework=None)
else:
self.lr_schedule = PiecewiseSchedule(
lr_schedule, outside_value=lr_schedule[-1][-1], framework=None)
@override(Policy)
def on_global_var_update(self, global_vars):
super(LearningRateSchedule, self).on_global_var_update(global_vars)
self.cur_lr = self.lr_schedule.value(global_vars["timestep"])
@override(TorchPolicy)
def optimizer(self):
for p in self._optimizer.param_groups:
p["lr"] = self.cur_lr
return self._optimizer
@DeveloperAPI
class EntropyCoeffSchedule:
"""Mixin for TorchPolicy that adds entropy coeff decay."""
@DeveloperAPI
def __init__(self, entropy_coeff, entropy_coeff_schedule):
self.entropy_coeff = entropy_coeff
if entropy_coeff_schedule is None:
self.entropy_coeff_schedule = ConstantSchedule(
entropy_coeff, framework=None)
else:
# Allows for custom schedule similar to lr_schedule format
if isinstance(entropy_coeff_schedule, list):
self.entropy_coeff_schedule = PiecewiseSchedule(
entropy_coeff_schedule,
outside_value=entropy_coeff_schedule[-1][-1],
framework=None)
else:
# Implements previous version but enforces outside_value
self.entropy_coeff_schedule = PiecewiseSchedule(
[[0, entropy_coeff], [entropy_coeff_schedule, 0.0]],
outside_value=0.0,
framework=None)
@override(Policy)
def on_global_var_update(self, global_vars):
super(EntropyCoeffSchedule, self).on_global_var_update(global_vars)
self.entropy_coeff = self.entropy_coeff_schedule.value(
global_vars["timestep"])
| 7,402 | 0 | 444 |
3788688439f869905b054a77ecac95a103f926f6 | 1,297 | py | Python | sshd_telegram_alert/__main__.py | nanih98/sshd-telegram-alert | 7f5adb662658bcf3dc9020113f6133b1184a971b | [
"MIT"
] | null | null | null | sshd_telegram_alert/__main__.py | nanih98/sshd-telegram-alert | 7f5adb662658bcf3dc9020113f6133b1184a971b | [
"MIT"
] | null | null | null | sshd_telegram_alert/__main__.py | nanih98/sshd-telegram-alert | 7f5adb662658bcf3dc9020113f6133b1184a971b | [
"MIT"
] | null | null | null | import os
from .parser import parse_args
from .logger import Logger
from .configuration import Configuration
from .requester import Requester
import platform
def main() -> None:
"""Main function where the program start"""
args = parse_args()
config_path = check_os_path()
log = Logger(debug_flag=True)
log.success("Starting the program")
config = Configuration()
# config.check_os()
config.create_config(args, config_path, platform.system())
message = config.message(args)
requester = Requester()
requester.requester(args, config_path, message)
#requester.requester(config_path, message, args, platform.system())
# # Initzialize logger
# logging.basicConfig(
# format="%(asctime)-5s %(name)-15s %(levelname)-8s %(message)s",
# level = args.log_level,
# #filename='/tmp/python-telegram-bot.log',
# encoding='utf-8'
# )
# Start program
if __name__ == "__main__":
main()
| 25.94 | 86 | 0.656901 | import os
from .parser import parse_args
from .logger import Logger
from .configuration import Configuration
from .requester import Requester
import platform
def check_os_path():
if platform.system() == "Darwin":
print(os.path.join(os.environ.get('HOME'),
".sshd-telegram-credentials.json"))
return os.path.join(os.environ.get('HOME'), ".sshd-telegram-credentials.json")
else:
return "/etc/ssh/.sshd-telegram-credentials.json"
def main() -> None:
"""Main function where the program start"""
args = parse_args()
config_path = check_os_path()
log = Logger(debug_flag=True)
log.success("Starting the program")
config = Configuration()
# config.check_os()
config.create_config(args, config_path, platform.system())
message = config.message(args)
requester = Requester()
requester.requester(args, config_path, message)
#requester.requester(config_path, message, args, platform.system())
# # Initzialize logger
# logging.basicConfig(
# format="%(asctime)-5s %(name)-15s %(levelname)-8s %(message)s",
# level = args.log_level,
# #filename='/tmp/python-telegram-bot.log',
# encoding='utf-8'
# )
# Start program
if __name__ == "__main__":
main()
| 293 | 0 | 23 |
5444b2b64f6468f6af4c6c70f4231a23dad1d247 | 4,773 | py | Python | bans/database/db_bans.py | examknow/bans | 2a405788770090e552e605e00e70cfb2cf910ac1 | [
"MIT"
] | null | null | null | bans/database/db_bans.py | examknow/bans | 2a405788770090e552e605e00e70cfb2cf910ac1 | [
"MIT"
] | null | null | null | bans/database/db_bans.py | examknow/bans | 2a405788770090e552e605e00e70cfb2cf910ac1 | [
"MIT"
] | null | null | null | from aiosqlite import connect as db_connect
from time import time
from typing import List, Optional
from dataclasses import dataclass
from .common import DBTable
@dataclass
| 30.208861 | 100 | 0.503247 | from aiosqlite import connect as db_connect
from time import time
from typing import List, Optional
from dataclasses import dataclass
from .common import DBTable
@dataclass
class DBBan(object):
id: int
channelid: int
setter: str
mode: str
ts: int
mask: Optional[int]
expiry: Optional[int]
removed: Optional[int]
remover: Optional[str]
reason: Optional[str]
class BansTable(DBTable):
async def add(self,
channel: int,
setter: str,
mode: str,
mask: Optional[str] = None,
expiry: Optional[int] = None,
reason: Optional[str] = None):
async with db_connect(self._db_location) as db:
await db.execute("""
INSERT INTO bans
(channel_id, setter, mode, mask, ts, expiry_ts, reason)
VALUES (?, ?, ?, ?, ?, ?, ?)
""", [channel, setter, mode, mask, int(time()), expiry, reason])
await db.commit()
cursor = await db.execute("""
SELECT id
FROM bans
ORDER BY id DESC
LIMIT 1
""")
return (await cursor.fetchone())[0]
async def _get(self,
where: str,
limit: Optional[int],
*args: str) -> List[DBBan]:
async with db_connect(self._db_location) as db:
limit_str = ""
if limit is not None:
limit_str = f"LIMIT {limit}"
query = f"""
SELECT id, channel_id, setter, mode, ts, mask, expiry_ts, remove_ts, remover, reason
FROM bans
{where}
{limit_str}
"""
cursor = await db.execute(query, args)
rows = await cursor.fetchall()
return [DBBan(*row) for row in rows]
async def get_by_id(self,
id: int) -> List[DBBan]:
try:
return (await self._get("WHERE id = ?", 1, id))[0]
except IndexError:
return None
async def get_by_channel(self,
channel: int,
by_active: Optional[bool] = None,
by_setter: Optional[str] = None,
limit: Optional[int] = 10) -> List[DBBan]:
args: List[str] = []
where = "WHERE channel_id = ?"
args.append(channel)
if by_active is not None:
where += by_active == True and " AND remove_ts IS NULL" or " AND remove_ts IS NOT NULL"
if by_setter is not None:
where += f" AND setter LIKE ?"
args.append(by_setter)
return await self._get(where, limit, *args)
async def get_expired(self) -> List[DBBan]:
return await self._get("WHERE expiry_ts < ? AND remove_ts IS NULL", None, int(time()))
async def get_last_by_setter(self, setter: str, count: int) -> List[DBBan]:
return await self._get("WHERE setter = ? AND remove_ts IS NULL", count, setter)
async def get_id(self,
channel: str,
mask: str) -> Optional[DBBan]:
async with db_connect(self._db_location) as db:
cursor = await db.execute("""
SELECT id
FROM bans
WHERE remove_ts IS NULL
ORDER BY id DESC
LIMIT 1""")
res = await cursor.fetchone()
if len(res) > 0:
return res[0]
else:
return None
async def set_reason(self,
id: int,
reason: str):
async with db_connect(self._db_location) as db:
await db.execute("""
UPDATE bans
SET reason = ?
WHERE id = ?""", [reason, id])
await db.commit()
async def set_expiry(self,
id: int,
expiry: Optional[int]):
async with db_connect(self._db_location) as db:
await db.execute("""
UPDATE bans
SET expiry_ts = ?
WHERE id = ?""", [expiry, id])
await db.commit()
async def set_reason(self,
id: int,
reason: str):
async with db_connect(self._db_location) as db:
await db.execute("""
UPDATE bans
SET reason = ?
WHERE id = ?""", [reason, id])
await db.commit()
async def remove(self,
id: int,
remover: Optional[str] = None):
async with db_connect(self._db_location) as db:
await db.execute("""
UPDATE bans
SET remove_ts = ?, remover = ?
WHERE id = ?""", [int(time()), remover, id])
await db.commit()
| 4,035 | 206 | 341 |
077847e78c05efc885c4770c62e1e3d7fa340596 | 6,461 | py | Python | azure-mgmt-search/azure/mgmt/search/models/search_service.py | JonathanGailliez/azure-sdk-for-python | f0f051bfd27f8ea512aea6fc0c3212ee9ee0029b | [
"MIT"
] | 1 | 2021-09-07T18:36:04.000Z | 2021-09-07T18:36:04.000Z | azure-mgmt-search/azure/mgmt/search/models/search_service.py | JonathanGailliez/azure-sdk-for-python | f0f051bfd27f8ea512aea6fc0c3212ee9ee0029b | [
"MIT"
] | 2 | 2019-10-02T23:37:38.000Z | 2020-10-02T01:17:31.000Z | azure-mgmt-search/azure/mgmt/search/models/search_service.py | JonathanGailliez/azure-sdk-for-python | f0f051bfd27f8ea512aea6fc0c3212ee9ee0029b | [
"MIT"
] | 1 | 2018-08-28T14:36:47.000Z | 2018-08-28T14:36:47.000Z | # coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
#
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may cause incorrect behavior and will be lost if the code is
# regenerated.
# --------------------------------------------------------------------------
from .resource import Resource
class SearchService(Resource):
"""Describes an Azure Search service and its current state.
Variables are only populated by the server, and will be ignored when
sending a request.
:ivar id: The ID of the resource. This can be used with the Azure Resource
Manager to link resources together.
:vartype id: str
:ivar name: The name of the resource.
:vartype name: str
:ivar type: The resource type.
:vartype type: str
:param location: The geographic location of the resource. This must be one
of the supported and registered Azure Geo Regions (for example, West US,
East US, Southeast Asia, and so forth). This property is required when
creating a new resource.
:type location: str
:param tags: Tags to help categorize the resource in the Azure portal.
:type tags: dict[str, str]
:param replica_count: The number of replicas in the Search service. If
specified, it must be a value between 1 and 12 inclusive for standard SKUs
or between 1 and 3 inclusive for basic SKU. Default value: 1 .
:type replica_count: int
:param partition_count: The number of partitions in the Search service; if
specified, it can be 1, 2, 3, 4, 6, or 12. Values greater than 1 are only
valid for standard SKUs. For 'standard3' services with hostingMode set to
'highDensity', the allowed values are between 1 and 3. Default value: 1 .
:type partition_count: int
:param hosting_mode: Applicable only for the standard3 SKU. You can set
this property to enable up to 3 high density partitions that allow up to
1000 indexes, which is much higher than the maximum indexes allowed for
any other SKU. For the standard3 SKU, the value is either 'default' or
'highDensity'. For all other SKUs, this value must be 'default'. Possible
values include: 'default', 'highDensity'. Default value: "default" .
:type hosting_mode: str or ~azure.mgmt.search.models.HostingMode
:ivar status: The status of the Search service. Possible values include:
'running': The Search service is running and no provisioning operations
are underway. 'provisioning': The Search service is being provisioned or
scaled up or down. 'deleting': The Search service is being deleted.
'degraded': The Search service is degraded. This can occur when the
underlying search units are not healthy. The Search service is most likely
operational, but performance might be slow and some requests might be
dropped. 'disabled': The Search service is disabled. In this state, the
service will reject all API requests. 'error': The Search service is in an
error state. If your service is in the degraded, disabled, or error
states, it means the Azure Search team is actively investigating the
underlying issue. Dedicated services in these states are still chargeable
based on the number of search units provisioned. Possible values include:
'running', 'provisioning', 'deleting', 'degraded', 'disabled', 'error'
:vartype status: str or ~azure.mgmt.search.models.SearchServiceStatus
:ivar status_details: The details of the Search service status.
:vartype status_details: str
:ivar provisioning_state: The state of the last provisioning operation
performed on the Search service. Provisioning is an intermediate state
that occurs while service capacity is being established. After capacity is
set up, provisioningState changes to either 'succeeded' or 'failed'.
Client applications can poll provisioning status (the recommended polling
interval is from 30 seconds to one minute) by using the Get Search Service
operation to see when an operation is completed. If you are using the free
service, this value tends to come back as 'succeeded' directly in the call
to Create Search service. This is because the free service uses capacity
that is already set up. Possible values include: 'succeeded',
'provisioning', 'failed'
:vartype provisioning_state: str or
~azure.mgmt.search.models.ProvisioningState
:param sku: The SKU of the Search Service, which determines price tier and
capacity limits. This property is required when creating a new Search
Service.
:type sku: ~azure.mgmt.search.models.Sku
"""
_validation = {
'id': {'readonly': True},
'name': {'readonly': True},
'type': {'readonly': True},
'replica_count': {'maximum': 12, 'minimum': 1},
'partition_count': {'maximum': 12, 'minimum': 1},
'status': {'readonly': True},
'status_details': {'readonly': True},
'provisioning_state': {'readonly': True},
}
_attribute_map = {
'id': {'key': 'id', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
'location': {'key': 'location', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'replica_count': {'key': 'properties.replicaCount', 'type': 'int'},
'partition_count': {'key': 'properties.partitionCount', 'type': 'int'},
'hosting_mode': {'key': 'properties.hostingMode', 'type': 'HostingMode'},
'status': {'key': 'properties.status', 'type': 'SearchServiceStatus'},
'status_details': {'key': 'properties.statusDetails', 'type': 'str'},
'provisioning_state': {'key': 'properties.provisioningState', 'type': 'ProvisioningState'},
'sku': {'key': 'sku', 'type': 'Sku'},
}
| 52.959016 | 99 | 0.67358 | # coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
#
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may cause incorrect behavior and will be lost if the code is
# regenerated.
# --------------------------------------------------------------------------
from .resource import Resource
class SearchService(Resource):
"""Describes an Azure Search service and its current state.
Variables are only populated by the server, and will be ignored when
sending a request.
:ivar id: The ID of the resource. This can be used with the Azure Resource
Manager to link resources together.
:vartype id: str
:ivar name: The name of the resource.
:vartype name: str
:ivar type: The resource type.
:vartype type: str
:param location: The geographic location of the resource. This must be one
of the supported and registered Azure Geo Regions (for example, West US,
East US, Southeast Asia, and so forth). This property is required when
creating a new resource.
:type location: str
:param tags: Tags to help categorize the resource in the Azure portal.
:type tags: dict[str, str]
:param replica_count: The number of replicas in the Search service. If
specified, it must be a value between 1 and 12 inclusive for standard SKUs
or between 1 and 3 inclusive for basic SKU. Default value: 1 .
:type replica_count: int
:param partition_count: The number of partitions in the Search service; if
specified, it can be 1, 2, 3, 4, 6, or 12. Values greater than 1 are only
valid for standard SKUs. For 'standard3' services with hostingMode set to
'highDensity', the allowed values are between 1 and 3. Default value: 1 .
:type partition_count: int
:param hosting_mode: Applicable only for the standard3 SKU. You can set
this property to enable up to 3 high density partitions that allow up to
1000 indexes, which is much higher than the maximum indexes allowed for
any other SKU. For the standard3 SKU, the value is either 'default' or
'highDensity'. For all other SKUs, this value must be 'default'. Possible
values include: 'default', 'highDensity'. Default value: "default" .
:type hosting_mode: str or ~azure.mgmt.search.models.HostingMode
:ivar status: The status of the Search service. Possible values include:
'running': The Search service is running and no provisioning operations
are underway. 'provisioning': The Search service is being provisioned or
scaled up or down. 'deleting': The Search service is being deleted.
'degraded': The Search service is degraded. This can occur when the
underlying search units are not healthy. The Search service is most likely
operational, but performance might be slow and some requests might be
dropped. 'disabled': The Search service is disabled. In this state, the
service will reject all API requests. 'error': The Search service is in an
error state. If your service is in the degraded, disabled, or error
states, it means the Azure Search team is actively investigating the
underlying issue. Dedicated services in these states are still chargeable
based on the number of search units provisioned. Possible values include:
'running', 'provisioning', 'deleting', 'degraded', 'disabled', 'error'
:vartype status: str or ~azure.mgmt.search.models.SearchServiceStatus
:ivar status_details: The details of the Search service status.
:vartype status_details: str
:ivar provisioning_state: The state of the last provisioning operation
performed on the Search service. Provisioning is an intermediate state
that occurs while service capacity is being established. After capacity is
set up, provisioningState changes to either 'succeeded' or 'failed'.
Client applications can poll provisioning status (the recommended polling
interval is from 30 seconds to one minute) by using the Get Search Service
operation to see when an operation is completed. If you are using the free
service, this value tends to come back as 'succeeded' directly in the call
to Create Search service. This is because the free service uses capacity
that is already set up. Possible values include: 'succeeded',
'provisioning', 'failed'
:vartype provisioning_state: str or
~azure.mgmt.search.models.ProvisioningState
:param sku: The SKU of the Search Service, which determines price tier and
capacity limits. This property is required when creating a new Search
Service.
:type sku: ~azure.mgmt.search.models.Sku
"""
_validation = {
'id': {'readonly': True},
'name': {'readonly': True},
'type': {'readonly': True},
'replica_count': {'maximum': 12, 'minimum': 1},
'partition_count': {'maximum': 12, 'minimum': 1},
'status': {'readonly': True},
'status_details': {'readonly': True},
'provisioning_state': {'readonly': True},
}
_attribute_map = {
'id': {'key': 'id', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
'location': {'key': 'location', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'replica_count': {'key': 'properties.replicaCount', 'type': 'int'},
'partition_count': {'key': 'properties.partitionCount', 'type': 'int'},
'hosting_mode': {'key': 'properties.hostingMode', 'type': 'HostingMode'},
'status': {'key': 'properties.status', 'type': 'SearchServiceStatus'},
'status_details': {'key': 'properties.statusDetails', 'type': 'str'},
'provisioning_state': {'key': 'properties.provisioningState', 'type': 'ProvisioningState'},
'sku': {'key': 'sku', 'type': 'Sku'},
}
def __init__(self, **kwargs):
super(SearchService, self).__init__(**kwargs)
self.replica_count = kwargs.get('replica_count', 1)
self.partition_count = kwargs.get('partition_count', 1)
self.hosting_mode = kwargs.get('hosting_mode', "default")
self.status = None
self.status_details = None
self.provisioning_state = None
self.sku = kwargs.get('sku', None)
| 396 | 0 | 27 |
a50e62be14975592aef74c3fa5ad879365e37cfa | 1,893 | py | Python | Prediction.py | sarthak25/Google-Stock-Price-Prediction | 017f78b20def5ca5a3e2727051bcbc735b9dc33c | [
"MIT"
] | 1 | 2021-07-04T12:09:35.000Z | 2021-07-04T12:09:35.000Z | Prediction.py | sarthak25/Google-Stock-Price-Prediction | 017f78b20def5ca5a3e2727051bcbc735b9dc33c | [
"MIT"
] | null | null | null | Prediction.py | sarthak25/Google-Stock-Price-Prediction | 017f78b20def5ca5a3e2727051bcbc735b9dc33c | [
"MIT"
] | 1 | 2020-11-16T18:31:57.000Z | 2020-11-16T18:31:57.000Z | # A PREDICTIVE MODEL FOR GOOGLE STOCK PRICES USING LINEAR REGRESSION
# IMPORTING ALL THE MODULES
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
# READING THE DATASET AND VISUALIZING A FEW ENTRIES
dt=pd.read_csv("Dataset/Google_Stock_Price_Train.csv")
print("Entries of the dataset:")
print(dt.head())
# PREPARATION OF THE TRAINING DATASET
df=dt.iloc[:,1:2].values
# NUMPY ARRAY
print("Shape of the training dataset:")
print(dt.shape)
x_train=[]
y_train=[]
for i in range(60,1258):
x_train.append(df[i-60:i,0])
y_train.append(df[i,0])
x_train,y_train= np.array(x_train), np.array(y_train)
print("The Converted X_train shape:")
print(x_train.shape)
print("The Converted Y_train shape:")
print(y_train.shape)
# PREPARATION OF THE TESTING DATASET
df_test = pd.read_csv('Dataset/Google_Stock_Price_Test.csv')
Real_stock_price = df_test.iloc[:,1:2].values
print("Shape of the testing dataset:")
print(Real_stock_price.shape)
total_dataset = pd.concat((dt['Open'], df_test['Open']),axis = 0 )
inputs = total_dataset[len(total_dataset)-len(df_test) - 60:].values
inputs = inputs.reshape(-1,1)
print("Shape of the dataset after conversion:")
print(inputs.shape)
x_test = []
for i in range (60,80):
x_test.append(inputs[i-60:i,0])
x_test=np.array(x_test)
print("Converted X_test shape:")
print(x_test.shape)
# FITTING THE DATA IN THE MODEL
lrm = LinearRegression()
print(lrm.fit(x_train,y_train))
pred_lrm = lrm.predict(x_test)
print("Prediction:")
print(pred_lrm)
# PLOTTING THE GRAPH
plt.plot(Real_stock_price,color='red', label='Real Result')
plt.plot(pred_lrm,color='green',label='Predicted by Model')
plt.title('Google Stock Price Prediction')
plt.xlabel('Time')
plt.ylabel('Price')
plt.legend()
plt.show()
| 27.042857 | 69 | 0.725304 | # A PREDICTIVE MODEL FOR GOOGLE STOCK PRICES USING LINEAR REGRESSION
# IMPORTING ALL THE MODULES
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
# READING THE DATASET AND VISUALIZING A FEW ENTRIES
dt=pd.read_csv("Dataset/Google_Stock_Price_Train.csv")
print("Entries of the dataset:")
print(dt.head())
# PREPARATION OF THE TRAINING DATASET
df=dt.iloc[:,1:2].values
# NUMPY ARRAY
print("Shape of the training dataset:")
print(dt.shape)
x_train=[]
y_train=[]
for i in range(60,1258):
x_train.append(df[i-60:i,0])
y_train.append(df[i,0])
x_train,y_train= np.array(x_train), np.array(y_train)
print("The Converted X_train shape:")
print(x_train.shape)
print("The Converted Y_train shape:")
print(y_train.shape)
# PREPARATION OF THE TESTING DATASET
df_test = pd.read_csv('Dataset/Google_Stock_Price_Test.csv')
Real_stock_price = df_test.iloc[:,1:2].values
print("Shape of the testing dataset:")
print(Real_stock_price.shape)
total_dataset = pd.concat((dt['Open'], df_test['Open']),axis = 0 )
inputs = total_dataset[len(total_dataset)-len(df_test) - 60:].values
inputs = inputs.reshape(-1,1)
print("Shape of the dataset after conversion:")
print(inputs.shape)
x_test = []
for i in range (60,80):
x_test.append(inputs[i-60:i,0])
x_test=np.array(x_test)
print("Converted X_test shape:")
print(x_test.shape)
# FITTING THE DATA IN THE MODEL
lrm = LinearRegression()
print(lrm.fit(x_train,y_train))
pred_lrm = lrm.predict(x_test)
print("Prediction:")
print(pred_lrm)
# PLOTTING THE GRAPH
plt.plot(Real_stock_price,color='red', label='Real Result')
plt.plot(pred_lrm,color='green',label='Predicted by Model')
plt.title('Google Stock Price Prediction')
plt.xlabel('Time')
plt.ylabel('Price')
plt.legend()
plt.show()
| 0 | 0 | 0 |
f95e1333ac543570c7afc2700e11021923f0fd0f | 579 | py | Python | torchtools/test.py | Takezo87/torchtools | 4230305d9063dabee3614f0dcd8557739b90f817 | [
"Apache-2.0"
] | null | null | null | torchtools/test.py | Takezo87/torchtools | 4230305d9063dabee3614f0dcd8557739b90f817 | [
"Apache-2.0"
] | 1 | 2022-02-26T06:23:52.000Z | 2022-02-26T06:23:52.000Z | torchtools/test.py | Takezo87/torchtools | 4230305d9063dabee3614f0dcd8557739b90f817 | [
"Apache-2.0"
] | null | null | null | # AUTOGENERATED! DO NOT EDIT! File to edit: 100_test.ipynb (unless otherwise specified).
__all__ = ['items_to_arrays']
# Cell
from .core import *
from .data import *
from .models import *
from .datasets import *
from .augmentations import *
from .datablock import *
from .dataloader import *
# Cell
def items_to_arrays(items):
'''convert list of item tuples into X,y numpy arrays (for use with numpy dataloader)'''
# return np.stack([x[0] for x in items]), np.stack([x[1] for x in items])
return tuple(np.stack([x[i] for x in items]) for i in range(len(items[0]))) | 32.166667 | 91 | 0.704663 | # AUTOGENERATED! DO NOT EDIT! File to edit: 100_test.ipynb (unless otherwise specified).
__all__ = ['items_to_arrays']
# Cell
from .core import *
from .data import *
from .models import *
from .datasets import *
from .augmentations import *
from .datablock import *
from .dataloader import *
# Cell
def items_to_arrays(items):
'''convert list of item tuples into X,y numpy arrays (for use with numpy dataloader)'''
# return np.stack([x[0] for x in items]), np.stack([x[1] for x in items])
return tuple(np.stack([x[i] for x in items]) for i in range(len(items[0]))) | 0 | 0 | 0 |
13d97300471566ef042dc75cbe53f61f814fbf4a | 28,681 | py | Python | sopel/tools/__init__.py | cottongin/sopel | 54e42747959d86ddc1ba40bc02098cb3ab7d9439 | [
"EFL-2.0"
] | null | null | null | sopel/tools/__init__.py | cottongin/sopel | 54e42747959d86ddc1ba40bc02098cb3ab7d9439 | [
"EFL-2.0"
] | null | null | null | sopel/tools/__init__.py | cottongin/sopel | 54e42747959d86ddc1ba40bc02098cb3ab7d9439 | [
"EFL-2.0"
] | null | null | null | # coding=utf-8
"""Useful miscellaneous tools and shortcuts for Sopel plugins
*Availability: 3+*
"""
# tools.py - Sopel misc tools
# Copyright 2008, Sean B. Palmer, inamidst.com
# Copyright © 2012, Elad Alfassa <elad@fedoraproject.org>
# Copyright 2012, Elsie Powell, embolalia.com
# Licensed under the Eiffel Forum License 2.
# https://sopel.chat
from __future__ import absolute_import, division, print_function, unicode_literals
import codecs
from collections import defaultdict
import functools
import logging
import os
import re
import sys
import threading
import traceback
from pkg_resources import parse_version
from sopel import __version__
from sopel.tools._events import events # NOQA
if sys.version_info.major >= 3:
raw_input = input
unicode = str
iteritems = dict.items
itervalues = dict.values
iterkeys = dict.keys
else:
iteritems = dict.iteritems
itervalues = dict.itervalues
iterkeys = dict.iterkeys
_channel_prefixes = ('#', '&', '+', '!')
# Can be implementation-dependent
_regex_type = type(re.compile(''))
def deprecated(
reason=None,
version=None,
removed_in=None,
warning_in=None,
func=None,
):
"""Decorator to mark deprecated functions in Sopel's API
:param str reason: optional text added to the deprecation warning
:param str version: optional version number when the decorated function
is deprecated
:param str removed_in: optional version number when the deprecated function
will be removed
:param str warning_in: optional version number when the decorated function
should start emitting a warning when called
:param callable func: deprecated function
:return: a callable that depends on how the decorator is called; either
the decorated function, or a decorator with the appropriate
parameters
Any time the decorated ``func`` is called, a deprecation warning will be
printed to ``sys.stderr``, with the last frame of the traceback. The
optional ``warning_in`` argument suppresses the warning on Sopel versions
older than that, allowing for multi-stage deprecation timelines.
The decorator can be used with or without arguments::
from sopel import tools
@tools.deprecated
def func1():
print('func 1')
@tools.deprecated()
def func2():
print('func 2')
@tools.deprecated(reason='obsolete', version='7.0', removed_in='8.0')
def func3():
print('func 3')
which will output the following in a console::
>>> func1()
Deprecated: func1
File "<stdin>", line 1, in <module>
func 1
>>> func2()
Deprecated: func2
File "<stdin>", line 1, in <module>
func 2
>>> func3()
Deprecated since 7.0, will be removed in 8.0: obsolete
File "<stdin>", line 1, in <module>
func 3
.. note::
There is nothing that prevents this decorator to be used on a class's
method, or on any existing callable.
.. versionadded:: 7.0
Parameters ``reason``, ``version``, and ``removed_in``.
.. versionadded:: 7.1
The ``warning_in`` parameter.
"""
if not any([reason, version, removed_in, warning_in, func]):
# common usage: @deprecated()
return deprecated
if callable(reason):
# common usage: @deprecated
return deprecated(func=reason)
if func is None:
# common usage: @deprecated(message, version, removed_in)
return decorator
# now, we have everything we need to have:
# - message is not a callable (could be None)
# - func is not None
# - version and removed_in can be None but that's OK
# so now we can return the actual decorated function
message = reason or getattr(func, '__name__', '<anonymous-function>')
template = 'Deprecated: {message}'
if version and removed_in:
template = (
'Deprecated since {version}, '
'will be removed in {removed_in}: '
'{message}')
elif version:
template = 'Deprecated since {version}: {message}'
elif removed_in:
template = 'Deprecated, will be removed in {removed_in}: {message}'
text = template.format(
message=message, version=version, removed_in=removed_in)
@functools.wraps(func)
return deprecated_func
@deprecated('Shim for Python 2 cross-compatibility, no longer needed. '
'Use built-in `input()` instead.',
version='7.1',
warning_in='8.0',
removed_in='8.1')
def get_input(prompt):
"""Get decoded input from the terminal (equivalent to Python 3's ``input``).
:param str prompt: what to display as a prompt on the terminal
:return: the user's input
:rtype: str
.. deprecated:: 7.1
Use of this function will become a warning when Python 2 support is
dropped in Sopel 8.0. The function will be removed in Sopel 8.1.
"""
if sys.version_info.major >= 3:
return input(prompt)
else:
return raw_input(prompt).decode('utf8')
def compile_rule(nick, pattern, alias_nicks):
"""Compile a rule regex and fill in nickname placeholders.
:param str nick: the nickname to use when replacing ``$nick`` and
``$nickname`` placeholders in the ``pattern``
:param str pattern: the rule regex pattern
:param list alias_nicks: a list of alternatives that should also be accepted
instead of ``nick``
:return: the compiled regex ``pattern``, with placeholders for ``$nick`` and
``$nickname`` filled in
:rtype: :ref:`re.Pattern <python:re-objects>`
Will not recompile an already compiled pattern.
"""
# Not sure why this happens on reloads, but it shouldn't cause problems…
if isinstance(pattern, _regex_type):
return pattern
if alias_nicks:
nicks = list(alias_nicks) # alias_nicks.copy() doesn't work in py2
nicks.append(nick)
nicks = map(re.escape, nicks)
nick = '(?:%s)' % '|'.join(nicks)
else:
nick = re.escape(nick)
pattern = pattern.replace('$nickname', nick)
pattern = pattern.replace('$nick ', r'{}[,:]\s*'.format(nick)) # @rule('$nick hi')
pattern = pattern.replace('$nick', r'{}[,:]\s+'.format(nick)) # @rule('$nickhi')
flags = re.IGNORECASE
if '\n' in pattern:
# See https://docs.python.org/3/library/re.html#re.VERBOSE
flags |= re.VERBOSE
return re.compile(pattern, flags)
def get_command_regexp(prefix, command):
"""Get a compiled regexp object that implements the command.
:param str prefix: the command prefix (interpreted as regex)
:param str command: the name of the command
:return: a compiled regexp object that implements the command
:rtype: :ref:`re.Pattern <python:re-objects>`
"""
# Escape all whitespace with a single backslash. This ensures that regexp
# in the prefix is treated as it was before the actual regexp was changed
# to use the verbose syntax.
prefix = re.sub(r"(\s)", r"\\\1", prefix)
pattern = get_command_pattern(prefix, command)
return re.compile(pattern, re.IGNORECASE | re.VERBOSE)
def get_command_pattern(prefix, command):
"""Get the uncompiled regex pattern for standard commands.
:param str prefix: the command prefix (interpreted as regex)
:param str command: the command name
:return: a regex pattern that will match the given command
:rtype: str
"""
# This regexp matches equivalently and produces the same
# groups 1 and 2 as the old regexp: r'^%s(%s)(?: +(.*))?$'
# The only differences should be handling all whitespace
# like spaces and the addition of groups 3-6.
return r"""
(?:{prefix})({command}) # Command as group 1.
(?:\s+ # Whitespace to end command.
( # Rest of the line as group 2.
(?:(\S+))? # Parameters 1-4 as groups 3-6.
(?:\s+(\S+))?
(?:\s+(\S+))?
(?:\s+(\S+))?
.* # Accept anything after the parameters.
# Leave it up to the plugin to parse
# the line.
))? # Group 2 must be None, if there are no
# parameters.
$ # EoL, so there are no partial matches.
""".format(prefix=prefix, command=command)
def get_nickname_command_regexp(nick, command, alias_nicks):
"""Get a compiled regexp object that implements the nickname command.
:param str nick: the bot's nickname
:param str command: the command name
:param list alias_nicks: a list of alternatives that should also be accepted
instead of ``nick``
:return: a compiled regex pattern that implements the given nickname command
:rtype: :ref:`re.Pattern <python:re-objects>`
"""
if isinstance(alias_nicks, unicode):
alias_nicks = [alias_nicks]
elif not isinstance(alias_nicks, (list, tuple)):
raise ValueError('A list or string is required.')
return compile_rule(nick, get_nickname_command_pattern(command), alias_nicks)
def get_nickname_command_pattern(command):
"""Get the uncompiled regex pattern for a nickname command.
:param str command: the command name
:return: a regex pattern that will match the given nickname command
:rtype: str
"""
return r"""
^
$nickname[:,]? # Nickname.
\s+({command}) # Command as group 1.
(?:\s+ # Whitespace to end command.
( # Rest of the line as group 2.
(?:(\S+))? # Parameters 1-4 as groups 3-6.
(?:\s+(\S+))?
(?:\s+(\S+))?
(?:\s+(\S+))?
.* # Accept anything after the parameters. Leave it up to
# the plugin to parse the line.
))? # Group 1 must be None, if there are no parameters.
$ # EoL, so there are no partial matches.
""".format(command=command)
def get_action_command_regexp(command):
"""Get a compiled regexp object that implements the command.
:param str command: the name of the command
:return: a compiled regexp object that implements the command
:rtype: :ref:`re.Pattern <python:re-objects>`
"""
pattern = get_action_command_pattern(command)
return re.compile(pattern, re.IGNORECASE | re.VERBOSE)
def get_action_command_pattern(command):
"""Get the uncompiled regex pattern for action commands.
:param str command: the command name
:return: a regex pattern that will match the given command
:rtype: str
"""
# This regexp matches equivalently and produces the same
# groups 1 and 2 as the old regexp: r'^%s(%s)(?: +(.*))?$'
# The only differences should be handling all whitespace
# like spaces and the addition of groups 3-6.
return r"""
({command}) # Command as group 1.
(?:\s+ # Whitespace to end command.
( # Rest of the line as group 2.
(?:(\S+))? # Parameters 1-4 as groups 3-6.
(?:\s+(\S+))?
(?:\s+(\S+))?
(?:\s+(\S+))?
.* # Accept anything after the parameters.
# Leave it up to the plugin to parse
# the line.
))? # Group 2 must be None, if there are no
# parameters.
$ # EoL, so there are no partial matches.
""".format(command=command)
def get_sendable_message(text, max_length=400):
"""Get a sendable ``text`` message, with its excess when needed.
:param str txt: text to send (expects Unicode-encoded string)
:param int max_length: maximum length of the message to be sendable
:return: a tuple of two values, the sendable text and its excess text
:rtype: (str, str)
We're arbitrarily saying that the max is 400 bytes of text when
messages will be split. Otherwise, we'd have to account for the bot's
hostmask, which is hard.
The ``max_length`` is the max length of text in **bytes**, but we take
care of Unicode 2-byte characters by working on the Unicode string,
then making sure the bytes version is smaller than the max length.
"""
unicode_max_length = max_length
excess = ''
while len(text.encode('utf-8')) > max_length:
last_space = text.rfind(' ', 0, unicode_max_length)
if last_space == -1:
# No last space, just split where it is possible
excess = text[unicode_max_length:] + excess
text = text[:unicode_max_length]
# Decrease max length for the unicode string
unicode_max_length = unicode_max_length - 1
else:
# Split at the last best space found
excess = text[last_space:]
text = text[:last_space]
return text, excess.lstrip()
# This class was useful before Python 2.5, when ``defaultdict`` was added
# to the built-in ``collections`` module.
# It is now deprecated.
class Ddict(dict):
"""A default dict implementation available for Python 2.x support.
It was used to make multi-dimensional ``dict``\\s easy to use when the
bot worked with Python version < 2.5.
.. deprecated:: 7.0
Use :class:`collections.defaultdict` instead.
"""
@deprecated('use "collections.defaultdict" instead', '7.0', '8.0')
class Identifier(unicode):
"""A `unicode` subclass which acts appropriately for IRC identifiers.
When used as normal `unicode` objects, case will be preserved.
However, when comparing two Identifier objects, or comparing a Identifier
object with a `unicode` object, the comparison will be case insensitive.
This case insensitivity includes the case convention conventions regarding
``[]``, ``{}``, ``|``, ``\\``, ``^`` and ``~`` described in RFC 2812.
"""
# May want to tweak this and update documentation accordingly when dropping
# Python 2 support, since in py3 plain str is Unicode and a "unicode" type
# no longer exists. Probably lots of code will need tweaking, tbh.
def lower(self):
"""Get the RFC 2812-compliant lowercase version of this identifier.
:return: RFC 2812-compliant lowercase version of the
:py:class:`Identifier` instance
:rtype: str
"""
return self._lowered
@staticmethod
def _lower(identifier):
"""Convert an identifier to lowercase per RFC 2812.
:param str identifier: the identifier (nickname or channel) to convert
:return: RFC 2812-compliant lowercase version of ``identifier``
:rtype: str
"""
if isinstance(identifier, Identifier):
return identifier._lowered
# The tilde replacement isn't needed for identifiers, but is for
# channels, which may be useful at some point in the future.
low = identifier.lower().replace('[', '{').replace(']', '}')
low = low.replace('\\', '|').replace('~', '^')
return low
@staticmethod
def _lower_swapped(identifier):
"""Backward-compatible version of :meth:`_lower`.
:param str identifier: the identifier (nickname or channel) to convert
:return: RFC 2812-non-compliant lowercase version of ``identifier``
:rtype: str
This is what the old :meth:`_lower` function did before Sopel 7.0. It maps
``{}``, ``[]``, ``|``, ``\\``, ``^``, and ``~`` incorrectly.
You shouldn't use this unless you need to migrate stored values from the
previous, incorrect "lowercase" representation to the correct one.
"""
# The tilde replacement isn't needed for identifiers, but is for
# channels, which may be useful at some point in the future.
low = identifier.lower().replace('{', '[').replace('}', ']')
low = low.replace('|', '\\').replace('^', '~')
return low
def is_nick(self):
"""Check if the Identifier is a nickname (i.e. not a channel)
:return: ``True`` if this :py:class:`Identifier` is a nickname;
``False`` if it appears to be a channel
"""
return self and not self.startswith(_channel_prefixes)
class OutputRedirect(object):
"""Redirect the output to the terminal and a log file.
A simplified object used to write to both the terminal and a log file.
"""
def __init__(self, logpath, stderr=False, quiet=False):
"""Create an object which will log to both a file and the terminal.
:param str logpath: path to the log file
:param bool stderr: write output to stderr if ``True``, or to stdout
otherwise
:param bool quiet: write to the log file only if ``True`` (and not to
the terminal)
Create an object which will log to the file at ``logpath`` as well as
the terminal.
"""
self.logpath = logpath
self.stderr = stderr
self.quiet = quiet
def write(self, string):
"""Write the given ``string`` to the logfile and terminal.
:param str string: the string to write
"""
if not self.quiet:
try:
if self.stderr:
sys.__stderr__.write(string)
else:
sys.__stdout__.write(string)
except Exception: # TODO: Be specific
pass
with codecs.open(self.logpath, 'ab', encoding="utf8",
errors='xmlcharrefreplace') as logfile:
try:
logfile.write(string)
except UnicodeDecodeError:
# we got an invalid string, safely encode it to utf-8
logfile.write(unicode(string, 'utf8', errors="replace"))
def flush(self):
"""Flush the file writing buffer."""
if self.stderr:
sys.__stderr__.flush()
else:
sys.__stdout__.flush()
# These seems to trace back to when we thought we needed a try/except on prints,
# because it looked like that was why we were having problems.
# We'll drop it in Sopel 8.0 because it has been here for far too long already.
@deprecated('Use `print()` instead of sopel.tools.stdout', removed_in='8.0')
def stderr(string):
"""Print the given ``string`` to stderr.
:param str string: the string to output
This is equivalent to ``print >> sys.stderr, string``
"""
print(string, file=sys.stderr)
def check_pid(pid):
"""Check if a process is running with the given ``PID``.
:param int pid: PID to check
:return bool: ``True`` if the given PID is running, ``False`` otherwise
*Availability: POSIX systems only.*
.. note::
Matching the :py:func:`os.kill` behavior this function needs on Windows
was rejected in
`Python issue #14480 <https://bugs.python.org/issue14480>`_, so
:py:func:`check_pid` cannot be used on Windows systems.
"""
try:
os.kill(pid, 0)
except OSError:
return False
else:
return True
def get_hostmask_regex(mask):
"""Get a compiled regex pattern for an IRC hostmask
:param str mask: the hostmask that the pattern should match
:return: a compiled regex pattern matching the given ``mask``
:rtype: :ref:`re.Pattern <python:re-objects>`
"""
mask = re.escape(mask)
mask = mask.replace(r'\*', '.*')
return re.compile(mask + '$', re.I)
def get_logger(plugin_name):
"""Return a logger for a plugin.
:param str plugin_name: name of the plugin
:return: the logger for the given plugin
This::
from sopel import plugins
LOGGER = plugins.get_logger('my_custom_plugin')
is equivalent to this::
import logging
LOGGER = logging.getLogger('sopel.externals.my_custom_plugin')
Internally, Sopel configures logging for the ``sopel`` namespace, so
external plugins can't benefit from it with ``logging.getLogger(__name__)``
as they won't be in the same namespace. This function uses the
``plugin_name`` with a prefix inside this namespace.
"""
return logging.getLogger('sopel.externals.%s' % plugin_name)
class SopelMemory(dict):
"""A simple thread-safe ``dict`` implementation.
In order to prevent exceptions when iterating over the values and changing
them at the same time from different threads, we use a blocking lock in
``__setitem__`` and ``contains``.
.. versionadded:: 3.1
As ``Willie.WillieMemory``
.. versionchanged:: 4.0
Moved to ``tools.WillieMemory``
.. versionchanged:: 6.0
Renamed from ``WillieMemory`` to ``SopelMemory``
"""
def __setitem__(self, key, value):
"""Set a key equal to a value.
The dict is locked for other writes while doing so.
"""
self.lock.acquire()
result = dict.__setitem__(self, key, value)
self.lock.release()
return result
def __contains__(self, key):
"""Check if a key is in the dict.
The dict is locked for writes while doing so.
"""
self.lock.acquire()
result = dict.__contains__(self, key)
self.lock.release()
return result
# Needed to make it explicit that we don't care about the `lock` attribute
# when comparing/hashing SopelMemory objects.
__eq__ = dict.__eq__
__ne__ = dict.__ne__
__hash__ = dict.__hash__
@deprecated
def contains(self, key):
"""Check if ``key`` is in the memory
:param str key: key to check for
.. deprecated:: 7.0
Will be removed in Sopel 8. If you aren't already using the ``in``
operator, you should be.
"""
return self.__contains__(key)
class SopelMemoryWithDefault(defaultdict):
"""Same as SopelMemory, but subclasses from collections.defaultdict.
.. versionadded:: 4.3
As ``WillieMemoryWithDefault``
.. versionchanged:: 6.0
Renamed to ``SopelMemoryWithDefault``
"""
def __setitem__(self, key, value):
"""Set a key equal to a value.
The dict is locked for other writes while doing so.
"""
self.lock.acquire()
result = defaultdict.__setitem__(self, key, value)
self.lock.release()
return result
def __contains__(self, key):
"""Check if a key is in the dict.
The dict is locked for writes while doing so.
"""
self.lock.acquire()
result = defaultdict.__contains__(self, key)
self.lock.release()
return result
@deprecated
def contains(self, key):
"""Check if ``key`` is in the memory
:param str key: key to check for
.. deprecated:: 7.0
Will be removed in Sopel 8. If you aren't already using the ``in``
operator, you should be.
"""
return self.__contains__(key)
class SopelIdentifierMemory(SopelMemory):
"""Special Sopel memory that stores ``Identifier`` as key.
This is a convenient subclass of :class:`SopelMemory` that always casts its
keys as instances of :class:`Identifier`::
>>> from sopel import tools
>>> memory = tools.SopelIdentifierMemory()
>>> memory['Exirel'] = 'king'
>>> list(memory.items())
[(Identifier('Exirel'), 'king')]
>>> tools.Identifier('exirel') in memory
True
>>> 'exirel' in memory
True
As seen in the example above, it is possible to perform various operations
with both ``Identifier`` and :class:`str` objects, taking advantage of the
case-insensitive behavior of ``Identifier``.
.. note::
Internally, it will try to do ``key = tools.Identifier(key)``, which
will raise an exception if it cannot instantiate the key properly::
>>> memory[1] = 'error'
AttributeError: 'int' object has no attribute 'lower'
.. versionadded:: 7.1
"""
@deprecated(version='7.0', removed_in='8.0')
def get_raising_file_and_line(tb=None):
"""Get the file and line number where an exception happened.
:param tb: the traceback (uses the most recent exception if not given)
:return: a tuple of the filename and line number
:rtype: (str, int)
.. deprecated:: 7.0
Use Python's built-in logging system, with the ``logger.exception``
method. This method makes sure to log the exception with the traceback
and the relevant information (filename, line number, etc.).
"""
if not tb:
tb = sys.exc_info()[2]
filename, lineno, _context, _line = traceback.extract_tb(tb)[-1]
return filename, lineno
def chain_loaders(*lazy_loaders):
"""Chain lazy loaders into one.
:param lazy_loaders: one or more lazy loader functions
:type lazy_loaders: :term:`function`
:return: a lazy loader that combines all of the given ones
:rtype: :term:`function`
This function takes any number of lazy loaders as arguments and merges them
together into one. It's primarily a helper for lazy rule decorators such as
:func:`sopel.plugin.url_lazy`.
.. important::
This function doesn't check the uniqueness of regexes generated by
all the loaders.
"""
return chained_loader
| 34.103448 | 87 | 0.62055 | # coding=utf-8
"""Useful miscellaneous tools and shortcuts for Sopel plugins
*Availability: 3+*
"""
# tools.py - Sopel misc tools
# Copyright 2008, Sean B. Palmer, inamidst.com
# Copyright © 2012, Elad Alfassa <elad@fedoraproject.org>
# Copyright 2012, Elsie Powell, embolalia.com
# Licensed under the Eiffel Forum License 2.
# https://sopel.chat
from __future__ import absolute_import, division, print_function, unicode_literals
import codecs
from collections import defaultdict
import functools
import logging
import os
import re
import sys
import threading
import traceback
from pkg_resources import parse_version
from sopel import __version__
from sopel.tools._events import events # NOQA
if sys.version_info.major >= 3:
raw_input = input
unicode = str
iteritems = dict.items
itervalues = dict.values
iterkeys = dict.keys
else:
iteritems = dict.iteritems
itervalues = dict.itervalues
iterkeys = dict.iterkeys
_channel_prefixes = ('#', '&', '+', '!')
# Can be implementation-dependent
_regex_type = type(re.compile(''))
def deprecated(
reason=None,
version=None,
removed_in=None,
warning_in=None,
func=None,
):
"""Decorator to mark deprecated functions in Sopel's API
:param str reason: optional text added to the deprecation warning
:param str version: optional version number when the decorated function
is deprecated
:param str removed_in: optional version number when the deprecated function
will be removed
:param str warning_in: optional version number when the decorated function
should start emitting a warning when called
:param callable func: deprecated function
:return: a callable that depends on how the decorator is called; either
the decorated function, or a decorator with the appropriate
parameters
Any time the decorated ``func`` is called, a deprecation warning will be
printed to ``sys.stderr``, with the last frame of the traceback. The
optional ``warning_in`` argument suppresses the warning on Sopel versions
older than that, allowing for multi-stage deprecation timelines.
The decorator can be used with or without arguments::
from sopel import tools
@tools.deprecated
def func1():
print('func 1')
@tools.deprecated()
def func2():
print('func 2')
@tools.deprecated(reason='obsolete', version='7.0', removed_in='8.0')
def func3():
print('func 3')
which will output the following in a console::
>>> func1()
Deprecated: func1
File "<stdin>", line 1, in <module>
func 1
>>> func2()
Deprecated: func2
File "<stdin>", line 1, in <module>
func 2
>>> func3()
Deprecated since 7.0, will be removed in 8.0: obsolete
File "<stdin>", line 1, in <module>
func 3
.. note::
There is nothing that prevents this decorator to be used on a class's
method, or on any existing callable.
.. versionadded:: 7.0
Parameters ``reason``, ``version``, and ``removed_in``.
.. versionadded:: 7.1
The ``warning_in`` parameter.
"""
if not any([reason, version, removed_in, warning_in, func]):
# common usage: @deprecated()
return deprecated
if callable(reason):
# common usage: @deprecated
return deprecated(func=reason)
if func is None:
# common usage: @deprecated(message, version, removed_in)
def decorator(func):
return deprecated(reason, version, removed_in, warning_in, func)
return decorator
# now, we have everything we need to have:
# - message is not a callable (could be None)
# - func is not None
# - version and removed_in can be None but that's OK
# so now we can return the actual decorated function
message = reason or getattr(func, '__name__', '<anonymous-function>')
template = 'Deprecated: {message}'
if version and removed_in:
template = (
'Deprecated since {version}, '
'will be removed in {removed_in}: '
'{message}')
elif version:
template = 'Deprecated since {version}: {message}'
elif removed_in:
template = 'Deprecated, will be removed in {removed_in}: {message}'
text = template.format(
message=message, version=version, removed_in=removed_in)
@functools.wraps(func)
def deprecated_func(*args, **kwargs):
if not (warning_in and
parse_version(warning_in) >= parse_version(__version__)):
stderr(text)
# Only display the last frame
trace = traceback.extract_stack()
stderr(traceback.format_list(trace[:-1])[-1][:-1])
return func(*args, **kwargs)
return deprecated_func
@deprecated('Shim for Python 2 cross-compatibility, no longer needed. '
'Use built-in `input()` instead.',
version='7.1',
warning_in='8.0',
removed_in='8.1')
def get_input(prompt):
"""Get decoded input from the terminal (equivalent to Python 3's ``input``).
:param str prompt: what to display as a prompt on the terminal
:return: the user's input
:rtype: str
.. deprecated:: 7.1
Use of this function will become a warning when Python 2 support is
dropped in Sopel 8.0. The function will be removed in Sopel 8.1.
"""
if sys.version_info.major >= 3:
return input(prompt)
else:
return raw_input(prompt).decode('utf8')
def compile_rule(nick, pattern, alias_nicks):
"""Compile a rule regex and fill in nickname placeholders.
:param str nick: the nickname to use when replacing ``$nick`` and
``$nickname`` placeholders in the ``pattern``
:param str pattern: the rule regex pattern
:param list alias_nicks: a list of alternatives that should also be accepted
instead of ``nick``
:return: the compiled regex ``pattern``, with placeholders for ``$nick`` and
``$nickname`` filled in
:rtype: :ref:`re.Pattern <python:re-objects>`
Will not recompile an already compiled pattern.
"""
# Not sure why this happens on reloads, but it shouldn't cause problems…
if isinstance(pattern, _regex_type):
return pattern
if alias_nicks:
nicks = list(alias_nicks) # alias_nicks.copy() doesn't work in py2
nicks.append(nick)
nicks = map(re.escape, nicks)
nick = '(?:%s)' % '|'.join(nicks)
else:
nick = re.escape(nick)
pattern = pattern.replace('$nickname', nick)
pattern = pattern.replace('$nick ', r'{}[,:]\s*'.format(nick)) # @rule('$nick hi')
pattern = pattern.replace('$nick', r'{}[,:]\s+'.format(nick)) # @rule('$nickhi')
flags = re.IGNORECASE
if '\n' in pattern:
# See https://docs.python.org/3/library/re.html#re.VERBOSE
flags |= re.VERBOSE
return re.compile(pattern, flags)
def get_command_regexp(prefix, command):
"""Get a compiled regexp object that implements the command.
:param str prefix: the command prefix (interpreted as regex)
:param str command: the name of the command
:return: a compiled regexp object that implements the command
:rtype: :ref:`re.Pattern <python:re-objects>`
"""
# Escape all whitespace with a single backslash. This ensures that regexp
# in the prefix is treated as it was before the actual regexp was changed
# to use the verbose syntax.
prefix = re.sub(r"(\s)", r"\\\1", prefix)
pattern = get_command_pattern(prefix, command)
return re.compile(pattern, re.IGNORECASE | re.VERBOSE)
def get_command_pattern(prefix, command):
"""Get the uncompiled regex pattern for standard commands.
:param str prefix: the command prefix (interpreted as regex)
:param str command: the command name
:return: a regex pattern that will match the given command
:rtype: str
"""
# This regexp matches equivalently and produces the same
# groups 1 and 2 as the old regexp: r'^%s(%s)(?: +(.*))?$'
# The only differences should be handling all whitespace
# like spaces and the addition of groups 3-6.
return r"""
(?:{prefix})({command}) # Command as group 1.
(?:\s+ # Whitespace to end command.
( # Rest of the line as group 2.
(?:(\S+))? # Parameters 1-4 as groups 3-6.
(?:\s+(\S+))?
(?:\s+(\S+))?
(?:\s+(\S+))?
.* # Accept anything after the parameters.
# Leave it up to the plugin to parse
# the line.
))? # Group 2 must be None, if there are no
# parameters.
$ # EoL, so there are no partial matches.
""".format(prefix=prefix, command=command)
def get_nickname_command_regexp(nick, command, alias_nicks):
"""Get a compiled regexp object that implements the nickname command.
:param str nick: the bot's nickname
:param str command: the command name
:param list alias_nicks: a list of alternatives that should also be accepted
instead of ``nick``
:return: a compiled regex pattern that implements the given nickname command
:rtype: :ref:`re.Pattern <python:re-objects>`
"""
if isinstance(alias_nicks, unicode):
alias_nicks = [alias_nicks]
elif not isinstance(alias_nicks, (list, tuple)):
raise ValueError('A list or string is required.')
return compile_rule(nick, get_nickname_command_pattern(command), alias_nicks)
def get_nickname_command_pattern(command):
"""Get the uncompiled regex pattern for a nickname command.
:param str command: the command name
:return: a regex pattern that will match the given nickname command
:rtype: str
"""
return r"""
^
$nickname[:,]? # Nickname.
\s+({command}) # Command as group 1.
(?:\s+ # Whitespace to end command.
( # Rest of the line as group 2.
(?:(\S+))? # Parameters 1-4 as groups 3-6.
(?:\s+(\S+))?
(?:\s+(\S+))?
(?:\s+(\S+))?
.* # Accept anything after the parameters. Leave it up to
# the plugin to parse the line.
))? # Group 1 must be None, if there are no parameters.
$ # EoL, so there are no partial matches.
""".format(command=command)
def get_action_command_regexp(command):
"""Get a compiled regexp object that implements the command.
:param str command: the name of the command
:return: a compiled regexp object that implements the command
:rtype: :ref:`re.Pattern <python:re-objects>`
"""
pattern = get_action_command_pattern(command)
return re.compile(pattern, re.IGNORECASE | re.VERBOSE)
def get_action_command_pattern(command):
"""Get the uncompiled regex pattern for action commands.
:param str command: the command name
:return: a regex pattern that will match the given command
:rtype: str
"""
# This regexp matches equivalently and produces the same
# groups 1 and 2 as the old regexp: r'^%s(%s)(?: +(.*))?$'
# The only differences should be handling all whitespace
# like spaces and the addition of groups 3-6.
return r"""
({command}) # Command as group 1.
(?:\s+ # Whitespace to end command.
( # Rest of the line as group 2.
(?:(\S+))? # Parameters 1-4 as groups 3-6.
(?:\s+(\S+))?
(?:\s+(\S+))?
(?:\s+(\S+))?
.* # Accept anything after the parameters.
# Leave it up to the plugin to parse
# the line.
))? # Group 2 must be None, if there are no
# parameters.
$ # EoL, so there are no partial matches.
""".format(command=command)
def get_sendable_message(text, max_length=400):
"""Get a sendable ``text`` message, with its excess when needed.
:param str txt: text to send (expects Unicode-encoded string)
:param int max_length: maximum length of the message to be sendable
:return: a tuple of two values, the sendable text and its excess text
:rtype: (str, str)
We're arbitrarily saying that the max is 400 bytes of text when
messages will be split. Otherwise, we'd have to account for the bot's
hostmask, which is hard.
The ``max_length`` is the max length of text in **bytes**, but we take
care of Unicode 2-byte characters by working on the Unicode string,
then making sure the bytes version is smaller than the max length.
"""
unicode_max_length = max_length
excess = ''
while len(text.encode('utf-8')) > max_length:
last_space = text.rfind(' ', 0, unicode_max_length)
if last_space == -1:
# No last space, just split where it is possible
excess = text[unicode_max_length:] + excess
text = text[:unicode_max_length]
# Decrease max length for the unicode string
unicode_max_length = unicode_max_length - 1
else:
# Split at the last best space found
excess = text[last_space:]
text = text[:last_space]
return text, excess.lstrip()
# This class was useful before Python 2.5, when ``defaultdict`` was added
# to the built-in ``collections`` module.
# It is now deprecated.
class Ddict(dict):
"""A default dict implementation available for Python 2.x support.
It was used to make multi-dimensional ``dict``\\s easy to use when the
bot worked with Python version < 2.5.
.. deprecated:: 7.0
Use :class:`collections.defaultdict` instead.
"""
@deprecated('use "collections.defaultdict" instead', '7.0', '8.0')
def __init__(self, default=None):
self.default = default
def __getitem__(self, key):
if key not in self:
self[key] = self.default()
return dict.__getitem__(self, key)
class Identifier(unicode):
"""A `unicode` subclass which acts appropriately for IRC identifiers.
When used as normal `unicode` objects, case will be preserved.
However, when comparing two Identifier objects, or comparing a Identifier
object with a `unicode` object, the comparison will be case insensitive.
This case insensitivity includes the case convention conventions regarding
``[]``, ``{}``, ``|``, ``\\``, ``^`` and ``~`` described in RFC 2812.
"""
# May want to tweak this and update documentation accordingly when dropping
# Python 2 support, since in py3 plain str is Unicode and a "unicode" type
# no longer exists. Probably lots of code will need tweaking, tbh.
def __new__(cls, identifier):
# According to RFC2812, identifiers have to be in the ASCII range.
# However, I think it's best to let the IRCd determine that, and we'll
# just assume unicode. It won't hurt anything, and is more internally
# consistent. And who knows, maybe there's another use case for this
# weird case convention.
s = unicode.__new__(cls, identifier)
s._lowered = Identifier._lower(identifier)
return s
def lower(self):
"""Get the RFC 2812-compliant lowercase version of this identifier.
:return: RFC 2812-compliant lowercase version of the
:py:class:`Identifier` instance
:rtype: str
"""
return self._lowered
@staticmethod
def _lower(identifier):
"""Convert an identifier to lowercase per RFC 2812.
:param str identifier: the identifier (nickname or channel) to convert
:return: RFC 2812-compliant lowercase version of ``identifier``
:rtype: str
"""
if isinstance(identifier, Identifier):
return identifier._lowered
# The tilde replacement isn't needed for identifiers, but is for
# channels, which may be useful at some point in the future.
low = identifier.lower().replace('[', '{').replace(']', '}')
low = low.replace('\\', '|').replace('~', '^')
return low
@staticmethod
def _lower_swapped(identifier):
"""Backward-compatible version of :meth:`_lower`.
:param str identifier: the identifier (nickname or channel) to convert
:return: RFC 2812-non-compliant lowercase version of ``identifier``
:rtype: str
This is what the old :meth:`_lower` function did before Sopel 7.0. It maps
``{}``, ``[]``, ``|``, ``\\``, ``^``, and ``~`` incorrectly.
You shouldn't use this unless you need to migrate stored values from the
previous, incorrect "lowercase" representation to the correct one.
"""
# The tilde replacement isn't needed for identifiers, but is for
# channels, which may be useful at some point in the future.
low = identifier.lower().replace('{', '[').replace('}', ']')
low = low.replace('|', '\\').replace('^', '~')
return low
def __repr__(self):
return "%s(%r)" % (
self.__class__.__name__,
self.__str__()
)
def __hash__(self):
return self._lowered.__hash__()
def __lt__(self, other):
if isinstance(other, unicode):
other = Identifier._lower(other)
return unicode.__lt__(self._lowered, other)
def __le__(self, other):
if isinstance(other, unicode):
other = Identifier._lower(other)
return unicode.__le__(self._lowered, other)
def __gt__(self, other):
if isinstance(other, unicode):
other = Identifier._lower(other)
return unicode.__gt__(self._lowered, other)
def __ge__(self, other):
if isinstance(other, unicode):
other = Identifier._lower(other)
return unicode.__ge__(self._lowered, other)
def __eq__(self, other):
if isinstance(other, unicode):
other = Identifier._lower(other)
return unicode.__eq__(self._lowered, other)
def __ne__(self, other):
return not (self == other)
def is_nick(self):
"""Check if the Identifier is a nickname (i.e. not a channel)
:return: ``True`` if this :py:class:`Identifier` is a nickname;
``False`` if it appears to be a channel
"""
return self and not self.startswith(_channel_prefixes)
class OutputRedirect(object):
"""Redirect the output to the terminal and a log file.
A simplified object used to write to both the terminal and a log file.
"""
def __init__(self, logpath, stderr=False, quiet=False):
"""Create an object which will log to both a file and the terminal.
:param str logpath: path to the log file
:param bool stderr: write output to stderr if ``True``, or to stdout
otherwise
:param bool quiet: write to the log file only if ``True`` (and not to
the terminal)
Create an object which will log to the file at ``logpath`` as well as
the terminal.
"""
self.logpath = logpath
self.stderr = stderr
self.quiet = quiet
def write(self, string):
"""Write the given ``string`` to the logfile and terminal.
:param str string: the string to write
"""
if not self.quiet:
try:
if self.stderr:
sys.__stderr__.write(string)
else:
sys.__stdout__.write(string)
except Exception: # TODO: Be specific
pass
with codecs.open(self.logpath, 'ab', encoding="utf8",
errors='xmlcharrefreplace') as logfile:
try:
logfile.write(string)
except UnicodeDecodeError:
# we got an invalid string, safely encode it to utf-8
logfile.write(unicode(string, 'utf8', errors="replace"))
def flush(self):
"""Flush the file writing buffer."""
if self.stderr:
sys.__stderr__.flush()
else:
sys.__stdout__.flush()
# These seems to trace back to when we thought we needed a try/except on prints,
# because it looked like that was why we were having problems.
# We'll drop it in Sopel 8.0 because it has been here for far too long already.
@deprecated('Use `print()` instead of sopel.tools.stdout', removed_in='8.0')
def stdout(string):
print(string)
def stderr(string):
"""Print the given ``string`` to stderr.
:param str string: the string to output
This is equivalent to ``print >> sys.stderr, string``
"""
print(string, file=sys.stderr)
def check_pid(pid):
"""Check if a process is running with the given ``PID``.
:param int pid: PID to check
:return bool: ``True`` if the given PID is running, ``False`` otherwise
*Availability: POSIX systems only.*
.. note::
Matching the :py:func:`os.kill` behavior this function needs on Windows
was rejected in
`Python issue #14480 <https://bugs.python.org/issue14480>`_, so
:py:func:`check_pid` cannot be used on Windows systems.
"""
try:
os.kill(pid, 0)
except OSError:
return False
else:
return True
def get_hostmask_regex(mask):
"""Get a compiled regex pattern for an IRC hostmask
:param str mask: the hostmask that the pattern should match
:return: a compiled regex pattern matching the given ``mask``
:rtype: :ref:`re.Pattern <python:re-objects>`
"""
mask = re.escape(mask)
mask = mask.replace(r'\*', '.*')
return re.compile(mask + '$', re.I)
def get_logger(plugin_name):
"""Return a logger for a plugin.
:param str plugin_name: name of the plugin
:return: the logger for the given plugin
This::
from sopel import plugins
LOGGER = plugins.get_logger('my_custom_plugin')
is equivalent to this::
import logging
LOGGER = logging.getLogger('sopel.externals.my_custom_plugin')
Internally, Sopel configures logging for the ``sopel`` namespace, so
external plugins can't benefit from it with ``logging.getLogger(__name__)``
as they won't be in the same namespace. This function uses the
``plugin_name`` with a prefix inside this namespace.
"""
return logging.getLogger('sopel.externals.%s' % plugin_name)
class SopelMemory(dict):
"""A simple thread-safe ``dict`` implementation.
In order to prevent exceptions when iterating over the values and changing
them at the same time from different threads, we use a blocking lock in
``__setitem__`` and ``contains``.
.. versionadded:: 3.1
As ``Willie.WillieMemory``
.. versionchanged:: 4.0
Moved to ``tools.WillieMemory``
.. versionchanged:: 6.0
Renamed from ``WillieMemory`` to ``SopelMemory``
"""
def __init__(self, *args):
dict.__init__(self, *args)
self.lock = threading.Lock()
def __setitem__(self, key, value):
"""Set a key equal to a value.
The dict is locked for other writes while doing so.
"""
self.lock.acquire()
result = dict.__setitem__(self, key, value)
self.lock.release()
return result
def __contains__(self, key):
"""Check if a key is in the dict.
The dict is locked for writes while doing so.
"""
self.lock.acquire()
result = dict.__contains__(self, key)
self.lock.release()
return result
# Needed to make it explicit that we don't care about the `lock` attribute
# when comparing/hashing SopelMemory objects.
__eq__ = dict.__eq__
__ne__ = dict.__ne__
__hash__ = dict.__hash__
@deprecated
def contains(self, key):
"""Check if ``key`` is in the memory
:param str key: key to check for
.. deprecated:: 7.0
Will be removed in Sopel 8. If you aren't already using the ``in``
operator, you should be.
"""
return self.__contains__(key)
class SopelMemoryWithDefault(defaultdict):
"""Same as SopelMemory, but subclasses from collections.defaultdict.
.. versionadded:: 4.3
As ``WillieMemoryWithDefault``
.. versionchanged:: 6.0
Renamed to ``SopelMemoryWithDefault``
"""
def __init__(self, *args):
defaultdict.__init__(self, *args)
self.lock = threading.Lock()
def __setitem__(self, key, value):
"""Set a key equal to a value.
The dict is locked for other writes while doing so.
"""
self.lock.acquire()
result = defaultdict.__setitem__(self, key, value)
self.lock.release()
return result
def __contains__(self, key):
"""Check if a key is in the dict.
The dict is locked for writes while doing so.
"""
self.lock.acquire()
result = defaultdict.__contains__(self, key)
self.lock.release()
return result
@deprecated
def contains(self, key):
"""Check if ``key`` is in the memory
:param str key: key to check for
.. deprecated:: 7.0
Will be removed in Sopel 8. If you aren't already using the ``in``
operator, you should be.
"""
return self.__contains__(key)
class SopelIdentifierMemory(SopelMemory):
"""Special Sopel memory that stores ``Identifier`` as key.
This is a convenient subclass of :class:`SopelMemory` that always casts its
keys as instances of :class:`Identifier`::
>>> from sopel import tools
>>> memory = tools.SopelIdentifierMemory()
>>> memory['Exirel'] = 'king'
>>> list(memory.items())
[(Identifier('Exirel'), 'king')]
>>> tools.Identifier('exirel') in memory
True
>>> 'exirel' in memory
True
As seen in the example above, it is possible to perform various operations
with both ``Identifier`` and :class:`str` objects, taking advantage of the
case-insensitive behavior of ``Identifier``.
.. note::
Internally, it will try to do ``key = tools.Identifier(key)``, which
will raise an exception if it cannot instantiate the key properly::
>>> memory[1] = 'error'
AttributeError: 'int' object has no attribute 'lower'
.. versionadded:: 7.1
"""
def __getitem__(self, key):
return super(SopelIdentifierMemory, self).__getitem__(Identifier(key))
def __contains__(self, key):
return super(SopelIdentifierMemory, self).__contains__(Identifier(key))
def __setitem__(self, key, value):
super(SopelIdentifierMemory, self).__setitem__(Identifier(key), value)
@deprecated(version='7.0', removed_in='8.0')
def get_raising_file_and_line(tb=None):
"""Get the file and line number where an exception happened.
:param tb: the traceback (uses the most recent exception if not given)
:return: a tuple of the filename and line number
:rtype: (str, int)
.. deprecated:: 7.0
Use Python's built-in logging system, with the ``logger.exception``
method. This method makes sure to log the exception with the traceback
and the relevant information (filename, line number, etc.).
"""
if not tb:
tb = sys.exc_info()[2]
filename, lineno, _context, _line = traceback.extract_tb(tb)[-1]
return filename, lineno
def chain_loaders(*lazy_loaders):
"""Chain lazy loaders into one.
:param lazy_loaders: one or more lazy loader functions
:type lazy_loaders: :term:`function`
:return: a lazy loader that combines all of the given ones
:rtype: :term:`function`
This function takes any number of lazy loaders as arguments and merges them
together into one. It's primarily a helper for lazy rule decorators such as
:func:`sopel.plugin.url_lazy`.
.. important::
This function doesn't check the uniqueness of regexes generated by
all the loaders.
"""
def chained_loader(settings):
return [
regex
for lazy_loader in lazy_loaders
for regex in lazy_loader(settings)
]
return chained_loader
| 2,488 | 0 | 532 |
22713b1b406683d4ed6ad898aa63ba71a1a28ac4 | 3,520 | py | Python | oracle-runs.py | fair-trec/trec2021-fair-public | d9f16bd7d70c350fa6e5906fac36c4f1af837573 | [
"MIT"
] | 3 | 2021-07-28T09:10:48.000Z | 2022-03-23T15:08:48.000Z | oracle-runs.py | fair-trec/trec2021-fair-public | d9f16bd7d70c350fa6e5906fac36c4f1af837573 | [
"MIT"
] | null | null | null | oracle-runs.py | fair-trec/trec2021-fair-public | d9f16bd7d70c350fa6e5906fac36c4f1af837573 | [
"MIT"
] | null | null | null | """
Produce TREC fair ranking runs from an oracle.
This script assumes the data lives in a directory 'data'. It loads the training
topics and uses them as an oracle for producing rankings.
Usage:
oracle-runs.py --task1 [options]
oracle-runs.py --task2 [options]
Options:
-v, --verbose
Write verbose logging output.
-o FILE
Write output to FILE.
-p PREC, --precision=PREC
Produce results with the specified precision [default: 0.9].
"""
import sys
from pathlib import Path
import logging
from tqdm import tqdm
from docopt import docopt
import pandas as pd
import numpy as np
_log = logging.getLogger('oracle-runs')
if __name__ == '__main__':
opts = docopt(__doc__)
main(opts) | 27.716535 | 95 | 0.641477 | """
Produce TREC fair ranking runs from an oracle.
This script assumes the data lives in a directory 'data'. It loads the training
topics and uses them as an oracle for producing rankings.
Usage:
oracle-runs.py --task1 [options]
oracle-runs.py --task2 [options]
Options:
-v, --verbose
Write verbose logging output.
-o FILE
Write output to FILE.
-p PREC, --precision=PREC
Produce results with the specified precision [default: 0.9].
"""
import sys
from pathlib import Path
import logging
from tqdm import tqdm
from docopt import docopt
import pandas as pd
import numpy as np
_log = logging.getLogger('oracle-runs')
def load_metadata():
meta_f = Path('data/trec_metadata.json.gz')
_log.info('reading %s', meta_f)
meta = pd.read_json(meta_f, lines=True, compression='gzip')
return meta.set_index('page_id')
def load_topics():
topic_f = Path('data/trec_topics.json.gz')
_log.info('reading %s', topic_f)
topics = pd.read_json(topic_f, lines=True, compression='gzip')
return topics
def sample_docs(rng, meta, rel, n, prec):
_log.debug('sampling %d rel items (n=%d, prec=%.2f)', len(rel), n, prec)
n_rel = min(int(n * prec), len(rel))
n_unrel = n - n_rel
rel = np.array(rel)
all = pd.Series(meta.index)
unrel = all[~all.isin(rel)].values
samp_rel = rng.choice(rel, n_rel, replace=False)
samp_unrel = rng.choice(unrel, n_unrel, replace=False)
samp = np.concatenate([samp_rel, samp_unrel])
rng.shuffle(samp)
return pd.Series(samp)
def task1_run(opts, meta, topics):
rng = np.random.default_rng()
rank_len = 1000
prec = float(opts['--precision'])
rels = topics[['id', 'rel_docs']].set_index('id').explode('rel_docs')
def sample(df):
return sample_docs(rng, meta, df['rel_docs'], rank_len, prec)
runs = rels.groupby('id').apply(sample)
runs.columns.name = 'rank'
runs = runs.stack().reset_index(name='page_id')
_log.info('sample runs:\n%s', runs)
return runs[['id', 'page_id']]
def task2_run(opts, meta, topics):
rng = np.random.default_rng()
rank_len = 50
run_count = 100
prec = float(opts['--precision'])
rels = topics[['id', 'rel_docs']].set_index('id').explode('rel_docs')
def one_sample(df):
return sample_docs(rng, meta, df['rel_docs'], rank_len, prec)
def multi_sample(df):
runs = dict((i+1, one_sample(df)) for i in tqdm(range(run_count), 'reps', leave=False))
rdf = pd.DataFrame(runs)
rdf.columns.name = 'seq_no'
rdf.index.name = 'rank'
return rdf.T
runs = rels.groupby('id').progress_apply(multi_sample)
runs = runs.stack().reset_index(name='page_id')
_log.info('multi-sample runs:\n%s', runs)
return runs[['id', 'seq_no', 'page_id']]
def main(opts):
level = logging.DEBUG if opts['--verbose'] else logging.INFO
logging.basicConfig(stream=sys.stderr, level=level)
tqdm.pandas(leave=False)
meta = load_metadata()
topics = load_topics()
if opts['--task1']:
runs = task1_run(opts, meta, topics)
dft_out = 'task1.tsv'
elif opts['--task2']:
runs = task2_run(opts, meta, topics)
dft_out = 'task2.tsv'
else:
raise ValueError('no task specified')
out_file = opts.get('-o', dft_out)
_log.info('writing to %s', out_file)
runs.to_csv(out_file, index=False, sep='\t')
if __name__ == '__main__':
opts = docopt(__doc__)
main(opts) | 2,641 | 0 | 138 |
4b44ab6318cc359b020659d482e8f114bcd8d62d | 6,394 | py | Python | scan_list.py | jeremyng123/cobaltstrike | 51a67ec918904d8a3b7ea07ab62a10b58183eb8f | [
"MIT"
] | 193 | 2020-12-21T03:20:27.000Z | 2022-02-28T11:11:08.000Z | scan_list.py | jeremyng123/cobaltstrike | 51a67ec918904d8a3b7ea07ab62a10b58183eb8f | [
"MIT"
] | 4 | 2021-01-09T11:27:57.000Z | 2021-12-06T13:38:17.000Z | scan_list.py | jeremyng123/cobaltstrike | 51a67ec918904d8a3b7ea07ab62a10b58183eb8f | [
"MIT"
] | 54 | 2020-12-20T20:24:54.000Z | 2022-02-28T11:11:09.000Z | import argparse
import csv
import requests
import urllib3
from urllib.parse import urljoin
from lib import decrypt_beacon, decode_config, JsonEncoder
import multiprocessing
from functools import partial
import datetime
import pandas as pd
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Extract Cobalt Strike beacon and configuration from a list of server')
parser.add_argument('HOSTLIST', help='List of IP addresses or domains from a file')
parser.add_argument('--PROCESS','-j', help='Number of process to be active simultaneously', default=10, type=int)
parser.add_argument('--PORT','-p', help='Specify port on which scan will occur. Default: port 443', default=443, type=int)
parser.add_argument('--BITS','-b', help='Specify which version of payload the script should request (32 or 64 bits). Default: 32', default=32, type=int, choices=[32, 64])
parser.add_argument('--HTTP', help='If specified, made request http and NOT https. Default : nothing', default=False, action='store_true')
parser.add_argument('--format','-f', help='Specify format (csv or json). Default : csv', default="csv", choices=['csv', 'json'])
args = parser.parse_args()
ua = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.77 Safari/537.36"
urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
df = mp_handler(args.HOSTLIST, args.PROCESS, args.BITS, args.PORT, args.HTTP)
header = ["host", "result", "ssl", "port", ".http-get.uri", ".http-post.uri", ".user-agent", ".watermark", "bits"]
df["port"] = args.PORT
df["ssl"] = not args.HTTP
df["bits"] = args.BITS
# Add columns if they are missing
for h in header:
if h not in df:
df[h] = ""
try:
if args.format == "csv":
df.to_csv(f'{datetime.date.today()}-{args.PORT}-test-output.csv', columns=header, index=False, doublequote=True, escapechar=",", quoting=csv.QUOTE_ALL)
print(f'[+] Output success : {datetime.date.today()}-{args.PORT}-test-output.csv')
else:
df.to_json(f'{datetime.date.today()}-{args.PORT}-test-output.json', orient='records')
print(f'[+] Output success : {datetime.date.today()}-{args.PORT}-test-output.json')
except Exception as e:
print("[-] Error during output : " + str(e))
| 43.202703 | 174 | 0.542853 | import argparse
import csv
import requests
import urllib3
from urllib.parse import urljoin
from lib import decrypt_beacon, decode_config, JsonEncoder
import multiprocessing
from functools import partial
import datetime
import pandas as pd
def mp_worker( BITS, PORT, HTTP,output_list, host ):
print("Checking {}".format(host))
if not host.startswith("http"):
if HTTP == True:
host = "http://{0}:{1}".format(host, PORT)
else :
host = "https://{0}:{1}".format(host, PORT)
if BITS == 64:
uri = "/aad7"
else :
uri = "/aaa9"
try:
r = requests.get(urljoin(host, uri ), headers={'user-agent': ua}, verify=False, timeout=5)
if r.status_code == 200:
data = r.content
if data.startswith(b"\xfc\xe8"):
beacon = decrypt_beacon(data)
if beacon:
config = decode_config(beacon)
if config:
print(f"Payload {BITS} bits found")
config["port"] = int(PORT)
config["host"] = host
config["result"] = "Found"
output_list.append(config)
else:
config = dict()
config["port"] = int(PORT)
config["host"] = host
config["result"] = "Config Extraction Failed"
output_list.append(config)
print("Config extraction failed")
else:
config = dict()
print("Beacon extraction failed")
config["port"] = int(PORT)
config["host"] = host
config["result"] = "Beacon Extraction Failed"
output_list.append(config)
elif data.startswith(b"MZ"):
config = decode_config(data)
if config:
print(f"Payload {BITS} bits found")
config["port"] = int(PORT)
config["host"] = host
config["result"] = "Found"
output_list.append(config)
else:
config = dict()
print("Config extraction failed")
config["port"] = int(PORT)
config["host"] = host
config["result"] = "Config Extraction Failed"
output_list.append(config)
else:
config = dict()
print(f"No {BITS} bits payload")
config["port"] = int(PORT)
config["host"] = host
config["result"] = "Not Found"
output_list.append(config)
else:
config = dict()
print(f"No {BITS} bits payload")
config["port"] = int(PORT)
config["host"] = host
config["result"] = "Not Found"
output_list.append(config)
except Exception as e :
print("Request failed : "+str(e))
config = dict()
config["port"] = int(PORT)
config["host"] = host
config["result"] = "Request Failed"
output_list.append(config)
def mp_handler(HOSTLIST,PROCESS, BITS, PORT, HTTP):
#Preparing multiprocessing
with multiprocessing.Pool(PROCESS) as p:
with multiprocessing.Manager() as manager:
#Shared list between process
output_list = manager.list()
func = partial(mp_worker, BITS, PORT, HTTP, output_list)
with open(HOSTLIST) as f:
hosts = f.read().split('\n')
hosts.remove('')
#Multiprocessing
p.imap(func, hosts)
p.close()
p.join()
#Transform list into Pandas DF to facilitate output (json, csv, ....)
real_list = list(output_list)
df = pd.DataFrame(real_list)
try:
df[".watermark"] = df[".watermark"].astype('Int64', errors='ignore')
except KeyError:
# Not watermark identified at al
df[".watermark"] = 0
return df
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Extract Cobalt Strike beacon and configuration from a list of server')
parser.add_argument('HOSTLIST', help='List of IP addresses or domains from a file')
parser.add_argument('--PROCESS','-j', help='Number of process to be active simultaneously', default=10, type=int)
parser.add_argument('--PORT','-p', help='Specify port on which scan will occur. Default: port 443', default=443, type=int)
parser.add_argument('--BITS','-b', help='Specify which version of payload the script should request (32 or 64 bits). Default: 32', default=32, type=int, choices=[32, 64])
parser.add_argument('--HTTP', help='If specified, made request http and NOT https. Default : nothing', default=False, action='store_true')
parser.add_argument('--format','-f', help='Specify format (csv or json). Default : csv', default="csv", choices=['csv', 'json'])
args = parser.parse_args()
ua = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.77 Safari/537.36"
urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
df = mp_handler(args.HOSTLIST, args.PROCESS, args.BITS, args.PORT, args.HTTP)
header = ["host", "result", "ssl", "port", ".http-get.uri", ".http-post.uri", ".user-agent", ".watermark", "bits"]
df["port"] = args.PORT
df["ssl"] = not args.HTTP
df["bits"] = args.BITS
# Add columns if they are missing
for h in header:
if h not in df:
df[h] = ""
try:
if args.format == "csv":
df.to_csv(f'{datetime.date.today()}-{args.PORT}-test-output.csv', columns=header, index=False, doublequote=True, escapechar=",", quoting=csv.QUOTE_ALL)
print(f'[+] Output success : {datetime.date.today()}-{args.PORT}-test-output.csv')
else:
df.to_json(f'{datetime.date.today()}-{args.PORT}-test-output.json', orient='records')
print(f'[+] Output success : {datetime.date.today()}-{args.PORT}-test-output.json')
except Exception as e:
print("[-] Error during output : " + str(e))
| 3,961 | 0 | 46 |
30ce9806dbf6f7dbafe0f568c2b2657bff6791b5 | 9,140 | py | Python | ar-cnn/utils/midi_utils.py | carniblood/aws-deepcomposer-retrogame | 6876eedd2a128362baff4daace73ee4f254676c3 | [
"MIT-0"
] | null | null | null | ar-cnn/utils/midi_utils.py | carniblood/aws-deepcomposer-retrogame | 6876eedd2a128362baff4daace73ee4f254676c3 | [
"MIT-0"
] | null | null | null | ar-cnn/utils/midi_utils.py | carniblood/aws-deepcomposer-retrogame | 6876eedd2a128362baff4daace73ee4f254676c3 | [
"MIT-0"
] | null | null | null | # The MIT-Zero License
# Copyright 2020 Amazon.com, Inc. or its affiliates. All Rights Reserved.
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
import numpy as np
from music21 import midi
import pypianoroll
from pypianoroll import Multitrack
from texttable import Texttable
from constants import Constants
def process_midi(midi_file, beat_resolution, program=0, ignore_warnings=False):
'''Takes path to an input midi file and parses it to pianoroll
:param input_midi: Path to midi file
:param beat_resolution
:return: parsed painoroll
'''
multi_track = pypianoroll.Multitrack(beat_resolution=beat_resolution)
multi_track_onsets = multi_track.copy()
try:
multi_track.parse_midi(midi_file,
skip_empty_tracks=False,
collect_onsets_only=False,
algorithm='custom',
first_beat_time=0)
multi_track_onsets.parse_midi(midi_file,
skip_empty_tracks=False,
collect_onsets_only=True,
algorithm='custom',
first_beat_time=0)
except:
print("ERROR - midi file: {} is invalid. Ignoring during preprocessing".format(
midi_file))
pass
# Merge regular pianoroll with onsets to make sure to not miss any notes
for track_onset in multi_track_onsets.tracks:
multi_track.append_track(track_onset)
# Convert the PianoRoll to binary ignoring the values of velocities
multi_track.pad_to_multiple(Constants.number_of_timesteps)
multi_track.binarize()
track_indices = [] # to merge all the voices
track_indices_drum = [] # to merge drums
for index,track in enumerate(multi_track.tracks):
if track.is_drum:
track_indices_drum.append(index)
else:
track_indices.append(index)
if len(track_indices) == 0 and not ignore_warnings:
print("WARNING - midi file: {} is empty. Ignoring during preprocessing".format(midi_file))
pass
if len(track_indices_drum) == 0 and not ignore_warnings:
print("WARNING - midi file: {} has no drum. Ignoring during preprocessing".format(midi_file))
pass
multi_track.merge_tracks(track_indices=track_indices,
mode='any',
program=program,
remove_merged=False)
pianoroll = multi_track.tracks[-1].pianoroll
drums = np.zeros((pianoroll.shape[0], 128), bool)
if len(track_indices_drum) > 0:
multi_track.merge_tracks(track_indices=track_indices_drum,
mode='any',
is_drum=True,
remove_merged=False)
drums = multi_track.tracks[-1].pianoroll
return pianoroll,drums
def process_pianoroll(pianoroll, drums,
total_time_steps_shifted_per_sample,
total_timesteps_per_nbars):
'''Takes path to an input midi file and parses it to pianoroll
:param pianoroll: pianoroll obtained after parsing midi file
:param time_steps_shifted_per_sample: number of bars to be shifted in timesteps
param timesteps_per_nbars: total number of timesteps to be included in processed pianoroll
:return: parsed painoroll sections
'''
if Constants.split_into_two_voices:
time_steps_shifted_per_sample = total_time_steps_shifted_per_sample // 2
timesteps_per_nbars = total_timesteps_per_nbars // 2
else:
time_steps_shifted_per_sample = total_time_steps_shifted_per_sample
timesteps_per_nbars = total_timesteps_per_nbars
# instead squeeze drums at the end of the pianoroll
pianoroll_start = Constants.voices_maximum
pianoroll_end = pianoroll_start + len(Constants.drums)
pianoroll[:,pianoroll_start:pianoroll_end] = np.take(drums, axis=1, indices=Constants.drums)
pianoroll[:,pianoroll_end:].fill(0)
pianoroll_sections = []
truncated_pianoroll_length = pianoroll.shape[0]# - (pianoroll.shape[0] % timesteps_per_nbars)
for i in range(0, truncated_pianoroll_length - timesteps_per_nbars + 1,
time_steps_shifted_per_sample):
if Constants.split_into_two_voices:
pianoroll_section = pianoroll[i:i + timesteps_per_nbars, :]
drum_section = drums[i:i + timesteps_per_nbars, :]
section = np.concatenate((pianoroll_section, drum_section))
else:
section = pianoroll[i:i + timesteps_per_nbars, :]
if section.any():
pianoroll_sections.append(section)
else:
break
return pianoroll_sections
def play_midi(input_midi):
'''Takes path to an input and plays the midi file in the notebook cell
:param input_midi: Path to midi file
:return:
'''
midi_object = midi.MidiFile()
midi_object.open(input_midi)
midi_object.read()
midi_object.close()
show_midi = midi.translate.midiFileToStream(midi_object)
show_midi.show('midi')
def plot_pianoroll(pianoroll, beat_resolution, fig_name=None):
'''Plot a Single Track Pianoroll image
:param pianoroll: Pianoroll tensor of shape time_steps * pitches
:return:
'''
pypianoroll.plot_track(pypianoroll.Track(pianoroll=pianoroll), fig_name,
beat_resolution)
def get_music_metrics(input_midi, beat_resolution, track=0):
"""Takes a midifile as an input and Returns the following metrics
:param input_midi: Path to midi file
:param beat_resolution:
:param trac: Instrument number in the multi track midi file
:return: The following metrics are returned
1.) n_pitch_classes_used is the unique pitch classes used in a pianoroll.
2.) polyphonic_rate ie ratio of the number of time steps where the number of pitches
being played is larger than `threshold` to the total number of time steps in
a pianoroll
3.) in_scale_rate is the ratio of the number of nonzero entries that lie in a specific scale
to the total number of nonzero entries in a pianoroll.
4.) n_pitches_used is the the number of unique pitches used in a pianoroll."""
midi_data = Multitrack(input_midi, beat_resolution)
piano_roll = midi_data.tracks[track].pianoroll
n_pitch_classes_used = pypianoroll.metrics.n_pitch_classes_used(piano_roll)
polyphonic_rate = pypianoroll.metrics.polyphonic_rate(piano_roll)
in_scale_rate = pypianoroll.metrics.in_scale_rate(piano_roll)
n_pitches_used = pypianoroll.metrics.n_pitches_used(piano_roll)
metrics = [
n_pitch_classes_used, polyphonic_rate, n_pitches_used, in_scale_rate
]
metrics_table = [[
"n_pitch_classes_used", "in_scale_rate", "polyphonic_rate",
"n_pitches_used"
], metrics]
table = Texttable()
table.add_rows(metrics_table)
print(table.draw())
| 42.511628 | 101 | 0.659956 | # The MIT-Zero License
# Copyright 2020 Amazon.com, Inc. or its affiliates. All Rights Reserved.
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
import numpy as np
from music21 import midi
import pypianoroll
from pypianoroll import Multitrack
from texttable import Texttable
from constants import Constants
def print_midi_info(midi_file, all_programs_used=None, all_voices_used=None, all_drums_used=None):
multi_track = pypianoroll.Multitrack()
try:
multi_track.parse_midi(midi_file,
algorithm='custom',
first_beat_time=0)
except:
print("ERROR - midi file: {} is invalid.".format(
midi_file))
pass
multi_track.binarize()
for track in multi_track.tracks:
is_drum = ""
if track.is_drum:
drums_used = np.nonzero(np.sum(track.pianoroll, axis=0))[0]
is_drum = "[DRUM] " + str(drums_used)
if all_drums_used is not None:
all_drums_used.extend(drums_used)
else:
voices_used = np.nonzero(np.sum(track.pianoroll, axis=0))[0]
if all_voices_used is not None:
all_voices_used.extend(voices_used)
if all_programs_used is not None:
all_programs_used.append(track.program)
print(" {}: {} {}".format(track.program, track.name, is_drum))
def process_midi(midi_file, beat_resolution, program=0, ignore_warnings=False):
'''Takes path to an input midi file and parses it to pianoroll
:param input_midi: Path to midi file
:param beat_resolution
:return: parsed painoroll
'''
multi_track = pypianoroll.Multitrack(beat_resolution=beat_resolution)
multi_track_onsets = multi_track.copy()
try:
multi_track.parse_midi(midi_file,
skip_empty_tracks=False,
collect_onsets_only=False,
algorithm='custom',
first_beat_time=0)
multi_track_onsets.parse_midi(midi_file,
skip_empty_tracks=False,
collect_onsets_only=True,
algorithm='custom',
first_beat_time=0)
except:
print("ERROR - midi file: {} is invalid. Ignoring during preprocessing".format(
midi_file))
pass
# Merge regular pianoroll with onsets to make sure to not miss any notes
for track_onset in multi_track_onsets.tracks:
multi_track.append_track(track_onset)
# Convert the PianoRoll to binary ignoring the values of velocities
multi_track.pad_to_multiple(Constants.number_of_timesteps)
multi_track.binarize()
track_indices = [] # to merge all the voices
track_indices_drum = [] # to merge drums
for index,track in enumerate(multi_track.tracks):
if track.is_drum:
track_indices_drum.append(index)
else:
track_indices.append(index)
if len(track_indices) == 0 and not ignore_warnings:
print("WARNING - midi file: {} is empty. Ignoring during preprocessing".format(midi_file))
pass
if len(track_indices_drum) == 0 and not ignore_warnings:
print("WARNING - midi file: {} has no drum. Ignoring during preprocessing".format(midi_file))
pass
multi_track.merge_tracks(track_indices=track_indices,
mode='any',
program=program,
remove_merged=False)
pianoroll = multi_track.tracks[-1].pianoroll
drums = np.zeros((pianoroll.shape[0], 128), bool)
if len(track_indices_drum) > 0:
multi_track.merge_tracks(track_indices=track_indices_drum,
mode='any',
is_drum=True,
remove_merged=False)
drums = multi_track.tracks[-1].pianoroll
return pianoroll,drums
def process_pianoroll(pianoroll, drums,
total_time_steps_shifted_per_sample,
total_timesteps_per_nbars):
'''Takes path to an input midi file and parses it to pianoroll
:param pianoroll: pianoroll obtained after parsing midi file
:param time_steps_shifted_per_sample: number of bars to be shifted in timesteps
param timesteps_per_nbars: total number of timesteps to be included in processed pianoroll
:return: parsed painoroll sections
'''
if Constants.split_into_two_voices:
time_steps_shifted_per_sample = total_time_steps_shifted_per_sample // 2
timesteps_per_nbars = total_timesteps_per_nbars // 2
else:
time_steps_shifted_per_sample = total_time_steps_shifted_per_sample
timesteps_per_nbars = total_timesteps_per_nbars
# instead squeeze drums at the end of the pianoroll
pianoroll_start = Constants.voices_maximum
pianoroll_end = pianoroll_start + len(Constants.drums)
pianoroll[:,pianoroll_start:pianoroll_end] = np.take(drums, axis=1, indices=Constants.drums)
pianoroll[:,pianoroll_end:].fill(0)
pianoroll_sections = []
truncated_pianoroll_length = pianoroll.shape[0]# - (pianoroll.shape[0] % timesteps_per_nbars)
for i in range(0, truncated_pianoroll_length - timesteps_per_nbars + 1,
time_steps_shifted_per_sample):
if Constants.split_into_two_voices:
pianoroll_section = pianoroll[i:i + timesteps_per_nbars, :]
drum_section = drums[i:i + timesteps_per_nbars, :]
section = np.concatenate((pianoroll_section, drum_section))
else:
section = pianoroll[i:i + timesteps_per_nbars, :]
if section.any():
pianoroll_sections.append(section)
else:
break
return pianoroll_sections
def play_midi(input_midi):
'''Takes path to an input and plays the midi file in the notebook cell
:param input_midi: Path to midi file
:return:
'''
midi_object = midi.MidiFile()
midi_object.open(input_midi)
midi_object.read()
midi_object.close()
show_midi = midi.translate.midiFileToStream(midi_object)
show_midi.show('midi')
def plot_pianoroll(pianoroll, beat_resolution, fig_name=None):
'''Plot a Single Track Pianoroll image
:param pianoroll: Pianoroll tensor of shape time_steps * pitches
:return:
'''
pypianoroll.plot_track(pypianoroll.Track(pianoroll=pianoroll), fig_name,
beat_resolution)
def get_music_metrics(input_midi, beat_resolution, track=0):
"""Takes a midifile as an input and Returns the following metrics
:param input_midi: Path to midi file
:param beat_resolution:
:param trac: Instrument number in the multi track midi file
:return: The following metrics are returned
1.) n_pitch_classes_used is the unique pitch classes used in a pianoroll.
2.) polyphonic_rate ie ratio of the number of time steps where the number of pitches
being played is larger than `threshold` to the total number of time steps in
a pianoroll
3.) in_scale_rate is the ratio of the number of nonzero entries that lie in a specific scale
to the total number of nonzero entries in a pianoroll.
4.) n_pitches_used is the the number of unique pitches used in a pianoroll."""
midi_data = Multitrack(input_midi, beat_resolution)
piano_roll = midi_data.tracks[track].pianoroll
n_pitch_classes_used = pypianoroll.metrics.n_pitch_classes_used(piano_roll)
polyphonic_rate = pypianoroll.metrics.polyphonic_rate(piano_roll)
in_scale_rate = pypianoroll.metrics.in_scale_rate(piano_roll)
n_pitches_used = pypianoroll.metrics.n_pitches_used(piano_roll)
metrics = [
n_pitch_classes_used, polyphonic_rate, n_pitches_used, in_scale_rate
]
metrics_table = [[
"n_pitch_classes_used", "in_scale_rate", "polyphonic_rate",
"n_pitches_used"
], metrics]
table = Texttable()
table.add_rows(metrics_table)
print(table.draw())
| 1,093 | 0 | 23 |
958a3b3c3c8bbbf71fdafc826be947de6c1e562c | 2,740 | py | Python | core/constraints/__init__.py | AluBhorta/UCSPy-Engine | 917eea6ab98338c5763c9fd07e24da7fa2ee1cda | [
"MIT"
] | 6 | 2021-09-29T06:18:55.000Z | 2021-09-29T18:56:13.000Z | core/constraints/__init__.py | AluBhorta/UCSPy-Engine | 917eea6ab98338c5763c9fd07e24da7fa2ee1cda | [
"MIT"
] | 1 | 2021-10-01T00:00:15.000Z | 2021-10-01T00:14:24.000Z | core/constraints/__init__.py | AluBhorta/UCSPy-Engine | 917eea6ab98338c5763c9fd07e24da7fa2ee1cda | [
"MIT"
] | 3 | 2020-06-26T05:59:56.000Z | 2021-09-29T06:25:23.000Z |
from core.constraints.hard.hard_constraint_1 import violates_hard_constraint_1
from core.constraints.hard.hard_constraint_2 import violates_hard_constraint_2
from core.constraints.soft.soft_constraint_1 import penalty_of_soft_constraint_1
from core.constraints.soft.soft_constraint_2 import penalty_of_soft_constraint_2
from core.constraints.soft.soft_constraint_3 import penalty_of_soft_constraint_3
from core.constraints.soft.soft_constraint_4 import penalty_of_soft_constraint_4
from core.constraints.soft.soft_constraint_5 import penalty_of_soft_constraint_5
from core.constraints.soft.soft_constraint_6 import penalty_of_soft_constraint_6
from core.constraints.soft.soft_constraint_7 import penalty_of_soft_constraint_7
from core.constraints.soft.soft_constraint_8 import penalty_of_soft_constraint_8
""" to learn how to add new constraints, read `core/constraints/modify_constraints.md` """
HARD_CONSTRAINT_FUNCS = [
{
"id": 1, "func": violates_hard_constraint_1,
"desc": "No two classes can take place in the same room at the same Timeslot."
},
{
"id": 2, "func": violates_hard_constraint_2,
"desc": "No Instructor can take more than one class at a given Timeslot."
},
]
SOFT_CONSTRAINT_FUNCS = [
{
"id": 1, "unit_penalty": 0.9, "func": penalty_of_soft_constraint_1,
"desc": "Instructors should only take certain courses they are are assigned to."
},
{
"id": 2, "unit_penalty": 0.85, "func": penalty_of_soft_constraint_2,
"desc": "A particular Room should only allow Classes of certain Courses."
},
{
"id": 3, "unit_penalty": 0.6, "func": penalty_of_soft_constraint_3,
"desc": "CourseGroups have Timeslot preferences."
},
{
"id": 4, "unit_penalty": 0.5, "func": penalty_of_soft_constraint_4,
"desc": "Instructors have Timeslot preferences."
},
{
"id": 5, "unit_penalty": 1.0, "func": penalty_of_soft_constraint_5,
"desc": "If a Course has 2 Lectures Per Week, it should take place in a composite Timeslot i.e. with Day code of 'ST' or 'MW'."
},
{
"id": 6, "unit_penalty": 0.8, "func": penalty_of_soft_constraint_6,
"desc": "The Lab Section of a Course (if any) should be placed in a Timeslot that is before or after the corresponding Theory Section."
},
{
"id": 7, "unit_penalty": 0.6, "func": penalty_of_soft_constraint_7,
"desc": "The Theory Section and the corresponding Lab Section of a Course (if any) should be taken by the same Instructor."
},
{
"id": 8, "unit_penalty": 0.9, "func": penalty_of_soft_constraint_8,
"desc": "Instructors have minimum credit load requirements."
},
]
| 44.193548 | 143 | 0.712409 |
from core.constraints.hard.hard_constraint_1 import violates_hard_constraint_1
from core.constraints.hard.hard_constraint_2 import violates_hard_constraint_2
from core.constraints.soft.soft_constraint_1 import penalty_of_soft_constraint_1
from core.constraints.soft.soft_constraint_2 import penalty_of_soft_constraint_2
from core.constraints.soft.soft_constraint_3 import penalty_of_soft_constraint_3
from core.constraints.soft.soft_constraint_4 import penalty_of_soft_constraint_4
from core.constraints.soft.soft_constraint_5 import penalty_of_soft_constraint_5
from core.constraints.soft.soft_constraint_6 import penalty_of_soft_constraint_6
from core.constraints.soft.soft_constraint_7 import penalty_of_soft_constraint_7
from core.constraints.soft.soft_constraint_8 import penalty_of_soft_constraint_8
""" to learn how to add new constraints, read `core/constraints/modify_constraints.md` """
HARD_CONSTRAINT_FUNCS = [
{
"id": 1, "func": violates_hard_constraint_1,
"desc": "No two classes can take place in the same room at the same Timeslot."
},
{
"id": 2, "func": violates_hard_constraint_2,
"desc": "No Instructor can take more than one class at a given Timeslot."
},
]
SOFT_CONSTRAINT_FUNCS = [
{
"id": 1, "unit_penalty": 0.9, "func": penalty_of_soft_constraint_1,
"desc": "Instructors should only take certain courses they are are assigned to."
},
{
"id": 2, "unit_penalty": 0.85, "func": penalty_of_soft_constraint_2,
"desc": "A particular Room should only allow Classes of certain Courses."
},
{
"id": 3, "unit_penalty": 0.6, "func": penalty_of_soft_constraint_3,
"desc": "CourseGroups have Timeslot preferences."
},
{
"id": 4, "unit_penalty": 0.5, "func": penalty_of_soft_constraint_4,
"desc": "Instructors have Timeslot preferences."
},
{
"id": 5, "unit_penalty": 1.0, "func": penalty_of_soft_constraint_5,
"desc": "If a Course has 2 Lectures Per Week, it should take place in a composite Timeslot i.e. with Day code of 'ST' or 'MW'."
},
{
"id": 6, "unit_penalty": 0.8, "func": penalty_of_soft_constraint_6,
"desc": "The Lab Section of a Course (if any) should be placed in a Timeslot that is before or after the corresponding Theory Section."
},
{
"id": 7, "unit_penalty": 0.6, "func": penalty_of_soft_constraint_7,
"desc": "The Theory Section and the corresponding Lab Section of a Course (if any) should be taken by the same Instructor."
},
{
"id": 8, "unit_penalty": 0.9, "func": penalty_of_soft_constraint_8,
"desc": "Instructors have minimum credit load requirements."
},
]
| 0 | 0 | 0 |
b38add5b43c8c288b94da71185957d3fc3fad700 | 963 | py | Python | extras/scripts/convert_pronto_codes.py | aneisch/home-assistant | d8a866d75f512d23ffa7b9e5d82d7c575ece3c76 | [
"Apache-2.0"
] | 18 | 2016-08-10T01:02:27.000Z | 2017-10-26T04:19:49.000Z | extras/scripts/convert_pronto_codes.py | aneisch/home-assistant | d8a866d75f512d23ffa7b9e5d82d7c575ece3c76 | [
"Apache-2.0"
] | null | null | null | extras/scripts/convert_pronto_codes.py | aneisch/home-assistant | d8a866d75f512d23ffa7b9e5d82d7c575ece3c76 | [
"Apache-2.0"
] | 4 | 2017-04-20T19:41:21.000Z | 2017-05-16T17:10:05.000Z | #!/usr/bin/env python
# Provide code as arg: ./whatever.py "0000 ... ..."
import binascii
import struct
if __name__ == '__main__':
import sys
for code in sys.argv[1:]:
pronto = bytearray.fromhex(code)
pulses = pronto2lirc(pronto)
output = ""
count = 0
for pulse in pulses:
if count%2 != 0:
output += "{},".format(pulse*-1)
else:
output += "{},".format(pulse)
count += 1
print "["+output[:-1]+"]"
| 27.514286 | 90 | 0.554517 | #!/usr/bin/env python
# Provide code as arg: ./whatever.py "0000 ... ..."
import binascii
import struct
def pronto2lirc(pronto):
codes = [long(binascii.hexlify(pronto[i:i+2]), 16) for i in xrange(0, len(pronto), 2)]
if codes[0]:
raise ValueError('Pronto code should start with 0000')
if len(codes) != 4 + 2 * (codes[2] + codes[3]):
raise ValueError('Number of pulse widths does not match the preamble')
frequency = 1 / (codes[1] * 0.241246)
return [int(round(code / frequency)) for code in codes[4:]]
if __name__ == '__main__':
import sys
for code in sys.argv[1:]:
pronto = bytearray.fromhex(code)
pulses = pronto2lirc(pronto)
output = ""
count = 0
for pulse in pulses:
if count%2 != 0:
output += "{},".format(pulse*-1)
else:
output += "{},".format(pulse)
count += 1
print "["+output[:-1]+"]"
| 413 | 0 | 23 |
43dab3bbe7439e488e44d82a67a8a105d29e8e3d | 7,328 | py | Python | models/losses.py | yueyu-stu/EdgeAwareSpixel | f7f9fcb15bfa8e31bd4ad9473f9058c44a8391d7 | [
"MIT"
] | null | null | null | models/losses.py | yueyu-stu/EdgeAwareSpixel | f7f9fcb15bfa8e31bd4ad9473f9058c44a8391d7 | [
"MIT"
] | null | null | null | models/losses.py | yueyu-stu/EdgeAwareSpixel | f7f9fcb15bfa8e31bd4ad9473f9058c44a8391d7 | [
"MIT"
] | null | null | null | import torch
from torch import nn
class MutualInfoLoss(nn.Module):
"""
Mutual Information Loss derived from ss-with-RIM that also applied in
this work.
First term enforces to generate a sparse nSpixel dimension vector for
each pixel; Second term indicates the cardinality of each spixel.
Args:
logits: torch.tensor
A trainable tensor of shape (b, nSpixel, h, w) that
represents the probability of each pixel belonging to all spixels.
It should be softmaxed before calling this loss funtion.
coef: float
A coefficient that controls the amplitude of second term.
"""
class SmoothContourLoss(nn.Module):
"""
Loss function that contains smoothness loss derived from ss-with-RIM
and contour-aware loss.
Smoothness loss concerns about smoothness of local patterns, while
contour-aware loss is interested in whether two patches are divided.
Cross entropy (or KL divergence) is applied to calculate Contour-aware
loss. When calculating the gradients of probability, i.e. dp, and that
of image, i.e. di, we desire that the distribution of dp should appoximate
that of di.
Args:
logits: torch.tensor
A trainable tensor of shape (b, nSpixel, h, w)
It should be softmaxed before calling this loss function.
image: torch.tensor
A tensor derived from color channels of input with shape
(b, c, h, w)
sigma: float
Parameter of transformed Gaussian kernel size
weights: List[float]
A List contains 2 coefficients that control the amplitudes of
2 losses
thresh: float
Parameter for controling the amplitude of edge
margin: int
Parameter for finding edge width
"""
class ReconstructionLoss(nn.Module):
"""
Reconstruction loss for validating whether the features extracted by
CNN are effective
Args:
pred_img: torch.tensor
A trainable tensor of shape (b, c, h, w), where c = 3 in default
image: torch.tensor
A tensor of shape (b, c, h, w) that is derived from color channels
of input
"""
class Loss(nn.Module):
"""
Calculate total loss
Args:
pred_spixel: List[List[torch.tensor]]
containing 2 Lists with spixel probabilities in x and y dir
pred_img: tensor.tensor
A trainable tensor of shape (b, c, h, w), where c is 3 in default
img_in: tensor.tensor
An input tensor of shape (b, c, h, w), where c is 20 in default
These features consists of 15-channel features extracted by CNN,
and 5-channel RGB ()
loss_weights: List[float]
Weights for MutualInfoLoss, SmoothContourLoss, CompactnessLoss
and Reconstruction in sequence
sc_weights: List[float]
Weights for SmoothContourLoss
thresh: float
A parameter for SmoothContourLoss
coef_card: float
Parameter for MutualInfoLoss
sigma: float
Parameter for SmoothContourLoss
margin: int
Parameter for SmoothContourLoss
Returns:
total_loss: torch.tensor
A trainable tensor indicating final loss
losses: List[torch.tensor]
A List containing aforementioned losses in sequence
"""
| 37.387755 | 83 | 0.553357 | import torch
from torch import nn
class MutualInfoLoss(nn.Module):
"""
Mutual Information Loss derived from ss-with-RIM that also applied in
this work.
First term enforces to generate a sparse nSpixel dimension vector for
each pixel; Second term indicates the cardinality of each spixel.
Args:
logits: torch.tensor
A trainable tensor of shape (b, nSpixel, h, w) that
represents the probability of each pixel belonging to all spixels.
It should be softmaxed before calling this loss funtion.
coef: float
A coefficient that controls the amplitude of second term.
"""
def __init__(self, coef=2):
super().__init__()
self.coef = coef
def forward(self, logits):
pixel_wise_ent = -(logits * torch.log(logits + 1e-16)).sum(1).mean()
marginal_prob = logits.mean((2, 3))
marginal_ent = -(marginal_prob *
torch.log(marginal_prob + 1e-16)).sum(1).mean()
return pixel_wise_ent - self.coef * marginal_ent
class SmoothContourLoss(nn.Module):
"""
Loss function that contains smoothness loss derived from ss-with-RIM
and contour-aware loss.
Smoothness loss concerns about smoothness of local patterns, while
contour-aware loss is interested in whether two patches are divided.
Cross entropy (or KL divergence) is applied to calculate Contour-aware
loss. When calculating the gradients of probability, i.e. dp, and that
of image, i.e. di, we desire that the distribution of dp should appoximate
that of di.
Args:
logits: torch.tensor
A trainable tensor of shape (b, nSpixel, h, w)
It should be softmaxed before calling this loss function.
image: torch.tensor
A tensor derived from color channels of input with shape
(b, c, h, w)
sigma: float
Parameter of transformed Gaussian kernel size
weights: List[float]
A List contains 2 coefficients that control the amplitudes of
2 losses
thresh: float
Parameter for controling the amplitude of edge
margin: int
Parameter for finding edge width
"""
def __init__(self, sigma=2, weights=[1, 1], thresh=1.0, margin=1):
super().__init__()
self.sigma = 2 * sigma**2
self.weights = weights
self.thresh = thresh
self.margin = margin
def forward(self, logits, image):
dp, di = self.get_gradients(logits, image)
smooth = 0.
contour = 0.
for idx in range(len(dp)):
smooth += self.smooth_loss(dp[idx], di[idx])
contour += self.contour_loss(dp[idx], di[idx])
return self.weights[0] * smooth + self.weights[1] * contour
def get_gradients(self, logits, image):
dp_dx = (logits[..., :-self.margin] - logits[..., self.margin:])
dp_dy = (logits[..., :-self.margin, :] - logits[..., self.margin:, :])
di_dx = (image[..., :-self.margin] - image[..., self.margin:])
di_dy = (image[..., :-self.margin, :] - image[..., self.margin:, :])
return [dp_dx, dp_dy], [di_dx, di_dy]
def smooth_loss(self, dp, di):
return (dp.abs().sum(1) *
(-di.pow(2).sum(1) / self.sigma).exp()).mean()
def contour_loss(self, dp, di):
di_norm = di.pow(2)
di_min = (di_norm.min(-1,
keepdim=True).values).min(-2,
keepdim=True).values
di_max = (di_norm.max(-1,
keepdim=True).values).max(-2,
keepdim=True).values
di_norm = ((di_norm - di_min) / (di_max - di_min + 1e-16)).sum(1) * 2
isValidEdges = di_norm > self.thresh
dp_valid = dp.abs().sum(1) * isValidEdges
di_valid = di_norm * isValidEdges
return -(di_valid * (torch.log(dp_valid + 1e-16))).mean()
class ReconstructionLoss(nn.Module):
"""
Reconstruction loss for validating whether the features extracted by
CNN are effective
Args:
pred_img: torch.tensor
A trainable tensor of shape (b, c, h, w), where c = 3 in default
image: torch.tensor
A tensor of shape (b, c, h, w) that is derived from color channels
of input
"""
def __init__(self):
super().__init__()
self.mse_loss = nn.MSELoss()
def forward(self, pred_img, image):
return self.mse_loss(pred_img, image)
class Loss(nn.Module):
"""
Calculate total loss
Args:
pred_spixel: List[List[torch.tensor]]
containing 2 Lists with spixel probabilities in x and y dir
pred_img: tensor.tensor
A trainable tensor of shape (b, c, h, w), where c is 3 in default
img_in: tensor.tensor
An input tensor of shape (b, c, h, w), where c is 20 in default
These features consists of 15-channel features extracted by CNN,
and 5-channel RGB ()
loss_weights: List[float]
Weights for MutualInfoLoss, SmoothContourLoss, CompactnessLoss
and Reconstruction in sequence
sc_weights: List[float]
Weights for SmoothContourLoss
thresh: float
A parameter for SmoothContourLoss
coef_card: float
Parameter for MutualInfoLoss
sigma: float
Parameter for SmoothContourLoss
margin: int
Parameter for SmoothContourLoss
Returns:
total_loss: torch.tensor
A trainable tensor indicating final loss
losses: List[torch.tensor]
A List containing aforementioned losses in sequence
"""
def __init__(self,
loss_weights=[1, 2, 10],
sc_weights=[1, .75],
thresh=1.0,
coef_card=2,
sigma=2,
margin=1):
super().__init__()
# parameters - weights
self.weights = loss_weights
# Loss classes
self.mutual_info_loss = MutualInfoLoss(coef_card)
self.sc_loss = SmoothContourLoss(sigma, sc_weights, thresh, margin)
self.recon_loss = ReconstructionLoss()
def forward(self, spixel_prob, recon_img, img_feat):
loss_mutual = self.mutual_info_loss(spixel_prob)
# use blurred & clahed image
loss_sc = self.sc_loss(spixel_prob, img_feat[:, 3:-2, ...])
# use original image
loss_recon = self.recon_loss(recon_img, img_feat[:, :3, ...])
loss_total = 0.
losses = [loss_mutual, loss_sc, loss_recon]
for idx in range(len(losses)):
loss_total += self.weights[idx] * losses[idx]
return loss_total, losses
| 3,197 | 0 | 319 |
2585b1efefd1186d26a369a8a60b9ee6612d3e1c | 1,302 | py | Python | cards/turret.py | MrCoft/EngiMod | 65c90bd9231ac388d8af7849a1835914f1eefc78 | [
"MIT"
] | null | null | null | cards/turret.py | MrCoft/EngiMod | 65c90bd9231ac388d8af7849a1835914f1eefc78 | [
"MIT"
] | null | null | null | cards/turret.py | MrCoft/EngiMod | 65c90bd9231ac388d8af7849a1835914f1eefc78 | [
"MIT"
] | null | null | null | from engi_mod import *
Card(
name = "Turret",
type = "power",
target = "self",
rarity = "uncommon",
cost = 1,
const = dict(
DAMAGE = 6,
DAMAGE_UPGRADE = 3,
MAGIC_NUMBER = "DAMAGE",
),
desc = "At the end of your turn, deal !M! damage to a random enemy.",
upgrade_code = """
upgradeName();
upgradeMagicNumber(DAMAGE_UPGRADE);
""",
)
Power(
name = "Turret",
desc = [
"At the end of your turn, deal #b",
" damage to a random enemy."
],
desc_expr = "DESCRIPTIONS[0] + amount + DESCRIPTIONS[1]",
code = """
@Override
public void atEndOfTurn(final boolean isPlayer) {
if (AbstractDungeon.getCurrRoom().monsters.areMonstersBasicallyDead()) {
AbstractDungeon.actionManager.clearPostCombatActions();
return;
}
flash();
AbstractMonster target = AbstractDungeon.getMonsters().getRandomMonster(true);
if (target.currentHealth > 0) {
AbstractDungeon.actionManager.addToBottom(
new DamageAction((AbstractCreature)target, new DamageInfo(owner, amount, DamageInfo.DamageType.THORNS))
);
}
}
""",
icon = "armor",
)
| 28.933333 | 123 | 0.554531 | from engi_mod import *
Card(
name = "Turret",
type = "power",
target = "self",
rarity = "uncommon",
cost = 1,
const = dict(
DAMAGE = 6,
DAMAGE_UPGRADE = 3,
MAGIC_NUMBER = "DAMAGE",
),
desc = "At the end of your turn, deal !M! damage to a random enemy.",
upgrade_code = """
upgradeName();
upgradeMagicNumber(DAMAGE_UPGRADE);
""",
)
Power(
name = "Turret",
desc = [
"At the end of your turn, deal #b",
" damage to a random enemy."
],
desc_expr = "DESCRIPTIONS[0] + amount + DESCRIPTIONS[1]",
code = """
@Override
public void atEndOfTurn(final boolean isPlayer) {
if (AbstractDungeon.getCurrRoom().monsters.areMonstersBasicallyDead()) {
AbstractDungeon.actionManager.clearPostCombatActions();
return;
}
flash();
AbstractMonster target = AbstractDungeon.getMonsters().getRandomMonster(true);
if (target.currentHealth > 0) {
AbstractDungeon.actionManager.addToBottom(
new DamageAction((AbstractCreature)target, new DamageInfo(owner, amount, DamageInfo.DamageType.THORNS))
);
}
}
""",
icon = "armor",
)
| 0 | 0 | 0 |
d31cdcc12511cabf5ac61c6151db8886c0888504 | 494 | py | Python | 0x0F-python-object_relational_mapping/model_city.py | C-distin/alx-higher_level_programming | ee018135b24ac07d40f2309a4febf21b8a25aee4 | [
"MIT"
] | null | null | null | 0x0F-python-object_relational_mapping/model_city.py | C-distin/alx-higher_level_programming | ee018135b24ac07d40f2309a4febf21b8a25aee4 | [
"MIT"
] | null | null | null | 0x0F-python-object_relational_mapping/model_city.py | C-distin/alx-higher_level_programming | ee018135b24ac07d40f2309a4febf21b8a25aee4 | [
"MIT"
] | null | null | null | #!/usr/bin/python3
"""
Define a class City that inherits from Base
"""
from sqlalchemy import Column, Integer, String, ForeignKey
from model_state import Base
class City(Base):
"""
A class City that inherits from Base
"""
__tablename__ = "cities"
id = Column(Integer, primary_key=True, nullable=False, unique=True,
autoincrement=True)
name = Column(String(128), nullable=False)
state_id = Column(Integer, ForeignKey('states.id'), nullable=False)
| 24.7 | 71 | 0.686235 | #!/usr/bin/python3
"""
Define a class City that inherits from Base
"""
from sqlalchemy import Column, Integer, String, ForeignKey
from model_state import Base
class City(Base):
"""
A class City that inherits from Base
"""
__tablename__ = "cities"
id = Column(Integer, primary_key=True, nullable=False, unique=True,
autoincrement=True)
name = Column(String(128), nullable=False)
state_id = Column(Integer, ForeignKey('states.id'), nullable=False)
| 0 | 0 | 0 |
86d16852ee15057e22545a2d7257027440ff9f5d | 2,857 | py | Python | static_grouper/templatetags/static_grouper.py | adrienlachaize/dezede | 584ec30cedab95152e2f95595b7691a04e6736e2 | [
"BSD-3-Clause"
] | 15 | 2015-02-10T21:16:31.000Z | 2021-03-25T16:46:20.000Z | static_grouper/templatetags/static_grouper.py | adrienlachaize/dezede | 584ec30cedab95152e2f95595b7691a04e6736e2 | [
"BSD-3-Clause"
] | 4 | 2021-02-10T15:42:08.000Z | 2022-03-11T23:20:38.000Z | static_grouper/templatetags/static_grouper.py | adrienlachaize/dezede | 584ec30cedab95152e2f95595b7691a04e6736e2 | [
"BSD-3-Clause"
] | 6 | 2016-07-10T14:20:48.000Z | 2022-01-19T18:34:02.000Z | from collections import defaultdict
from compressor.templatetags.compress import CompressorNode
from django.template import (
Library, Node, Template, TemplateSyntaxError, Context)
register = Library()
CONTEXT_VARIABLE_NAME = 'static_grouper_dict'
register.tag('addstatic', AddStaticNode)
register.tag('static_list', StaticListNode)
| 32.465909 | 83 | 0.621281 | from collections import defaultdict
from compressor.templatetags.compress import CompressorNode
from django.template import (
Library, Node, Template, TemplateSyntaxError, Context)
register = Library()
CONTEXT_VARIABLE_NAME = 'static_grouper_dict'
class AddStaticNode(Node):
def __init__(self, parser, token):
contents = token.split_contents()
if len(contents) not in (2, 3):
raise TemplateSyntaxError
if len(contents) == 3:
assert contents[2] == 'nocompress'
self.compress = False
else:
self.compress = True
self.static_type = contents[1]
self.nodelist = parser.parse(('endaddstatic',))
parser.delete_first_token()
def render(self, context):
output = self.nodelist.render(context).strip()
static_grouper_dict = context.get(CONTEXT_VARIABLE_NAME)
if static_grouper_dict is None:
root_context = context.dicts[0]
root_context[CONTEXT_VARIABLE_NAME] = \
static_grouper_dict = defaultdict(list)
item = (self.compress, output)
if item not in static_grouper_dict[self.static_type]:
static_grouper_dict[self.static_type].append(item)
return ''
register.tag('addstatic', AddStaticNode)
class StaticListNode(Node):
def __init__(self, parser, token):
contents = token.split_contents()
if len(contents) not in (2, 3):
raise TemplateSyntaxError
self.static_type = contents[1]
if len(contents) == 3:
assert contents[2] == 'compress'
self.compress = True
else:
self.compress = False
self.following_nodelist = parser.parse()
def groups_iterator(self, static_grouper_dict):
compressed_group = []
for compress, output in static_grouper_dict[self.static_type]:
if compress:
compressed_group.append(output)
else:
if compressed_group:
yield True, ''.join(compressed_group)
compressed_group = []
yield False, output
if compressed_group:
yield True, ''.join(compressed_group)
def render(self, context):
static_grouper_dict = context.get(CONTEXT_VARIABLE_NAME, defaultdict(list))
following = self.following_nodelist.render(context)
inner = ''
for compress, output in self.groups_iterator(static_grouper_dict):
if compress and self.compress:
inner += CompressorNode(
nodelist=Template(output).nodelist, kind=self.static_type,
mode='file').render(context=context)
else:
inner += output
return inner + following
register.tag('static_list', StaticListNode)
| 2,322 | 11 | 179 |
b9d69527abbd1d87104316935718861ead22d02c | 549 | py | Python | solfasol/issues/migrations/0009_auto_20200918_0020.py | rekognize/solfasol | c960c3364c753d75161242eccac4f085d800c843 | [
"MIT"
] | null | null | null | solfasol/issues/migrations/0009_auto_20200918_0020.py | rekognize/solfasol | c960c3364c753d75161242eccac4f085d800c843 | [
"MIT"
] | 1 | 2020-06-18T13:08:47.000Z | 2020-06-18T13:08:47.000Z | solfasol/issues/migrations/0009_auto_20200918_0020.py | Solfasol/solfasol | c960c3364c753d75161242eccac4f085d800c843 | [
"MIT"
] | null | null | null | # Generated by Django 3.1.1 on 2020-09-17 21:20
from django.db import migrations, models
| 23.869565 | 109 | 0.591985 | # Generated by Django 3.1.1 on 2020-09-17 21:20
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('issues', '0008_auto_20200918_0006'),
]
operations = [
migrations.RemoveField(
model_name='issue',
name='file_data',
),
migrations.AddField(
model_name='issue',
name='page_data',
field=models.TextField(blank=True, help_text='page data for creating pages remotely', null=True),
),
]
| 0 | 435 | 23 |
c7e38cb251d253e29429b6f992fbff071d6adf2c | 627 | py | Python | osm_austria_building_coverage/urls.py | thomaskonrad/osm_austria_building_coverage | 3f678837b6800adfdd165f9b8424d1a258ca63de | [
"MIT"
] | 2 | 2015-06-21T19:39:05.000Z | 2015-06-22T10:54:17.000Z | osm_austria_building_coverage/urls.py | thomaskonrad/osm-austria-building-coverage | 3f678837b6800adfdd165f9b8424d1a258ca63de | [
"MIT"
] | 2 | 2018-02-17T16:51:06.000Z | 2018-02-23T07:11:13.000Z | osm_austria_building_coverage/urls.py | thomaskonrad/osm_austria_building_coverage | 3f678837b6800adfdd165f9b8424d1a258ca63de | [
"MIT"
] | 3 | 2019-10-16T08:46:13.000Z | 2021-04-14T23:49:07.000Z | from django.conf.urls import url
from coverage_score_viewer.views import index, states, districts, municipalities, details, search, coverage_chart
from map.views import map
urlpatterns = [
url(r'^$', index, name='index'),
url(r'^states/$', states, name='states'),
url(r'^districts/$', districts, name='districts'),
url(r'^municipalities/$', municipalities, name='municipalities'),
url(r'^details/(?P<boundary_id>\d+)$', details, name='details'),
url(r'^search$', search, name='search'),
url(r'^coverage-chart.svg', coverage_chart, name='coverage_chart'),
url(r'^map/$', map, name='map'),
]
| 33 | 113 | 0.673046 | from django.conf.urls import url
from coverage_score_viewer.views import index, states, districts, municipalities, details, search, coverage_chart
from map.views import map
urlpatterns = [
url(r'^$', index, name='index'),
url(r'^states/$', states, name='states'),
url(r'^districts/$', districts, name='districts'),
url(r'^municipalities/$', municipalities, name='municipalities'),
url(r'^details/(?P<boundary_id>\d+)$', details, name='details'),
url(r'^search$', search, name='search'),
url(r'^coverage-chart.svg', coverage_chart, name='coverage_chart'),
url(r'^map/$', map, name='map'),
]
| 0 | 0 | 0 |
3cc7f8fa749c9cf61233fe046871543a9e762b27 | 672 | py | Python | PythonApps/hanoi_tower.py | zactsiap/SB4-sample | b9a70d267db3a330170a5cd6aa2c0bf37bfdd5d2 | [
"MIT"
] | 1 | 2018-03-28T08:55:10.000Z | 2018-03-28T08:55:10.000Z | PythonApps/hanoi_tower.py | zactsiap/Python | b9a70d267db3a330170a5cd6aa2c0bf37bfdd5d2 | [
"MIT"
] | null | null | null | PythonApps/hanoi_tower.py | zactsiap/Python | b9a70d267db3a330170a5cd6aa2c0bf37bfdd5d2 | [
"MIT"
] | null | null | null | #Hanoi tower
import time
diskNumber = 1
while diskNumber != 0:
print("============================")
diskNumber = input("Give how many disk you have.\nGive 0 to end.\n============================\n")
print("============================")
diskNumber=int(diskNumber)
moveTower(diskNumber,"A","C","B")
print("Thanks for your time.")
time.sleep(4)
| 28 | 103 | 0.552083 | #Hanoi tower
import time
def moveTower(height,fromPole, toPole, withPole):
if height >= 1:
moveTower(height-1,fromPole,withPole,toPole)
moveDisk(fromPole,toPole)
moveTower(height-1,withPole,toPole,fromPole)
def moveDisk(fp,tp):
print("Moving disk from",fp,"to",tp)
diskNumber = 1
while diskNumber != 0:
print("============================")
diskNumber = input("Give how many disk you have.\nGive 0 to end.\n============================\n")
print("============================")
diskNumber=int(diskNumber)
moveTower(diskNumber,"A","C","B")
print("Thanks for your time.")
time.sleep(4)
| 233 | 0 | 58 |
0c47d0dab31b2f5af3dda0da867f4d530e0f3323 | 8,665 | py | Python | build/lib/Pythactyl/client.py | IAmGadget/Pythactyl | b0cca838d434e8ceba1a90328708b9cde3d9fbcc | [
"MIT"
] | 2 | 2021-07-13T21:43:44.000Z | 2021-09-06T08:05:35.000Z | build/lib/Pythactyl/client.py | IAmGadget/Pythactyl | b0cca838d434e8ceba1a90328708b9cde3d9fbcc | [
"MIT"
] | 1 | 2021-09-11T16:08:43.000Z | 2021-10-30T16:10:56.000Z | build/lib/Pythactyl/client.py | IAmGadget/Pythactyl | b0cca838d434e8ceba1a90328708b9cde3d9fbcc | [
"MIT"
] | null | null | null | import requests
from errors import PermissionsMissing
# Servers
# Subusers
if __name__ == "__main__":
print("I dont run. No use in trying to make me lol")
| 36.716102 | 130 | 0.552222 | import requests
from errors import PermissionsMissing
class PterodactylClient(object):
def __init__(self, url, api_key):
self.url = url + "/api/client"
self.api_key = api_key
def account(self):
headers = {
"Authorization": "Bearer {}".format(self.api_key),
"Accept": "application/json",
"Content-type": "application/json"
}
r = requests.get(self.url + "/account", headers=headers)
return r.json()
def check2fa(self):
headers = {
"Authorization": "Bearer {}".format(self.api_key),
"Accept": "application/json",
"Content-type": "application/json"
}
r = requests.get(self.url + "/account/two-factor", headers=headers)
return r.json()
def updateEmail(self, email, password):
headers = {
"Authorization": "Bearer {}".format(self.api_key),
"Accept": "application/json",
"Content-type": "application/json"
}
data = {
"email": email,
"password": password
}
r = requests.put(self.url + "/account/email", headers=headers, json=data)
return r.status_code
def updatePassword(self, password, newpass, confpass):
if newpass != confpass:
return PasswordsDontMatch(f"{newpass} does not match {confpass}. Check spelling")
headers = {
"Authorization": "Bearer {}".format(self.api_key),
"Accept": "application/json",
"Content-type": "application/json"
}
data = {
"current_password": password,
"password": newpass,
"password_confirmation": confpass
}
r = requests.put(self.url + "/account/email", headers=headers, json=data)
return r.json()
def listApikeys(self):
headers = {
"Authorization": "Bearer {}".format(self.api_key),
"Accept": "application/json",
"Content-type": "application/json"
}
r = requests.get(self.url + "/account/api-keys", headers=headers)
return r.json()
def createApiKey(self, description, allowed_ips=[]):
headers = {
"Authorization": "Bearer {}".format(self.api_key),
"Accept": "application/json",
"Content-type": "application/json"
}
data = {
"description": description,
"allowed_ips": allowed_ips
}
r = requests.post(self.url + "/account/api-keys", headers=headers, json=data)
return r.json()
def removeApiKey(self, code):
headers = {
"Authorization": "Bearer {}".format(self.api_key),
"Accept": "application/json",
"Content-type": "application/json"
}
r = requests.delete(self.url + "/account/api-keys/" + str(code), headers=headers)
if r.status_code == 404:
return {'error': 'An error has occured','tips': 'Check the code is correct and/or existing'}
else:
return r.status_code
# Servers
def listServers(self):
headers = {
"Authorization": "Bearer {}".format(self.api_key),
"Accept": "application/json",
"Content-type": "application/json"
}
r = requests.get(self.url, headers=headers)
return r.json()
def getServer(self, identifier):
headers = {
"Authorization": "Bearer {}".format(self.api_key),
"Accept": "application/json",
"Content-type": "application/json"
}
r = requests.get(self.url + "/servers/" + str(identifier), headers=headers)
return r.json()
def sendPowerAction(self, identifier, action):
signals = ['start', 'stop','restart','kill']
if action.lower() not in signals:
return {'error': 'Incorrect signal sent','available signals': signal}
headers = {
"Authorization": "Bearer {}".format(self.api_key),
"Accept": "application/json",
"Content-type": "application/json"
}
data = {
"signal": action
}
r = requests.post(self.url + "/servers/" + identifier + "/power", headers=headers, json=data)
return r
def sendCommand(self, identifier, command):
headers = {
"Authorization": "Bearer {}".format(self.api_key),
"Accept": "application/json",
"Content-type": "application/json"
}
data = {
"command": command
}
r = requests.post(self.url + "/servers/" + identifier + "/command", headers=headers, json=data)
return r
def listDatabases(self, identifier):
headers = {
"Authorization": "Bearer {}".format(self.api_key),
"Accept": "application/json",
"Content-type": "application/json"
}
r = requests.get(self.url + "/servers/" + str(identifier) + "/databases", headers=headers)
return r.json()
def createDatabase(self, identifier, db_name, remote_addr="%"):
headers = {
"Authorization": "Bearer {}".format(self.api_key),
"Accept": "application/json",
"Content-type": "application/json"
}
data = {
"database": db_name,
"remote": remote_addr
}
r = requests.post(self.url + "/servers/" + str(identifier) + "/databases", headers=headers, json=data)
return r.json()
def resetDatabasePassword(self, identifier, db_id):
headers = {
"Authorization": "Bearer {}".format(self.api_key),
"Accept": "application/json",
"Content-type": "application/json"
}
r = requests.post(self.url + "/servers/" + str(identifier) + "/databases/" + db_id + "/rotate-password", headers=headers)
return r.json()
def removeDatabase(self, identifier, db_id):
headers = {
"Authorization": "Bearer {}".format(self.api_key),
"Accept": "application/json",
"Content-type": "application/json"
}
r = requests.delete(self.url + "/servers/" + str(identifier) + "/databases/" + db_id + "rotate-password", headers=headers)
return r.json()
# Subusers
def listSubusers(self, identifier):
headers = {
"Authorization": "Bearer {}".format(self.api_key),
"Accept": "application/json",
"Content-type": "application/json"
}
r = requests.get(self.url + "/servers/" + str(identifier) + "/users", headers=headers)
return r.json()
def addSubuser(self, identifier, email, permissions=[]):
if len(permissions) <= 0:
raise PermissionsMissing("You must specify at least 1 permission node")
headers = {
"Authorization": "Bearer {}".format(self.api_key),
"Accept": "application/json",
"Content-type": "application/json"
}
data = {
"email": email,
"permissions": permissions
}
r = requests.post(self.url + "/servers/" + str(identifier) + "/users", headers=headers, json=data)
return r.json()
def getSubuser(self, identifier, uuid):
headers = {
"Authorization": "Bearer {}".format(self.api_key),
"Accept": "application/json",
"Content-type": "application/json"
}
r = requests.get(self.url + "/servers/" + str(identifier) + "/users/" + uuid, headers=headers)
return r.json()
def updateSubuser(self, identifier, uuid, permissions=[]):
if len(permissions) <= 0:
raise PermissionsMissing("You must specify at least 1 permission node")
headers = {
"Authorization": "Bearer {}".format(self.api_key),
"Accept": "application/json",
"Content-type": "application/json"
}
data = {
"permissions": permissions
}
r = requests.post(self.url + "/servers/" + str(identifier) + "/users/" + uuid, headers=headers, json=data)
return r.json()
def removeSubuser(self, identifier, uuid):
headers = {
"Authorization": "Bearer {}".format(self.api_key),
"Accept": "application/json",
"Content-type": "application/json"
}
r = requests.delete(self.url + "/servers/" + str(identifier) + "/users/" + uuid, headers=headers, json=data)
return r.json()
if __name__ == "__main__":
print("I dont run. No use in trying to make me lol")
| 7,888 | 11 | 588 |
2958aa609c7322fec1b8dd23a67c95f349cb1219 | 1,110 | py | Python | terse/Grammar/Command_Assign.py | talipovm/Terse | e6bfd3ac0d411b18d5167019623d5451695e787c | [
"MIT"
] | 2 | 2015-04-08T21:28:35.000Z | 2015-04-08T23:25:04.000Z | terse/Grammar/Command_Assign.py | talipovm/Terse | e6bfd3ac0d411b18d5167019623d5451695e787c | [
"MIT"
] | null | null | null | terse/Grammar/Command_Assign.py | talipovm/Terse | e6bfd3ac0d411b18d5167019623d5451695e787c | [
"MIT"
] | 1 | 2015-04-08T21:29:22.000Z | 2015-04-08T21:29:22.000Z | from Containers.ParsedStructure import ParsedElement
from Tools.misc import strip_all
from Grammar.Top_Grammar import Top_Grammar
from Grammar.Functions import ExpressionFactory
import logging
log = logging.getLogger(__name__) | 32.647059 | 101 | 0.667568 | from Containers.ParsedStructure import ParsedElement
from Tools.misc import strip_all
from Grammar.Top_Grammar import Top_Grammar
from Grammar.Functions import ExpressionFactory
import logging
log = logging.getLogger(__name__)
class Command_Assign(Top_Grammar):
def __init__(self, GI, FI, parsed_container, troublemakers):
super().__init__(GI, FI, parsed_container, troublemakers)
s = self.GI.s
v = s.split('=', maxsplit=1)
s_keys, s_function = strip_all(v)
self.keys = strip_all(s_keys.split(','))
self.f = ExpressionFactory(s_function,f_get_params=self.parsed_container.last_value).assign()
def execute(self):
vals = self.f.get_value(self.FI.s)
if vals is None: # expression was not found
return
if self.keys == ['row']: # are we parsing a table row?
return vals
if len(self.keys) != len(vals):
raise SyntaxError
for key, val in zip(self.keys,vals):
assigned_element = ParsedElement(key, val)
self.parsed_container.append(assigned_element) | 794 | 13 | 76 |
2543280c31c85783d24075e8fcbb3059c3236aa9 | 136 | py | Python | minos/aggregate/models/diffs/__init__.py | Clariteia/minos_microservice_aggregate | 517db2f1abb11f5a8f2d77da76613061590ee5a7 | [
"MIT"
] | 3 | 2021-11-05T08:47:45.000Z | 2021-11-17T09:37:26.000Z | minos/aggregate/models/diffs/__init__.py | Clariteia/minos_microservice_aggregate | 517db2f1abb11f5a8f2d77da76613061590ee5a7 | [
"MIT"
] | 30 | 2021-11-05T08:49:28.000Z | 2022-01-28T12:00:56.000Z | minos/aggregate/models/diffs/__init__.py | Clariteia/minos_microservice_aggregate | 517db2f1abb11f5a8f2d77da76613061590ee5a7 | [
"MIT"
] | null | null | null | from .aggregates import (
AggregateDiff,
)
from .fields import (
FieldDiff,
FieldDiffContainer,
IncrementalFieldDiff,
)
| 15.111111 | 25 | 0.705882 | from .aggregates import (
AggregateDiff,
)
from .fields import (
FieldDiff,
FieldDiffContainer,
IncrementalFieldDiff,
)
| 0 | 0 | 0 |
54e7f05eb53e54dca85ec1cb19c90651ae041cb3 | 2,596 | py | Python | app/api/schema/image_sizes.py | akashtalole/python-flask-restful-api | 475d8fd7be1724183716a197aac4257f8fbbeac4 | [
"MIT"
] | 3 | 2019-09-05T05:28:49.000Z | 2020-06-10T09:03:37.000Z | app/api/schema/image_sizes.py | akashtalole/python-flask-restful-api | 475d8fd7be1724183716a197aac4257f8fbbeac4 | [
"MIT"
] | null | null | null | app/api/schema/image_sizes.py | akashtalole/python-flask-restful-api | 475d8fd7be1724183716a197aac4257f8fbbeac4 | [
"MIT"
] | null | null | null | from marshmallow_jsonapi import fields
from marshmallow_jsonapi.flask import Schema
from app.api.helpers.utilities import dasherize
from utils.common import use_defaults
@use_defaults()
class EventImageSizeSchema(Schema):
"""
Api schema for image_size Model
"""
class Meta:
"""
Meta class for image_size Api Schema
"""
type_ = 'event-image-size'
self_view = 'v1.event_image_size_detail'
self_view_kwargs = {'id': '<id>'}
inflect = dasherize
id = fields.Str(dump_only=True)
type = fields.Str(allow_none=True)
full_width = fields.Integer(validate=lambda n: n >= 0, allow_none=True)
full_height = fields.Integer(validate=lambda n: n >= 0, allow_none=True)
full_aspect = fields.Boolean(default=False)
full_quality = fields.Integer(validate=lambda n: 0 <= n <= 100, allow_none=True)
icon_width = fields.Integer(validate=lambda n: n >= 0, allow_none=True)
icon_height = fields.Integer(validate=lambda n: n >= 0, allow_none=True)
icon_aspect = fields.Boolean(default=False)
icon_quality = fields.Integer(validate=lambda n: 0 <= n <= 100, allow_none=True)
thumbnail_width = fields.Integer(validate=lambda n: n >= 0, allow_none=True)
thumbnail_height = fields.Integer(validate=lambda n: n >= 0, allow_none=True)
thumbnail_aspect = fields.Boolean(default=False)
thumbnail_quality = fields.Integer(validate=lambda n: 0 <= n <= 100, allow_none=True)
logo_width = fields.Integer(validate=lambda n: n >= 0, allow_none=True)
logo_height = fields.Integer(validate=lambda n: n >= 0, allow_none=True)
@use_defaults()
class SpeakerImageSizeSchema(Schema):
"""
Api schema for image_size Model
"""
class Meta:
"""
Meta class for image_size Api Schema
"""
type_ = 'speaker-image-size'
self_view = 'v1.speaker_image_size_detail'
self_view_kwargs = {'id': '<id>'}
inflect = dasherize
id = fields.Str(dump_only=True)
type = fields.Str(allow_none=True)
small_size_width_height = fields.Integer(validate=lambda n: n >= 0, allow_none=True)
small_size_quality = fields.Integer(validate=lambda n: 0 <= n <= 100, allow_none=True)
thumbnail_size_width_height = fields.Integer(validate=lambda n: n >= 0, allow_none=True)
thumbnail_size_quality = fields.Integer(validate=lambda n: 0 <= n <= 100, allow_none=True)
icon_size_width_height = fields.Integer(validate=lambda n: n >= 0, allow_none=True)
icon_size_quality = fields.Integer(validate=lambda n: 0 <= n <= 100, allow_none=True)
| 41.870968 | 94 | 0.692604 | from marshmallow_jsonapi import fields
from marshmallow_jsonapi.flask import Schema
from app.api.helpers.utilities import dasherize
from utils.common import use_defaults
@use_defaults()
class EventImageSizeSchema(Schema):
"""
Api schema for image_size Model
"""
class Meta:
"""
Meta class for image_size Api Schema
"""
type_ = 'event-image-size'
self_view = 'v1.event_image_size_detail'
self_view_kwargs = {'id': '<id>'}
inflect = dasherize
id = fields.Str(dump_only=True)
type = fields.Str(allow_none=True)
full_width = fields.Integer(validate=lambda n: n >= 0, allow_none=True)
full_height = fields.Integer(validate=lambda n: n >= 0, allow_none=True)
full_aspect = fields.Boolean(default=False)
full_quality = fields.Integer(validate=lambda n: 0 <= n <= 100, allow_none=True)
icon_width = fields.Integer(validate=lambda n: n >= 0, allow_none=True)
icon_height = fields.Integer(validate=lambda n: n >= 0, allow_none=True)
icon_aspect = fields.Boolean(default=False)
icon_quality = fields.Integer(validate=lambda n: 0 <= n <= 100, allow_none=True)
thumbnail_width = fields.Integer(validate=lambda n: n >= 0, allow_none=True)
thumbnail_height = fields.Integer(validate=lambda n: n >= 0, allow_none=True)
thumbnail_aspect = fields.Boolean(default=False)
thumbnail_quality = fields.Integer(validate=lambda n: 0 <= n <= 100, allow_none=True)
logo_width = fields.Integer(validate=lambda n: n >= 0, allow_none=True)
logo_height = fields.Integer(validate=lambda n: n >= 0, allow_none=True)
@use_defaults()
class SpeakerImageSizeSchema(Schema):
"""
Api schema for image_size Model
"""
class Meta:
"""
Meta class for image_size Api Schema
"""
type_ = 'speaker-image-size'
self_view = 'v1.speaker_image_size_detail'
self_view_kwargs = {'id': '<id>'}
inflect = dasherize
id = fields.Str(dump_only=True)
type = fields.Str(allow_none=True)
small_size_width_height = fields.Integer(validate=lambda n: n >= 0, allow_none=True)
small_size_quality = fields.Integer(validate=lambda n: 0 <= n <= 100, allow_none=True)
thumbnail_size_width_height = fields.Integer(validate=lambda n: n >= 0, allow_none=True)
thumbnail_size_quality = fields.Integer(validate=lambda n: 0 <= n <= 100, allow_none=True)
icon_size_width_height = fields.Integer(validate=lambda n: n >= 0, allow_none=True)
icon_size_quality = fields.Integer(validate=lambda n: 0 <= n <= 100, allow_none=True)
| 0 | 0 | 0 |
80ccd07a50d9e26f0f3c296670920b067e2d2e5e | 1,043 | py | Python | Algorithms/0097_Interleaving_String/Python/Interleaving_String_Solution_1.py | lht19900714/Leetcode_Solutions | dac7a038329a5c1f8a78e86cc6f49116b963f1fb | [
"MIT"
] | null | null | null | Algorithms/0097_Interleaving_String/Python/Interleaving_String_Solution_1.py | lht19900714/Leetcode_Solutions | dac7a038329a5c1f8a78e86cc6f49116b963f1fb | [
"MIT"
] | null | null | null | Algorithms/0097_Interleaving_String/Python/Interleaving_String_Solution_1.py | lht19900714/Leetcode_Solutions | dac7a038329a5c1f8a78e86cc6f49116b963f1fb | [
"MIT"
] | null | null | null |
# Space: O(n)
# Time: O(n)
| 28.972222 | 99 | 0.540748 |
# Space: O(n)
# Time: O(n)
class Solution:
def isInterleave(self, s1: str, s2: str, s3: str) -> bool:
length_1, length_2, length_3 = len(s1), len(s2), len(s3)
if length_1 == length_2 == length_3 == 0: return True
if length_1 + length_2 != length_3: return False
cache = [[None for _ in range(length_1)] for _ in range(length_2)]
def dfs(board, s1, s2, s3, index1, index2, index3):
if index1 == len(s1): return s2[index2:] == s3[index3:]
if index2 == len(s2): return s1[index1:] == s3[index3:]
if board[index2][index1] is not None: return board[index2][index1]
ans = False
if s1[index1] == s3[index3] and dfs(board, s1, s2, s3, index1 + 1, index2, index3 + 1):
ans = True
if s2[index2] == s3[index3] and dfs(board, s1, s2, s3, index1, index2 + 1, index3 + 1):
ans = True
board[index2][index1] = ans
return ans
return dfs(cache, s1, s2, s3, 0, 0, 0)
| 964 | -6 | 49 |
a2cfb3dd847c2c2f892bf3b4da52f1a13e5ade61 | 338 | py | Python | carProject/carApp/models.py | cs-fullstack-2019-spring/django-validation-cw-cgarciapieto | 51df6fbd8d4e5ad878c4a620ca822ecd9df55711 | [
"Apache-2.0"
] | null | null | null | carProject/carApp/models.py | cs-fullstack-2019-spring/django-validation-cw-cgarciapieto | 51df6fbd8d4e5ad878c4a620ca822ecd9df55711 | [
"Apache-2.0"
] | null | null | null | carProject/carApp/models.py | cs-fullstack-2019-spring/django-validation-cw-cgarciapieto | 51df6fbd8d4e5ad878c4a620ca822ecd9df55711 | [
"Apache-2.0"
] | null | null | null | from django.db import models
# car Model with input/attributes make, model, year, mpg | 28.166667 | 56 | 0.718935 | from django.db import models
# car Model with input/attributes make, model, year, mpg
class CarModel(models.Model):
make = models.CharField(max_length=200)
model = models.CharField(max_length=200)
year = models.IntegerField(default=0)
mpg = models.IntegerField(default=0)
def __str__(self):
return self.make | 22 | 207 | 23 |
e8525b400d730f96fedff9ad040d2992ff14e0fd | 193 | py | Python | ecfactory/mnt_cycles/mnt_cycles_examples.py | weikengchen/ecfactory | f509c00b7cf66f4b8dbe9540599a4c95b9742bfd | [
"MIT"
] | 39 | 2016-06-09T13:47:57.000Z | 2022-02-10T14:06:20.000Z | ecfactory/mnt_cycles/mnt_cycles_examples.py | frevson/ecfactory-A-SageMath-Library-for-Constructing-Elliptic-Curves | f509c00b7cf66f4b8dbe9540599a4c95b9742bfd | [
"MIT"
] | 3 | 2019-04-26T14:15:34.000Z | 2021-02-03T09:21:37.000Z | ecfactory/mnt_cycles/mnt_cycles_examples.py | frevson/ecfactory-A-SageMath-Library-for-Constructing-Elliptic-Curves | f509c00b7cf66f4b8dbe9540599a4c95b9742bfd | [
"MIT"
] | 13 | 2017-09-27T08:08:49.000Z | 2022-03-28T12:11:20.000Z | import ecfactory.mnt_cycles as mnt_cycles
# Example (find an MNT cycle with D = -19)
cycles = mnt_cycles.make_cycle(-19)
print('Found a cycle: ' + str(cycles[0][0]) + ', ' + str(cycles[0][1])) | 38.6 | 71 | 0.678756 | import ecfactory.mnt_cycles as mnt_cycles
# Example (find an MNT cycle with D = -19)
cycles = mnt_cycles.make_cycle(-19)
print('Found a cycle: ' + str(cycles[0][0]) + ', ' + str(cycles[0][1])) | 0 | 0 | 0 |
394e112047884d425fd5bac24f0c8c865dbcab28 | 2,159 | py | Python | tests/unit/test_core.py | ehebert/pipenv | b771621274fcdb6980b4c9682bd2b2879e3354d1 | [
"MIT"
] | 2 | 2018-11-06T04:53:13.000Z | 2018-11-08T22:10:20.000Z | tests/unit/test_core.py | ehebert/pipenv | b771621274fcdb6980b4c9682bd2b2879e3354d1 | [
"MIT"
] | 1 | 2018-11-25T10:50:57.000Z | 2018-11-25T10:50:57.000Z | tests/unit/test_core.py | ehebert/pipenv | b771621274fcdb6980b4c9682bd2b2879e3354d1 | [
"MIT"
] | 1 | 2021-07-03T03:30:45.000Z | 2021-07-03T03:30:45.000Z | import os
import pytest
import mock
from pipenv._compat import TemporaryDirectory
from pipenv.core import warn_in_virtualenv, load_dot_env
from pipenv.utils import temp_environ
@mock.patch('pipenv.environments.PIPENV_VIRTUALENV', 'totallyrealenv')
@mock.patch('pipenv.environments.PIPENV_VERBOSITY', -1)
@pytest.mark.core
@pytest.mark.core
@pytest.mark.core
@pytest.mark.core
| 35.983333 | 84 | 0.692913 | import os
import pytest
import mock
from pipenv._compat import TemporaryDirectory
from pipenv.core import warn_in_virtualenv, load_dot_env
from pipenv.utils import temp_environ
@mock.patch('pipenv.environments.PIPENV_VIRTUALENV', 'totallyrealenv')
@mock.patch('pipenv.environments.PIPENV_VERBOSITY', -1)
@pytest.mark.core
def test_suppress_nested_venv_warning(capsys):
# Capture the stderr of warn_in_virtualenv to test for the presence of the
# courtesy notice.
warn_in_virtualenv()
output, err = capsys.readouterr()
assert 'Courtesy Notice' not in err
@pytest.mark.core
def test_load_dot_env_from_environment_variable_location(capsys):
with temp_environ(), TemporaryDirectory(prefix='pipenv-', suffix='') as tempdir:
dotenv_path = os.path.join(tempdir.name, 'test.env')
key, val = 'SOME_KEY', 'some_value'
with open(dotenv_path, 'w') as f:
f.write('{}={}'.format(key, val))
with mock.patch('pipenv.environments.PIPENV_DOTENV_LOCATION', dotenv_path):
load_dot_env()
assert os.environ[key] == val
@pytest.mark.core
def test_doesnt_load_dot_env_if_disabled(capsys):
with temp_environ(), TemporaryDirectory(prefix='pipenv-', suffix='') as tempdir:
dotenv_path = os.path.join(tempdir.name, 'test.env')
key, val = 'SOME_KEY', 'some_value'
with open(dotenv_path, 'w') as f:
f.write('{}={}'.format(key, val))
with mock.patch('pipenv.environments.PIPENV_DOTENV_LOCATION', dotenv_path):
with mock.patch('pipenv.environments.PIPENV_DONT_LOAD_ENV', '1'):
load_dot_env()
assert key not in os.environ
load_dot_env()
assert key in os.environ
@pytest.mark.core
def test_load_dot_env_warns_if_file_doesnt_exist(capsys):
with temp_environ(), TemporaryDirectory(prefix='pipenv-', suffix='') as tempdir:
dotenv_path = os.path.join(tempdir.name, 'does-not-exist.env')
with mock.patch('pipenv.environments.PIPENV_DOTENV_LOCATION', dotenv_path):
load_dot_env()
output, err = capsys.readouterr()
assert 'Warning' in err
| 1,685 | 0 | 88 |
19600211c81f047065f97d9d51dc1a3189acd673 | 4,107 | py | Python | data_collector_app.py | durgeshsamariya/foodify.ai | c058996fb095103f3b28e1d439073cf4ddbd26b0 | [
"MIT"
] | 2 | 2022-02-17T06:25:10.000Z | 2022-02-20T03:57:37.000Z | data_collector_app.py | durgeshsamariya/foodify.ai | c058996fb095103f3b28e1d439073cf4ddbd26b0 | [
"MIT"
] | null | null | null | data_collector_app.py | durgeshsamariya/foodify.ai | c058996fb095103f3b28e1d439073cf4ddbd26b0 | [
"MIT"
] | null | null | null | import PIL
import streamlit as st
import datetime
import uuid
from streamlit.uploaded_file_manager import UploadedFile
from utils.utils import upload_image, create_unique_filename, save_to_google_sheet
st.title("Foodify.ai Data Collection 🍔🌯🍫")
st.write(
"Upload or take a photo of your food and help us to build the world's biggest \
indian food image database!"
)
# Store image upload ID as key, this will be changed once image is uploaded
if "upload_key" not in st.session_state:
st.session_state["upload_key"] = str(uuid.uuid4())
uploaded_image = st.file_uploader(
label="Upload an image of any indian food",
type=["png", "jpeg", "jpg"],
help="Tip: if you're on a mobile device you can also take a photo",
# set the key for the uploaded file
key=st.session_state["upload_key"],
)
def display_image(img: UploadedFile) -> PIL.Image:
"""
Displays an image if the image exists.
"""
displayed_image = None
if img is not None:
# Show the image
img = PIL.Image.open(img)
print("Displaying image...")
print(img.height, img.width)
displayed_image = st.image(img, use_column_width="auto")
return img, displayed_image
image, displayed_image = display_image(uploaded_image)
# Create image label form to submit
st.write("## Image details")
with st.form(key="image_metadata_submit_form", clear_on_submit=True):
# Image label
label = st.text_input(
label="What food(s) it is in the image that you have uploaded? \
You can enter text like: '*biryani*' or '*parotha, aloo parotha*' or '*dosa, masala dosa*' ",
max_chars=100,
)
# Image upload location
place = st.text_input(
label="Where are you uploading this images? \
You can enter state name if you are uploading image from India else you can enter country name. ",
autocomplete="place",
max_chars=100,
)
# Person email
email = st.text_input(
label="What's your email? (optional, we'll use this to contact you \
about the app/say thank you for your image(s))",
autocomplete="email",
)
# Disclaimer
st.info(
'**Note:** If you click "upload image", your image will be stored on \
our servers and we will use this image to the largest indian food image database\
in the world! *(Do not upload anything sensitive, \
as we will make it publically available soon)*'
)
# Submit button + logic
submit_button = st.form_submit_button(
label="Upload image",
help="Click to upload your image and label to Foodify.ai servers.",
)
if submit_button:
if uploaded_image is None:
st.error("Please upload an image.")
else:
# Generate unique filename for the image
unique_image_id = create_unique_filename()
# Make timestamp
current_time = datetime.datetime.now().strftime(
"%Y-%m-%d %H:%M:%S"
)
#Upload image object to AWS S3 Bucket
with st.spinner("Uploading Image to our server..."):
upload_image(
source_file=uploaded_image,
destination_file_name=unique_image_id + ".jpg",
)
st.success(
f"Your image of {label} has been uploaded sucessfully! Thank you for your contribution :)"
)
info = [
[
unique_image_id,
current_time,
image.height,
image.width,
label,
place,
email
]
]
save_to_google_sheet(info)
# Remove (displayed) image after upload successful
displayed_image.empty()
# To do this, the key it's stored under Streamlit's
# UploadedFile gets changed to something random
st.session_state["upload_key"] = str(uuid.uuid4())
| 33.390244 | 110 | 0.600195 | import PIL
import streamlit as st
import datetime
import uuid
from streamlit.uploaded_file_manager import UploadedFile
from utils.utils import upload_image, create_unique_filename, save_to_google_sheet
st.title("Foodify.ai Data Collection 🍔🌯🍫")
st.write(
"Upload or take a photo of your food and help us to build the world's biggest \
indian food image database!"
)
# Store image upload ID as key, this will be changed once image is uploaded
if "upload_key" not in st.session_state:
st.session_state["upload_key"] = str(uuid.uuid4())
uploaded_image = st.file_uploader(
label="Upload an image of any indian food",
type=["png", "jpeg", "jpg"],
help="Tip: if you're on a mobile device you can also take a photo",
# set the key for the uploaded file
key=st.session_state["upload_key"],
)
def display_image(img: UploadedFile) -> PIL.Image:
"""
Displays an image if the image exists.
"""
displayed_image = None
if img is not None:
# Show the image
img = PIL.Image.open(img)
print("Displaying image...")
print(img.height, img.width)
displayed_image = st.image(img, use_column_width="auto")
return img, displayed_image
image, displayed_image = display_image(uploaded_image)
# Create image label form to submit
st.write("## Image details")
with st.form(key="image_metadata_submit_form", clear_on_submit=True):
# Image label
label = st.text_input(
label="What food(s) it is in the image that you have uploaded? \
You can enter text like: '*biryani*' or '*parotha, aloo parotha*' or '*dosa, masala dosa*' ",
max_chars=100,
)
# Image upload location
place = st.text_input(
label="Where are you uploading this images? \
You can enter state name if you are uploading image from India else you can enter country name. ",
autocomplete="place",
max_chars=100,
)
# Person email
email = st.text_input(
label="What's your email? (optional, we'll use this to contact you \
about the app/say thank you for your image(s))",
autocomplete="email",
)
# Disclaimer
st.info(
'**Note:** If you click "upload image", your image will be stored on \
our servers and we will use this image to the largest indian food image database\
in the world! *(Do not upload anything sensitive, \
as we will make it publically available soon)*'
)
# Submit button + logic
submit_button = st.form_submit_button(
label="Upload image",
help="Click to upload your image and label to Foodify.ai servers.",
)
if submit_button:
if uploaded_image is None:
st.error("Please upload an image.")
else:
# Generate unique filename for the image
unique_image_id = create_unique_filename()
# Make timestamp
current_time = datetime.datetime.now().strftime(
"%Y-%m-%d %H:%M:%S"
)
#Upload image object to AWS S3 Bucket
with st.spinner("Uploading Image to our server..."):
upload_image(
source_file=uploaded_image,
destination_file_name=unique_image_id + ".jpg",
)
st.success(
f"Your image of {label} has been uploaded sucessfully! Thank you for your contribution :)"
)
info = [
[
unique_image_id,
current_time,
image.height,
image.width,
label,
place,
email
]
]
save_to_google_sheet(info)
# Remove (displayed) image after upload successful
displayed_image.empty()
# To do this, the key it's stored under Streamlit's
# UploadedFile gets changed to something random
st.session_state["upload_key"] = str(uuid.uuid4())
| 0 | 0 | 0 |
62c7d6156a97c6551bc35203a263f248aed6d089 | 2,525 | py | Python | libs/orm.py | BreakUnrealGod/TanTan | 043454a76ee27d61e7d9aede7818f9127e34aaf2 | [
"MIT"
] | null | null | null | libs/orm.py | BreakUnrealGod/TanTan | 043454a76ee27d61e7d9aede7818f9127e34aaf2 | [
"MIT"
] | 10 | 2019-12-04T23:38:04.000Z | 2022-02-10T09:53:59.000Z | swiper/libs/orm.py | lijiaqipy/test1 | ab628a794ab67e153b929c819c876c5a676ab068 | [
"MIT"
] | null | null | null | from django.db import models
from django.core.cache import cache
from common import config
# class ModelToDictMixin(object):
def get(cls, *args, **kwargs):
"""
为 objects 管理类的 get 方法增加缓存功能
Model.get(pk=123)
:param self:
:param args:
:param kwargs:
:return:
"""
# 1、从缓存中获取数据
# 根据 pk 或 id 字段获得模型主键
if 'pk' in kwargs:
pk = kwargs.get('pk')
else:
pk = kwargs.get('id')
if pk is not None:
# 根据主键生成的 key，从缓存中获得数据
key = config.MODEL_CACHE_PREFIX % (cls.__name__, pk)
model_obj = cache.get(key)
# 如果缓存中不为空，则返回，否则执行原有数据库操作
if isinstance(model_obj, cls):
return model_obj
# 2、如果缓存中不存在，则从数据库中获得数据
model_obj = cls.objects.get(*args, **kwargs)
# 3、将数据库中返回的数据保存至缓存
key = config.MODEL_CACHE_PREFIX % (cls.__name__, model_obj.pk)
cache.set(key, model_obj)
return model_obj
def get_or_create(cls, defaults=None, **kwargs):
"""
为 objects 管理类的 get_or_create 增加缓存操作
User.get_or_create()
:param cls:
:param defaults:
:param kwargs:
:return:
"""
# 根据 pk 或 id 字段获得模型主键
if 'pk' in kwargs:
pk = kwargs.get('pk')
else:
pk = kwargs.get('id')
if pk is not None:
# 根据主键生成的 key，从缓存中获得数据
key = config.MODEL_CACHE_PREFIX % (cls.__name__, pk)
model_obj = cache.get(key)
# 如果缓存中不为空，则返回，否则执行原有数据库操作
if isinstance(model_obj, cls):
return model_obj, False
model_obj, created = cls.objects.get_or_create(defaults=None, **kwargs)
key = config.MODEL_CACHE_PREFIX % (cls.__name__, model_obj.pk)
cache.set(key, model_obj)
return model_obj, created
| 22.954545 | 75 | 0.636832 | from django.db import models
from django.core.cache import cache
from common import config
# class ModelToDictMixin(object):
def to_dict(self, exclude=[]):
attr_dict = {}
for field in self._meta.fields:
field_name = field.attname
if field_name not in exclude:
attr_dict[field_name] = getattr(self, field_name)
return attr_dict
def get(cls, *args, **kwargs):
"""
为 objects 管理类的 get 方法增加缓存功能
Model.get(pk=123)
:param self:
:param args:
:param kwargs:
:return:
"""
# 1、从缓存中获取数据
# 根据 pk 或 id 字段获得模型主键
if 'pk' in kwargs:
pk = kwargs.get('pk')
else:
pk = kwargs.get('id')
if pk is not None:
# 根据主键生成的 key，从缓存中获得数据
key = config.MODEL_CACHE_PREFIX % (cls.__name__, pk)
model_obj = cache.get(key)
# 如果缓存中不为空，则返回，否则执行原有数据库操作
if isinstance(model_obj, cls):
return model_obj
# 2、如果缓存中不存在，则从数据库中获得数据
model_obj = cls.objects.get(*args, **kwargs)
# 3、将数据库中返回的数据保存至缓存
key = config.MODEL_CACHE_PREFIX % (cls.__name__, model_obj.pk)
cache.set(key, model_obj)
return model_obj
def get_or_create(cls, defaults=None, **kwargs):
"""
为 objects 管理类的 get_or_create 增加缓存操作
User.get_or_create()
:param cls:
:param defaults:
:param kwargs:
:return:
"""
# 根据 pk 或 id 字段获得模型主键
if 'pk' in kwargs:
pk = kwargs.get('pk')
else:
pk = kwargs.get('id')
if pk is not None:
# 根据主键生成的 key，从缓存中获得数据
key = config.MODEL_CACHE_PREFIX % (cls.__name__, pk)
model_obj = cache.get(key)
# 如果缓存中不为空，则返回，否则执行原有数据库操作
if isinstance(model_obj, cls):
return model_obj, False
model_obj, created = cls.objects.get_or_create(defaults=None, **kwargs)
key = config.MODEL_CACHE_PREFIX % (cls.__name__, model_obj.pk)
cache.set(key, model_obj)
return model_obj, created
def save(self, force_insert=False, force_update=False, using=None,
update_fields=None):
# 使用 models.Model 原有的实例方法保存数据
self._save(force_insert=False, force_update=False, using=None,
update_fields=None)
# 将实例保存到缓存
key = config.MODEL_CACHE_PREFIX % (self.__class__.__name__, self.pk)
cache.set(key, self)
def path_model():
models.Model.get = classmethod(get)
models.Model.get_or_create = classmethod(get_or_create)
models.Model._save = models.Model.save
models.Model.save = save
models.Model.to_dict = to_dict
| 794 | 0 | 68 |
a3a6bcc50693cebd7f3c061c9e8471bb491ad207 | 3,142 | py | Python | airbyte-integrations/connectors/source-trello/unit_tests/test_control_rate_limit.py | OTRI-Unipd/OTRI-airbyte | 50eeeb773f75246e86c6e167b0cd7d2dda6efe0d | [
"MIT"
] | 6,215 | 2020-09-21T13:45:56.000Z | 2022-03-31T21:21:45.000Z | airbyte-integrations/connectors/source-trello/unit_tests/test_control_rate_limit.py | OTRI-Unipd/OTRI-airbyte | 50eeeb773f75246e86c6e167b0cd7d2dda6efe0d | [
"MIT"
] | 8,448 | 2020-09-21T00:43:50.000Z | 2022-03-31T23:56:06.000Z | airbyte-integrations/connectors/source-trello/unit_tests/test_control_rate_limit.py | OTRI-Unipd/OTRI-airbyte | 50eeeb773f75246e86c6e167b0cd7d2dda6efe0d | [
"MIT"
] | 1,251 | 2020-09-20T05:48:47.000Z | 2022-03-31T10:41:29.000Z | #
# Copyright (c) 2021 Airbyte, Inc., all rights reserved.
#
from functools import wraps
import requests
# Define standard timings in seconds
SLEEP_ON_HIGH_LOAD: float = 9.0
TEST_DATA_FIELD = "some_data_field"
TEST_RATE_LIMIT_THRESHOLD = 0.1
TEST_HEADERS_NAME = [
("x-rate-limit-api-key-remaining", "x-rate-limit-api-key-max"),
("x-rate-limit-api-token-remaining", "x-rate-limit-api-token-max"),
]
def control_request_rate_limit_decorator(threshold: float = 0.05, limit_headers=None):
"""
This decorator was replicated completely, as separeted function in order to be tested.
The only difference is:
:: the real one inside utils.py sleeps the actual defined time and returns the function back,
:: and this fake one simply sleeps and returns the wait_time as actual sleep time in order to be tested.
"""
return decorator
# Simulating real function call based CDK's parse_response() method
@control_request_rate_limit_decorator(TEST_RATE_LIMIT_THRESHOLD, TEST_HEADERS_NAME)
def test_with_load(requests_mock):
"""
Test simulates high load of rate limit.
In this case we should wait at least 9 sec before next API call.
"""
test_response_header = {
"x-rate-limit-api-token-max": "300",
"x-rate-limit-api-token-remaining": "10",
"x-rate-limit-api-key-max": "300",
"x-rate-limit-api-key-remaining": "100",
}
requests_mock.get("https://test.trello.com/", headers=test_response_header)
test_response = requests.get("https://test.trello.com/")
actual_sleep_time = fake_parse_response(test_response)
assert SLEEP_ON_HIGH_LOAD == actual_sleep_time
| 34.911111 | 116 | 0.668046 | #
# Copyright (c) 2021 Airbyte, Inc., all rights reserved.
#
from functools import wraps
import requests
# Define standard timings in seconds
SLEEP_ON_HIGH_LOAD: float = 9.0
TEST_DATA_FIELD = "some_data_field"
TEST_RATE_LIMIT_THRESHOLD = 0.1
TEST_HEADERS_NAME = [
("x-rate-limit-api-key-remaining", "x-rate-limit-api-key-max"),
("x-rate-limit-api-token-remaining", "x-rate-limit-api-token-max"),
]
def control_request_rate_limit_decorator(threshold: float = 0.05, limit_headers=None):
"""
This decorator was replicated completely, as separeted function in order to be tested.
The only difference is:
:: the real one inside utils.py sleeps the actual defined time and returns the function back,
:: and this fake one simply sleeps and returns the wait_time as actual sleep time in order to be tested.
"""
def decorator(func):
@wraps(func)
def wrapper_control_request_rate_limit(*args, **kwargs):
sleep_time = 0
free_load = float("inf")
# find the Response inside args list
for arg in args:
response = arg if type(arg) is requests.models.Response else None
# Get the rate_limits from response
rate_limits = (
[
(response.headers.get(rate_remaining_limit_header), response.headers.get(rate_max_limit_header))
for rate_remaining_limit_header, rate_max_limit_header in limit_headers
]
if response
else None
)
# define current load from rate_limits
if rate_limits:
for current_rate, max_rate_limit in rate_limits:
free_load = min(free_load, int(current_rate) / int(max_rate_limit))
# define sleep time based on load conditions
if free_load <= threshold:
sleep_time = SLEEP_ON_HIGH_LOAD
# for this test RETURN sleep_time based on load conditions
return sleep_time
return wrapper_control_request_rate_limit
return decorator
# Simulating real function call based CDK's parse_response() method
@control_request_rate_limit_decorator(TEST_RATE_LIMIT_THRESHOLD, TEST_HEADERS_NAME)
def fake_parse_response(response: requests.Response, **kwargs):
json_response = response.json()
records = json_response.get(TEST_DATA_FIELD, []) if TEST_DATA_FIELD is not None else json_response
yield from records
def test_with_load(requests_mock):
"""
Test simulates high load of rate limit.
In this case we should wait at least 9 sec before next API call.
"""
test_response_header = {
"x-rate-limit-api-token-max": "300",
"x-rate-limit-api-token-remaining": "10",
"x-rate-limit-api-key-max": "300",
"x-rate-limit-api-key-remaining": "100",
}
requests_mock.get("https://test.trello.com/", headers=test_response_header)
test_response = requests.get("https://test.trello.com/")
actual_sleep_time = fake_parse_response(test_response)
assert SLEEP_ON_HIGH_LOAD == actual_sleep_time
| 1,434 | 0 | 49 |
d9d1654b34f40e0c7abcc17dff20fa72909b9f64 | 8,810 | py | Python | xmonitor/db/sqlalchemy/migrate_repo/versions/017_quote_encrypted_swift_credentials.py | froyobin/xmonitor | 092dcaa01f834353ffd8dd3c40edf9e97543bfe8 | [
"Apache-2.0"
] | null | null | null | xmonitor/db/sqlalchemy/migrate_repo/versions/017_quote_encrypted_swift_credentials.py | froyobin/xmonitor | 092dcaa01f834353ffd8dd3c40edf9e97543bfe8 | [
"Apache-2.0"
] | null | null | null | xmonitor/db/sqlalchemy/migrate_repo/versions/017_quote_encrypted_swift_credentials.py | froyobin/xmonitor | 092dcaa01f834353ffd8dd3c40edf9e97543bfe8 | [
"Apache-2.0"
] | null | null | null | # Copyright 2013 OpenStack Foundation
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
"""
This migration handles migrating encrypted image location values from
the unquoted form to the quoted form.
If 'metadata_encryption_key' is specified in the config then this
migration performs the following steps for every entry in the images table:
1. Decrypt the location value with the metadata_encryption_key
2. Changes the value to its quoted form
3. Encrypts the new value with the metadata_encryption_key
4. Inserts the new value back into the row
Fixes bug #1081043
"""
import types # noqa
from oslo_config import cfg
from oslo_log import log as logging
from oslo_utils import encodeutils
import six.moves.urllib.parse as urlparse
import sqlalchemy
from xmonitor.common import crypt
from xmonitor.common import exception
from xmonitor.i18n import _, _LE, _LI, _LW
LOG = logging.getLogger(__name__)
CONF = cfg.CONF
CONF.import_opt('metadata_encryption_key', 'xmonitor.common.config')
def migrate_location_credentials(migrate_engine, to_quoted):
"""
Migrate location credentials for encrypted swift uri's between the
quoted and unquoted forms.
:param migrate_engine: The configured db engine
:param to_quoted: If True, migrate location credentials from
unquoted to quoted form. If False, do the
reverse.
"""
if not CONF.metadata_encryption_key:
msg = _LI("'metadata_encryption_key' was not specified in the config"
" file or a config file was not specified. This means that"
" this migration is a NOOP.")
LOG.info(msg)
return
meta = sqlalchemy.schema.MetaData()
meta.bind = migrate_engine
images_table = sqlalchemy.Table('images', meta, autoload=True)
images = list(images_table.select().execute())
for image in images:
try:
fixed_uri = fix_uri_credentials(image['location'], to_quoted)
images_table.update().where(
images_table.c.id == image['id']).values(
location=fixed_uri).execute()
except exception.Invalid:
msg = _LW("Failed to decrypt location value for image"
" %(image_id)s") % {'image_id': image['id']}
LOG.warn(msg)
except exception.BadStoreUri as e:
reason = encodeutils.exception_to_unicode(e)
msg = _LE("Invalid store uri for image: %(image_id)s. "
"Details: %(reason)s") % {'image_id': image.id,
'reason': reason}
LOG.exception(msg)
raise
def fix_uri_credentials(uri, to_quoted):
"""
Fix the given uri's embedded credentials by round-tripping with
StoreLocation.
If to_quoted is True, the uri is assumed to have credentials that
have not been quoted, and the resulting uri will contain quoted
credentials.
If to_quoted is False, the uri is assumed to have credentials that
have been quoted, and the resulting uri will contain credentials
that have not been quoted.
"""
if not uri:
return
try:
decrypted_uri = decrypt_location(uri)
# NOTE (ameade): If a uri is not encrypted or incorrectly encoded then we
# we raise an exception.
except (TypeError, ValueError) as e:
raise exception.Invalid(str(e))
return legacy_parse_uri(decrypted_uri, to_quoted)
def legacy_parse_uri(uri, to_quote):
"""
Parse URLs. This method fixes an issue where credentials specified
in the URL are interpreted differently in Python 2.6.1+ than prior
versions of Python. It also deals with the peculiarity that new-style
Swift URIs have where a username can contain a ':', like so:
swift://account:user:pass@authurl.com/container/obj
If to_quoted is True, the uri is assumed to have credentials that
have not been quoted, and the resulting uri will contain quoted
credentials.
If to_quoted is False, the uri is assumed to have credentials that
have been quoted, and the resulting uri will contain credentials
that have not been quoted.
"""
# Make sure that URIs that contain multiple schemes, such as:
# swift://user:pass@http://authurl.com/v1/container/obj
# are immediately rejected.
if uri.count('://') != 1:
reason = _("URI cannot contain more than one occurrence of a scheme."
"If you have specified a URI like "
"swift://user:pass@http://authurl.com/v1/container/obj"
", you need to change it to use the swift+http:// scheme, "
"like so: "
"swift+http://user:pass@authurl.com/v1/container/obj")
raise exception.BadStoreUri(message=reason)
pieces = urlparse.urlparse(uri)
if pieces.scheme not in ('swift', 'swift+http', 'swift+https'):
raise exception.BadStoreUri(message="Unacceptable scheme: '%s'" %
pieces.scheme)
scheme = pieces.scheme
netloc = pieces.netloc
path = pieces.path.lstrip('/')
if netloc != '':
# > Python 2.6.1
if '@' in netloc:
creds, netloc = netloc.split('@')
else:
creds = None
else:
# Python 2.6.1 compat
# see lp659445 and Python issue7904
if '@' in path:
creds, path = path.split('@')
else:
creds = None
netloc = path[0:path.find('/')].strip('/')
path = path[path.find('/'):].strip('/')
if creds:
cred_parts = creds.split(':')
# User can be account:user, in which case cred_parts[0:2] will be
# the account and user. Combine them into a single username of
# account:user
if to_quote:
if len(cred_parts) == 1:
reason = (_("Badly formed credentials '%(creds)s' in Swift "
"URI") % {'creds': creds})
raise exception.BadStoreUri(message=reason)
elif len(cred_parts) == 3:
user = ':'.join(cred_parts[0:2])
else:
user = cred_parts[0]
key = cred_parts[-1]
user = user
key = key
else:
if len(cred_parts) != 2:
reason = (_("Badly formed credentials in Swift URI."))
raise exception.BadStoreUri(message=reason)
user, key = cred_parts
user = urlparse.unquote(user)
key = urlparse.unquote(key)
else:
user = None
key = None
path_parts = path.split('/')
try:
obj = path_parts.pop()
container = path_parts.pop()
if not netloc.startswith('http'):
# push hostname back into the remaining to build full authurl
path_parts.insert(0, netloc)
auth_or_store_url = '/'.join(path_parts)
except IndexError:
reason = _("Badly formed S3 URI: %(uri)s") % {'uri': uri}
raise exception.BadStoreUri(message=reason)
if auth_or_store_url.startswith('http://'):
auth_or_store_url = auth_or_store_url[len('http://'):]
elif auth_or_store_url.startswith('https://'):
auth_or_store_url = auth_or_store_url[len('https://'):]
credstring = ''
if user and key:
if to_quote:
quote_user = urlparse.quote(user)
quote_key = urlparse.quote(key)
else:
quote_user = user
quote_key = key
credstring = '%s:%s@' % (quote_user, quote_key)
auth_or_store_url = auth_or_store_url.strip('/')
container = container.strip('/')
obj = obj.strip('/')
uri = '%s://%s%s/%s/%s' % (scheme, credstring, auth_or_store_url,
container, obj)
return encrypt_location(uri)
| 36.106557 | 78 | 0.632123 | # Copyright 2013 OpenStack Foundation
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
"""
This migration handles migrating encrypted image location values from
the unquoted form to the quoted form.
If 'metadata_encryption_key' is specified in the config then this
migration performs the following steps for every entry in the images table:
1. Decrypt the location value with the metadata_encryption_key
2. Changes the value to its quoted form
3. Encrypts the new value with the metadata_encryption_key
4. Inserts the new value back into the row
Fixes bug #1081043
"""
import types # noqa
from oslo_config import cfg
from oslo_log import log as logging
from oslo_utils import encodeutils
import six.moves.urllib.parse as urlparse
import sqlalchemy
from xmonitor.common import crypt
from xmonitor.common import exception
from xmonitor.i18n import _, _LE, _LI, _LW
LOG = logging.getLogger(__name__)
CONF = cfg.CONF
CONF.import_opt('metadata_encryption_key', 'xmonitor.common.config')
def upgrade(migrate_engine):
migrate_location_credentials(migrate_engine, to_quoted=True)
def downgrade(migrate_engine):
migrate_location_credentials(migrate_engine, to_quoted=False)
def migrate_location_credentials(migrate_engine, to_quoted):
"""
Migrate location credentials for encrypted swift uri's between the
quoted and unquoted forms.
:param migrate_engine: The configured db engine
:param to_quoted: If True, migrate location credentials from
unquoted to quoted form. If False, do the
reverse.
"""
if not CONF.metadata_encryption_key:
msg = _LI("'metadata_encryption_key' was not specified in the config"
" file or a config file was not specified. This means that"
" this migration is a NOOP.")
LOG.info(msg)
return
meta = sqlalchemy.schema.MetaData()
meta.bind = migrate_engine
images_table = sqlalchemy.Table('images', meta, autoload=True)
images = list(images_table.select().execute())
for image in images:
try:
fixed_uri = fix_uri_credentials(image['location'], to_quoted)
images_table.update().where(
images_table.c.id == image['id']).values(
location=fixed_uri).execute()
except exception.Invalid:
msg = _LW("Failed to decrypt location value for image"
" %(image_id)s") % {'image_id': image['id']}
LOG.warn(msg)
except exception.BadStoreUri as e:
reason = encodeutils.exception_to_unicode(e)
msg = _LE("Invalid store uri for image: %(image_id)s. "
"Details: %(reason)s") % {'image_id': image.id,
'reason': reason}
LOG.exception(msg)
raise
def decrypt_location(uri):
return crypt.urlsafe_decrypt(CONF.metadata_encryption_key, uri)
def encrypt_location(uri):
return crypt.urlsafe_encrypt(CONF.metadata_encryption_key, uri, 64)
def fix_uri_credentials(uri, to_quoted):
"""
Fix the given uri's embedded credentials by round-tripping with
StoreLocation.
If to_quoted is True, the uri is assumed to have credentials that
have not been quoted, and the resulting uri will contain quoted
credentials.
If to_quoted is False, the uri is assumed to have credentials that
have been quoted, and the resulting uri will contain credentials
that have not been quoted.
"""
if not uri:
return
try:
decrypted_uri = decrypt_location(uri)
# NOTE (ameade): If a uri is not encrypted or incorrectly encoded then we
# we raise an exception.
except (TypeError, ValueError) as e:
raise exception.Invalid(str(e))
return legacy_parse_uri(decrypted_uri, to_quoted)
def legacy_parse_uri(uri, to_quote):
"""
Parse URLs. This method fixes an issue where credentials specified
in the URL are interpreted differently in Python 2.6.1+ than prior
versions of Python. It also deals with the peculiarity that new-style
Swift URIs have where a username can contain a ':', like so:
swift://account:user:pass@authurl.com/container/obj
If to_quoted is True, the uri is assumed to have credentials that
have not been quoted, and the resulting uri will contain quoted
credentials.
If to_quoted is False, the uri is assumed to have credentials that
have been quoted, and the resulting uri will contain credentials
that have not been quoted.
"""
# Make sure that URIs that contain multiple schemes, such as:
# swift://user:pass@http://authurl.com/v1/container/obj
# are immediately rejected.
if uri.count('://') != 1:
reason = _("URI cannot contain more than one occurrence of a scheme."
"If you have specified a URI like "
"swift://user:pass@http://authurl.com/v1/container/obj"
", you need to change it to use the swift+http:// scheme, "
"like so: "
"swift+http://user:pass@authurl.com/v1/container/obj")
raise exception.BadStoreUri(message=reason)
pieces = urlparse.urlparse(uri)
if pieces.scheme not in ('swift', 'swift+http', 'swift+https'):
raise exception.BadStoreUri(message="Unacceptable scheme: '%s'" %
pieces.scheme)
scheme = pieces.scheme
netloc = pieces.netloc
path = pieces.path.lstrip('/')
if netloc != '':
# > Python 2.6.1
if '@' in netloc:
creds, netloc = netloc.split('@')
else:
creds = None
else:
# Python 2.6.1 compat
# see lp659445 and Python issue7904
if '@' in path:
creds, path = path.split('@')
else:
creds = None
netloc = path[0:path.find('/')].strip('/')
path = path[path.find('/'):].strip('/')
if creds:
cred_parts = creds.split(':')
# User can be account:user, in which case cred_parts[0:2] will be
# the account and user. Combine them into a single username of
# account:user
if to_quote:
if len(cred_parts) == 1:
reason = (_("Badly formed credentials '%(creds)s' in Swift "
"URI") % {'creds': creds})
raise exception.BadStoreUri(message=reason)
elif len(cred_parts) == 3:
user = ':'.join(cred_parts[0:2])
else:
user = cred_parts[0]
key = cred_parts[-1]
user = user
key = key
else:
if len(cred_parts) != 2:
reason = (_("Badly formed credentials in Swift URI."))
raise exception.BadStoreUri(message=reason)
user, key = cred_parts
user = urlparse.unquote(user)
key = urlparse.unquote(key)
else:
user = None
key = None
path_parts = path.split('/')
try:
obj = path_parts.pop()
container = path_parts.pop()
if not netloc.startswith('http'):
# push hostname back into the remaining to build full authurl
path_parts.insert(0, netloc)
auth_or_store_url = '/'.join(path_parts)
except IndexError:
reason = _("Badly formed S3 URI: %(uri)s") % {'uri': uri}
raise exception.BadStoreUri(message=reason)
if auth_or_store_url.startswith('http://'):
auth_or_store_url = auth_or_store_url[len('http://'):]
elif auth_or_store_url.startswith('https://'):
auth_or_store_url = auth_or_store_url[len('https://'):]
credstring = ''
if user and key:
if to_quote:
quote_user = urlparse.quote(user)
quote_key = urlparse.quote(key)
else:
quote_user = user
quote_key = key
credstring = '%s:%s@' % (quote_user, quote_key)
auth_or_store_url = auth_or_store_url.strip('/')
container = container.strip('/')
obj = obj.strip('/')
uri = '%s://%s%s/%s/%s' % (scheme, credstring, auth_or_store_url,
container, obj)
return encrypt_location(uri)
| 297 | 0 | 92 |
54fcba5a90b30f291e1a2fab18963885b8029fe5 | 1,885 | py | Python | cm/cm_dhexppot/CM/CM_TUW19/array2raster.py | historeno/enermaps | ad3a97636baa153a56367e374d0fef7f009bf19d | [
"Apache-2.0"
] | null | null | null | cm/cm_dhexppot/CM/CM_TUW19/array2raster.py | historeno/enermaps | ad3a97636baa153a56367e374d0fef7f009bf19d | [
"Apache-2.0"
] | null | null | null | cm/cm_dhexppot/CM/CM_TUW19/array2raster.py | historeno/enermaps | ad3a97636baa153a56367e374d0fef7f009bf19d | [
"Apache-2.0"
] | null | null | null | """
Created on August 14 2018
@author: fallahnejad@eeg.tuwien.ac.at
"""
import os
import sys
from osgeo import gdal, osr
path = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
if path not in sys.path:
sys.path.append(path)
def array2raster(
outRasterPath, geo_transform, dataType, array, noDataValue=0, OutputRasterSRS=3035
):
"""
This function rasterizes the input numpy array. The input array and the
geo_transform must be given for EPSG3035.
"""
# conversion of data types from numpy to gdal
dict_varTyp = {
"int8": gdal.GDT_Byte,
"int16": gdal.GDT_Int16,
"int32": gdal.GDT_Int32,
"uint16": gdal.GDT_UInt16,
"uint32": gdal.GDT_UInt32,
"float32": gdal.GDT_Float32,
"float64": gdal.GDT_Float64,
}
cols = array.shape[1]
rows = array.shape[0]
driver = gdal.GetDriverByName("GTiff")
outRaster = driver.Create(
outRasterPath,
cols,
rows,
1,
dict_varTyp[dataType],
["compress=DEFLATE", "TILED=YES", "TFW=YES", "ZLEVEL=9", "PREDICTOR=1"],
)
outRaster.SetGeoTransform(geo_transform)
outRasterSRS = osr.SpatialReference()
outRasterSRS.ImportFromEPSG(OutputRasterSRS)
outRaster.SetProjection(outRasterSRS.ExportToWkt())
outRaster.GetRasterBand(1).SetNoDataValue(noDataValue)
if dataType == "int8" or dataType == "uint16":
# This can be used for dtype int8
ct = gdal.ColorTable()
ct.SetColorEntry(noDataValue, (0, 0, 0, 255))
ct.SetColorEntry(1, (250, 159, 181, 255))
"""
for i in range(1, 1+np.max(array)):
ct.SetColorEntry(i, tuple(np.random.choice(range(256), size=4)))
"""
outRaster.GetRasterBand(1).SetColorTable(ct)
outRaster.GetRasterBand(1).WriteArray(array)
outRaster.FlushCache()
| 29.920635 | 86 | 0.646154 | """
Created on August 14 2018
@author: fallahnejad@eeg.tuwien.ac.at
"""
import os
import sys
from osgeo import gdal, osr
path = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
if path not in sys.path:
sys.path.append(path)
def array2raster(
outRasterPath, geo_transform, dataType, array, noDataValue=0, OutputRasterSRS=3035
):
"""
This function rasterizes the input numpy array. The input array and the
geo_transform must be given for EPSG3035.
"""
# conversion of data types from numpy to gdal
dict_varTyp = {
"int8": gdal.GDT_Byte,
"int16": gdal.GDT_Int16,
"int32": gdal.GDT_Int32,
"uint16": gdal.GDT_UInt16,
"uint32": gdal.GDT_UInt32,
"float32": gdal.GDT_Float32,
"float64": gdal.GDT_Float64,
}
cols = array.shape[1]
rows = array.shape[0]
driver = gdal.GetDriverByName("GTiff")
outRaster = driver.Create(
outRasterPath,
cols,
rows,
1,
dict_varTyp[dataType],
["compress=DEFLATE", "TILED=YES", "TFW=YES", "ZLEVEL=9", "PREDICTOR=1"],
)
outRaster.SetGeoTransform(geo_transform)
outRasterSRS = osr.SpatialReference()
outRasterSRS.ImportFromEPSG(OutputRasterSRS)
outRaster.SetProjection(outRasterSRS.ExportToWkt())
outRaster.GetRasterBand(1).SetNoDataValue(noDataValue)
if dataType == "int8" or dataType == "uint16":
# This can be used for dtype int8
ct = gdal.ColorTable()
ct.SetColorEntry(noDataValue, (0, 0, 0, 255))
ct.SetColorEntry(1, (250, 159, 181, 255))
"""
for i in range(1, 1+np.max(array)):
ct.SetColorEntry(i, tuple(np.random.choice(range(256), size=4)))
"""
outRaster.GetRasterBand(1).SetColorTable(ct)
outRaster.GetRasterBand(1).WriteArray(array)
outRaster.FlushCache()
| 0 | 0 | 0 |
9ba66ac0118865fada74771be7bf4006b25265ce | 5,286 | py | Python | util/scripts/stack-depth-info.py | systems-nuts/popcorn-compiler-alpine | 5c225c7d3055db83dc654b6b5525c34bbdacded1 | [
"Linux-OpenIB"
] | 30 | 2019-04-07T14:58:31.000Z | 2021-05-24T19:07:20.000Z | util/scripts/stack-depth-info.py | XRDevIEEE/popcorn-compiler | 2cb2eccc0c75b5963d9fec26ad80a7b881316b1d | [
"Linux-OpenIB"
] | 11 | 2018-07-24T19:31:26.000Z | 2020-09-03T08:56:23.000Z | util/scripts/stack-depth-info.py | XRDevIEEE/popcorn-compiler | 2cb2eccc0c75b5963d9fec26ad80a7b881316b1d | [
"Linux-OpenIB"
] | 17 | 2018-08-26T12:43:15.000Z | 2022-03-18T12:08:40.000Z | #!/usr/bin/python3
import sys, subprocess
###############################################################################
# Config
###############################################################################
dataFile = None
binFile = None
onlyFunc = False
verbose = False
###############################################################################
# Utility functions
###############################################################################
###############################################################################
# Driver
###############################################################################
skip = False
for i in range(len(sys.argv)):
if skip:
skip = False
continue
elif sys.argv[i] == "-h" or sys.argv[i] == "--help":
printHelp()
sys.exit(0)
continue
elif sys.argv[i] == "-d":
dataFile = sys.argv[i+1]
skip = True
continue
elif sys.argv[i] == "-b":
binFile = sys.argv[i+1]
skip = True
continue
elif sys.argv[i] == "-f":
onlyFunc = True
continue
elif sys.argv[i] == "-v":
verbose = True
continue
if dataFile == None:
print("Please supply a data file!")
printHelp()
sys.exit(1)
if onlyFunc and binFile == None:
print("Please supply a binary to print function names!")
printHelp()
sys.exit(1)
avgDepth, maxDepth, funcCalls = parseData(dataFile)
if binFile == None:
printRaw(dataFile, avgDepth, maxDepth, funcCalls)
else:
symbols = getSymbolTable(binFile)
printDetailed(dataFile, binFile, symbols, avgDepth, maxDepth, funcCalls)
| 30.732558 | 104 | 0.574915 | #!/usr/bin/python3
import sys, subprocess
###############################################################################
# Config
###############################################################################
dataFile = None
binFile = None
onlyFunc = False
verbose = False
###############################################################################
# Utility functions
###############################################################################
def printHelp():
print("stack-depth-info.py: parse stack depth data and summarize information\n")
print("Usage: ./stack-depth-info.py -d file [ OPTIONS ]")
print("Options:")
print(" -h / --help : print help & exit")
print(" -d file : stack depth file dumped by library (usually stack_depth.dat)")
print(" -b file : binary from which data was dumped, gives more detailed information")
print(" -f : only print names of functions who called the stack depth library (requires -b)")
print(" -v : verbose output, prints caller information")
def parseData(fileName):
numCalls = 0
avgDepth = 0.0
maxDepth = (0, 0, 0)
funcCalls = []
# Read in raw data, which is of the format:
# (<function>, <# of calls>, <average depth>, (max depth caller, max depth), [<callers>])
fp = open(fileName, 'r')
for line in fp:
tup = eval(line.strip())
numCalls += tup[1]
avgDepth += tup[1] * tup[2]
if tup[3][1] > maxDepth[2]:
maxDepth = (tup[0], tup[3][0], tup[3][1])
funcCalls.append(tup)
fp.close()
avgDepth /= float(numCalls)
return avgDepth, maxDepth, sorted(funcCalls, key=lambda func: func[1], reverse=True)
def printRaw(dataFile, avgDepth, maxDepth, funcCalls):
global verbose
print("Data from " + dataFile)
print("Average depth: {:4.3f}".format(avgDepth))
print("Max depth: " + str(maxDepth[2]) + ", " + hex(maxDepth[0]) + " called by " + hex(maxDepth[1]))
print()
print("{0:<14s} {1:>12s} {2:>12s}".format("Function:", "Num Calls", "Avg. Depth"))
for val in funcCalls:
print("0x{0:<12x} {1:>12d} {2:>12.3f}".format(val[0], val[1], val[2]))
if verbose:
for val in funcCalls:
print("\n\r" + hex(val[0]) + " called by:")
callers = sorted(val[4], key=lambda func: func[1], reverse=True)
for caller in callers:
print(" 0x{0:<x}: {1:d} time(s)".format(caller[0], caller[1]))
def getSymbolTable(binFile):
symbols = {}
out = subprocess.check_output(["readelf", "-sW", binFile])
outlines = out.decode("utf-8").split("\n")
for line in outlines:
if "Symbol table" in line or "Num:" in line:
continue
else:
toks = line.strip().split()
if len(toks) < 8:
continue
startAddr = int(toks[1], base=16)
if startAddr == 0: # Skip undefined symbols
continue
if "x" in toks[2]:
size = int(toks[2], base=16)
else:
size = int(toks[2])
if size == 0: # For dynamically loaded symbols
size = 1
name = toks[7].split("@")[0]
symbols[name] = (startAddr, size)
return symbols
def printDetailed(dataFile, binFile, symbols, avgDepth, maxDepth, funcCalls):
global onlyFunc
global verbose
def getSymbol(symbols, addr):
for sym in symbols:
endAddr = symbols[sym][0] + symbols[sym][1]
if symbols[sym][0] <= addr and addr < endAddr:
return sym
return "(n/a)"
if onlyFunc:
for val in funcCalls:
sym = getSymbol(symbols, val[0])
print(sym)
else:
print("Data from " + dataFile + ", generated by " + binFile)
print("Average depth: {:4.3f}".format(avgDepth))
print("Max depth: " + str(maxDepth[2]) + ", " + \
getSymbol(symbols, maxDepth[0]) + " (" + hex(maxDepth[0]) + ") called by " + \
getSymbol(symbols, maxDepth[1]) + " (" + hex(maxDepth[1]) + ")")
print()
print("{0:<55s} {1:>12s} {2:>12s}".format("Function:", "Num Calls", "Avg. Depth"))
for val in funcCalls:
sym = getSymbol(symbols, val[0])
print("{0:<55s} {1:>12d} {2:>12.3f}".format(sym + " (" + hex(val[0]) + ")", val[1], val[2]))
if verbose:
for val in funcCalls:
print("\n\r" + getSymbol(symbols, val[0]) + " called by:")
callers = sorted(val[4], key=lambda func: func[1], reverse=True)
for caller in callers:
sym = getSymbol(symbols, caller[0])
print(" {0:s}: {1:d} time(s)".format(sym + " (" + hex(caller[0]) + ")", caller[1]))
###############################################################################
# Driver
###############################################################################
skip = False
for i in range(len(sys.argv)):
if skip:
skip = False
continue
elif sys.argv[i] == "-h" or sys.argv[i] == "--help":
printHelp()
sys.exit(0)
continue
elif sys.argv[i] == "-d":
dataFile = sys.argv[i+1]
skip = True
continue
elif sys.argv[i] == "-b":
binFile = sys.argv[i+1]
skip = True
continue
elif sys.argv[i] == "-f":
onlyFunc = True
continue
elif sys.argv[i] == "-v":
verbose = True
continue
if dataFile == None:
print("Please supply a data file!")
printHelp()
sys.exit(1)
if onlyFunc and binFile == None:
print("Please supply a binary to print function names!")
printHelp()
sys.exit(1)
avgDepth, maxDepth, funcCalls = parseData(dataFile)
if binFile == None:
printRaw(dataFile, avgDepth, maxDepth, funcCalls)
else:
symbols = getSymbolTable(binFile)
printDetailed(dataFile, binFile, symbols, avgDepth, maxDepth, funcCalls)
| 3,657 | 0 | 115 |
d6d71f011810b5ff2754627d1771f5d19d8fd950 | 178 | py | Python | examples/get_user_info.py | suyashjawale/InstagramCLI | 56b7f8e1f97ad723c41a15874d5ab5c8967d08c3 | [
"MIT"
] | 5 | 2022-01-26T00:15:52.000Z | 2022-03-16T17:18:29.000Z | examples/get_user_info.py | suyashjawale/InstagramCLI | 56b7f8e1f97ad723c41a15874d5ab5c8967d08c3 | [
"MIT"
] | 1 | 2021-11-03T17:26:11.000Z | 2021-11-14T05:06:20.000Z | examples/get_user_info.py | suyashjawale/InstagramCLI | 56b7f8e1f97ad723c41a15874d5ab5c8967d08c3 | [
"MIT"
] | 1 | 2022-01-02T03:50:29.000Z | 2022-01-02T03:50:29.000Z | from InstagramCLI import InstagramCLI
cli = InstagramCLI(username="", password="")
data= cli.get_user_info(target_username="usnjsi77",save_to_device=True)
print(data)
cli.close() | 35.6 | 71 | 0.803371 | from InstagramCLI import InstagramCLI
cli = InstagramCLI(username="", password="")
data= cli.get_user_info(target_username="usnjsi77",save_to_device=True)
print(data)
cli.close() | 0 | 0 | 0 |
6a8f79a8e7bb2e7ba26110a94905fe82627d84c0 | 8,592 | py | Python | pymatgen/analysis/molecule_structure_comparator.py | exenGT/pymatgen | a8ffb820ab8fc3f60251099e38c8888f45eae618 | [
"MIT"
] | 1 | 2022-03-22T22:08:16.000Z | 2022-03-22T22:08:16.000Z | pymatgen/analysis/molecule_structure_comparator.py | exenGT/pymatgen | a8ffb820ab8fc3f60251099e38c8888f45eae618 | [
"MIT"
] | null | null | null | pymatgen/analysis/molecule_structure_comparator.py | exenGT/pymatgen | a8ffb820ab8fc3f60251099e38c8888f45eae618 | [
"MIT"
] | null | null | null | # Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.
"""
This module provides classes to comparsion the structures of the two
molecule. As long as the two molecule have the same bond connection tables,
the molecules are deemed to be same. The atom in the two molecule must be
paired accordingly.
This module is supposed to perform rough comparisons with the atom order
correspondence prerequisite, while molecule_matcher is supposed to do exact
comparisons without the atom order correspondence prerequisite.
"""
import itertools
from monty.json import MSONable
__author__ = "Xiaohui Qu"
__copyright__ = "Copyright 2011, The Materials Project"
__version__ = "1.0"
__maintainer__ = "Xiaohui Qu"
__email__ = "xhqu1981@gmail.com"
__status__ = "Experimental"
__date__ = "Jan 22, 2014"
class CovalentRadius:
"""
Covalent Radius of the elements.
Beatriz C. et al. Dalton Trans. 2008, 2832-2838. DOI: 10.1039/b801115j
"""
radius = {
"H": 0.31,
"He": 0.28,
"Li": 1.28,
"Be": 0.96,
"B": 0.84,
"C": 0.73,
"N": 0.71,
"O": 0.66,
"F": 0.57,
"Ne": 0.58,
"Na": 1.66,
"Mg": 1.41,
"Al": 1.21,
"Si": 1.11,
"P": 1.07,
"S": 1.05,
"Cl": 1.02,
"Ar": 1.06,
"K": 2.03,
"Ca": 1.76,
"Sc": 1.70,
"Ti": 1.60,
"V": 1.53,
"Cr": 1.39,
"Mn": 1.50,
"Fe": 1.42,
"Co": 1.38,
"Ni": 1.24,
"Cu": 1.32,
"Zn": 1.22,
"Ga": 1.22,
"Ge": 1.20,
"As": 1.19,
"Se": 1.20,
"Br": 1.20,
"Kr": 1.16,
"Rb": 2.20,
"Sr": 1.95,
"Y": 1.90,
"Zr": 1.75,
"Nb": 1.64,
"Mo": 1.54,
"Tc": 1.47,
"Ru": 1.46,
"Rh": 1.42,
"Pd": 1.39,
"Ag": 1.45,
"Cd": 1.44,
"In": 1.42,
"Sn": 1.39,
"Sb": 1.39,
"Te": 1.38,
"I": 1.39,
"Xe": 1.40,
"Cs": 2.44,
"Ba": 2.15,
"La": 2.07,
"Ce": 2.04,
"Pr": 2.03,
"Nd": 2.01,
"Pm": 1.99,
"Sm": 1.98,
"Eu": 1.98,
"Gd": 1.96,
"Tb": 1.94,
"Dy": 1.92,
"Ho": 1.92,
"Er": 1.89,
"Tm": 1.90,
"Yb": 1.87,
"Lu": 1.87,
"Hf": 1.75,
"Ta": 1.70,
"W": 1.62,
"Re": 1.51,
"Os": 1.44,
"Ir": 1.41,
"Pt": 1.36,
"Au": 1.36,
"Hg": 1.32,
"Tl": 1.45,
"Pb": 1.46,
"Bi": 1.48,
"Po": 1.40,
"At": 1.50,
"Rn": 1.50,
"Fr": 2.60,
"Ra": 2.21,
"Ac": 2.15,
"Th": 2.06,
"Pa": 2.00,
"U": 1.96,
"Np": 1.90,
"Pu": 1.87,
"Am": 1.80,
"Cm": 1.69,
}
class MoleculeStructureComparator(MSONable):
"""
Class to check whether the connection tables of the two molecules are the
same. The atom in the two molecule must be paired accordingly.
"""
ionic_element_list = [
"Na",
"Mg",
"Al",
"Sc",
"V",
"Cr",
"Mn",
"Fe",
"Co",
"Ni",
"Cu",
"Zn",
"Ga",
"Rb",
"Sr",
]
halogen_list = ["F", "Cl", "Br", "I"]
def __init__(
self,
bond_length_cap=0.3,
covalent_radius=CovalentRadius.radius,
priority_bonds=(),
priority_cap=0.8,
ignore_ionic_bond=True,
bond_13_cap=0.05,
):
"""
Args:
bond_length_cap: The ratio of the elongation of the bond to be
acknowledged. If the distance between two atoms is less than (
empirical covalent bond length) X (1 + bond_length_cap), the bond
between the two atoms will be acknowledged.
covalent_radius: The covalent radius of the atoms.
dict (element symbol -> radius)
priority_bonds: The bonds that are known to be existed in the initial
molecule. Such bonds will be acknowledged in a loose criteria.
The index should start from 0.
priority_cap: The ratio of the elongation of the bond to be
acknowledged for the priority bonds.
"""
self.bond_length_cap = bond_length_cap
self.covalent_radius = covalent_radius
self.priority_bonds = [tuple(sorted(b)) for b in priority_bonds]
self.priority_cap = priority_cap
self.ignore_ionic_bond = ignore_ionic_bond
self.ignore_halogen_self_bond = True
self.bond_13_cap = bond_13_cap
def are_equal(self, mol1, mol2):
"""
Compare the bond table of the two molecules.
Args:
mol1: first molecule. pymatgen Molecule object.
mol2: second moleculs. pymatgen Molecule objec.
"""
b1 = set(self._get_bonds(mol1))
b2 = set(self._get_bonds(mol2))
return b1 == b2
@staticmethod
def get_13_bonds(priority_bonds):
"""
Args:
priority_bonds ():
Returns:
"""
all_bond_pairs = list(itertools.combinations(priority_bonds, r=2))
all_2_bond_atoms = [set(b1 + b2) for b1, b2 in all_bond_pairs]
all_13_bond_atoms = [a for a in all_2_bond_atoms if len(a) == 3]
all_2_and_13_bonds = {
tuple(sorted(b)) for b in itertools.chain(*(itertools.combinations(p, 2) for p in all_13_bond_atoms))
}
bonds_13 = all_2_and_13_bonds - {tuple(b) for b in priority_bonds}
return tuple(sorted(bonds_13))
def _get_bonds(self, mol):
"""
Find all the bond in a molcule
Args:
mol: the molecule. pymatgen Molecule object
Returns:
List of tuple. Each tuple correspond to a bond represented by the
id of the two end atoms.
"""
num_atoms = len(mol)
# index starting from 0
if self.ignore_ionic_bond:
covalent_atoms = [i for i in range(num_atoms) if mol.species[i].symbol not in self.ionic_element_list]
else:
covalent_atoms = list(range(num_atoms))
all_pairs = list(itertools.combinations(covalent_atoms, 2))
pair_dists = [mol.get_distance(*p) for p in all_pairs]
elements = mol.composition.as_dict().keys()
unavailable_elements = list(set(elements) - set(self.covalent_radius.keys()))
if len(unavailable_elements) > 0:
raise ValueError(f"The covalent radius for element {unavailable_elements} is not available")
bond_13 = self.get_13_bonds(self.priority_bonds)
max_length = [
(self.covalent_radius[mol.sites[p[0]].specie.symbol] + self.covalent_radius[mol.sites[p[1]].specie.symbol])
* (
1
+ (
self.priority_cap
if p in self.priority_bonds
else (self.bond_length_cap if p not in bond_13 else self.bond_13_cap)
)
)
* (
0.1
if (
self.ignore_halogen_self_bond
and p not in self.priority_bonds
and mol.sites[p[0]].specie.symbol in self.halogen_list
and mol.sites[p[1]].specie.symbol in self.halogen_list
)
else 1.0
)
for p in all_pairs
]
bonds = [bond for bond, dist, cap in zip(all_pairs, pair_dists, max_length) if dist <= cap]
return bonds
def as_dict(self):
"""
Returns: MSONable dict
"""
return {
"version": __version__,
"@module": self.__class__.__module__,
"@class": self.__class__.__name__,
"bond_length_cap": self.bond_length_cap,
"covalent_radius": self.covalent_radius,
"priority_bonds": self.priority_bonds,
"priority_cap": self.priority_cap,
}
@classmethod
def from_dict(cls, d):
"""
Args:
d (dict): Dict representation
Returns:
MoleculeStructureComparator
"""
return MoleculeStructureComparator(
bond_length_cap=d["bond_length_cap"],
covalent_radius=d["covalent_radius"],
priority_bonds=d["priority_bonds"],
priority_cap=d["priority_cap"],
)
| 28.54485 | 119 | 0.516527 | # Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.
"""
This module provides classes to comparsion the structures of the two
molecule. As long as the two molecule have the same bond connection tables,
the molecules are deemed to be same. The atom in the two molecule must be
paired accordingly.
This module is supposed to perform rough comparisons with the atom order
correspondence prerequisite, while molecule_matcher is supposed to do exact
comparisons without the atom order correspondence prerequisite.
"""
import itertools
from monty.json import MSONable
__author__ = "Xiaohui Qu"
__copyright__ = "Copyright 2011, The Materials Project"
__version__ = "1.0"
__maintainer__ = "Xiaohui Qu"
__email__ = "xhqu1981@gmail.com"
__status__ = "Experimental"
__date__ = "Jan 22, 2014"
class CovalentRadius:
"""
Covalent Radius of the elements.
Beatriz C. et al. Dalton Trans. 2008, 2832-2838. DOI: 10.1039/b801115j
"""
radius = {
"H": 0.31,
"He": 0.28,
"Li": 1.28,
"Be": 0.96,
"B": 0.84,
"C": 0.73,
"N": 0.71,
"O": 0.66,
"F": 0.57,
"Ne": 0.58,
"Na": 1.66,
"Mg": 1.41,
"Al": 1.21,
"Si": 1.11,
"P": 1.07,
"S": 1.05,
"Cl": 1.02,
"Ar": 1.06,
"K": 2.03,
"Ca": 1.76,
"Sc": 1.70,
"Ti": 1.60,
"V": 1.53,
"Cr": 1.39,
"Mn": 1.50,
"Fe": 1.42,
"Co": 1.38,
"Ni": 1.24,
"Cu": 1.32,
"Zn": 1.22,
"Ga": 1.22,
"Ge": 1.20,
"As": 1.19,
"Se": 1.20,
"Br": 1.20,
"Kr": 1.16,
"Rb": 2.20,
"Sr": 1.95,
"Y": 1.90,
"Zr": 1.75,
"Nb": 1.64,
"Mo": 1.54,
"Tc": 1.47,
"Ru": 1.46,
"Rh": 1.42,
"Pd": 1.39,
"Ag": 1.45,
"Cd": 1.44,
"In": 1.42,
"Sn": 1.39,
"Sb": 1.39,
"Te": 1.38,
"I": 1.39,
"Xe": 1.40,
"Cs": 2.44,
"Ba": 2.15,
"La": 2.07,
"Ce": 2.04,
"Pr": 2.03,
"Nd": 2.01,
"Pm": 1.99,
"Sm": 1.98,
"Eu": 1.98,
"Gd": 1.96,
"Tb": 1.94,
"Dy": 1.92,
"Ho": 1.92,
"Er": 1.89,
"Tm": 1.90,
"Yb": 1.87,
"Lu": 1.87,
"Hf": 1.75,
"Ta": 1.70,
"W": 1.62,
"Re": 1.51,
"Os": 1.44,
"Ir": 1.41,
"Pt": 1.36,
"Au": 1.36,
"Hg": 1.32,
"Tl": 1.45,
"Pb": 1.46,
"Bi": 1.48,
"Po": 1.40,
"At": 1.50,
"Rn": 1.50,
"Fr": 2.60,
"Ra": 2.21,
"Ac": 2.15,
"Th": 2.06,
"Pa": 2.00,
"U": 1.96,
"Np": 1.90,
"Pu": 1.87,
"Am": 1.80,
"Cm": 1.69,
}
class MoleculeStructureComparator(MSONable):
"""
Class to check whether the connection tables of the two molecules are the
same. The atom in the two molecule must be paired accordingly.
"""
ionic_element_list = [
"Na",
"Mg",
"Al",
"Sc",
"V",
"Cr",
"Mn",
"Fe",
"Co",
"Ni",
"Cu",
"Zn",
"Ga",
"Rb",
"Sr",
]
halogen_list = ["F", "Cl", "Br", "I"]
def __init__(
self,
bond_length_cap=0.3,
covalent_radius=CovalentRadius.radius,
priority_bonds=(),
priority_cap=0.8,
ignore_ionic_bond=True,
bond_13_cap=0.05,
):
"""
Args:
bond_length_cap: The ratio of the elongation of the bond to be
acknowledged. If the distance between two atoms is less than (
empirical covalent bond length) X (1 + bond_length_cap), the bond
between the two atoms will be acknowledged.
covalent_radius: The covalent radius of the atoms.
dict (element symbol -> radius)
priority_bonds: The bonds that are known to be existed in the initial
molecule. Such bonds will be acknowledged in a loose criteria.
The index should start from 0.
priority_cap: The ratio of the elongation of the bond to be
acknowledged for the priority bonds.
"""
self.bond_length_cap = bond_length_cap
self.covalent_radius = covalent_radius
self.priority_bonds = [tuple(sorted(b)) for b in priority_bonds]
self.priority_cap = priority_cap
self.ignore_ionic_bond = ignore_ionic_bond
self.ignore_halogen_self_bond = True
self.bond_13_cap = bond_13_cap
def are_equal(self, mol1, mol2):
"""
Compare the bond table of the two molecules.
Args:
mol1: first molecule. pymatgen Molecule object.
mol2: second moleculs. pymatgen Molecule objec.
"""
b1 = set(self._get_bonds(mol1))
b2 = set(self._get_bonds(mol2))
return b1 == b2
@staticmethod
def get_13_bonds(priority_bonds):
"""
Args:
priority_bonds ():
Returns:
"""
all_bond_pairs = list(itertools.combinations(priority_bonds, r=2))
all_2_bond_atoms = [set(b1 + b2) for b1, b2 in all_bond_pairs]
all_13_bond_atoms = [a for a in all_2_bond_atoms if len(a) == 3]
all_2_and_13_bonds = {
tuple(sorted(b)) for b in itertools.chain(*(itertools.combinations(p, 2) for p in all_13_bond_atoms))
}
bonds_13 = all_2_and_13_bonds - {tuple(b) for b in priority_bonds}
return tuple(sorted(bonds_13))
def _get_bonds(self, mol):
"""
Find all the bond in a molcule
Args:
mol: the molecule. pymatgen Molecule object
Returns:
List of tuple. Each tuple correspond to a bond represented by the
id of the two end atoms.
"""
num_atoms = len(mol)
# index starting from 0
if self.ignore_ionic_bond:
covalent_atoms = [i for i in range(num_atoms) if mol.species[i].symbol not in self.ionic_element_list]
else:
covalent_atoms = list(range(num_atoms))
all_pairs = list(itertools.combinations(covalent_atoms, 2))
pair_dists = [mol.get_distance(*p) for p in all_pairs]
elements = mol.composition.as_dict().keys()
unavailable_elements = list(set(elements) - set(self.covalent_radius.keys()))
if len(unavailable_elements) > 0:
raise ValueError(f"The covalent radius for element {unavailable_elements} is not available")
bond_13 = self.get_13_bonds(self.priority_bonds)
max_length = [
(self.covalent_radius[mol.sites[p[0]].specie.symbol] + self.covalent_radius[mol.sites[p[1]].specie.symbol])
* (
1
+ (
self.priority_cap
if p in self.priority_bonds
else (self.bond_length_cap if p not in bond_13 else self.bond_13_cap)
)
)
* (
0.1
if (
self.ignore_halogen_self_bond
and p not in self.priority_bonds
and mol.sites[p[0]].specie.symbol in self.halogen_list
and mol.sites[p[1]].specie.symbol in self.halogen_list
)
else 1.0
)
for p in all_pairs
]
bonds = [bond for bond, dist, cap in zip(all_pairs, pair_dists, max_length) if dist <= cap]
return bonds
def as_dict(self):
"""
Returns: MSONable dict
"""
return {
"version": __version__,
"@module": self.__class__.__module__,
"@class": self.__class__.__name__,
"bond_length_cap": self.bond_length_cap,
"covalent_radius": self.covalent_radius,
"priority_bonds": self.priority_bonds,
"priority_cap": self.priority_cap,
}
@classmethod
def from_dict(cls, d):
"""
Args:
d (dict): Dict representation
Returns:
MoleculeStructureComparator
"""
return MoleculeStructureComparator(
bond_length_cap=d["bond_length_cap"],
covalent_radius=d["covalent_radius"],
priority_bonds=d["priority_bonds"],
priority_cap=d["priority_cap"],
)
| 0 | 0 | 0 |
f836aea2e60665ebbee4033654498c2a4b470d74 | 1,858 | py | Python | ThenWhatTree/execute_tree.py | intel/ThenWhatTree | ddcd5af58e55705ba2dd6a824828f3fc48bd68da | [
"BSD-3-Clause"
] | 3 | 2018-05-23T17:15:39.000Z | 2019-03-17T18:44:19.000Z | ThenWhatTree/execute_tree.py | intel/ThenWhatTree | ddcd5af58e55705ba2dd6a824828f3fc48bd68da | [
"BSD-3-Clause"
] | null | null | null | ThenWhatTree/execute_tree.py | intel/ThenWhatTree | ddcd5af58e55705ba2dd6a824828f3fc48bd68da | [
"BSD-3-Clause"
] | 4 | 2019-11-02T01:15:21.000Z | 2021-09-16T11:01:29.000Z | #!/usr/bin/env python3
# Copyright (C) 2018 Intel Corporation
# SPDX-License-Identifier: BSD-3-Clause
"""Function for evaluating the nodes of a decision tree and extracting out the results from each of the nodes."""
# Import built in modules
import argparse
import sys
import os
# Import 3rd party modules
# Import local modules
from ThenWhatTree import _get_file_type
from ThenWhatTree import evaluate
from ThenWhatTree import extract
# Module authorship metadata
__author__ = "Erik W Berg"
__copyright__ = "Copyright 2018, Intel Corporation"
__credits__ = [""]
__license__ = "BSD-3-Clause"
__version__ = "1.0"
__maintainer__ = "Erik W Berg"
__email__ = ""
__status__ = "Production" # Prototype, Development, Production
# Code starts here
def parse_args():
'''
Function to parse the cmdline, check that required args are present and that the file types are correct
:return:
'''
parser = argparse.ArgumentParser()
parser.add_argument('--xml', help='path to xml file defining the decision tree', nargs='?', type=str)
args = parser.parse_args()
check_cmd_line_args(args)
check_xml_file_type(args)
return args
def check_cmd_line_args(args):
'''
Raise an exception if an XML file was not passed in on the cmd line.
:param args:
:return: none
'''
if not args.xml:
raise Exception('--xml switch must be populated')
def check_xml_file_type(args):
'''
Raise an exception if the cmd line switch does not match the type of the passed file
:param args:
:return: none
'''
if 'XML' not in _get_file_type(args.xml):
raise Exception(sys.argv[1] + ' is not a XML file')
if __name__ == '__main__':
args = parse_args()
os.environ['PYTHONDONTWRITEBYTECODE'] = 'TRUE'
tree_object = evaluate(args.xml)
print(extract(tree_object.getroot()))
| 25.108108 | 113 | 0.703983 | #!/usr/bin/env python3
# Copyright (C) 2018 Intel Corporation
# SPDX-License-Identifier: BSD-3-Clause
"""Function for evaluating the nodes of a decision tree and extracting out the results from each of the nodes."""
# Import built in modules
import argparse
import sys
import os
# Import 3rd party modules
# Import local modules
from ThenWhatTree import _get_file_type
from ThenWhatTree import evaluate
from ThenWhatTree import extract
# Module authorship metadata
__author__ = "Erik W Berg"
__copyright__ = "Copyright 2018, Intel Corporation"
__credits__ = [""]
__license__ = "BSD-3-Clause"
__version__ = "1.0"
__maintainer__ = "Erik W Berg"
__email__ = ""
__status__ = "Production" # Prototype, Development, Production
# Code starts here
def parse_args():
'''
Function to parse the cmdline, check that required args are present and that the file types are correct
:return:
'''
parser = argparse.ArgumentParser()
parser.add_argument('--xml', help='path to xml file defining the decision tree', nargs='?', type=str)
args = parser.parse_args()
check_cmd_line_args(args)
check_xml_file_type(args)
return args
def check_cmd_line_args(args):
'''
Raise an exception if an XML file was not passed in on the cmd line.
:param args:
:return: none
'''
if not args.xml:
raise Exception('--xml switch must be populated')
def check_xml_file_type(args):
'''
Raise an exception if the cmd line switch does not match the type of the passed file
:param args:
:return: none
'''
if 'XML' not in _get_file_type(args.xml):
raise Exception(sys.argv[1] + ' is not a XML file')
if __name__ == '__main__':
args = parse_args()
os.environ['PYTHONDONTWRITEBYTECODE'] = 'TRUE'
tree_object = evaluate(args.xml)
print(extract(tree_object.getroot()))
| 0 | 0 | 0 |
5e9d965ec0e648c42b7858dcd55dc6a562d14c96 | 140 | py | Python | DOM/game/item.py | AlexDev-py/CubIC | 7932d2789c0b45ebc9ce631d21f6bed99d3a3a51 | [
"MIT"
] | 2 | 2022-02-05T13:06:28.000Z | 2022-02-09T07:07:11.000Z | DOM/game/item.py | AlexDev-py/CubIC | 7932d2789c0b45ebc9ce631d21f6bed99d3a3a51 | [
"MIT"
] | null | null | null | DOM/game/item.py | AlexDev-py/CubIC | 7932d2789c0b45ebc9ce631d21f6bed99d3a3a51 | [
"MIT"
] | 2 | 2022-01-24T13:42:45.000Z | 2022-02-08T09:18:58.000Z | from dataclasses import dataclass
@dataclass
| 12.727273 | 33 | 0.642857 | from dataclasses import dataclass
@dataclass
class Item:
name: str
lvl: int
price: int
icon: str
desc: dict[str, str]
| 0 | 71 | 22 |
2773a2b5fb1ba7a66e740bb2804aab93cde6bb13 | 10,125 | py | Python | sfw_torch.py | bwilder0/tb_code_release | e0994116cff1ec0c04e0297f2b558ec5dd53f765 | [
"MIT"
] | null | null | null | sfw_torch.py | bwilder0/tb_code_release | e0994116cff1ec0c04e0297f2b558ec5dd53f765 | [
"MIT"
] | null | null | null | sfw_torch.py | bwilder0/tb_code_release | e0994116cff1ec0c04e0297f2b558ec5dd53f765 | [
"MIT"
] | null | null | null | import torch
import numpy as np
def run_sis(T, G, S, I, newI, nu, mu, d, beta, N):
'''
Runs the linearized SIS model, returning the total number of infected agents
summed over all time steps.
'''
#duplicate these variables along an additional axis to match the batch size
nu = torch.diag(1 - nu).expand_as(beta)
d = torch.diag(1 - d).expand_as(beta)
G = G.expand_as(beta)
#run the main loop for the linearized disease dynamics
total_infected = I.mean(dim=0).sum()
for t in range(1, T):
new_infections = S[t-1] @ mu[t-1] @ beta @ N[t-1]
old_infections = nu @ d
A = G @ (new_infections + old_infections)
I = A @ I
I = I + newI[t]
total_infected += I.mean(dim=0).sum()
return total_infected
def run_seis(T, G, S, I, newI, nu, mu, d, beta, N, alpha_fast, alpha_slow, E):
'''
Runs the linearized SEIS model, returning the total number of infected agents
summed over all time steps.
'''
#duplicate these variables along an additional axis to match the batch size
nu = torch.diag(1 - nu).expand_as(beta)
d = torch.diag(1 - d).expand_as(beta)
G = G.expand_as(beta)
E = E.expand_as(beta)
alpha_fast = torch.diag(alpha_fast).expand_as(beta)
alpha_slow = alpha_slow.expand_as(beta)
#run the main loop for the linearized disease dynamics
total_infected = I.mean(dim=0).sum()
import numpy as np
infections_time = np.zeros((T, 100))
for t in range(1, T):
new_infections = S[t-1] @ mu[t-1] @ beta @ N[t-1] @ I
# print(new_infections.shape)
# print(alpha_fast.shape)
new_infections_active = alpha_fast @ new_infections
new_infections_latent = new_infections - new_infections_active
E = mu[t-1] @ E
activations = alpha_slow*E
E = (1 - alpha_slow)*E
# print(E[:, 0].shape)
# print(new_infections_latent.squeeze().shape)
E[:, 0] += new_infections_latent.squeeze()
E = G @ E @ G
old_infections = nu @ d @ I
# print(new_infections_active.shape)
# print(old_infections.shape)
# print(newI[t].shape)
# print(activations.sum(dim=2).shape)
# print(I.shape)
I = new_infections_active + old_infections + newI[t] + activations.sum(dim=2).view_as(I)
I = G @ I
# infections_time.append(I.mean(dim=0).sum().item())
for j in range(100):
total_pop = (1./torch.diag(N[t, j])).sum()
infections_time[t, j] = I[j].sum()/total_pop
if t == 1 and j == 0:
print(total_pop.item(), I[j].sum().item())
# total_infected += I.mean(dim=0).sum()
return infections_time
def run_seis_information(T, G, S, I, newI, nu, mu, d, beta, N, alpha_fast, alpha_slow, E, beta_information, nu_max):
'''
Runs the linearized SEIS model, returning the total number of infected agents
summed over all time steps.
'''
#duplicate these variables along an additional axis to match the batch size
informed = nu.view(len(nu), 1)
informed = informed.expand(beta.shape[0], *informed.shape)
nu = torch.diag(1 - nu).expand_as(beta)
d = torch.diag(1 - d).expand_as(beta)
G = G.expand_as(beta)
E = E.expand_as(beta)
alpha_fast = torch.diag(alpha_fast).expand_as(beta)
alpha_slow = alpha_slow.expand_as(beta)
#keep track of infected, latent, and informed at each time step
all_infections = torch.zeros(T, beta.shape[1], 1)
all_E = torch.zeros(T, E.shape[1], E.shape[2])
all_F = torch.zeros_like(all_infections)
#run the main loop for the linearized disease dynamics
for t in range(1, T):
#update nu with new information spread
not_informed_fraction = 1 - informed
not_informed_fraction_diag = vector_to_diag(not_informed_fraction, beta)
#constant scaling the beta for information spread
informed = 0.1*not_informed_fraction_diag@beta_information@informed + informed
nu = nu_max*informed
nu = vector_to_diag(1 - nu, beta)
#infections
new_infections = S[t-1] @ mu[t-1] @ beta @ N[t-1] @ I
new_infections_active = alpha_fast @ new_infections
new_infections_latent = new_infections - new_infections_active
E = mu[t-1] @ E
activations = alpha_slow*E
E = (1 - alpha_slow)*E
E[:, 0] += new_infections_latent.squeeze()
E = G @ E @ G
old_infections = nu @ d @ I
I = new_infections_active + old_infections + newI[t] + activations.sum(dim=2).view_as(I)
I = G @ I
#return E, I, F by time and age group
#mean across samples
all_infections[t] = I.mean(dim=0)
all_E[t] = E.mean(dim=0)
all_F[t] = informed.mean(dim = 0)
return all_infections, all_E, all_F
def run_seis_information_new(T, G, S, I, migration_I, migration_E, nu, mu, d, beta, N, alpha_fast, alpha_slow, E, beta_information, nu_max):
'''
Runs the linearized SEIS model, returning the total number of infected agents
summed over all time steps.
'''
#read in for first period of F, informed
#nu_sq = np.loadtxt('ann2018_clearanceProb.csv.csv', delimiter=',', skiprows=1)
#nu_sq[np.isnan(nu_sq)] = 0
#nu_sq = nu_sq.mean(axis = 0)
#nu_sq = torch.from_numpy(nu_sq)
#duplicate these variables along an additional axis to match the batch size
beta = beta.expand_as(G)
informed = nu.view(len(nu), 1)
informed = informed.expand(beta.shape[0], *informed.shape)
nu = torch.diag(1 - nu).expand_as(beta)
num_samples = G.shape[0]
#keep track of infected, latent, and informed at each time step
all_I = torch.zeros(T, num_samples, beta.shape[1], 1).double()
all_E = torch.zeros(T, num_samples, E.shape[1], E.shape[2]).double()
all_F = torch.zeros_like(all_I).double()
all_I[0] = I[0]
all_E[0] = E[0]
#all_I[0] = I[30]
#all_E[0] = E[30]
all_F[0] = informed
#run the main loop for the linearized disease dynamics
for t in range(1, T):
#update nu with new information spread
not_informed_fraction = 1 - informed
not_informed_fraction_diag = vector_to_diag(not_informed_fraction, beta)
#constant scaling the beta for information spread
informed = not_informed_fraction_diag@beta_information@informed + informed
#print('here is info beta mat')
#print(beta_information)
#print('here is informed')
#print(informed)
#debug sze
nu = nu_max*informed
nu = vector_to_diag(1 - nu, beta)
#infections
new_infections = S[t-1] @ mu @ beta @ N[t-1] @ I
new_infections_active = alpha_fast @ new_infections
new_infections_latent = new_infections - new_infections_active
E = mu @ E
activations = alpha_slow@E
E = E - activations
E += new_infections_latent
E = G @ E + migration_E[t] #CHANGING TO USING THE LAST MIGRATION PERIOD
#E = G @ E + migration_E[30]
old_infections = nu @ d @ I
I = new_infections_active + old_infections + activations
I = G @ I + migration_I[t] #CHANGING TO USING THE LAST MIGRATION PERIOD
#I = G @ I + migration_I[30]
#return E, I, F by time and age group
#mean across samples
all_I[t] = I
all_E[t] = E
all_F[t] = informed
#print(all_I)
return all_I, all_E, all_F
class SISInstance():
"""
Represents an instantiation of the SIS model with a particular (distribution
over) parameters. Foward pass computes total infections as a function of nu,
backward computes gradient wrt nu.
"""
def greedy(grad, U, L, K):
'''
Greedily select budget number of elements with highest weight according to
grad
'''
sorted_groups = torch.sort(grad)[1]
nu = L.clone()
curr = 0
while (nu - L).sum() < K and curr < len(grad):
amount_add = min([U[sorted_groups[curr]] - L[sorted_groups[curr]], K - (nu - L).sum()])
nu[[sorted_groups[curr]]] += amount_add
curr += 1
return nu
| 37.5 | 169 | 0.618469 | import torch
import numpy as np
def run_sis(T, G, S, I, newI, nu, mu, d, beta, N):
'''
Runs the linearized SIS model, returning the total number of infected agents
summed over all time steps.
'''
#duplicate these variables along an additional axis to match the batch size
nu = torch.diag(1 - nu).expand_as(beta)
d = torch.diag(1 - d).expand_as(beta)
G = G.expand_as(beta)
#run the main loop for the linearized disease dynamics
total_infected = I.mean(dim=0).sum()
for t in range(1, T):
new_infections = S[t-1] @ mu[t-1] @ beta @ N[t-1]
old_infections = nu @ d
A = G @ (new_infections + old_infections)
I = A @ I
I = I + newI[t]
total_infected += I.mean(dim=0).sum()
return total_infected
def run_seis(T, G, S, I, newI, nu, mu, d, beta, N, alpha_fast, alpha_slow, E):
'''
Runs the linearized SEIS model, returning the total number of infected agents
summed over all time steps.
'''
#duplicate these variables along an additional axis to match the batch size
nu = torch.diag(1 - nu).expand_as(beta)
d = torch.diag(1 - d).expand_as(beta)
G = G.expand_as(beta)
E = E.expand_as(beta)
alpha_fast = torch.diag(alpha_fast).expand_as(beta)
alpha_slow = alpha_slow.expand_as(beta)
#run the main loop for the linearized disease dynamics
total_infected = I.mean(dim=0).sum()
import numpy as np
infections_time = np.zeros((T, 100))
for t in range(1, T):
new_infections = S[t-1] @ mu[t-1] @ beta @ N[t-1] @ I
# print(new_infections.shape)
# print(alpha_fast.shape)
new_infections_active = alpha_fast @ new_infections
new_infections_latent = new_infections - new_infections_active
E = mu[t-1] @ E
activations = alpha_slow*E
E = (1 - alpha_slow)*E
# print(E[:, 0].shape)
# print(new_infections_latent.squeeze().shape)
E[:, 0] += new_infections_latent.squeeze()
E = G @ E @ G
old_infections = nu @ d @ I
# print(new_infections_active.shape)
# print(old_infections.shape)
# print(newI[t].shape)
# print(activations.sum(dim=2).shape)
# print(I.shape)
I = new_infections_active + old_infections + newI[t] + activations.sum(dim=2).view_as(I)
I = G @ I
# infections_time.append(I.mean(dim=0).sum().item())
for j in range(100):
total_pop = (1./torch.diag(N[t, j])).sum()
infections_time[t, j] = I[j].sum()/total_pop
if t == 1 and j == 0:
print(total_pop.item(), I[j].sum().item())
# total_infected += I.mean(dim=0).sum()
return infections_time
def vector_to_diag(not_informed_fraction, beta):
not_informed_fraction_diag = torch.zeros_like(beta)
for s in range(beta.shape[0]):
not_informed_fraction_diag[s] = torch.diag(not_informed_fraction[s].squeeze())
# print('diag output')
# print(not_informed_fraction_diag)
return not_informed_fraction_diag
def run_seis_information(T, G, S, I, newI, nu, mu, d, beta, N, alpha_fast, alpha_slow, E, beta_information, nu_max):
'''
Runs the linearized SEIS model, returning the total number of infected agents
summed over all time steps.
'''
#duplicate these variables along an additional axis to match the batch size
informed = nu.view(len(nu), 1)
informed = informed.expand(beta.shape[0], *informed.shape)
nu = torch.diag(1 - nu).expand_as(beta)
d = torch.diag(1 - d).expand_as(beta)
G = G.expand_as(beta)
E = E.expand_as(beta)
alpha_fast = torch.diag(alpha_fast).expand_as(beta)
alpha_slow = alpha_slow.expand_as(beta)
#keep track of infected, latent, and informed at each time step
all_infections = torch.zeros(T, beta.shape[1], 1)
all_E = torch.zeros(T, E.shape[1], E.shape[2])
all_F = torch.zeros_like(all_infections)
#run the main loop for the linearized disease dynamics
for t in range(1, T):
#update nu with new information spread
not_informed_fraction = 1 - informed
not_informed_fraction_diag = vector_to_diag(not_informed_fraction, beta)
#constant scaling the beta for information spread
informed = 0.1*not_informed_fraction_diag@beta_information@informed + informed
nu = nu_max*informed
nu = vector_to_diag(1 - nu, beta)
#infections
new_infections = S[t-1] @ mu[t-1] @ beta @ N[t-1] @ I
new_infections_active = alpha_fast @ new_infections
new_infections_latent = new_infections - new_infections_active
E = mu[t-1] @ E
activations = alpha_slow*E
E = (1 - alpha_slow)*E
E[:, 0] += new_infections_latent.squeeze()
E = G @ E @ G
old_infections = nu @ d @ I
I = new_infections_active + old_infections + newI[t] + activations.sum(dim=2).view_as(I)
I = G @ I
#return E, I, F by time and age group
#mean across samples
all_infections[t] = I.mean(dim=0)
all_E[t] = E.mean(dim=0)
all_F[t] = informed.mean(dim = 0)
return all_infections, all_E, all_F
def run_seis_information_new(T, G, S, I, migration_I, migration_E, nu, mu, d, beta, N, alpha_fast, alpha_slow, E, beta_information, nu_max):
'''
Runs the linearized SEIS model, returning the total number of infected agents
summed over all time steps.
'''
#read in for first period of F, informed
#nu_sq = np.loadtxt('ann2018_clearanceProb.csv.csv', delimiter=',', skiprows=1)
#nu_sq[np.isnan(nu_sq)] = 0
#nu_sq = nu_sq.mean(axis = 0)
#nu_sq = torch.from_numpy(nu_sq)
#duplicate these variables along an additional axis to match the batch size
beta = beta.expand_as(G)
informed = nu.view(len(nu), 1)
informed = informed.expand(beta.shape[0], *informed.shape)
nu = torch.diag(1 - nu).expand_as(beta)
num_samples = G.shape[0]
#keep track of infected, latent, and informed at each time step
all_I = torch.zeros(T, num_samples, beta.shape[1], 1).double()
all_E = torch.zeros(T, num_samples, E.shape[1], E.shape[2]).double()
all_F = torch.zeros_like(all_I).double()
all_I[0] = I[0]
all_E[0] = E[0]
#all_I[0] = I[30]
#all_E[0] = E[30]
all_F[0] = informed
#run the main loop for the linearized disease dynamics
for t in range(1, T):
#update nu with new information spread
not_informed_fraction = 1 - informed
not_informed_fraction_diag = vector_to_diag(not_informed_fraction, beta)
#constant scaling the beta for information spread
informed = not_informed_fraction_diag@beta_information@informed + informed
#print('here is info beta mat')
#print(beta_information)
#print('here is informed')
#print(informed)
#debug sze
nu = nu_max*informed
nu = vector_to_diag(1 - nu, beta)
#infections
new_infections = S[t-1] @ mu @ beta @ N[t-1] @ I
new_infections_active = alpha_fast @ new_infections
new_infections_latent = new_infections - new_infections_active
E = mu @ E
activations = alpha_slow@E
E = E - activations
E += new_infections_latent
E = G @ E + migration_E[t] #CHANGING TO USING THE LAST MIGRATION PERIOD
#E = G @ E + migration_E[30]
old_infections = nu @ d @ I
I = new_infections_active + old_infections + activations
I = G @ I + migration_I[t] #CHANGING TO USING THE LAST MIGRATION PERIOD
#I = G @ I + migration_I[30]
#return E, I, F by time and age group
#mean across samples
all_I[t] = I
all_E[t] = E
all_F[t] = informed
#print(all_I)
return all_I, all_E, all_F
class SISInstance():
"""
Represents an instantiation of the SIS model with a particular (distribution
over) parameters. Foward pass computes total infections as a function of nu,
backward computes gradient wrt nu.
"""
def __init__(self, T, G, S, I, newI, mu, d, beta, N):
self.T = T
self.G = G
self.S = S
self.I = I
self.newI = newI
self.mu = mu
self.d = d
self.beta = beta
self.N = N
def __call__(self, nu):
return run_sis(self.T, self.G, self.S, self.I, self.newI, nu, self.mu, self.d, self.beta, self.N)
def greedy(grad, U, L, K):
'''
Greedily select budget number of elements with highest weight according to
grad
'''
sorted_groups = torch.sort(grad)[1]
nu = L.clone()
curr = 0
while (nu - L).sum() < K and curr < len(grad):
amount_add = min([U[sorted_groups[curr]] - L[sorted_groups[curr]], K - (nu - L).sum()])
nu[[sorted_groups[curr]]] += amount_add
curr += 1
return nu
def sfw_torch(L, U, K, T, G, S, I, newI, mu, d, beta, N, num_iters = 100):
sis = SISInstance(T, G, S, I, newI, mu, d, beta, N)
nu = torch.rand_like(L, requires_grad=True)
nu.data.zero_()
nu.grad = torch.zeros_like(nu)
for i in range(num_iters):
val = sis(nu + L)
nu.grad.zero_()
val.backward()
nu.data += 1./num_iters * greedy(nu.grad, U - L, torch.zeros_like(nu), K)
nu.data += L
return nu
def sfw_seis_torch(L, U, K, T, G, S, I, migration_I, migration_E, mu, d, beta, N, alpha_fast, alpha_slow, E, beta_information, nu_max, num_iters = 100):
nu = torch.rand_like(L, requires_grad=True)
nu.data.zero_()
nu.grad = torch.zeros_like(nu)
for i in range(num_iters):
print('optimizing: {}/{}'.format(i, num_iters))
all_I, all_E, all_F = run_seis_information_new(T, G, S, I, migration_I, migration_E, nu + L, mu, d, beta, N, alpha_fast, alpha_slow, E, beta_information, nu_max)
val = all_I.sum()
nu.grad.zero_()
val.backward()
nu.data += 1./num_iters * greedy(nu.grad, U - L, torch.zeros_like(nu), K)
nu.data += L
#print(nu)
return nu
| 1,754 | 0 | 126 |
886d846b1bd5e2c59d450a892dc2cfd09102c8c5 | 3,042 | py | Python | cj_2nd/problem_2/problem2.py | yskang/AlgorithmPracticeWithPython | f7129bd1924a7961489198f0ee052d2cd1e9cf40 | [
"MIT"
] | null | null | null | cj_2nd/problem_2/problem2.py | yskang/AlgorithmPracticeWithPython | f7129bd1924a7961489198f0ee052d2cd1e9cf40 | [
"MIT"
] | 1 | 2019-11-04T06:44:04.000Z | 2019-11-04T06:46:55.000Z | cj_2nd/problem_2/problem2.py | yskang/AlgorithmPractice | 31b76e38b4c2f1e3e29fb029587662a745437912 | [
"MIT"
] | null | null | null | import sys
filename = sys.argv[1]
test_input = open(filename, 'r')
numberOfTest = int(test_input.readline().replace('\n', '').replace('\r', '').strip())
nodesR = []
distanceR = []
nodesC = []
distanceC = []
branchPoint = 0
output = open(filename.replace('in', 'out'), 'w')
for i in range(numberOfTest):
initData()
numNodeRow = int(test_input.readline().replace('\n', '').replace('\r', '').strip())
line = test_input.readline().replace('\n', '').replace('\r', '').strip()
ins = line.split(' ')
for j in range(0, 2 * (numNodeRow - 1), 2):
nodesR.append(int(ins[j]))
distanceR.append(int(ins[j+1]))
ins = test_input.readline().replace('\n', '').replace('\r', '').strip().split(' ')
branchPoint = int(ins[0])
numNodeCol = int(ins[1])
line = test_input.readline().replace('\n', '').replace('\r', '').strip()
ins = line.split(' ')
for j in range(0, 2 * numNodeCol, 2):
distanceC.append(int(ins[j]))
nodesC.append(int(ins[j+1]))
if branchPoint-1 >= len(nodesR):
nodesC.insert(0, 100)
else:
nodesC.insert(0, nodesR[branchPoint-1])
checkCost()
test_input.close()
output.close() | 28.698113 | 87 | 0.606838 | import sys
filename = sys.argv[1]
test_input = open(filename, 'r')
numberOfTest = int(test_input.readline().replace('\n', '').replace('\r', '').strip())
nodesR = []
distanceR = []
nodesC = []
distanceC = []
branchPoint = 0
def initData():
nodesR[:] = []
distanceR[:] = []
nodesC[:] = []
distanceC[:] = []
branchPoint = 0
def getCostA():
cost = 0
currentCost = nodesR[0]
for i in range(branchPoint - 1):
if currentCost > nodesR[i]:
currentCost = nodesR[i]
cost = cost + currentCost * distanceR[i]
return (cost, currentCost)
def getCostB(initCost):
cost = 0
currentCost = initCost
for i in range(branchPoint - 1, len(nodesR)):
if currentCost > nodesR[i]:
currentCost = nodesR[i]
cost = cost + currentCost * distanceR[i]
return cost
def getForwardCost(initCost, returnPoint):
cost = 0
currentCost = initCost
for i in range(returnPoint):
if currentCost > nodesC[i]:
currentCost = nodesC[i]
cost = cost + currentCost * distanceC[i]
return (cost, currentCost)
def getBackwardCost(initCost, returnPoint):
cost = 0
currentCost = initCost
for i in range(returnPoint, 0, -1):
if currentCost > nodesC[i]:
currentCost = nodesC[i]
cost = cost + currentCost * distanceC[i - 1]
return (cost, currentCost)
def getBranchCost(initCost, returnPoint):
(forwardCost, lastCost) = getForwardCost(initCost, returnPoint)
(backwardCost, lastCost) = getBackwardCost(lastCost, returnPoint)
return (forwardCost + backwardCost, lastCost)
def checkCost():
(costA, lastCost) = getCostA()
costB = getCostB(lastCost)
totalCosts = []
totalCosts.append(costA + costB)
for i in range(len(nodesC)):
if lastCost > nodesC[i]:
(branchCost, branchLastCost) = getBranchCost(lastCost, i)
costB = getCostB(branchLastCost)
totalCosts.append(costA + branchCost + costB)
# print(min(totalCosts))
output.write(str(min(totalCosts)) + '\n')
output = open(filename.replace('in', 'out'), 'w')
for i in range(numberOfTest):
initData()
numNodeRow = int(test_input.readline().replace('\n', '').replace('\r', '').strip())
line = test_input.readline().replace('\n', '').replace('\r', '').strip()
ins = line.split(' ')
for j in range(0, 2 * (numNodeRow - 1), 2):
nodesR.append(int(ins[j]))
distanceR.append(int(ins[j+1]))
ins = test_input.readline().replace('\n', '').replace('\r', '').strip().split(' ')
branchPoint = int(ins[0])
numNodeCol = int(ins[1])
line = test_input.readline().replace('\n', '').replace('\r', '').strip()
ins = line.split(' ')
for j in range(0, 2 * numNodeCol, 2):
distanceC.append(int(ins[j]))
nodesC.append(int(ins[j+1]))
if branchPoint-1 >= len(nodesR):
nodesC.insert(0, 100)
else:
nodesC.insert(0, nodesR[branchPoint-1])
checkCost()
test_input.close()
output.close() | 1,696 | 0 | 161 |
ece9c2de7d3783b49ddc26cdb15385cc57ba75f8 | 1,094 | py | Python | LinearRegression/pcr.py | jayshonzs/ESL | 2b51b1e01cb204d17588b7a2a8f765daa0a1e0c5 | [
"MIT"
] | 9 | 2016-09-26T15:56:16.000Z | 2019-10-21T05:59:14.000Z | LinearRegression/pcr.py | jayshonzs/ESL | 2b51b1e01cb204d17588b7a2a8f765daa0a1e0c5 | [
"MIT"
] | null | null | null | LinearRegression/pcr.py | jayshonzs/ESL | 2b51b1e01cb204d17588b7a2a8f765daa0a1e0c5 | [
"MIT"
] | 3 | 2016-08-15T00:18:22.000Z | 2019-02-28T14:54:55.000Z | '''
Created on 2014-5-25
@author: xiajie
'''
import numpy as np
import prostate
import preprocess
if __name__ == '__main__':
inputs, output, Ttype = prostate.loaddata()
train_data, train_out, test_data, test_out = prostate.cookdata2(inputs, output, Ttype)
X, X_mean, Y_mean, X_std = preprocess.center_data(train_data, train_out, True)
beta = PCR(X,train_out)
print beta
RSS = 0
for i in range(len(test_out)):
x = test_data[i]
x = (x-X_mean)/X_std
print test_out[i], predict(x,beta,Y_mean)
RSS += (test_out[i]-predict(x,beta,Y_mean))**2
print RSS/len(test_out) | 26.682927 | 90 | 0.617002 | '''
Created on 2014-5-25
@author: xiajie
'''
import numpy as np
import prostate
import preprocess
def theta(z, y):
return np.transpose(z).dot(y)/np.transpose(z).dot(z)
def PCR(X,Y,m=7):
z = []
thetas = []
beta = np.zeros(len(X[0]))
U, S, V = np.linalg.svd(X, full_matrices=True)
for i in range(m):
z.append(X.dot(V[i]))
for i in range(m):
thetas.append(theta(z[i],Y))
for i in range(m):
beta = beta + V[i]*thetas[i]
return beta
def predict(x, beta, Y_mean):
return Y_mean + np.transpose(x).dot(beta)
if __name__ == '__main__':
inputs, output, Ttype = prostate.loaddata()
train_data, train_out, test_data, test_out = prostate.cookdata2(inputs, output, Ttype)
X, X_mean, Y_mean, X_std = preprocess.center_data(train_data, train_out, True)
beta = PCR(X,train_out)
print beta
RSS = 0
for i in range(len(test_out)):
x = test_data[i]
x = (x-X_mean)/X_std
print test_out[i], predict(x,beta,Y_mean)
RSS += (test_out[i]-predict(x,beta,Y_mean))**2
print RSS/len(test_out) | 400 | 0 | 69 |
46fd6c109844df9a60a301cc1c15e3eb17f850a2 | 11,188 | py | Python | tests/unit/profiles/test_views.py | etienne86/oc_p13_team_spirit | fd3d45618d349ecd0a03e63c4a7e9c1044eeffaa | [
"MIT"
] | null | null | null | tests/unit/profiles/test_views.py | etienne86/oc_p13_team_spirit | fd3d45618d349ecd0a03e63c4a7e9c1044eeffaa | [
"MIT"
] | null | null | null | tests/unit/profiles/test_views.py | etienne86/oc_p13_team_spirit | fd3d45618d349ecd0a03e63c4a7e9c1044eeffaa | [
"MIT"
] | null | null | null | """Contain the unit tests related to the views in app ``profiles``."""
from django.http.request import HttpRequest
from django.test import TestCase
from teamspirit.core.models import Address
from teamspirit.profiles.models import Personal
from teamspirit.profiles.views import (
add_id_file_view,
add_medical_file_view,
custom_password_reset_complete_view,
custom_password_reset_done_view,
drop_file_view,
drop_id_file_view,
drop_medical_file_view,
password_changed_view,
personal_info_view,
phone_address_view,
profile_view,
)
from teamspirit.users.models import User
class ProfilesViewsTestCase(TestCase):
"""Test the views in the app ``profiles``."""
def test_profile_view(self):
"""Unit test - app ``profiles`` - view ``profile_view``
Test the profile view.
"""
view = profile_view
response = view(self.get_request) # type is TemplateResponse
# render the response content
response.render()
html = response.content.decode('utf8')
self.assertEqual(response.status_code, 200)
self.assertTrue(html.startswith('<!DOCTYPE html>'))
self.assertIn('<title>Team Spirit - Profil</title>', html)
# next test: status 403 (Forbidden), problem with CSRF?
# def test_custom_password_change_view(self):
# """Unit test - app ``profiles`` - view ...
# [complete view: ``custom_password_change_view``]
# Test the custom password change view.
# """
# view = custom_password_change_view
# view = requires_csrf_token(view)
# response = view(self.get_request)
# print(response)
# print(type(response))
# # response = csrf_exempt(view)(self.get_request)
# # render the response content
# # response.render()
# html = response.content.decode('utf8')
# self.assertEqual(response.status_code, 200)
# self.assertTrue(html.startswith('<!DOCTYPE html>'))
# self.assertIn(
# '<title>Team Spirit - Changement de mot de passe</title>',
# html
# )
def test_password_changed_view(self):
"""Unit test - app ``profiles`` - view ``password_changed_view``
Test the 'password changed' (confirmation) view.
"""
view = password_changed_view
response = view(self.get_request) # type is TemplateResponse
# render the response content
response.render()
html = response.content.decode('utf8')
self.assertEqual(response.status_code, 200)
self.assertTrue(html.startswith('<!DOCTYPE html>'))
self.assertIn('<title>Team Spirit - Mot de passe changé</title>', html)
# next test: status 403 (Forbidden), problem with CSRF?
# def test_password_reset_view(self):
# """Unit test - app ``profiles`` - view ``custom_password_reset_view``
# Test the custom password reset view.
# """
# view = custom_password_reset_view
# response = view(self.get_request)
# # response = csrf_exempt(view)(self.get_request)
# # render the response content
# # response.render()
# html = response.content.decode('utf8')
# self.assertEqual(response.status_code, 200)
# self.assertTrue(html.startswith('<!DOCTYPE html>'))
# self.assertIn(
# '<title>Team Spirit - Réinitialisation du mot de passe</title>',
# html
# )
def test_password_reset_done_view(self):
"""Unit test - app ``profiles`` - view ...
[complete view: ``custom_password_reset_done_view``]
Test the custom password reset (done) view.
"""
view = custom_password_reset_done_view
response = view(self.get_request) # type is TemplateResponse
# render the response content
response.render()
html = response.content.decode('utf8')
self.assertEqual(response.status_code, 200)
self.assertTrue(html.startswith('<!DOCTYPE html>'))
self.assertIn(
'<title>Team Spirit - '
'Envoi d\'un mail pour réinitialisation du mot de passe</title>',
html
)
# next test: AttributeError: 'NoneType' object has no attribute 'is_bound'
# I do not know how to generate/mock the `uidb64` and `token`.
# def test_password_reset_confirm_view(self):
# """Unit test - app ``profiles`` - view ...
# [complete view: ``custom_password_reset_confirm_view``]
# Test the custom password reset confirm view.
# """
# view = custom_password_reset_confirm_view
# response = view(self.get_request, uidb64='uidb64', token='token')
# print(type(response))
# print(response)
# # response = csrf_exempt(view)(self.get_request)
# # render the response content
# response.render()
# html = response.content.decode('utf8')
# self.assertEqual(response.status_code, 200)
# self.assertTrue(html.startswith('<!DOCTYPE html>'))
# self.assertIn(
# '<title>Team Spirit - '
# 'Définition du nouveau mot de passe</title>',
# html
# )
def test_password_reset_complete_view(self):
"""Unit test - app ``profiles`` - view ...
[complete view: ``custom_password_reset_complete_view``]
Test the custom password reset (complete) view.
"""
view = custom_password_reset_complete_view
response = view(self.get_request) # type is TemplateResponse
# render the response content
response.render()
html = response.content.decode('utf8')
self.assertEqual(response.status_code, 200)
self.assertTrue(html.startswith('<!DOCTYPE html>'))
self.assertIn('<title>Team Spirit - Mot de passe réinitialisé', html)
def test_personal_info_view(self):
"""Unit test - app ``profiles`` - view ``personal_info_view``
Test the personal info view.
"""
view = personal_info_view
response = view(self.get_request) # type is TemplateResponse
# render the response content
response.render()
html = response.content.decode('utf8')
self.assertEqual(response.status_code, 200)
self.assertTrue(html.startswith('<!DOCTYPE html>'))
self.assertIn(
'<title>Team Spirit - Mise à jour des informations personnelles',
html
)
def test_update_phone_address_view(self):
"""Unit test - app ``profiles`` - view ``phone_address_view``
Test the phone and address view.
"""
view = phone_address_view
response = view(self.get_request) # type is TemplateResponse
html = response.content.decode('utf8')
self.assertEqual(response.status_code, 200)
self.assertTrue(html.startswith('<!DOCTYPE html>'))
self.assertIn(
'<title>Team Spirit - Mise à jour des coordonnées',
html
)
def test_add_medical_file_view(self):
"""Unit test - app ``profiles`` - view ``add_medical_file_view``
Test the 'medical file add' view.
"""
view = add_medical_file_view
response = view(self.get_request) # type is TemplateResponse
# render the response content
response.render()
html = response.content.decode('utf8')
self.assertEqual(response.status_code, 200)
self.assertTrue(html.startswith('<!DOCTYPE html>'))
self.assertIn(
'<title>Team Spirit - Mise à jour du certificat médical ou de la'
' licence',
html
)
def test_add_id_file_view(self):
"""Unit test - app ``profiles`` - view ``add_id_file_view``
Test the 'id file add' view.
"""
view = add_id_file_view
response = view(self.get_request) # type is TemplateResponse
# render the response content
response.render()
html = response.content.decode('utf8')
self.assertEqual(response.status_code, 200)
self.assertTrue(html.startswith('<!DOCTYPE html>'))
self.assertIn(
'<title>Team Spirit - Mise à jour de la pièce d\'identité',
html
)
def test_drop_medical_file_view(self):
"""Unit test - app ``profiles`` - view ``drop_medical_file_view``
Test the 'medical file drop' view.
"""
view = drop_medical_file_view
response = view(self.get_request) # type is TemplateResponse
# render the response content
response.render()
html = response.content.decode('utf8')
self.assertEqual(response.status_code, 200)
self.assertTrue(html.startswith('<!DOCTYPE html>'))
self.assertIn(
'<title>Team Spirit - Suppression du certificat médical ou de la'
' licence',
html
)
def test_drop_id_file_view(self):
"""Unit test - app ``profiles`` - view ``drop_id_file_view``
Test the 'id file drop' view.
"""
view = drop_id_file_view
response = view(self.get_request) # type is TemplateResponse
# render the response content
response.render()
html = response.content.decode('utf8')
self.assertEqual(response.status_code, 200)
self.assertTrue(html.startswith('<!DOCTYPE html>'))
self.assertIn(
'<title>Team Spirit - Suppression de la pièce d\'identité',
html
)
def test_drop_file_view(self):
"""Unit test - app ``profiles`` - view ``drop_file_view``
Test the 'file drop' view.
"""
view = drop_file_view
response = view(self.get_request) # type is TemplateResponse
# render the response content
response.render()
html = response.content.decode('utf8')
self.assertEqual(response.status_code, 200)
self.assertTrue(html.startswith('<!DOCTYPE html>'))
self.assertIn(
'<title>Team Spirit - Suppression d\'un fichier',
html
)
| 36.442997 | 79 | 0.611459 | """Contain the unit tests related to the views in app ``profiles``."""
from django.http.request import HttpRequest
from django.test import TestCase
from teamspirit.core.models import Address
from teamspirit.profiles.models import Personal
from teamspirit.profiles.views import (
add_id_file_view,
add_medical_file_view,
custom_password_reset_complete_view,
custom_password_reset_done_view,
drop_file_view,
drop_id_file_view,
drop_medical_file_view,
password_changed_view,
personal_info_view,
phone_address_view,
profile_view,
)
from teamspirit.users.models import User
class ProfilesViewsTestCase(TestCase):
"""Test the views in the app ``profiles``."""
def setUp(self):
super().setUp()
# a user in database
self.address = Address.objects.create(
label_first="1 rue de l'impasse",
label_second="",
postal_code="75000",
city="Paris",
country="France"
)
self.personal = Personal.objects.create(
phone_number="01 02 03 04 05",
address=self.address
)
self.user = User.objects.create_user(
email="toto@mail.com",
first_name="Toto",
password="TopSecret",
personal=self.personal
)
# log this user in
self.client.login(email="toto@mail.com", password="TopSecret")
# a 'get' request
self.get_request = HttpRequest()
self.get_request.method = 'get'
self.get_request.user = self.user
# a 'post' request
self.post_request = HttpRequest()
self.post_request.method = 'post'
self.post_request.user = self.user
def test_profile_view(self):
"""Unit test - app ``profiles`` - view ``profile_view``
Test the profile view.
"""
view = profile_view
response = view(self.get_request) # type is TemplateResponse
# render the response content
response.render()
html = response.content.decode('utf8')
self.assertEqual(response.status_code, 200)
self.assertTrue(html.startswith('<!DOCTYPE html>'))
self.assertIn('<title>Team Spirit - Profil</title>', html)
# next test: status 403 (Forbidden), problem with CSRF?
# def test_custom_password_change_view(self):
# """Unit test - app ``profiles`` - view ...
# [complete view: ``custom_password_change_view``]
# Test the custom password change view.
# """
# view = custom_password_change_view
# view = requires_csrf_token(view)
# response = view(self.get_request)
# print(response)
# print(type(response))
# # response = csrf_exempt(view)(self.get_request)
# # render the response content
# # response.render()
# html = response.content.decode('utf8')
# self.assertEqual(response.status_code, 200)
# self.assertTrue(html.startswith('<!DOCTYPE html>'))
# self.assertIn(
# '<title>Team Spirit - Changement de mot de passe</title>',
# html
# )
def test_password_changed_view(self):
"""Unit test - app ``profiles`` - view ``password_changed_view``
Test the 'password changed' (confirmation) view.
"""
view = password_changed_view
response = view(self.get_request) # type is TemplateResponse
# render the response content
response.render()
html = response.content.decode('utf8')
self.assertEqual(response.status_code, 200)
self.assertTrue(html.startswith('<!DOCTYPE html>'))
self.assertIn('<title>Team Spirit - Mot de passe changé</title>', html)
# next test: status 403 (Forbidden), problem with CSRF?
# def test_password_reset_view(self):
# """Unit test - app ``profiles`` - view ``custom_password_reset_view``
# Test the custom password reset view.
# """
# view = custom_password_reset_view
# response = view(self.get_request)
# # response = csrf_exempt(view)(self.get_request)
# # render the response content
# # response.render()
# html = response.content.decode('utf8')
# self.assertEqual(response.status_code, 200)
# self.assertTrue(html.startswith('<!DOCTYPE html>'))
# self.assertIn(
# '<title>Team Spirit - Réinitialisation du mot de passe</title>',
# html
# )
def test_password_reset_done_view(self):
"""Unit test - app ``profiles`` - view ...
[complete view: ``custom_password_reset_done_view``]
Test the custom password reset (done) view.
"""
view = custom_password_reset_done_view
response = view(self.get_request) # type is TemplateResponse
# render the response content
response.render()
html = response.content.decode('utf8')
self.assertEqual(response.status_code, 200)
self.assertTrue(html.startswith('<!DOCTYPE html>'))
self.assertIn(
'<title>Team Spirit - '
'Envoi d\'un mail pour réinitialisation du mot de passe</title>',
html
)
# next test: AttributeError: 'NoneType' object has no attribute 'is_bound'
# I do not know how to generate/mock the `uidb64` and `token`.
# def test_password_reset_confirm_view(self):
# """Unit test - app ``profiles`` - view ...
# [complete view: ``custom_password_reset_confirm_view``]
# Test the custom password reset confirm view.
# """
# view = custom_password_reset_confirm_view
# response = view(self.get_request, uidb64='uidb64', token='token')
# print(type(response))
# print(response)
# # response = csrf_exempt(view)(self.get_request)
# # render the response content
# response.render()
# html = response.content.decode('utf8')
# self.assertEqual(response.status_code, 200)
# self.assertTrue(html.startswith('<!DOCTYPE html>'))
# self.assertIn(
# '<title>Team Spirit - '
# 'Définition du nouveau mot de passe</title>',
# html
# )
def test_password_reset_complete_view(self):
"""Unit test - app ``profiles`` - view ...
[complete view: ``custom_password_reset_complete_view``]
Test the custom password reset (complete) view.
"""
view = custom_password_reset_complete_view
response = view(self.get_request) # type is TemplateResponse
# render the response content
response.render()
html = response.content.decode('utf8')
self.assertEqual(response.status_code, 200)
self.assertTrue(html.startswith('<!DOCTYPE html>'))
self.assertIn('<title>Team Spirit - Mot de passe réinitialisé', html)
def test_personal_info_view(self):
"""Unit test - app ``profiles`` - view ``personal_info_view``
Test the personal info view.
"""
view = personal_info_view
response = view(self.get_request) # type is TemplateResponse
# render the response content
response.render()
html = response.content.decode('utf8')
self.assertEqual(response.status_code, 200)
self.assertTrue(html.startswith('<!DOCTYPE html>'))
self.assertIn(
'<title>Team Spirit - Mise à jour des informations personnelles',
html
)
def test_update_phone_address_view(self):
"""Unit test - app ``profiles`` - view ``phone_address_view``
Test the phone and address view.
"""
view = phone_address_view
response = view(self.get_request) # type is TemplateResponse
html = response.content.decode('utf8')
self.assertEqual(response.status_code, 200)
self.assertTrue(html.startswith('<!DOCTYPE html>'))
self.assertIn(
'<title>Team Spirit - Mise à jour des coordonnées',
html
)
def test_add_medical_file_view(self):
"""Unit test - app ``profiles`` - view ``add_medical_file_view``
Test the 'medical file add' view.
"""
view = add_medical_file_view
response = view(self.get_request) # type is TemplateResponse
# render the response content
response.render()
html = response.content.decode('utf8')
self.assertEqual(response.status_code, 200)
self.assertTrue(html.startswith('<!DOCTYPE html>'))
self.assertIn(
'<title>Team Spirit - Mise à jour du certificat médical ou de la'
' licence',
html
)
def test_add_id_file_view(self):
"""Unit test - app ``profiles`` - view ``add_id_file_view``
Test the 'id file add' view.
"""
view = add_id_file_view
response = view(self.get_request) # type is TemplateResponse
# render the response content
response.render()
html = response.content.decode('utf8')
self.assertEqual(response.status_code, 200)
self.assertTrue(html.startswith('<!DOCTYPE html>'))
self.assertIn(
'<title>Team Spirit - Mise à jour de la pièce d\'identité',
html
)
def test_drop_medical_file_view(self):
"""Unit test - app ``profiles`` - view ``drop_medical_file_view``
Test the 'medical file drop' view.
"""
view = drop_medical_file_view
response = view(self.get_request) # type is TemplateResponse
# render the response content
response.render()
html = response.content.decode('utf8')
self.assertEqual(response.status_code, 200)
self.assertTrue(html.startswith('<!DOCTYPE html>'))
self.assertIn(
'<title>Team Spirit - Suppression du certificat médical ou de la'
' licence',
html
)
def test_drop_id_file_view(self):
"""Unit test - app ``profiles`` - view ``drop_id_file_view``
Test the 'id file drop' view.
"""
view = drop_id_file_view
response = view(self.get_request) # type is TemplateResponse
# render the response content
response.render()
html = response.content.decode('utf8')
self.assertEqual(response.status_code, 200)
self.assertTrue(html.startswith('<!DOCTYPE html>'))
self.assertIn(
'<title>Team Spirit - Suppression de la pièce d\'identité',
html
)
def test_drop_file_view(self):
"""Unit test - app ``profiles`` - view ``drop_file_view``
Test the 'file drop' view.
"""
view = drop_file_view
response = view(self.get_request) # type is TemplateResponse
# render the response content
response.render()
html = response.content.decode('utf8')
self.assertEqual(response.status_code, 200)
self.assertTrue(html.startswith('<!DOCTYPE html>'))
self.assertIn(
'<title>Team Spirit - Suppression d\'un fichier',
html
)
| 995 | 0 | 27 |
86a8ad2afb7633e6e47ada84d04ae169c3278ecc | 4,831 | py | Python | modules/benchmark_common_functions.py | liushiyu1994/random_binary_file | 17c317b777387346114a4ad20c1bc3cb8a4147ce | [
"MIT"
] | null | null | null | modules/benchmark_common_functions.py | liushiyu1994/random_binary_file | 17c317b777387346114a4ad20c1bc3cb8a4147ce | [
"MIT"
] | null | null | null | modules/benchmark_common_functions.py | liushiyu1994/random_binary_file | 17c317b777387346114a4ad20c1bc3cb8a4147ce | [
"MIT"
] | null | null | null | from .packages import os, plt
from .common_functions import parameter_check_function, random_binary_files_single, time_stamp
| 49.295918 | 114 | 0.742703 | from .packages import os, plt
from .common_functions import parameter_check_function, random_binary_files_single, time_stamp
def benchmark_text_output(result_sorted_list, output_file_name, output_folder_name):
headline = "Generator name,\t\t Running time,\t\t File count rate (count/s),\t\t File size rate (MiB/s)"
text_output_line_list = [headline]
for generator_name, current_time, file_num_rate, file_size_rate in result_sorted_list:
newline = "{},\t\t {},\t\t {},\t\t {}".format(generator_name, current_time, file_num_rate, file_size_rate)
text_output_line_list.append(newline)
output_file_path = "{}/{}".format(output_folder_name, output_file_name)
with open(output_file_path, 'w') as f_out:
f_out.write("\n".join(text_output_line_list))
def benchmark_plotting(result_sorted_list, output_figure_name, output_folder_name):
x_loc_left_offset = 0.5
width = 0.5
x_loc_list = []
x_label_list = []
data_list = []
for index, (generator_name, _, _, file_size_rate) in enumerate(result_sorted_list):
x_loc_list.append(x_loc_left_offset + index)
x_label_list.append(generator_name)
data_list.append(file_size_rate)
fig, ax = plt.subplots(figsize=(12, 8))
ax.bar(x_loc_list, data_list, width)
ax.set_xlim([0, len(result_sorted_list) + x_loc_left_offset])
ax.set_ylabel('File size rate (MiB/s)')
ax.set_xticks(x_loc_list)
ax.set_xticklabels(x_label_list, rotation=15)
save_path = "{}/{}".format(output_folder_name, output_figure_name)
fig.savefig(save_path, dpi=fig.dpi)
def benchmark_parameter_check_function(config):
error_string = "config.py must include following attributes for benchmark: {}"
try:
generator_name_set = config.generator_name_list
except AttributeError:
raise ValueError(error_string.format("generator_name_list"))
try:
benchmark_output_folder = config.benchmark_output_folder
except AttributeError:
raise ValueError(error_string.format("benchmark_output_folder"))
if os is not None and not os.path.isdir(benchmark_output_folder):
raise ValueError("Cannot find benchmark output folder: {}".format(benchmark_output_folder))
try:
with open("{}/test.txt".format(benchmark_output_folder), 'w') as f_out:
f_out.write("This is just a test for the benchmark output folder")
except Exception:
raise ValueError("Cannot write file to folder: {}".format(benchmark_output_folder))
return generator_name_set, benchmark_output_folder
def make_son_folder(parent_folder_path, target_son_folder_name):
target_son_folder_path = "{}/{}".format(parent_folder_path, target_son_folder_name)
if os is not None:
if not os.path.isdir(target_son_folder_path):
os.mkdir(target_son_folder_path)
return target_son_folder_path
else:
return parent_folder_path
def benchmark_entry_function(config):
(
_, current_file_num, current_file_size, current_file_name_length, current_output_folder,
seed_length, parallel, parallel_num, parallel_file_name_prefix_length) = parameter_check_function(config)
def current_print_func(file_count):
print("[{}] {}({:.2f}%) finished".format(time_stamp(), file_count, file_count / current_file_num * 100))
generator_name_list, benchmark_output_folder = benchmark_parameter_check_function(config)
benchmark_file_output_folder = make_son_folder(benchmark_output_folder, "output_files")
total_file_size = current_file_size * current_file_num
final_time_list = []
print("[{}] Start benchmark".format(time_stamp()))
for generator_name in generator_name_list:
print("[{}] Start generator: {}".format(time_stamp(), generator_name))
current_generator_output_folder = make_son_folder(benchmark_file_output_folder, generator_name)
current_time = random_binary_files_single(
generator_name, current_file_num, current_file_size, current_file_name_length,
current_generator_output_folder, seed_length=seed_length, print_func=current_print_func)
file_num_rate = current_file_num / current_time
file_size_rate = total_file_size / current_time / 10**6
final_time_list.append((generator_name, current_time, file_num_rate, file_size_rate))
print("[{}] Finish generator: {}".format(time_stamp(), generator_name))
sorted_final_time_list = sorted(final_time_list, key=lambda x: x[3])
text_output_file_name = "benchmark_result.csv"
benchmark_text_output(sorted_final_time_list, text_output_file_name, benchmark_output_folder)
if plt is not None:
figure_name = "benchmark_result.png"
benchmark_plotting(sorted_final_time_list, figure_name, benchmark_output_folder)
plt.show()
| 4,586 | 0 | 115 |
6ad8ac63ac2ca2e4e626cb985983826a1b89907e | 10,317 | py | Python | bin/vrc_ssh.py | osrf/cloudsim-legacy | 01ea7dd2708ed9797a860ac839028ec62fd96a23 | [
"Apache-2.0"
] | null | null | null | bin/vrc_ssh.py | osrf/cloudsim-legacy | 01ea7dd2708ed9797a860ac839028ec62fd96a23 | [
"Apache-2.0"
] | null | null | null | bin/vrc_ssh.py | osrf/cloudsim-legacy | 01ea7dd2708ed9797a860ac839028ec62fd96a23 | [
"Apache-2.0"
] | 1 | 2021-03-16T15:00:51.000Z | 2021-03-16T15:00:51.000Z | #!/usr/bin/env python
"""
Program that runs a script or copy a file into a set of machines.
"""
import argparse
import os
import sys
from threading import Thread
import abc
import subprocess
import getpass
import Queue
NORMAL = '\033[00m'
RED = '\033[0;31m'
CLOUDSIM_PREFIX = 'OSRF_CloudSim_'
CONST_PREFIX = 'OSRF_VRC_Constellation_'
MACHINES = ['cs', 'router', 'sim', 'fc1', 'fc2']
class Ssh_base(object):
'''
Abstract class for running a ssh/scp command.
'''
DEFAULT_SSH_OPTS = ('-o UserKnownHostsFile=/dev/null '
'-o StrictHostKeyChecking=no '
'-o ConnectTimeout=5')
__metaclass__ = abc.ABCMeta
def get_credentials(self):
'''
To be implemented by derived classes.
'''
return
def run_remote_command(self, cmd, queue):
'''
Execute a command, and put in a shared queue the stdout, stderr outputs.
@param cmd Command to be executed
@param queue Shared queue (thread safe)
'''
po = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
out, err = po.communicate()
if po.returncode == 0:
msg = self.name + ': Success'
if out != '':
msg += '\n\t' + out.replace('\n', '\n\t')
queue.put(msg)
else:
queue.put(self.name + ': Error\n\t%s' % err)
def get_cmd_context(self, ssh_options):
'''
Returns the current ssh options, key file and ip of the current machine.
@param ssh_options Extra ssh options to be used
'''
ssh_options = Ssh_base.DEFAULT_SSH_OPTS + ssh_options
key, ip = self.get_credentials()
if not os.path.exists(key):
raise NameError('Identity file not found (%s)...Aborting' % key)
return ssh_options, key, ip
def copy(self, ssh_options, src, user, dst, queue):
'''
Copy a file into a remote machine.
@param ssh_options Extra ssh options
@param src Full path to the file to be uploaded
@param user Remote user used by ssh
@param dst Destination path
@param queue Shared queue to store the output of the command
'''
ssh_options, key, ip = self.get_cmd_context(ssh_options)
cmd = ('scp ' + ssh_options + ' -i ' + key + ' ' + src + ' ' +
user + '@' + ip + ':' + dst)
self.run_remote_command(cmd, queue)
def run(self, ssh_options, cmd, user, queue):
'''
Run a command in a remote machine.
@param ssh_options Extra ssh options
@param cmd Command to be executed
@param user Remote user used by ssh
@param queue Shared queue to store the output of the command
'''
ssh_options, key, ip = self.get_cmd_context(ssh_options)
cmd = ('ssh ' + ssh_options + ' -i ' + key + ' ' +
user + '@' + ip + ' ' + cmd)
self.run_remote_command(cmd, queue)
class Ssh_cloudsim(Ssh_base):
'''
Derived class for running a ssh/scp command in a CloudSim machine.
'''
def get_credentials(self):
'''
Return the CloudSim credentials.
'''
directory = self.constellation['constellation_directory']
ip = self.constellation['simulation_ip']
key = os.path.join(directory, 'cs', 'key-cs.pem')
return (key, ip)
class Ssh_router(Ssh_base):
'''
Derived class for running a ssh/scp command in a router machine.
'''
def get_credentials(self):
'''
Return the Router credentials.
'''
directory = self.constellation['constellation_directory']
ip = self.constellation['router_public_ip']
key = os.path.join(directory, 'router', 'key-router.pem')
return (key, ip)
class Ssh_machine(Ssh_base):
'''
Derived class for running a ssh/scp command in a sim, fc1, or fc2 machine.
'''
def get_credentials(self):
'''
Return the Sim, FC1, or FC2 credentials.
'''
if self.machine_type == 'sim':
ip = '10.0.0.51'
key_name = 'key-sim.pem'
elif self.machine_type == 'fc1':
ip = '10.0.0.52'
key_name = 'key-fc1.pem'
elif self.machine_type == 'fc2':
ip = '10.0.0.53'
key_name = 'key-fc2.pem'
else:
raise NameError('Invalid machine type: %s' % self.machine_type)
key = os.path.join('/home/ubuntu/cloudsim', key_name)
return (key, ip)
def go(args):
'''
Function that run a command or copy a file in multiples machines. A thread
is created to the job in every machine. A shared queue among the threads
is used to capture the returned value of the commands, stdout, and stderr.
@param args Command line arguments
'''
# Retrieve command line arguments
ssh_options = args.ssh_options
machine_type = args.type
subcommand = args.which
if subcommand == 'run':
arg = args.cmd
stats_msg = 'Command executed in'
elif subcommand == 'copy':
arg = args.src
dst = args.dst
stats_msg = 'File uploaded into'
if not os.path.exists(arg):
print '%sFile not found: (%s)...Aborting%s' % (RED, arg, NORMAL)
sys.exit(1)
else:
print 'Invalid subcommand (%s)...Aborting' % subcommand
sys.exit(1)
# Sanity check
if getpass.getuser() != 'root':
print "Invalid user, you should run this program as root...Aborting"
sys.exit(1)
# Counter for stats, threads for the job, and a queue for the returned vals11
counter = 0
threads = []
succeed_queue = Queue.Queue()
if machine_type in ['cs', 'router']:
try:
# Import cloudsimd
basepath = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
if not os.path.exists(os.path.join(basepath, 'cloudsimd')):
print 'Your DB does not contain any remote CloudSim...Aborting'
sys.exit(1)
sys.path.insert(0, basepath)
from cloudsimd import cloudsimd
const_list = cloudsimd.list_constellations()
except Exception, excep:
print ('%sError importing cloudsimd: %s%s'
% (RED, repr(excep), NORMAL))
sys.exit(1)
# Iterate over the list of CloudSims
for constellation in const_list:
try:
name = constellation['constellation_name']
if name.startswith(CLOUDSIM_PREFIX) and machine_type == 'cs':
machine = Ssh_cloudsim(name, constellation)
elif name.startswith(CONST_PREFIX) and machine_type == 'router':
machine = Ssh_router(name, constellation)
else:
continue
if subcommand == 'run':
params = [ssh_options, arg, 'ubuntu', succeed_queue]
t = Thread(target=machine.run, args=params)
elif subcommand == 'copy':
params = [ssh_options, arg, 'ubuntu', dst, succeed_queue]
t = Thread(target=machine.copy, args=params)
threads.append(t)
t.start()
counter += 1
except Exception, excep:
print ('%sError running command: %s%s'
% (RED, repr(excep), NORMAL))
counter -= 1
elif machine_type in ['sim', 'fc1', 'fc2']:
try:
machine = Ssh_machine(machine_type)
if subcommand == 'run':
machine.run(ssh_options, arg, 'ubuntu', succeed_queue)
elif subcommand == 'copy':
machine.copy(ssh_options, arg, 'ubuntu', dst, succeed_queue)
counter += 1
except Exception, excep:
print repr(excep)
sys.exit(1)
else:
print 'Invalid machine type (%s)...Aborting' % machine_type
sys.exit(1)
# Wait for all the threads to finish
[x.join() for x in threads]
# Print some stats
for elem in sorted(list(succeed_queue.queue)):
print elem
print '%s %d machine/s' % (stats_msg, counter)
if __name__ == '__main__':
# Specify top level command line arguments
parser = argparse.ArgumentParser(description='Manage multiple VRC SSH/SCP')
parser.add_argument('-o', '--ssh_options', default='', help='ssh options')
subparsers = parser.add_subparsers(title='subcommands',
description='valid subcommands',
help='additional help')
# create the parser for the "run" command
parser_run = subparsers.add_parser('run',
help='Run a command in several machines')
parser_run.add_argument('cmd', help='Command to run remotely')
parser_run.set_defaults(which='run')
parser_run.add_argument('type', choices=MACHINES, help='Remote machine type')
parser_run.set_defaults(func=go)
# create the parser for the "copy" command
parser_copy = subparsers.add_parser('copy',
help='Copy a file into several machines')
parser_copy.add_argument('src', help='File to upload')
parser_copy.set_defaults(which='copy')
parser_copy.add_argument('dst', help='Destination path')
parser_copy.add_argument('type', choices=MACHINES, help='Remote machine type')
parser_copy.set_defaults(func=go)
# Parse command line arguments
args = parser.parse_args()
args.func(args)
| 34.275748 | 85 | 0.583794 | #!/usr/bin/env python
"""
Program that runs a script or copy a file into a set of machines.
"""
import argparse
import os
import sys
from threading import Thread
import abc
import subprocess
import getpass
import Queue
NORMAL = '\033[00m'
RED = '\033[0;31m'
CLOUDSIM_PREFIX = 'OSRF_CloudSim_'
CONST_PREFIX = 'OSRF_VRC_Constellation_'
MACHINES = ['cs', 'router', 'sim', 'fc1', 'fc2']
class Ssh_base(object):
'''
Abstract class for running a ssh/scp command.
'''
DEFAULT_SSH_OPTS = ('-o UserKnownHostsFile=/dev/null '
'-o StrictHostKeyChecking=no '
'-o ConnectTimeout=5')
__metaclass__ = abc.ABCMeta
def __init__(self, name, constellation=None):
self.constellation = constellation
self.name = name
def get_credentials(self):
'''
To be implemented by derived classes.
'''
return
def run_remote_command(self, cmd, queue):
'''
Execute a command, and put in a shared queue the stdout, stderr outputs.
@param cmd Command to be executed
@param queue Shared queue (thread safe)
'''
po = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
out, err = po.communicate()
if po.returncode == 0:
msg = self.name + ': Success'
if out != '':
msg += '\n\t' + out.replace('\n', '\n\t')
queue.put(msg)
else:
queue.put(self.name + ': Error\n\t%s' % err)
def get_cmd_context(self, ssh_options):
'''
Returns the current ssh options, key file and ip of the current machine.
@param ssh_options Extra ssh options to be used
'''
ssh_options = Ssh_base.DEFAULT_SSH_OPTS + ssh_options
key, ip = self.get_credentials()
if not os.path.exists(key):
raise NameError('Identity file not found (%s)...Aborting' % key)
return ssh_options, key, ip
def copy(self, ssh_options, src, user, dst, queue):
'''
Copy a file into a remote machine.
@param ssh_options Extra ssh options
@param src Full path to the file to be uploaded
@param user Remote user used by ssh
@param dst Destination path
@param queue Shared queue to store the output of the command
'''
ssh_options, key, ip = self.get_cmd_context(ssh_options)
cmd = ('scp ' + ssh_options + ' -i ' + key + ' ' + src + ' ' +
user + '@' + ip + ':' + dst)
self.run_remote_command(cmd, queue)
def run(self, ssh_options, cmd, user, queue):
'''
Run a command in a remote machine.
@param ssh_options Extra ssh options
@param cmd Command to be executed
@param user Remote user used by ssh
@param queue Shared queue to store the output of the command
'''
ssh_options, key, ip = self.get_cmd_context(ssh_options)
cmd = ('ssh ' + ssh_options + ' -i ' + key + ' ' +
user + '@' + ip + ' ' + cmd)
self.run_remote_command(cmd, queue)
class Ssh_cloudsim(Ssh_base):
'''
Derived class for running a ssh/scp command in a CloudSim machine.
'''
def __init__(self, name, constellation):
super(Ssh_cloudsim, self).__init__(name, constellation)
def get_credentials(self):
'''
Return the CloudSim credentials.
'''
directory = self.constellation['constellation_directory']
ip = self.constellation['simulation_ip']
key = os.path.join(directory, 'cs', 'key-cs.pem')
return (key, ip)
class Ssh_router(Ssh_base):
'''
Derived class for running a ssh/scp command in a router machine.
'''
def __init__(self, name, constellation):
super(Ssh_router, self).__init__(name, constellation)
def get_credentials(self):
'''
Return the Router credentials.
'''
directory = self.constellation['constellation_directory']
ip = self.constellation['router_public_ip']
key = os.path.join(directory, 'router', 'key-router.pem')
return (key, ip)
class Ssh_machine(Ssh_base):
'''
Derived class for running a ssh/scp command in a sim, fc1, or fc2 machine.
'''
def __init__(self, machine_type):
super(Ssh_machine, self).__init__(machine_type)
if machine_type not in ['sim', 'fc1', 'fc2']:
raise NameError('Invalid machine type: %s' % machine_type)
else:
self.machine_type = machine_type
def get_credentials(self):
'''
Return the Sim, FC1, or FC2 credentials.
'''
if self.machine_type == 'sim':
ip = '10.0.0.51'
key_name = 'key-sim.pem'
elif self.machine_type == 'fc1':
ip = '10.0.0.52'
key_name = 'key-fc1.pem'
elif self.machine_type == 'fc2':
ip = '10.0.0.53'
key_name = 'key-fc2.pem'
else:
raise NameError('Invalid machine type: %s' % self.machine_type)
key = os.path.join('/home/ubuntu/cloudsim', key_name)
return (key, ip)
def go(args):
'''
Function that run a command or copy a file in multiples machines. A thread
is created to the job in every machine. A shared queue among the threads
is used to capture the returned value of the commands, stdout, and stderr.
@param args Command line arguments
'''
# Retrieve command line arguments
ssh_options = args.ssh_options
machine_type = args.type
subcommand = args.which
if subcommand == 'run':
arg = args.cmd
stats_msg = 'Command executed in'
elif subcommand == 'copy':
arg = args.src
dst = args.dst
stats_msg = 'File uploaded into'
if not os.path.exists(arg):
print '%sFile not found: (%s)...Aborting%s' % (RED, arg, NORMAL)
sys.exit(1)
else:
print 'Invalid subcommand (%s)...Aborting' % subcommand
sys.exit(1)
# Sanity check
if getpass.getuser() != 'root':
print "Invalid user, you should run this program as root...Aborting"
sys.exit(1)
# Counter for stats, threads for the job, and a queue for the returned vals11
counter = 0
threads = []
succeed_queue = Queue.Queue()
if machine_type in ['cs', 'router']:
try:
# Import cloudsimd
basepath = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
if not os.path.exists(os.path.join(basepath, 'cloudsimd')):
print 'Your DB does not contain any remote CloudSim...Aborting'
sys.exit(1)
sys.path.insert(0, basepath)
from cloudsimd import cloudsimd
const_list = cloudsimd.list_constellations()
except Exception, excep:
print ('%sError importing cloudsimd: %s%s'
% (RED, repr(excep), NORMAL))
sys.exit(1)
# Iterate over the list of CloudSims
for constellation in const_list:
try:
name = constellation['constellation_name']
if name.startswith(CLOUDSIM_PREFIX) and machine_type == 'cs':
machine = Ssh_cloudsim(name, constellation)
elif name.startswith(CONST_PREFIX) and machine_type == 'router':
machine = Ssh_router(name, constellation)
else:
continue
if subcommand == 'run':
params = [ssh_options, arg, 'ubuntu', succeed_queue]
t = Thread(target=machine.run, args=params)
elif subcommand == 'copy':
params = [ssh_options, arg, 'ubuntu', dst, succeed_queue]
t = Thread(target=machine.copy, args=params)
threads.append(t)
t.start()
counter += 1
except Exception, excep:
print ('%sError running command: %s%s'
% (RED, repr(excep), NORMAL))
counter -= 1
elif machine_type in ['sim', 'fc1', 'fc2']:
try:
machine = Ssh_machine(machine_type)
if subcommand == 'run':
machine.run(ssh_options, arg, 'ubuntu', succeed_queue)
elif subcommand == 'copy':
machine.copy(ssh_options, arg, 'ubuntu', dst, succeed_queue)
counter += 1
except Exception, excep:
print repr(excep)
sys.exit(1)
else:
print 'Invalid machine type (%s)...Aborting' % machine_type
sys.exit(1)
# Wait for all the threads to finish
[x.join() for x in threads]
# Print some stats
for elem in sorted(list(succeed_queue.queue)):
print elem
print '%s %d machine/s' % (stats_msg, counter)
if __name__ == '__main__':
# Specify top level command line arguments
parser = argparse.ArgumentParser(description='Manage multiple VRC SSH/SCP')
parser.add_argument('-o', '--ssh_options', default='', help='ssh options')
subparsers = parser.add_subparsers(title='subcommands',
description='valid subcommands',
help='additional help')
# create the parser for the "run" command
parser_run = subparsers.add_parser('run',
help='Run a command in several machines')
parser_run.add_argument('cmd', help='Command to run remotely')
parser_run.set_defaults(which='run')
parser_run.add_argument('type', choices=MACHINES, help='Remote machine type')
parser_run.set_defaults(func=go)
# create the parser for the "copy" command
parser_copy = subparsers.add_parser('copy',
help='Copy a file into several machines')
parser_copy.add_argument('src', help='File to upload')
parser_copy.set_defaults(which='copy')
parser_copy.add_argument('dst', help='Destination path')
parser_copy.add_argument('type', choices=MACHINES, help='Remote machine type')
parser_copy.set_defaults(func=go)
# Parse command line arguments
args = parser.parse_args()
args.func(args)
| 508 | 0 | 105 |
d4cd314bd47d95820381415300f80a6c264fbc93 | 10,264 | py | Python | sandbox/finetuning/algos/concurrent_continuous_ppo.py | andrewli77/rllab-finetuning | 2dae9141d0fdc284d04f18931907131d66b43023 | [
"MIT"
] | 23 | 2020-04-27T23:53:44.000Z | 2022-03-10T03:13:16.000Z | sandbox/finetuning/algos/concurrent_continuous_ppo.py | WeiChengTseng/rllab-finetuning | 2dae9141d0fdc284d04f18931907131d66b43023 | [
"MIT"
] | 1 | 2021-11-14T13:30:22.000Z | 2021-11-14T13:30:22.000Z | sandbox/finetuning/algos/concurrent_continuous_ppo.py | WeiChengTseng/rllab-finetuning | 2dae9141d0fdc284d04f18931907131d66b43023 | [
"MIT"
] | 8 | 2020-06-17T03:28:34.000Z | 2022-03-09T03:13:03.000Z | import theano
import theano.tensor as TT
from rllab.misc import ext
import numpy as np
import copy
import rllab.misc.logger as logger
from rllab.spaces.box import Box
from rllab.envs.env_spec import EnvSpec
from sandbox.finetuning.policies.concurrent_hier_policy2 import HierarchicalPolicy
from sandbox.finetuning.algos.hier_batch_polopt import BatchPolopt, \
BatchSampler # note that I use my own BatchPolopt class here
from sandbox.finetuning.algos.hier_batch_sampler import HierBatchSampler
from rllab.optimizers.first_order_optimizer import FirstOrderOptimizer
from rllab.distributions.diagonal_gaussian import DiagonalGaussian
from rllab.baselines.linear_feature_baseline import LinearFeatureBaseline
from rllab.baselines.gaussian_mlp_baseline import GaussianMLPBaseline
class ConcurrentContinuousPPO(BatchPolopt):
"""
Designed to enable concurrent training of a SNN that parameterizes skills
and also train the manager at the same time
Note that, if I'm not trying to do the sample approximation of the weird log of sum term,
I don't need to know which skill was picked, just need to know the action
"""
# double check this constructor later
# initialize the computation graph
# optimize is run on >= 1 trajectory at a time
# assumptions: 1 trajectory, which is a multiple of p; that the obs_var_probs is valid
# do the optimization
| 48.87619 | 118 | 0.667771 | import theano
import theano.tensor as TT
from rllab.misc import ext
import numpy as np
import copy
import rllab.misc.logger as logger
from rllab.spaces.box import Box
from rllab.envs.env_spec import EnvSpec
from sandbox.finetuning.policies.concurrent_hier_policy2 import HierarchicalPolicy
from sandbox.finetuning.algos.hier_batch_polopt import BatchPolopt, \
BatchSampler # note that I use my own BatchPolopt class here
from sandbox.finetuning.algos.hier_batch_sampler import HierBatchSampler
from rllab.optimizers.first_order_optimizer import FirstOrderOptimizer
from rllab.distributions.diagonal_gaussian import DiagonalGaussian
from rllab.baselines.linear_feature_baseline import LinearFeatureBaseline
from rllab.baselines.gaussian_mlp_baseline import GaussianMLPBaseline
class ConcurrentContinuousPPO(BatchPolopt):
"""
Designed to enable concurrent training of a SNN that parameterizes skills
and also train the manager at the same time
Note that, if I'm not trying to do the sample approximation of the weird log of sum term,
I don't need to know which skill was picked, just need to know the action
"""
# double check this constructor later
def __init__(self,
optimizer=None,
optimizer_args=None,
step_size=0.003,
num_latents=6,
latents=None, # some sort of iterable of the actual latent vectors
period=10, # how often I choose a latent
truncate_local_is_ratio=None,
epsilon=0.1,
train_pi_iters=10,
use_skill_dependent_baseline=False,
mlp_skill_dependent_baseline=False,
freeze_manager=False,
freeze_skills=False,
**kwargs):
if optimizer is None:
if optimizer_args is None:
# optimizer_args = dict()
optimizer_args = dict(batch_size=None)
self.optimizer = FirstOrderOptimizer(learning_rate=step_size, max_epochs=train_pi_iters, **optimizer_args)
self.step_size = step_size
self.truncate_local_is_ratio = truncate_local_is_ratio
self.epsilon = epsilon
super(ConcurrentContinuousPPO, self).__init__(**kwargs) # not sure if this line is correct
self.num_latents = kwargs['policy'].latent_dim
self.latents = latents
self.period = period
self.freeze_manager = freeze_manager
self.freeze_skills = freeze_skills
assert (not freeze_manager) or (not freeze_skills)
# todo: fix this sampler stuff
# import pdb; pdb.set_trace()
self.sampler = HierBatchSampler(self, self.period)
# self.sampler = BatchSampler(self)
# i hope this is right
self.diagonal = DiagonalGaussian(self.policy.low_policy.action_space.flat_dim)
self.debug_fns = []
assert isinstance(self.policy, HierarchicalPolicy)
self.period = self.policy.period
assert self.policy.period == self.period
self.continuous_latent = self.policy.continuous_latent
assert self.continuous_latent
# self.old_policy = copy.deepcopy(self.policy)
# skill dependent baseline
self.use_skill_dependent_baseline = use_skill_dependent_baseline
self.mlp_skill_dependent_baseline = mlp_skill_dependent_baseline
if use_skill_dependent_baseline:
curr_env = kwargs['env']
skill_dependent_action_space = curr_env.action_space
new_obs_space_no_bi = curr_env.observation_space.shape[0] + 1 # 1 for the t_remaining
skill_dependent_obs_space_dim = (new_obs_space_no_bi * (self.num_latents + 1) + self.num_latents,)
skill_dependent_obs_space = Box(-1.0, 1.0, shape=skill_dependent_obs_space_dim)
skill_dependent_env_spec = EnvSpec(skill_dependent_obs_space, skill_dependent_action_space)
if self.mlp_skill_dependent_baseline:
self.skill_dependent_baseline = GaussianMLPBaseline(env_spec=skill_dependent_env_spec)
else:
self.skill_dependent_baseline = LinearFeatureBaseline(env_spec=skill_dependent_env_spec)
# initialize the computation graph
# optimize is run on >= 1 trajectory at a time
# assumptions: 1 trajectory, which is a multiple of p; that the obs_var_probs is valid
def init_opt(self):
assert isinstance(self.policy, HierarchicalPolicy)
assert not self.freeze_manager and not self.freeze_skills
manager_surr_loss = 0
# skill_surr_loss = 0
obs_var_sparse = ext.new_tensor('sparse_obs', ndim=2, dtype=theano.config.floatX)
obs_var_raw = ext.new_tensor('obs', ndim=3, dtype=theano.config.floatX) # todo: check the dtype
action_var = self.env.action_space.new_tensor_variable('action', extra_dims=1, )
advantage_var = ext.new_tensor('advantage', ndim=1, dtype=theano.config.floatX)
# latent_var = ext.new_tensor('latents', ndim=2, dtype=theano.config.floatX)
mean_var = ext.new_tensor('mean', ndim=2, dtype=theano.config.floatX)
log_std_var = ext.new_tensor('log_std', ndim=2, dtype=theano.config.floatX)
# undoing the reshape, so that batch sampling is ok
obs_var = TT.reshape(obs_var_raw, [obs_var_raw.shape[0] * obs_var_raw.shape[1], obs_var_raw.shape[2]])
############################################################
### calculating the skills portion of the surrogate loss ###
############################################################
latent_var_sparse = self.policy.manager.dist_info_sym(obs_var_sparse)['mean']
latent_var = TT.extra_ops.repeat(latent_var_sparse, self.period, axis=0) #.dimshuffle(0, 'x')
dist_info_var = self.policy.low_policy.dist_info_sym(obs_var, state_info_var=latent_var)
old_dist_info_var = dict(mean=mean_var, log_std=log_std_var)
skill_lr = self.diagonal.likelihood_ratio_sym(action_var, old_dist_info_var, dist_info_var)
skill_surr_loss_vector = TT.minimum(skill_lr * advantage_var,
TT.clip(skill_lr, 1 - self.epsilon, 1 + self.epsilon) * advantage_var)
skill_surr_loss = -TT.mean(skill_surr_loss_vector)
surr_loss = skill_surr_loss # so that the relative magnitudes are correct
if self.freeze_skills and not self.freeze_manager:
raise NotImplementedError
elif self.freeze_manager and not self.freeze_skills:
raise NotImplementedError
else:
assert (not self.freeze_manager) or (not self.freeze_skills)
input_list = [obs_var_raw, obs_var_sparse, action_var, advantage_var, mean_var, log_std_var]
self.optimizer.update_opt(
loss=surr_loss,
target=self.policy,
inputs=input_list
)
return dict()
# do the optimization
def optimize_policy(self, itr, samples_data):
print(len(samples_data['observations']), self.period)
assert len(samples_data['observations']) % self.period == 0
# note that I have to do extra preprocessing to the advantages, and also create obs_var_sparse
if self.use_skill_dependent_baseline:
input_values = tuple(ext.extract(
samples_data, "observations", "actions", "advantages", "agent_infos", "skill_advantages"))
else:
input_values = tuple(ext.extract(
samples_data, "observations", "actions", "advantages", "agent_infos"))
obs_raw = input_values[0].reshape(input_values[0].shape[0] // self.period, self.period,
input_values[0].shape[1])
obs_sparse = input_values[0].take([i for i in range(0, input_values[0].shape[0], self.period)], axis=0)
if not self.continuous_latent:
advantage_sparse = input_values[2].reshape([input_values[2].shape[0] // self.period, self.period])[:, 0]
latents = input_values[3]['latents']
latents_sparse = latents.take([i for i in range(0, latents.shape[0], self.period)], axis=0)
prob = np.array(
list(input_values[3]['prob'].take([i for i in range(0, latents.shape[0], self.period)], axis=0)),
dtype=np.float32)
mean = input_values[3]['mean']
log_std = input_values[3]['log_std']
if self.use_skill_dependent_baseline:
advantage_var = input_values[4]
else:
advantage_var = input_values[2]
# import ipdb; ipdb.set_trace()
if self.freeze_skills and not self.freeze_manager:
raise NotImplementedError
elif self.freeze_manager and not self.freeze_skills:
raise NotImplementedError
else:
assert (not self.freeze_manager) or (not self.freeze_skills)
all_input_values = (obs_raw, obs_sparse, input_values[1], advantage_var, mean, log_std)
# todo: assign current parameters to old policy; does this work?
# old_param_values = self.policy.get_param_values(trainable=True)
# self.old_policy.set_param_values(old_param_values, trainable=True)
# old_param_values = self.policy.get_param_values()
# self.old_policy.set_param_values(old_param_values)
loss_before = self.optimizer.loss(all_input_values)
self.optimizer.optimize(all_input_values)
loss_after = self.optimizer.loss(all_input_values)
logger.record_tabular('LossBefore', loss_before)
logger.record_tabular('LossAfter', loss_after)
logger.record_tabular('dLoss', loss_before - loss_after)
return dict()
def get_itr_snapshot(self, itr, samples_data):
return dict(
itr=itr,
policy=self.policy,
baseline=self.baseline,
env=self.env
)
def log_diagnostics(self, paths):
# paths obtained by self.sampler.obtain_samples
BatchPolopt.log_diagnostics(self, paths)
# self.sampler.log_diagnostics(paths) # wasn't doing anything anyways
# want to log the standard deviations
# want to log the max and min of the actions
| 8,738 | 0 | 132 |
db8d1e3d87eeb6f3a0f5a5bfe9414cd167fe9148 | 1,064 | py | Python | test/perspective.py | cburggie/py3D | d0fe21aae1d5ab77ec80f85533e941c902dad6f4 | [
"MIT"
] | null | null | null | test/perspective.py | cburggie/py3D | d0fe21aae1d5ab77ec80f85533e941c902dad6f4 | [
"MIT"
] | null | null | null | test/perspective.py | cburggie/py3D | d0fe21aae1d5ab77ec80f85533e941c902dad6f4 | [
"MIT"
] | null | null | null | import pytrace
import py3D
filename = 'perspective.png'
ppu = 400
passes = 16
S = py3D.Sphere
C = py3D.Color
V = py3D.Vector
cam = pytrace.Camera(V(0.0,10.0,0.0), V(0.0,0.0,0.0), 2.0, 2.0, V(0.0,0.0,1.0))
cam.set_ppu(ppu)
center = S(V(0.0,0.0,0.0), 1.0, C(0.99,0.99,0.99)).set_reflectivity(0.8)
bodies = []
bodies.append( S(V(0.0,0.0,3.0), 1.0, C(0.01,0.01,0.01)) )
bodies.append( S(V(3.0,0.0,3.0).norm().scale(3.0), 1.0, C(0.99,0.01,0.01)) )
bodies.append( S(V(3.0,0.0,0.0), 1.0, C(0.99,0.99,0.01)) )
bodies.append( S(V(3.0,0.0,-3.0).norm().scale(3.0), 1.0, C(0.01,0.99,0.01)) )
bodies.append( S(V(0.0,0.0,-3.0), 1.0, C(0.01,0.99,0.99)) )
bodies.append( S(V(-3.0,0.0,-3.0).norm().scale(3.0), 1.0, C(0.01,0.01,0.99)) )
bodies.append( S(V(-3.0,0.0,0.0), 1.0, C(0.99,0.01,0.99)) )
bodies.append( S(V(-3.0,0.0,3.0).norm().scale(3.0), 1.0, C(0.99,0.99,0.99)) )
for b in bodies:
b.set_reflectivity(0.4)
bodies.append(center)
world = pytrace.World(bodies, py3D.Sky(V(0.0,1.0,0.0), C(0.01,0.01,0.3)) )
pytrace.Tracer(world, cam).draw(passes).write(filename)
| 30.4 | 79 | 0.596805 | import pytrace
import py3D
filename = 'perspective.png'
ppu = 400
passes = 16
S = py3D.Sphere
C = py3D.Color
V = py3D.Vector
cam = pytrace.Camera(V(0.0,10.0,0.0), V(0.0,0.0,0.0), 2.0, 2.0, V(0.0,0.0,1.0))
cam.set_ppu(ppu)
center = S(V(0.0,0.0,0.0), 1.0, C(0.99,0.99,0.99)).set_reflectivity(0.8)
bodies = []
bodies.append( S(V(0.0,0.0,3.0), 1.0, C(0.01,0.01,0.01)) )
bodies.append( S(V(3.0,0.0,3.0).norm().scale(3.0), 1.0, C(0.99,0.01,0.01)) )
bodies.append( S(V(3.0,0.0,0.0), 1.0, C(0.99,0.99,0.01)) )
bodies.append( S(V(3.0,0.0,-3.0).norm().scale(3.0), 1.0, C(0.01,0.99,0.01)) )
bodies.append( S(V(0.0,0.0,-3.0), 1.0, C(0.01,0.99,0.99)) )
bodies.append( S(V(-3.0,0.0,-3.0).norm().scale(3.0), 1.0, C(0.01,0.01,0.99)) )
bodies.append( S(V(-3.0,0.0,0.0), 1.0, C(0.99,0.01,0.99)) )
bodies.append( S(V(-3.0,0.0,3.0).norm().scale(3.0), 1.0, C(0.99,0.99,0.99)) )
for b in bodies:
b.set_reflectivity(0.4)
bodies.append(center)
world = pytrace.World(bodies, py3D.Sky(V(0.0,1.0,0.0), C(0.01,0.01,0.3)) )
pytrace.Tracer(world, cam).draw(passes).write(filename)
| 0 | 0 | 0 |
7d9b7841c0014afe5edcc26667fa135680bc684d | 6,061 | py | Python | flask_app/app.py | bulatcute/tatfac | 1bbfcff2622dd11a8caf69ff17268156720bb2c1 | [
"MIT"
] | null | null | null | flask_app/app.py | bulatcute/tatfac | 1bbfcff2622dd11a8caf69ff17268156720bb2c1 | [
"MIT"
] | null | null | null | flask_app/app.py | bulatcute/tatfac | 1bbfcff2622dd11a8caf69ff17268156720bb2c1 | [
"MIT"
] | null | null | null | import asyncio
import atexit
import os
import dotenv
import requests
from apscheduler.schedulers.background import BackgroundScheduler
from flask import (Flask, make_response, redirect, render_template, request,
send_from_directory, url_for)
from flask_login import (
LoginManager, UserMixin, current_user, login_required, login_user,
logout_user)
from flask_sqlalchemy import SQLAlchemy
from flask_app.edutatar import (check_login, facultative_info, get_diary,
get_home_params, login_edu, my_facultatives,
my_stars)
from flask_app.forms import LoginForm
dotenv.load_dotenv()
application = Flask(__name__)
application.config['SECRET_KEY'] = os.environ['SECRET_KEY']
application.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = True
# region DATABASE SETUP
application.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///database.db'
db = SQLAlchemy(application)
db.create_all()
# endregion
# region LOGIN SETUP
login_manager = LoginManager()
login_manager.init_app(application)
@login_manager.user_loader
@login_manager.unauthorized_handler
sessions = {}
for user in User.query.all():
s = requests.Session()
login_edu(s, user.login, user.password)
sessions[user.login] = s
scheduler = BackgroundScheduler()
scheduler.add_job(func=reload_sessions, trigger='interval', seconds=900)
scheduler.start()
atexit.register(lambda: scheduler.shutdown())
# endregion
@application.route('/sw.js', methods=['GET'])
@application.route('/', methods=['GET'])
@login_required
@application.route('/marks', methods=['GET'])
@login_required
@application.route('/marks/<int:term>', methods=['GET'])
@login_required
@application.route('/facultatives', methods=['GET'])
@login_required
@application.route('/facultative/<int:index>')
@login_required
@application.route('/diary')
@login_required
@application.route('/diary/<int:date>')
@login_required
@application.route('/login', methods=['GET', 'POST'])
@application.route('/logout')
@login_required
| 31.082051 | 107 | 0.671176 | import asyncio
import atexit
import os
import dotenv
import requests
from apscheduler.schedulers.background import BackgroundScheduler
from flask import (Flask, make_response, redirect, render_template, request,
send_from_directory, url_for)
from flask_login import (
LoginManager, UserMixin, current_user, login_required, login_user,
logout_user)
from flask_sqlalchemy import SQLAlchemy
from flask_app.edutatar import (check_login, facultative_info, get_diary,
get_home_params, login_edu, my_facultatives,
my_stars)
from flask_app.forms import LoginForm
dotenv.load_dotenv()
application = Flask(__name__)
application.config['SECRET_KEY'] = os.environ['SECRET_KEY']
application.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = True
# region DATABASE SETUP
application.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///database.db'
db = SQLAlchemy(application)
class User(UserMixin, db.Model):
id = db.Column(db.Integer, primary_key=True)
login = db.Column(db.String, nullable=False, unique=True)
avatar = db.Column(db.String)
name = db.Column(db.String, nullable=False)
password = db.Column(db.String, nullable=False)
db.create_all()
# endregion
# region LOGIN SETUP
login_manager = LoginManager()
login_manager.init_app(application)
@login_manager.user_loader
def load_user(user_id):
return User.query.get(user_id)
@login_manager.unauthorized_handler
def unauthorized_callback():
return redirect('/login?next=' + request.path)
sessions = {}
for user in User.query.all():
s = requests.Session()
login_edu(s, user.login, user.password)
sessions[user.login] = s
def reload_sessions():
print('sessions reloading')
for user in User.query.all():
s = requests.Session()
login_edu(s, user.login, user.password)
sessions[user.login] = s
scheduler = BackgroundScheduler()
scheduler.add_job(func=reload_sessions, trigger='interval', seconds=900)
scheduler.start()
atexit.register(lambda: scheduler.shutdown())
# endregion
@application.route('/sw.js', methods=['GET'])
def sw():
response = make_response(send_from_directory('static', 'sw.js'))
response.headers['Cache-Control'] = 'no-cache'
response.headers['Content-Type'] = 'application/javascript'
return response
@application.route('/', methods=['GET'])
@login_required
def index():
return redirect(url_for('diary'))
@application.route('/marks', methods=['GET'])
@login_required
def marks():
data = {'login': current_user.login,
'name': current_user.name, 'avatar': current_user.avatar}
stars = my_stars(sessions[current_user.login])
return render_template('marks.html', data=data, stars=stars[0], term=stars[1])
@application.route('/marks/<int:term>', methods=['GET'])
@login_required
def marks_with_term(term):
data = {'login': current_user.login,
'name': current_user.name, 'avatar': current_user.avatar}
stars = my_stars(sessions[current_user.login], term=term)
return render_template('marks.html', data=data, stars=stars[0], term=str(stars[1]))
@application.route('/facultatives', methods=['GET'])
@login_required
def facultatives():
data = {'login': current_user.login,
'name': current_user.name, 'avatar': current_user.avatar}
facs = my_facultatives(sessions[current_user.login])
return render_template('facultatives.html', data=data, facs=facs)
@application.route('/facultative/<int:index>')
@login_required
def facultative(index):
data = {'login': current_user.login,
'name': current_user.name, 'avatar': current_user.avatar}
info = facultative_info(sessions[current_user.login], index=index)
return render_template('facultative.html', data=data, info=info)
@application.route('/diary')
@login_required
def diary():
data = {'login': current_user.login,
'name': current_user.name, 'avatar': current_user.avatar}
diary = get_diary(sessions[current_user.login])
return render_template('diary.html', data=data, diary=diary[0], next_page=diary[1], prev_page=diary[2])
@application.route('/diary/<int:date>')
@login_required
def diary_with_date(date):
data = {'login': current_user.login,
'name': current_user.name, 'avatar': current_user.avatar}
diary = get_diary(
sessions[current_user.login], url=f'https://edu.tatar.ru/user/diary/week?date={date}')
return render_template('diary.html', data=data, diary=diary[0], next_page=diary[1], prev_page=diary[2])
@application.route('/login', methods=['GET', 'POST'])
def login():
form = LoginForm()
if form.validate_on_submit():
login = form.login.data
password = form.password.data
s = requests.Session()
login_edu(s, login, password)
code = check_login(s)
next = request.args.get('next')
if code:
user = User.query.filter_by(login=form.login.data).first()
if not user:
user_data = get_home_params(s)
if not user_data['avatar']:
user_data['avatar'] = url_for(
'static', filename='img/grayman.png')
new_user = User(
login=form.login.data, password=password, name=user_data[
'name'], avatar=user_data['avatar']
)
db.session.add(new_user)
db.session.commit()
login_user(new_user, remember=True)
login_edu(s, login, password)
sessions[new_user.login] = s
else:
login_user(user, remember=True)
return redirect(next or url_for('index'))
else:
return render_template('auth/login.html', form=form, message="Неправильный логин или пароль")
return render_template('auth/login.html', form=form)
@application.route('/logout')
@login_required
def logout():
logout_user()
return redirect(url_for('login'))
| 3,445 | 256 | 310 |
b7db7352d4a55e0347fe2a602885c256089ac79d | 62 | py | Python | Clase1/HolaNombre.py | JoseCordobaEAN/EstructurasDeDatosUE4P | 86a5c426d83d9d9ae86656c3c78324a1c07f608d | [
"MIT"
] | 2 | 2019-08-17T21:15:47.000Z | 2019-09-21T12:15:19.000Z | Clase1/HolaNombre.py | JoseCordobaEAN/EstructurasDeDatosUE4P | 86a5c426d83d9d9ae86656c3c78324a1c07f608d | [
"MIT"
] | null | null | null | Clase1/HolaNombre.py | JoseCordobaEAN/EstructurasDeDatosUE4P | 86a5c426d83d9d9ae86656c3c78324a1c07f608d | [
"MIT"
] | null | null | null | nombre = input("¿Cual es tu nombre?")
print(f'Hola {nombre}')
| 20.666667 | 37 | 0.66129 | nombre = input("¿Cual es tu nombre?")
print(f'Hola {nombre}')
| 0 | 0 | 0 |
011f123996b9652d7897969a4b6e12700739981a | 765 | py | Python | main/MouseAtlas.py | jimmayxu/scVI | 23cb7597e0b0677736a14c903c8053ad62c74938 | [
"MIT"
] | null | null | null | main/MouseAtlas.py | jimmayxu/scVI | 23cb7597e0b0677736a14c903c8053ad62c74938 | [
"MIT"
] | null | null | null | main/MouseAtlas.py | jimmayxu/scVI | 23cb7597e0b0677736a14c903c8053ad62c74938 | [
"MIT"
] | null | null | null | import os
os.getcwd()
import sys
sys.path.append('scvi')
import torch
import numpy as np
import pandas as pd
import scanpy as sc
save_path = "/lustre/scratch117/cellgen/team205/tpcg/backup/backup_20190401/sc_sclassification/CellTypist/data_repo/MouseAtlas/MouseAtlas.total.h5ad"
save_path2 = "/lustre/scratch117/cellgen/team205/tpcg/human_data/HumanAtlas.h5ad"
adata_mouse = sc.read_h5ad(save_path)
adata_human = sc.read_h5ad(save_path2)
# sc.read_10x_mtx
save_path = "/lustre/scratch117/cellgen/team205/zx3/pooled_2019-03-21"
adata = sc.read_10x_mtx(save_path)
import scvi
from dataset.anndata import AnnDataset
from inference import UnsupervisedTrainer
from models.vae import VAE
from typing import Tuple
import scvi
from models.modules import Encoder
| 22.5 | 149 | 0.816993 | import os
os.getcwd()
import sys
sys.path.append('scvi')
import torch
import numpy as np
import pandas as pd
import scanpy as sc
save_path = "/lustre/scratch117/cellgen/team205/tpcg/backup/backup_20190401/sc_sclassification/CellTypist/data_repo/MouseAtlas/MouseAtlas.total.h5ad"
save_path2 = "/lustre/scratch117/cellgen/team205/tpcg/human_data/HumanAtlas.h5ad"
adata_mouse = sc.read_h5ad(save_path)
adata_human = sc.read_h5ad(save_path2)
# sc.read_10x_mtx
save_path = "/lustre/scratch117/cellgen/team205/zx3/pooled_2019-03-21"
adata = sc.read_10x_mtx(save_path)
import scvi
from dataset.anndata import AnnDataset
from inference import UnsupervisedTrainer
from models.vae import VAE
from typing import Tuple
import scvi
from models.modules import Encoder
| 0 | 0 | 0 |
c7f59edb3a5c8d3619ad68f6b6967bea28d3ba40 | 3,792 | py | Python | shell/shell.py | kreusada/JackCogs | f1c5b30e0656cb9634fa8bb0d6b00aa7c2384c40 | [
"Apache-2.0"
] | null | null | null | shell/shell.py | kreusada/JackCogs | f1c5b30e0656cb9634fa8bb0d6b00aa7c2384c40 | [
"Apache-2.0"
] | null | null | null | shell/shell.py | kreusada/JackCogs | f1c5b30e0656cb9634fa8bb0d6b00aa7c2384c40 | [
"Apache-2.0"
] | null | null | null | # Copyright 2018-2021 Jakub Kuczys (https://github.com/jack1142)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import asyncio
import asyncio.subprocess as asp
import contextlib
from typing import Any, Dict, List, Literal
from redbot.core import commands
from redbot.core.bot import Red
from .errors import ProcessTerminatedEarly
from .utils import get_env, send_pages, wait_for_result
RequestType = Literal["discord_deleted_user", "owner", "user", "user_strict"]
class Shell(commands.Cog):
"""Run shell commands on bot's system from Discord."""
@commands.is_owner()
@commands.command()
async def shell(self, ctx: commands.Context, *, command: str) -> None:
"""Run shell command."""
await self._shell_command(ctx, command)
@commands.is_owner()
@commands.command()
async def shellq(self, ctx: commands.Context, *, command: str) -> None:
"""
Run shell command quietly.
If command's exit code is 0, `[p]shellq` will only send a tick reaction.
Otherwise, the result will be shown as with regular `[p]shell` command.
"""
await self._shell_command(ctx, command, send_message_on_success=False)
@commands.is_owner()
@commands.command()
async def killshells(self, ctx: commands.Context) -> None:
"""Kill all shell processes started by Shell cog."""
async with self._killing_lock:
for p in reversed(self.active_processes):
# in case some Process is still here after it terminated
if p.returncode is None:
p.kill()
self.active_processes.pop()
await ctx.send("Killed all active shell processes.")
| 35.111111 | 86 | 0.629483 | # Copyright 2018-2021 Jakub Kuczys (https://github.com/jack1142)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import asyncio
import asyncio.subprocess as asp
import contextlib
from typing import Any, Dict, List, Literal
from redbot.core import commands
from redbot.core.bot import Red
from .errors import ProcessTerminatedEarly
from .utils import get_env, send_pages, wait_for_result
RequestType = Literal["discord_deleted_user", "owner", "user", "user_strict"]
class Shell(commands.Cog):
"""Run shell commands on bot's system from Discord."""
def __init__(self, bot: Red) -> None:
self.bot = bot
self.active_processes: List[asp.Process] = []
self._killing_lock = asyncio.Lock()
async def red_get_data_for_user(self, *, user_id: int) -> Dict[str, Any]:
# this cog does not story any data
return {}
async def red_delete_data_for_user(
self, *, requester: RequestType, user_id: int
) -> None:
# this cog does not story any data
pass
@commands.is_owner()
@commands.command()
async def shell(self, ctx: commands.Context, *, command: str) -> None:
"""Run shell command."""
await self._shell_command(ctx, command)
@commands.is_owner()
@commands.command()
async def shellq(self, ctx: commands.Context, *, command: str) -> None:
"""
Run shell command quietly.
If command's exit code is 0, `[p]shellq` will only send a tick reaction.
Otherwise, the result will be shown as with regular `[p]shell` command.
"""
await self._shell_command(ctx, command, send_message_on_success=False)
async def _shell_command(
self,
ctx: commands.Context,
command: str,
*,
send_message_on_success: bool = True,
) -> None:
async with ctx.typing():
async with self._killing_lock:
p = await asp.create_subprocess_shell(
command, stdout=asp.PIPE, stderr=asp.STDOUT, env=get_env()
)
self.active_processes.append(p)
try:
output = await wait_for_result(p)
except ProcessTerminatedEarly as e:
output = e.partial_output
prefix = (
"**Command was terminated early and this is a partial output.**\n"
)
else:
prefix = ""
async with self._killing_lock:
with contextlib.suppress(ValueError):
self.active_processes.remove(p)
if not send_message_on_success and p.returncode == 0:
await ctx.tick()
else:
await send_pages(ctx, command=command, output=output, prefix=prefix)
@commands.is_owner()
@commands.command()
async def killshells(self, ctx: commands.Context) -> None:
"""Kill all shell processes started by Shell cog."""
async with self._killing_lock:
for p in reversed(self.active_processes):
# in case some Process is still here after it terminated
if p.returncode is None:
p.kill()
self.active_processes.pop()
await ctx.send("Killed all active shell processes.")
| 1,481 | 0 | 108 |
1aca8312e8b3a002362d2b49a0e848391cf47680 | 2,032 | py | Python | markov.py | AVMatthews/Outlet | c84765ef1288d206f09cdace5841d36192782875 | [
"CC0-1.0"
] | null | null | null | markov.py | AVMatthews/Outlet | c84765ef1288d206f09cdace5841d36192782875 | [
"CC0-1.0"
] | null | null | null | markov.py | AVMatthews/Outlet | c84765ef1288d206f09cdace5841d36192782875 | [
"CC0-1.0"
] | null | null | null | import re;
import random
import sys
# splitter = re.compile('.', re.MULTILINE)
if __name__ == '__main__':
k = int(sys.argv[1])
n = int(sys.argv[2])
ma = Markov(n)
for i in sys.argv[3:]:
ma.Feed(open(i, 'r').read())
#print ma.probs
#print ma.Dump(int(sys.argv[1]))
words, hits = ma.Generate(k, random.choice)
print ' '.join(words)
#print ''.join(words)
print>>sys.stderr, hits, 'hits', (100*float(hits)/k), '%'
| 32.774194 | 79 | 0.468012 | import re;
import random
import sys
class Markov(object):
splitter = re.compile(r'([\w\'!\?\.,<>="\'/:]+)')
# splitter = re.compile('.', re.MULTILINE)
def __init__(self, n = 1):
self.probs = {}
self.n = n
def Feed(self, str):
self.Update(map(lambda x: x.lower(), self.splitter.findall(str)))
#self.Update(map(lambda x: x.lower(), str))
def Update(self, words):
n = self.n
if len(words) < 2:
return
for idx in range(len(words)):
for i in range(1, n + 1):
if idx < i:
continue
vector = tuple(words[idx - i:idx])
self.probs.setdefault(vector, []).append(words[idx])
def Generate(self, k, choicef):
all_wls = self.probs.values()
word = choicef(choicef(all_wls))
result = [None]*k
hits = 0
for i in range(k):
result[i] = word
if i < self.n:
word = choicef(choicef(all_wls))
else:
vector = tuple(result[i - self.n + 1:i + 1])
while vector:
wl = self.probs.get(vector, [])
if wl:
hits += 1
word = choicef(wl)
#print 'prev:', vector, 'words:', len(wl), 'word', word
break
else:
vector = vector[1:]
else:
word = choicef(choicef(all_wls))
return result, hits
def Dump(self, k):
return ' '.join(self.Generate(k, random.choice)[0])
if __name__ == '__main__':
k = int(sys.argv[1])
n = int(sys.argv[2])
ma = Markov(n)
for i in sys.argv[3:]:
ma.Feed(open(i, 'r').read())
#print ma.probs
#print ma.Dump(int(sys.argv[1]))
words, hits = ma.Generate(k, random.choice)
print ' '.join(words)
#print ''.join(words)
print>>sys.stderr, hits, 'hits', (100*float(hits)/k), '%'
| 1,363 | 54 | 153 |
d05ba470162792a18c8b6797afd697c420796a34 | 3,882 | py | Python | batchflow/models/tf/pyramidnet.py | bestetc/batchflow | d2a843640383fbe860654236881483f755227e06 | [
"Apache-2.0"
] | 87 | 2018-11-16T08:04:12.000Z | 2022-03-24T20:08:44.000Z | batchflow/models/tf/pyramidnet.py | bestetc/batchflow | d2a843640383fbe860654236881483f755227e06 | [
"Apache-2.0"
] | 243 | 2018-11-29T02:03:55.000Z | 2022-02-21T08:28:29.000Z | batchflow/models/tf/pyramidnet.py | bestetc/batchflow | d2a843640383fbe860654236881483f755227e06 | [
"Apache-2.0"
] | 35 | 2019-01-29T14:26:14.000Z | 2021-12-30T01:39:02.000Z | """
Dongyoon Han et al. "`Deep Pyramidal Residual Networks
<https://arxiv.org/abs/1610.02915>`_"
"""
from . import ResNet
class PyramidNet(ResNet):
""" The base PyramidNet model
Notes
-----
This class is intended to define custom PyramidNets.
For more convenience use predefined :class:`.tf.PyramidNet18`, :class:`.tf.PyramidNet34`,
and others described down below.
**Configuration**
inputs : dict
dict with 'images' and 'labels' (see :meth:`~.TFModel._make_inputs`)
initial_block : dict
parameters for the initial block (see :func:`.conv_block`).
body : dict
num_blocks : list of int
number of blocks in each group with the same number of filters.
block : dict
widening : int
an increment of filters number in each block (default=8)
and other :class:`~.tf.ResNet` block params
head : dict
'Vdf' with dropout_rate=.4
Notes
-----
Also see :class:`~.TFModel` and :class:`~.tf.ResNet` configuration.
"""
@classmethod
def default_config(cls):
""" Define model defaults. See :meth: `~.TFModel.default_config` """
config = ResNet.default_config()
config['body/block/widening'] = 8
config['body/block/zero_pad'] = True
return config
@classmethod
def default_layout(cls, bottleneck, **kwargs):
""" Define conv block layout """
_ = kwargs
return 'nc nac nac n' if bottleneck else 'nc nac n'
def build_config(self, names=None):
""" Define model's architecture configuration. See :meth: `~.TFModel.build_config` """
config = super(ResNet, self).build_config(names)
if config.get('body/filters') is None:
w = config['body/block/widening']
filters = config['initial_block/filters']
config['body/filters'] = []
for g in config['body/num_blocks']:
bfilters = [filters + w * b for b in range(1, g + 1)]
filters = bfilters[-1]
config['body/filters'].append(bfilters)
if config.get('head/units') is None:
config['head/units'] = self.num_classes('targets')
if config.get('head/filters') is None:
config['head/filters'] = self.num_classes('targets')
return config
class PyramidNet18(PyramidNet):
""" 18-layer PyramidNet architecture """
@classmethod
class PyramidNet34(PyramidNet):
""" 34-layer PyramidNet architecture """
@classmethod
class PyramidNet50(PyramidNet):
""" 50-layer PyramidNet architecture with bottleneck blocks """
@classmethod
class PyramidNet101(PyramidNet):
""" 101-layer PyramidNet architecture with bottleneck blocks """
@classmethod
class PyramidNet152(PyramidNet):
""" 152-layer PyramidNet architecture with bottleneck blocks """
@classmethod
| 31.056 | 94 | 0.617723 | """
Dongyoon Han et al. "`Deep Pyramidal Residual Networks
<https://arxiv.org/abs/1610.02915>`_"
"""
from . import ResNet
class PyramidNet(ResNet):
""" The base PyramidNet model
Notes
-----
This class is intended to define custom PyramidNets.
For more convenience use predefined :class:`.tf.PyramidNet18`, :class:`.tf.PyramidNet34`,
and others described down below.
**Configuration**
inputs : dict
dict with 'images' and 'labels' (see :meth:`~.TFModel._make_inputs`)
initial_block : dict
parameters for the initial block (see :func:`.conv_block`).
body : dict
num_blocks : list of int
number of blocks in each group with the same number of filters.
block : dict
widening : int
an increment of filters number in each block (default=8)
and other :class:`~.tf.ResNet` block params
head : dict
'Vdf' with dropout_rate=.4
Notes
-----
Also see :class:`~.TFModel` and :class:`~.tf.ResNet` configuration.
"""
@classmethod
def default_config(cls):
""" Define model defaults. See :meth: `~.TFModel.default_config` """
config = ResNet.default_config()
config['body/block/widening'] = 8
config['body/block/zero_pad'] = True
return config
@classmethod
def default_layout(cls, bottleneck, **kwargs):
""" Define conv block layout """
_ = kwargs
return 'nc nac nac n' if bottleneck else 'nc nac n'
def build_config(self, names=None):
""" Define model's architecture configuration. See :meth: `~.TFModel.build_config` """
config = super(ResNet, self).build_config(names)
if config.get('body/filters') is None:
w = config['body/block/widening']
filters = config['initial_block/filters']
config['body/filters'] = []
for g in config['body/num_blocks']:
bfilters = [filters + w * b for b in range(1, g + 1)]
filters = bfilters[-1]
config['body/filters'].append(bfilters)
if config.get('head/units') is None:
config['head/units'] = self.num_classes('targets')
if config.get('head/filters') is None:
config['head/filters'] = self.num_classes('targets')
return config
class PyramidNet18(PyramidNet):
""" 18-layer PyramidNet architecture """
@classmethod
def default_config(cls):
config = PyramidNet.default_config()
config['body/num_blocks'] = [2, 2, 2, 2]
config['body/block/bottleneck'] = False
return config
class PyramidNet34(PyramidNet):
""" 34-layer PyramidNet architecture """
@classmethod
def default_config(cls):
config = PyramidNet.default_config()
config['body/num_blocks'] = [3, 4, 6, 3]
config['body/block/bottleneck'] = False
return config
class PyramidNet50(PyramidNet):
""" 50-layer PyramidNet architecture with bottleneck blocks """
@classmethod
def default_config(cls):
config = PyramidNet.default_config()
config['body/num_blocks'] = [3, 4, 6, 3]
config['body/block/bottleneck'] = True
return config
class PyramidNet101(PyramidNet):
""" 101-layer PyramidNet architecture with bottleneck blocks """
@classmethod
def default_config(cls):
config = PyramidNet.default_config()
config['body/num_blocks'] = [3, 4, 23, 3]
config['body/block/bottleneck'] = True
return config
class PyramidNet152(PyramidNet):
""" 152-layer PyramidNet architecture with bottleneck blocks """
@classmethod
def default_config(cls):
config = PyramidNet.default_config()
config['body/num_blocks'] = [3, 8, 36, 3]
config['body/block/bottleneck'] = True
return config
| 834 | 0 | 130 |
814151f26dafa0c3a95ca7fedafaed5b2414e027 | 18,627 | py | Python | MiNTiF_Utils/cnn/model_zoos/model_zoo_default.py | CAiM-lab/MiNTiF | b292670d3cc97f884fa02fb60229869df59fccbc | [
"MIT"
] | 2 | 2021-02-25T10:22:10.000Z | 2022-02-09T12:42:31.000Z | MiNTiF_Utils/cnn/model_zoos/model_zoo_default.py | CAiM-lab/MiNTiF | b292670d3cc97f884fa02fb60229869df59fccbc | [
"MIT"
] | null | null | null | MiNTiF_Utils/cnn/model_zoos/model_zoo_default.py | CAiM-lab/MiNTiF | b292670d3cc97f884fa02fb60229869df59fccbc | [
"MIT"
] | 2 | 2021-09-22T12:56:18.000Z | 2021-11-29T18:22:03.000Z | # (c) 2019-2021, Alvaro Gomariz @ ETH Zurich
# Computer-assisted Applications in Medicine (CAiM) Group, Prof. Orcun Goksel
import numpy as np
import tensorflow as tf
from cnn import layers_keras as clayers, losses, metrics_keras as cmetrics
import logging
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
class CustomModel:
"""
Base class to build other models on top
"""
optimizer = tf.keras.optimizers.Adam()
# optimizer = tf.keras.optimizers.Adam(0.000001)
activation = 'linear'
nlabels = 0
loss_name = 'crossentropy'
loss_weights = None
att_minput = ('att_domchcomb', 'att_chcomb', 'att_domchcomb_cust1', 'att_domchcomb_sm', 'att_domchcomb_cust1sm',
'att_domchcomb_cust2')
name_metrictrack = "Fscore_labelmean"
def __init__(self, msettings, class_weights=None, name='CustomModel', **kwargs):
"""
Class initialization
Parameters
----------
msettings : dict
dictionary describing the model settings. Normally loaded from an external .json file
class_weights : list or None
list of floats indicating the weight for each of the classes
name: str
name assigned to identify the created model
"""
# Data settings
self.name = name
self.msettings = msettings
self.msettings.update({
'activation': self.activation,
'nchannels': len(msettings['channels']),
'nlabels': len(msettings['labels']) + 1
})
self.class_weights = class_weights
self.patch_size = msettings['patch_size']
self.patch_size_out = msettings['patch_size_out']
self.nchannels = len(msettings['channels'])
self.input_aux = tf.keras.Input(shape=self.patch_size + [self.nchannels], name="input")
self.input_shape_aux = self.input_aux.shape
# self.input_shape = self.patch_size + [self.nchannels]
# Network settings
if 'nnNodes' in msettings:
self.nn_nodes = np.array(msettings['nnNodes'])
else:
self.nn_nodes = np.array([[64, 128, 256, 512, 1024], [1024, 512, 256, 128, 64]])
self.do_batch_norm = msettings['batchnorm']
self.padding = msettings['padding']
# Use extra label in segmentation (for bg)
extra_labels = 0 if (
('dataset_type' in self.msettings) and self.msettings['dataset_type'] in ('detection')) else 1
self.nlabels = len(msettings['labels']) + extra_labels
self.scale_factor = msettings['scale_factor']
self.ksize = msettings['kernel_size']
self.nlevels = msettings['nlevels']
# Graph settings
self._graph_built = False
self._model_built = False
# self.run_eagerly = system.eager_mode
# if self.run_eagerly:
# logger.warning("The model is running in eager mode, so it will be slower")
if not (self.activation in ('softmax', 'sigmoid')):
self.is_logits = True
def get_loss(self):
"""
Parses the string in self.loss_name to assign the proper loss
Returns
-------
class:`tf.losses.Loss`
loss function for the model
"""
if self.loss_name == 'dice':
return losses.DiceLoss(mode='macro_robust', only_foreground=True)
elif self.loss_name == 'crossentropy':
if self.class_weights is None:
self.class_weights = [1] * self.nlabels
logger.warning("class_weights should be declared for weighted cross entropy")
return losses.CrossEntropyWeighted(self.class_weights)
else:
raise Exception("The loss {} has not been implemented".format(self.loss_name))
def model_fromlayer(self, nlayer, name=None):
"""
Create a model from a specific layer
Parameters
----------
nlayer : int or str
layer number or name that can be recognized within the model
name str or None
name assigned to the newly created model
Returns
-------
class:`tf.keras.Model`
newly created model
"""
m_aux = self.build_model()
if isinstance(nlayer, int):
layer = m_aux.layers[nlayer]
elif isinstance(nlayer, str):
layer = m_aux.get_layer(nlayer)
else:
raise Exception("The layer type was not recognized")
return tf.keras.Model(inputs=self.input_aux, outputs=layer.output, name=name)
def build_model(self, name=None):
"""
Created a keras model from the layers stablished in this class
Parameters
----------
name str or None
name of the model
Returns
-------
class:`tf.keras.Model`
keras model
"""
name = name or self.name
model = tf.keras.Model(self.input_aux, self.call(self.input_aux), name=name)
return model
def build_metrics(self, do_idclass=False, **kwargs):
"""
Builds the custom metrics required for the defined mdoel
Parameters
----------
do_idclass: bool
Indicates if classes are treated separately (True) or aggregated together (False)
Returns
-------
class:`tf.keras.metrics.Metric`
Metrics for the model
"""
return cmetrics.get_confmetrics(self.nlabels, self.is_logits, do_idclass=do_idclass)
def get_vislayers(self, model=None, names=None):
"""
Defines the layers that will be visualized in tensorboard
Parameters
----------
model: class:`tf.keras.Model` or None
model from which the visualizations are to be obtained
names: list (of str) or None
names of the layers to be visualized
Returns
-------
class:`tf.keras.Model`
Keras model that outputs the different defined layers
"""
if names:
layers = [layer.output for layer in model.layers
if not isinstance(layer.output, list) and
any([n in layer.name for n in names])]
else:
layers = [layer.output for layer in model.layers
if not isinstance(layer.output, list) and len(layer.output.shape) > 3]
try:
mlayers = tf.keras.Model(inputs=model.input, outputs=layers)
except ValueError:
logger.warning("Layers could not be obtained for visualization")
mlayers = None
return mlayers
class UNet(CustomModel):
"""
Custom 2D U-Net model
References
----------
.. [1] Olaf Ronneberger, Philipp Fischer, Thomas Brox, *U-Net: Convolutional Networks for Biomedical Image Segmentation*, MICCAI 2015
"""
def __init__(self,
nodes_factor=2,
do_outlayer=True,
channel_gather=None,
chdrop=None,
drop_rate=0.5,
do_att=None,
pos_att=None,
nn_nodes=None,
ndims=2,
unsupervised_settings=None,
**kwargs):
"""
Class initialization
Parameters
----------
nodes_factor : int
Reduce the number of nodes in every layer by the factor indicated here
do_outlayer : bool
Employ a last layer that outputs the classes (True) or output logits (False)
channel_gather : int or None
If int, the channel corresponding to that id will be employed by the network
chdrop : str or None
If str, defines the type of MarkerSampling employed as defined in `cnn.layers_keras.lchdrop`
drop_rate : float
If chdrop is True, this indicated the sampling ratio
do_att : str or None
If str, defines the type of attention employed as defined in `cnn.layers_keras.get_attention_layer`
pos_att : list (of strs) or None
It indicates the parts of the UNet where attention is employed. If None, it is used in ('encoder', 'bottleneck', 'decoder')
nn_nodes : list (of lists (of ints)) or None
Defines the number of nodes in every layer of the model.
kwargs : key, value mappings
Other keyword arguments are passed through to class:`CustomModel`
"""
super().__init__(**kwargs)
self.do_outlayer = do_outlayer
if nn_nodes is not None:
self.nn_nodes = nn_nodes
else:
self.nn_nodes = self.nn_nodes // nodes_factor
if channel_gather is not None:
if chdrop:
raise Exception("chdrop and channel_gather cannot be used together")
self.do_channelgather = True
self.channelgather = clayers.ChannelGather(channel_gather)
chname = "BE" + str(channel_gather) + "_"
else:
self.do_channelgather = False
chname = ""
self.chdrop = chdrop
self.chdrop_multi = False
self.drop_rate = drop_rate
self.ndims = ndims
if self.chdrop is not None:
self.chdrop_multi = 'chcomb' in self.chdrop
self.drop_channels = clayers.lchdrop(self.chdrop, drop_rate)
if self.chdrop == 'att_chcomb':
self.drop_channels_att = clayers.ChCombAtt(name="ChDrop_auxAtt")
self.encoder = [None] * (self.nlevels)
self.encoder_skip = [None] * (self.nlevels - 1)
self.decoder = [None] * (self.nlevels - 1)
self.crop_encoder = [None] * (self.nlevels - 1)
self.concat = [tf.keras.layers.Concatenate() for _ in range(self.nlevels - 1)]
if ndims == 2:
self.down = [
tf.keras.layers.MaxPool2D(self.scale_factor, padding=self.padding,
name=chname + "downsample_level" + str(l))
for l in range(self.nlevels - 1)]
elif ndims == 3:
self.down = [
tf.keras.layers.MaxPool3D(self.scale_factor, padding=self.padding,
name=chname + "downsample_level" + str(l))
for l in range(self.nlevels - 1)]
else:
logger.error("Method not implemented for {} dimensions".format(ndims))
self.up = [clayers.upsampling_custom(
nodes=self.nn_nodes[1][l + 1],
scale_factor=self.scale_factor,
ndims=ndims,
name=chname + "upsample_level" + str(l)
) for l in range(self.nlevels - 1)]
self.do_att = do_att
self.act2 = None if self.do_att == 'att_chcomb_postact' else 'relu'
if self.do_att is None:
self.pos_att = {k: False for k in ('encoder', 'bottleneck', 'decoder')}
self.att_multi = False
else:
self.pos_att = pos_att or {k: True for k in ('encoder', 'bottleneck', 'decoder')}
self.att_multi = 'chcomb' in self.do_att
fatt = clayers.get_attention_layer(self.do_att)
# self.latt = [[
# fatt("SE_" + net + str(nl)) for nl in range(self.nlevels)
# ] for laux, net in zip((self.nlevels, self.nlevels - 1), ('e', 'd'))]
self.latt_enc = [
fatt("SE_enc_l" + str(nl)) for nl in range(self.nlevels - 1)
] if self.pos_att['encoder'] else None
self.latt_dec = [
fatt("SE_dec_l" + str(nl)) for nl in range(self.nlevels - 1)
] if self.pos_att['decoder'] else None
self.latt_bottleneck = fatt("SE_bn") if self.pos_att['bottleneck'] else None
self.encoder[0] = clayers.blockconv(self.nn_nodes[0][0],
do_batchnorm=self.do_batch_norm,
ksize=self.ksize,
padding=self.padding,
ndims=ndims,
name=chname + "BlockDown_level0",
rdrop=float(self.msettings["dropout"]))
for level in range(1, self.nlevels):
self.encoder[level] = clayers.blockconv(self.nn_nodes[0][level],
do_batchnorm=self.do_batch_norm,
ksize=self.ksize,
padding=self.padding,
activation2=self.act2,
ndims=ndims,
name=chname + "BlockDown_level" + str(level),
rdrop=float(self.msettings["dropout"]))
self.decoder[level - 1] = clayers.blockconv(self.nn_nodes[1][level],
do_batchnorm=self.do_batch_norm,
ksize=self.ksize,
padding=self.padding,
activation2=self.act2,
ndims=ndims,
name=chname + "BlockUp_level" + str(level),
rdrop=float(self.msettings["dropout"]))
self.crop_encoder[level - 1] = clayers.skipconnect(
level - 1, self.nlevels, scale_factor=self.scale_factor, padding=self.padding, ndims=ndims,
name=chname + "cropping_level" + str(level))
if self.do_outlayer:
if ndims == 2:
self.final_layer = tf.keras.layers.Conv2D(filters=self.nlabels,
kernel_size=(1, 1),
padding=self.padding,
activation=self.activation,
name=chname + 'output_layer')
elif ndims == 3:
self.final_layer = tf.keras.layers.Conv3D(filters=self.nlabels,
kernel_size=(1, 1, 1),
padding=self.padding,
activation=self.activation,
name=chname + 'output_layer')
else:
logger.error("Method not implemented for {} dimensions".format(ndims))
else:
self.final_layer = tf.keras.layers.Lambda(lambda x: x, name=chname + 'output_layer')
# @tf.function
class UNet_MarkerDrop_MarkerExcite(UNet2D):
"""
MS-ME model
"""
class MarkerSampling_MarkerExcite(UNet_MarkerDrop_MarkerExcite):
"""
Alias for UNet_MarkerDrop_MarkerExcite
"""
pass
class UNet3D(UNet):
"""
Custom 3D U-Net model
""" | 40.231102 | 137 | 0.543995 | # (c) 2019-2021, Alvaro Gomariz @ ETH Zurich
# Computer-assisted Applications in Medicine (CAiM) Group, Prof. Orcun Goksel
import numpy as np
import tensorflow as tf
from cnn import layers_keras as clayers, losses, metrics_keras as cmetrics
import logging
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
class CustomModel:
"""
Base class to build other models on top
"""
optimizer = tf.keras.optimizers.Adam()
# optimizer = tf.keras.optimizers.Adam(0.000001)
activation = 'linear'
nlabels = 0
loss_name = 'crossentropy'
loss_weights = None
att_minput = ('att_domchcomb', 'att_chcomb', 'att_domchcomb_cust1', 'att_domchcomb_sm', 'att_domchcomb_cust1sm',
'att_domchcomb_cust2')
name_metrictrack = "Fscore_labelmean"
def __init__(self, msettings, class_weights=None, name='CustomModel', **kwargs):
"""
Class initialization
Parameters
----------
msettings : dict
dictionary describing the model settings. Normally loaded from an external .json file
class_weights : list or None
list of floats indicating the weight for each of the classes
name: str
name assigned to identify the created model
"""
# Data settings
self.name = name
self.msettings = msettings
self.msettings.update({
'activation': self.activation,
'nchannels': len(msettings['channels']),
'nlabels': len(msettings['labels']) + 1
})
self.class_weights = class_weights
self.patch_size = msettings['patch_size']
self.patch_size_out = msettings['patch_size_out']
self.nchannels = len(msettings['channels'])
self.input_aux = tf.keras.Input(shape=self.patch_size + [self.nchannels], name="input")
self.input_shape_aux = self.input_aux.shape
# self.input_shape = self.patch_size + [self.nchannels]
# Network settings
if 'nnNodes' in msettings:
self.nn_nodes = np.array(msettings['nnNodes'])
else:
self.nn_nodes = np.array([[64, 128, 256, 512, 1024], [1024, 512, 256, 128, 64]])
self.do_batch_norm = msettings['batchnorm']
self.padding = msettings['padding']
# Use extra label in segmentation (for bg)
extra_labels = 0 if (
('dataset_type' in self.msettings) and self.msettings['dataset_type'] in ('detection')) else 1
self.nlabels = len(msettings['labels']) + extra_labels
self.scale_factor = msettings['scale_factor']
self.ksize = msettings['kernel_size']
self.nlevels = msettings['nlevels']
# Graph settings
self._graph_built = False
self._model_built = False
# self.run_eagerly = system.eager_mode
# if self.run_eagerly:
# logger.warning("The model is running in eager mode, so it will be slower")
if not (self.activation in ('softmax', 'sigmoid')):
self.is_logits = True
def get_loss(self):
"""
Parses the string in self.loss_name to assign the proper loss
Returns
-------
class:`tf.losses.Loss`
loss function for the model
"""
if self.loss_name == 'dice':
return losses.DiceLoss(mode='macro_robust', only_foreground=True)
elif self.loss_name == 'crossentropy':
if self.class_weights is None:
self.class_weights = [1] * self.nlabels
logger.warning("class_weights should be declared for weighted cross entropy")
return losses.CrossEntropyWeighted(self.class_weights)
else:
raise Exception("The loss {} has not been implemented".format(self.loss_name))
def compute_output_shape(self, input_shape=None):
# super(CustomModel, self).build(self.input_shape_aux)
return self.patch_size_out + [self.nlabels]
def get_summary_scalars(self, **kwargs):
return None
def call(self, inputs):
logger.warning("Calling wrong method")
return
def model_fromlayer(self, nlayer, name=None):
"""
Create a model from a specific layer
Parameters
----------
nlayer : int or str
layer number or name that can be recognized within the model
name str or None
name assigned to the newly created model
Returns
-------
class:`tf.keras.Model`
newly created model
"""
m_aux = self.build_model()
if isinstance(nlayer, int):
layer = m_aux.layers[nlayer]
elif isinstance(nlayer, str):
layer = m_aux.get_layer(nlayer)
else:
raise Exception("The layer type was not recognized")
return tf.keras.Model(inputs=self.input_aux, outputs=layer.output, name=name)
def build_model(self, name=None):
"""
Created a keras model from the layers stablished in this class
Parameters
----------
name str or None
name of the model
Returns
-------
class:`tf.keras.Model`
keras model
"""
name = name or self.name
model = tf.keras.Model(self.input_aux, self.call(self.input_aux), name=name)
return model
def build_metrics(self, do_idclass=False, **kwargs):
"""
Builds the custom metrics required for the defined mdoel
Parameters
----------
do_idclass: bool
Indicates if classes are treated separately (True) or aggregated together (False)
Returns
-------
class:`tf.keras.metrics.Metric`
Metrics for the model
"""
return cmetrics.get_confmetrics(self.nlabels, self.is_logits, do_idclass=do_idclass)
def set_metrics(self):
return {
"train": cmetrics.get_confmetrics(self.nlabels, self.is_logits),
"val": cmetrics.get_confmetrics(self.nlabels, self.is_logits),
"test": cmetrics.get_confmetrics(self.nlabels, self.is_logits)
}
def get_vislayers(self, model=None, names=None):
"""
Defines the layers that will be visualized in tensorboard
Parameters
----------
model: class:`tf.keras.Model` or None
model from which the visualizations are to be obtained
names: list (of str) or None
names of the layers to be visualized
Returns
-------
class:`tf.keras.Model`
Keras model that outputs the different defined layers
"""
if names:
layers = [layer.output for layer in model.layers
if not isinstance(layer.output, list) and
any([n in layer.name for n in names])]
else:
layers = [layer.output for layer in model.layers
if not isinstance(layer.output, list) and len(layer.output.shape) > 3]
try:
mlayers = tf.keras.Model(inputs=model.input, outputs=layers)
except ValueError:
logger.warning("Layers could not be obtained for visualization")
mlayers = None
return mlayers
class UNet(CustomModel):
"""
Custom 2D U-Net model
References
----------
.. [1] Olaf Ronneberger, Philipp Fischer, Thomas Brox, *U-Net: Convolutional Networks for Biomedical Image Segmentation*, MICCAI 2015
"""
def __init__(self,
nodes_factor=2,
do_outlayer=True,
channel_gather=None,
chdrop=None,
drop_rate=0.5,
do_att=None,
pos_att=None,
nn_nodes=None,
ndims=2,
unsupervised_settings=None,
**kwargs):
"""
Class initialization
Parameters
----------
nodes_factor : int
Reduce the number of nodes in every layer by the factor indicated here
do_outlayer : bool
Employ a last layer that outputs the classes (True) or output logits (False)
channel_gather : int or None
If int, the channel corresponding to that id will be employed by the network
chdrop : str or None
If str, defines the type of MarkerSampling employed as defined in `cnn.layers_keras.lchdrop`
drop_rate : float
If chdrop is True, this indicated the sampling ratio
do_att : str or None
If str, defines the type of attention employed as defined in `cnn.layers_keras.get_attention_layer`
pos_att : list (of strs) or None
It indicates the parts of the UNet where attention is employed. If None, it is used in ('encoder', 'bottleneck', 'decoder')
nn_nodes : list (of lists (of ints)) or None
Defines the number of nodes in every layer of the model.
kwargs : key, value mappings
Other keyword arguments are passed through to class:`CustomModel`
"""
super().__init__(**kwargs)
self.do_outlayer = do_outlayer
if nn_nodes is not None:
self.nn_nodes = nn_nodes
else:
self.nn_nodes = self.nn_nodes // nodes_factor
if channel_gather is not None:
if chdrop:
raise Exception("chdrop and channel_gather cannot be used together")
self.do_channelgather = True
self.channelgather = clayers.ChannelGather(channel_gather)
chname = "BE" + str(channel_gather) + "_"
else:
self.do_channelgather = False
chname = ""
self.chdrop = chdrop
self.chdrop_multi = False
self.drop_rate = drop_rate
self.ndims = ndims
if self.chdrop is not None:
self.chdrop_multi = 'chcomb' in self.chdrop
self.drop_channels = clayers.lchdrop(self.chdrop, drop_rate)
if self.chdrop == 'att_chcomb':
self.drop_channels_att = clayers.ChCombAtt(name="ChDrop_auxAtt")
self.encoder = [None] * (self.nlevels)
self.encoder_skip = [None] * (self.nlevels - 1)
self.decoder = [None] * (self.nlevels - 1)
self.crop_encoder = [None] * (self.nlevels - 1)
self.concat = [tf.keras.layers.Concatenate() for _ in range(self.nlevels - 1)]
if ndims == 2:
self.down = [
tf.keras.layers.MaxPool2D(self.scale_factor, padding=self.padding,
name=chname + "downsample_level" + str(l))
for l in range(self.nlevels - 1)]
elif ndims == 3:
self.down = [
tf.keras.layers.MaxPool3D(self.scale_factor, padding=self.padding,
name=chname + "downsample_level" + str(l))
for l in range(self.nlevels - 1)]
else:
logger.error("Method not implemented for {} dimensions".format(ndims))
self.up = [clayers.upsampling_custom(
nodes=self.nn_nodes[1][l + 1],
scale_factor=self.scale_factor,
ndims=ndims,
name=chname + "upsample_level" + str(l)
) for l in range(self.nlevels - 1)]
self.do_att = do_att
self.act2 = None if self.do_att == 'att_chcomb_postact' else 'relu'
if self.do_att is None:
self.pos_att = {k: False for k in ('encoder', 'bottleneck', 'decoder')}
self.att_multi = False
else:
self.pos_att = pos_att or {k: True for k in ('encoder', 'bottleneck', 'decoder')}
self.att_multi = 'chcomb' in self.do_att
fatt = clayers.get_attention_layer(self.do_att)
# self.latt = [[
# fatt("SE_" + net + str(nl)) for nl in range(self.nlevels)
# ] for laux, net in zip((self.nlevels, self.nlevels - 1), ('e', 'd'))]
self.latt_enc = [
fatt("SE_enc_l" + str(nl)) for nl in range(self.nlevels - 1)
] if self.pos_att['encoder'] else None
self.latt_dec = [
fatt("SE_dec_l" + str(nl)) for nl in range(self.nlevels - 1)
] if self.pos_att['decoder'] else None
self.latt_bottleneck = fatt("SE_bn") if self.pos_att['bottleneck'] else None
self.encoder[0] = clayers.blockconv(self.nn_nodes[0][0],
do_batchnorm=self.do_batch_norm,
ksize=self.ksize,
padding=self.padding,
ndims=ndims,
name=chname + "BlockDown_level0",
rdrop=float(self.msettings["dropout"]))
for level in range(1, self.nlevels):
self.encoder[level] = clayers.blockconv(self.nn_nodes[0][level],
do_batchnorm=self.do_batch_norm,
ksize=self.ksize,
padding=self.padding,
activation2=self.act2,
ndims=ndims,
name=chname + "BlockDown_level" + str(level),
rdrop=float(self.msettings["dropout"]))
self.decoder[level - 1] = clayers.blockconv(self.nn_nodes[1][level],
do_batchnorm=self.do_batch_norm,
ksize=self.ksize,
padding=self.padding,
activation2=self.act2,
ndims=ndims,
name=chname + "BlockUp_level" + str(level),
rdrop=float(self.msettings["dropout"]))
self.crop_encoder[level - 1] = clayers.skipconnect(
level - 1, self.nlevels, scale_factor=self.scale_factor, padding=self.padding, ndims=ndims,
name=chname + "cropping_level" + str(level))
if self.do_outlayer:
if ndims == 2:
self.final_layer = tf.keras.layers.Conv2D(filters=self.nlabels,
kernel_size=(1, 1),
padding=self.padding,
activation=self.activation,
name=chname + 'output_layer')
elif ndims == 3:
self.final_layer = tf.keras.layers.Conv3D(filters=self.nlabels,
kernel_size=(1, 1, 1),
padding=self.padding,
activation=self.activation,
name=chname + 'output_layer')
else:
logger.error("Method not implemented for {} dimensions".format(ndims))
else:
self.final_layer = tf.keras.layers.Lambda(lambda x: x, name=chname + 'output_layer')
# @tf.function
def call(self, inputs):
x = inputs
if self.chdrop:
x = self.drop_channels(x)
redaxis = [1, 2] if self.ndims == 2 else [1, 2, 3]
x_chcomb = tf.cast(tf.not_equal(tf.reduce_sum(x, axis=redaxis), 0), tf.float32)
if self.chdrop_multi:
self.drop_channels_att([x, x_chcomb])
# l_skips = tf.TensorArray(dtype=tf.float32, size=self.nlevels - 1, infer_shape=False)
l_skips = [None] * (self.nlevels - 1)
if self.do_channelgather:
x = self.channelgather(x)
x = self.encoder[0](x)
for level, (fencoder, bdown) in enumerate(zip(self.encoder[1:], self.down)):
if self.pos_att['encoder']:
if self.att_multi:
xatt_aux = [x, x_chcomb]
else:
xatt_aux = x
x = self.latt_enc[level](xatt_aux)
# l_skips = l_skips.write(level, x)
l_skips[level] = x
x = bdown(x)
x = fencoder(x)
if self.pos_att['bottleneck']:
if self.att_multi:
xatt_aux = [x, x_chcomb]
else:
xatt_aux = x
x = self.latt_bottleneck(xatt_aux)
max_level = self.nlevels - 1
for level2, (fdecoder, bup, bconcat, skip_crop) in enumerate(zip(
self.decoder, self.up, self.concat, reversed(self.crop_encoder))):
max_level -= 1
# x_skip = l_skips.read(max_level)
x_skip = l_skips[max_level]
x = bup(x)
x_skip_crop = skip_crop(x_skip)
# x_skip_crop = clayers.crop_match(x_skip, x)
x = bconcat([x, x_skip_crop])
x = fdecoder(x)
if self.pos_att['decoder']:
if self.att_multi:
xatt_aux = [x, x_chcomb]
else:
xatt_aux = x
x = self.latt_dec[level2](xatt_aux)
xout = self.final_layer(x)
return xout
class UNet2D(UNet):
def __init__(self, ndims=2, **kwargs):
super(UNet2D, self).__init__(ndims=ndims, **kwargs)
class UNet_MarkerDrop_MarkerExcite(UNet2D):
"""
MS-ME model
"""
def __init__(self, **kwargs):
super(UNet_MarkerDrop_MarkerExcite, self).__init__(do_att="att_chcomb", chdrop="nonorm", **kwargs)
class MarkerSampling_MarkerExcite(UNet_MarkerDrop_MarkerExcite):
"""
Alias for UNet_MarkerDrop_MarkerExcite
"""
pass
class UNet3D(UNet):
"""
Custom 3D U-Net model
"""
def __init__(self, **kwargs):
super(UNet3D, self).__init__(ndims=3, nodes_factor=4, **kwargs)
self.name_metrictrack = 'fscore'
def get_loss(self):
return tf.keras.losses.MeanSquaredError()
def build_metrics(self, data_set=None, **kwargs):
if data_set == 'train':
return [tf.keras.metrics.MeanSquaredError()]
else:
return [cmetrics.CellMetrics()] | 3,025 | -2 | 292 |
bd11547a58a12b55ae74731078e2652183703ae4 | 3,559 | py | Python | src/euros/en.py | ThbtSprt/euros | 750be3d31eab995b162a916234351e47a2dc27fa | [
"MIT"
] | 5 | 2021-06-11T08:32:35.000Z | 2021-08-13T15:48:22.000Z | src/euros/en.py | ThbtSprt/euros | 750be3d31eab995b162a916234351e47a2dc27fa | [
"MIT"
] | 1 | 2021-07-24T13:47:15.000Z | 2021-07-24T17:19:55.000Z | src/euros/en.py | ThbtSprt/euros | 750be3d31eab995b162a916234351e47a2dc27fa | [
"MIT"
] | null | null | null | from re import sub
chiffres = ['','one','two','three','four','five','six','seven','eight','nine','ten','eleven','twelve','thirteen','fourteen','fifteen','sixteen','seventeen','eighteen','nineteen']
nombres = ['','ten','twenty','thirty','forty','fifty','sixty','seventy','eighty','ninety']
def formater(x):
"""
Function to clean up the string input and convert it into a float
One mandatory arg : int, float, Decimal, or str representing numbers
"""
if type(x)==str:
if ',' in x and len(x.split(',')[1])==3:
x=x.replace(',','')
elif ',' in x:
x=x.replace(',','.')
x=sub(r'\s*[a-zA-Z]*','',x)
x=round(float(x),2)
if x<0:
return str(0.00)
return str(x)
def unite(x):
"""
Converts x into letters when int(x)<1000
One mandatory argument : any string input representing an integer < 1000
"""
x = str(x)
if len(x)==2:
x= '0'+x
elif len(x) ==1:
x = '00'+x
elif len(x) !=3:
return ''
if int(x[-2:])<20:
dizaines = chiffres[int(x[-2:])]
elif int(x[-1])==0:
dizaines = nombres[int(x[-2])]
else:
dizaines = nombres[int(x[-2])]+'-'+chiffres[int(x[-1])]
if x[0] == '0':
return dizaines
else:
centaines = chiffres[int(x[0])]+' hundred'
if dizaines!='':
centaines+=' and '
return centaines+dizaines
def nombre2lettres(x):
"""
This function converts an integer into letters.
It is called twice by conv() : firstly, for the integer part of the input, and secondly for the decimals if there are some.
"""
x=str(x)
if len(x)<=3:
total = unite(x)
else:
milliards, millions, milliers = '','',''
sp,sp2,sp3='','',''
if unite(x[-3:]) != '':#nécessité d'un espace avant les centaines
sp= ', '
if unite(x[-6:-3]) != '' and len(x)>6:#nécessité d'un espace avant les milliers
sp2 = ', '
if unite(x[-9:-6]) != '' and len(x)>9:#nécessité d'un espace avant les millions
sp3 = ', '
#MILLIERS
if x[-6:-3] == '000':
milliers = ''+sp+unite(x[-3:])
elif x == '1000':
milliers = 'a thousand'
else:
milliers = unite(x[-6:-3])+' thousand'+sp+unite(x[-3:])
#MILLIONS
if len(x)>6:
if x[-9:-6] == '000':
millions = ''
else:
millions = unite(x[-9:-6])+' million'
#MILLIARDS
if len(x)>9:
if x[-12:-9] == '000' or len(x)>12:
milliards = 'more than trillion'
else:
milliards = unite(x[-12:-9])+' billion'
#TOTAL
total=milliards+sp3+millions+sp2+milliers
return total
def conv(x):
"""
Principal function of this package :
It takes only one argument (int, float or str) and converts it into letters.
Example :
conv(10) returns 'ten euros'
"""
x=formater(x)
e,c = x.split('.')[0],x.split('.')[1]
if len(c)==1:
c=c+'0'
if int(c)==0:
c=''
elif int(c)==1:
c='one cent'
else:
c=nombre2lettres(c)+' cents'
if int(e)==0:
if c=='':
return 'zero euro'
else:
return c
elif int(e)==1:
e='one euro'
else:
e=nombre2lettres(e)+' euros'
if c=='':
return e
else:
return e+' and '+c
if __name__ == '__main__':
result=conv(str(input('Enter here the amount in figures :')))
print(result)
| 27.376923 | 178 | 0.503793 | from re import sub
chiffres = ['','one','two','three','four','five','six','seven','eight','nine','ten','eleven','twelve','thirteen','fourteen','fifteen','sixteen','seventeen','eighteen','nineteen']
nombres = ['','ten','twenty','thirty','forty','fifty','sixty','seventy','eighty','ninety']
def formater(x):
"""
Function to clean up the string input and convert it into a float
One mandatory arg : int, float, Decimal, or str representing numbers
"""
if type(x)==str:
if ',' in x and len(x.split(',')[1])==3:
x=x.replace(',','')
elif ',' in x:
x=x.replace(',','.')
x=sub(r'\s*[a-zA-Z]*','',x)
x=round(float(x),2)
if x<0:
return str(0.00)
return str(x)
def unite(x):
"""
Converts x into letters when int(x)<1000
One mandatory argument : any string input representing an integer < 1000
"""
x = str(x)
if len(x)==2:
x= '0'+x
elif len(x) ==1:
x = '00'+x
elif len(x) !=3:
return ''
if int(x[-2:])<20:
dizaines = chiffres[int(x[-2:])]
elif int(x[-1])==0:
dizaines = nombres[int(x[-2])]
else:
dizaines = nombres[int(x[-2])]+'-'+chiffres[int(x[-1])]
if x[0] == '0':
return dizaines
else:
centaines = chiffres[int(x[0])]+' hundred'
if dizaines!='':
centaines+=' and '
return centaines+dizaines
def nombre2lettres(x):
"""
This function converts an integer into letters.
It is called twice by conv() : firstly, for the integer part of the input, and secondly for the decimals if there are some.
"""
x=str(x)
if len(x)<=3:
total = unite(x)
else:
milliards, millions, milliers = '','',''
sp,sp2,sp3='','',''
if unite(x[-3:]) != '':#nécessité d'un espace avant les centaines
sp= ', '
if unite(x[-6:-3]) != '' and len(x)>6:#nécessité d'un espace avant les milliers
sp2 = ', '
if unite(x[-9:-6]) != '' and len(x)>9:#nécessité d'un espace avant les millions
sp3 = ', '
#MILLIERS
if x[-6:-3] == '000':
milliers = ''+sp+unite(x[-3:])
elif x == '1000':
milliers = 'a thousand'
else:
milliers = unite(x[-6:-3])+' thousand'+sp+unite(x[-3:])
#MILLIONS
if len(x)>6:
if x[-9:-6] == '000':
millions = ''
else:
millions = unite(x[-9:-6])+' million'
#MILLIARDS
if len(x)>9:
if x[-12:-9] == '000' or len(x)>12:
milliards = 'more than trillion'
else:
milliards = unite(x[-12:-9])+' billion'
#TOTAL
total=milliards+sp3+millions+sp2+milliers
return total
def conv(x):
"""
Principal function of this package :
It takes only one argument (int, float or str) and converts it into letters.
Example :
conv(10) returns 'ten euros'
"""
x=formater(x)
e,c = x.split('.')[0],x.split('.')[1]
if len(c)==1:
c=c+'0'
if int(c)==0:
c=''
elif int(c)==1:
c='one cent'
else:
c=nombre2lettres(c)+' cents'
if int(e)==0:
if c=='':
return 'zero euro'
else:
return c
elif int(e)==1:
e='one euro'
else:
e=nombre2lettres(e)+' euros'
if c=='':
return e
else:
return e+' and '+c
if __name__ == '__main__':
result=conv(str(input('Enter here the amount in figures :')))
print(result)
| 0 | 0 | 0 |
17845ad1df35ea63d566e55450a0df8d9184b04b | 6,077 | py | Python | data/external/repositories_2to3/197834/Kaggle_Avito-2015-master/model2.py | Keesiu/meta-kaggle | 87de739aba2399fd31072ee81b391f9b7a63f540 | [
"MIT"
] | null | null | null | data/external/repositories_2to3/197834/Kaggle_Avito-2015-master/model2.py | Keesiu/meta-kaggle | 87de739aba2399fd31072ee81b391f9b7a63f540 | [
"MIT"
] | null | null | null | data/external/repositories_2to3/197834/Kaggle_Avito-2015-master/model2.py | Keesiu/meta-kaggle | 87de739aba2399fd31072ee81b391f9b7a63f540 | [
"MIT"
] | 1 | 2019-12-04T08:23:33.000Z | 2019-12-04T08:23:33.000Z | '''
This script runs the ftrl-proximal model on data from combo.gl and extras.gl.
author: David Thaler
date: July 2015
'''
import avito2_io
from run_model import compute_offset
import sframes
from ftrl_proximal import ftrl_proximal
from hash_features import hash_features
from eval import logloss
from datetime import datetime
from math import log, exp, ceil
import os
import argparse
import numpy as np
import pandas as pd
import pdb
DROP_COLS = ['rowId','SearchID', 'isDS', 'isVal', 'isTest', 'IsClick','ID']
ROUND_COLS = ['HistCTR', 'cat_pos', 'spe_cat','spe_pos','sqe_cat','sqe_pos']
COL_NAMES = None
VAL_START = 0
TEST_START = 0
def chunk_iterator(combo, chunk_size = 50000, start=0):
'''
Returns a generator that yields chunks of data.
combo - the SFrame to draw from.
'''
global COL_NAMES
names = combo.column_names()
names.insert(0, 'rowId')
COL_NAMES = names
chunk_start = start
data_end = combo.shape[0]
chunk_end = min(data_end, start + chunk_size)
while chunk_start < data_end:
print('chunk [%d:%d]' % (chunk_start, chunk_end))
chunk = combo[chunk_start:chunk_end].to_dataframe()
chunk_start += chunk_size
chunk_end = min(data_end, chunk_end + chunk_size)
yield chunk
def select_train(chunk_it, all=True):
'''
A generator to filter rows from the other generator.
The val/test/ds sets are now files.
This filters train rows for full-train or all-data-train-val.
'''
for df in chunk_it:
if all: # all labeled data incl. usual val set
out = df[df.isTest == 0]
else: # all labeled data except val set
out = df[np.logical_and(df.isTest == 0, df.isVal == 0)]
if out.shape[0] > 0:
yield out
def train(data, alpha=0.1, beta=1.0, L1=0.0, L2=0.1, D=2**26):
'''
Runs one training pass.
'''
model = ftrl_proximal(alpha, beta, L1, L2, D, False)
for df in data:
for t in df.itertuples():
x, y = process_line(t, False)
f = hash_features(x, D)
p = model.predict(f)
model.update(f, p, y)
return model
def prepareSFrame(data):
'''
Call this on the SFrames before calling chunk_iterator, but after
adding in extra.gl, if necessary.
'''
data['SearchQuery'] = data['SearchQuery'].apply(lambda s : s.strip().lower()[:5])
data['ad_sq'] = data.apply(lambda x : x['SearchQuery'] + str(x['AdID']) if x['sqe'] else '_')
data['st_pos'] = data['Position'] + 0.1 * data['seenToday']
data['st_cat'] = data['CategoryID'] + 0.1 * data['seenToday']
data['st_spe'] = data['spe'] + 0.1 * data['seenToday']
data['st_sqe'] = data['sqe'] + 0.1 * data['seenToday']
data['st_ad'] = data['AdID'] + 0.1 * data['seenToday']
data['ad_pos'] = data['AdID'] + 0.1 * data['Position']
data['ad_spe'] = data['AdID'] + 0.1 * data['spe']
data['ad_sqe'] = data['AdID'] + 0.1 * data['sqe']
# Dead Code:
def extendSFrame(data):
'''
For now, call after prepareSFrame, and adding extras2.gl, is needed.
Later, we'll merge this in.
'''
# ucat == user_clicked_ad_today
data['ucat_pos'] = data['user_clicked_ad_today'] + 0.1 * data['Position']
data['ucat_cat'] = data['user_clicked_ad_today'] + 0.1 * data['CategoryID']
data['ucat_spe'] = data['user_clicked_ad_today'] + 0.1 * data['spe']
data['ucat_sqe'] = data['user_clicked_ad_today'] + 0.1 * data['sqe']
# NB: ucat_ad doesn't make sense
# uc = user_clicked_today
data['uc_pos'] = data['user_clicked_today'] + 0.1 * data['Position']
data['uc_cat'] = data['user_clicked_today'] + 0.1 * data['CategoryID']
data['uc_spe'] = data['user_clicked_today'] + 0.1 * data['spe']
data['uc_sqe'] = data['user_clicked_today'] + 0.1 * data['sqe']
data['uc_ad'] = data['user_clicked_today'] + 0.1 * data['AdID']
| 30.691919 | 96 | 0.611486 | '''
This script runs the ftrl-proximal model on data from combo.gl and extras.gl.
author: David Thaler
date: July 2015
'''
import avito2_io
from run_model import compute_offset
import sframes
from ftrl_proximal import ftrl_proximal
from hash_features import hash_features
from eval import logloss
from datetime import datetime
from math import log, exp, ceil
import os
import argparse
import numpy as np
import pandas as pd
import pdb
DROP_COLS = ['rowId','SearchID', 'isDS', 'isVal', 'isTest', 'IsClick','ID']
ROUND_COLS = ['HistCTR', 'cat_pos', 'spe_cat','spe_pos','sqe_cat','sqe_pos']
COL_NAMES = None
VAL_START = 0
TEST_START = 0
def chunk_iterator(combo, chunk_size = 50000, start=0):
'''
Returns a generator that yields chunks of data.
combo - the SFrame to draw from.
'''
global COL_NAMES
names = combo.column_names()
names.insert(0, 'rowId')
COL_NAMES = names
chunk_start = start
data_end = combo.shape[0]
chunk_end = min(data_end, start + chunk_size)
while chunk_start < data_end:
print('chunk [%d:%d]' % (chunk_start, chunk_end))
chunk = combo[chunk_start:chunk_end].to_dataframe()
chunk_start += chunk_size
chunk_end = min(data_end, chunk_end + chunk_size)
yield chunk
def select_train(chunk_it, all=True):
'''
A generator to filter rows from the other generator.
The val/test/ds sets are now files.
This filters train rows for full-train or all-data-train-val.
'''
for df in chunk_it:
if all: # all labeled data incl. usual val set
out = df[df.isTest == 0]
else: # all labeled data except val set
out = df[np.logical_and(df.isTest == 0, df.isVal == 0)]
if out.shape[0] > 0:
yield out
def process_line(t, isTest):
line = dict(list(zip(COL_NAMES, t)))
if isTest:
y = line['ID']
else:
y = line['IsClick']
for col in DROP_COLS:
line.pop(col)
for col in ROUND_COLS:
line[col] = round(line[col], 1)
line.pop('SearchParams')
line.pop('dt')
return line, y
def train(data, alpha=0.1, beta=1.0, L1=0.0, L2=0.1, D=2**26):
'''
Runs one training pass.
'''
model = ftrl_proximal(alpha, beta, L1, L2, D, False)
for df in data:
for t in df.itertuples():
x, y = process_line(t, False)
f = hash_features(x, D)
p = model.predict(f)
model.update(f, p, y)
return model
def validate(data, model, offset=0.0):
loss = 0.0
count = 0
for k, df in enumerate(data):
for t in df.itertuples():
count += 1
x, y = process_line(t, False)
f = hash_features(x, model.D)
dv = model.predict(f, False)
dv += offset
p = 1.0/(1.0 + exp(-dv))
loss += logloss(p, y)
return loss/count
def predict(data, model, offset=0.0):
out = []
for k, df in enumerate(data):
for t in df.itertuples():
x, id = process_line(t, True)
f = hash_features(x, model.D)
dv = model.predict(f, False)
dv += offset
p = 1.0/(1.0 + exp(-dv))
out.append((id, p))
return pd.DataFrame(out, columns=['ID','IsClick'])
def write_submission(submit_id, preds):
submit_name = 'submission%s.csv' % str(submit_id)
submit_path = os.path.join(avito2_io.SUBMIT, submit_name)
preds.to_csv(submit_path, index=False)
def val_run(tr,
val,
alpha=0.1,
beta=1.0,
L1=0.0,
L2=0.1,
D=2**26,
offset=-2.7):
start = datetime.now()
it = chunk_iterator(tr, chunk_size=50000)
print('training...')
m = train(it, alpha=alpha, beta=beta, L1=L1, L2=L2, D=D)
it = chunk_iterator(val, chunk_size=50000)
print('evaluating...')
loss = validate(it, m, offset)
print('loss: %.5f' % loss)
print('elapsed time: %s' % (datetime.now() - start))
return m
def prepareSFrame(data):
'''
Call this on the SFrames before calling chunk_iterator, but after
adding in extra.gl, if necessary.
'''
data['SearchQuery'] = data['SearchQuery'].apply(lambda s : s.strip().lower()[:5])
data['ad_sq'] = data.apply(lambda x : x['SearchQuery'] + str(x['AdID']) if x['sqe'] else '_')
data['st_pos'] = data['Position'] + 0.1 * data['seenToday']
data['st_cat'] = data['CategoryID'] + 0.1 * data['seenToday']
data['st_spe'] = data['spe'] + 0.1 * data['seenToday']
data['st_sqe'] = data['sqe'] + 0.1 * data['seenToday']
data['st_ad'] = data['AdID'] + 0.1 * data['seenToday']
data['ad_pos'] = data['AdID'] + 0.1 * data['Position']
data['ad_spe'] = data['AdID'] + 0.1 * data['spe']
data['ad_sqe'] = data['AdID'] + 0.1 * data['sqe']
def full_train():
combo = sframes.load('combo.gl')
extras = sframes.load('extras.gl')
combo.add_columms(extras)
prepareSFrame(combo)
cit = chunk_iterator(combo)
sit = select_train(cit, True)
model = train(sit)
return model
def run_test(model):
test = sframes.load('combo_test.gl')
prepareSFrame(test)
cit = chunk_iterator(test)
pred = predict(cit, model)
return pred
# Dead Code:
def extendSFrame(data):
'''
For now, call after prepareSFrame, and adding extras2.gl, is needed.
Later, we'll merge this in.
'''
# ucat == user_clicked_ad_today
data['ucat_pos'] = data['user_clicked_ad_today'] + 0.1 * data['Position']
data['ucat_cat'] = data['user_clicked_ad_today'] + 0.1 * data['CategoryID']
data['ucat_spe'] = data['user_clicked_ad_today'] + 0.1 * data['spe']
data['ucat_sqe'] = data['user_clicked_ad_today'] + 0.1 * data['sqe']
# NB: ucat_ad doesn't make sense
# uc = user_clicked_today
data['uc_pos'] = data['user_clicked_today'] + 0.1 * data['Position']
data['uc_cat'] = data['user_clicked_today'] + 0.1 * data['CategoryID']
data['uc_spe'] = data['user_clicked_today'] + 0.1 * data['spe']
data['uc_sqe'] = data['user_clicked_today'] + 0.1 * data['sqe']
data['uc_ad'] = data['user_clicked_today'] + 0.1 * data['AdID']
| 2,011 | 0 | 183 |
d295c9c8af4367b5caeee5f1b85613c99b24d519 | 159 | py | Python | python/ql/test/library-tests/modules/__all__/no_all.py | timoles/codeql | 2d24387e9e300bf03be35694816b1e76ae88a50c | [
"MIT"
] | 4,036 | 2020-04-29T00:09:57.000Z | 2022-03-31T14:16:38.000Z | python/ql/test/library-tests/modules/__all__/no_all.py | timoles/codeql | 2d24387e9e300bf03be35694816b1e76ae88a50c | [
"MIT"
] | 2,970 | 2020-04-28T17:24:18.000Z | 2022-03-31T22:40:46.000Z | python/ql/test/library-tests/modules/__all__/no_all.py | ScriptBox99/github-codeql | 2ecf0d3264db8fb4904b2056964da469372a235c | [
"MIT"
] | 794 | 2020-04-29T00:28:25.000Z | 2022-03-30T08:21:46.000Z | foo = "foo"
bar = "bar"
baz = "baz"
# When `__all__` is not defined, names starting with underscore is not imported with `from <module> import *`
_qux = "qux"
| 26.5 | 109 | 0.679245 | foo = "foo"
bar = "bar"
baz = "baz"
# When `__all__` is not defined, names starting with underscore is not imported with `from <module> import *`
_qux = "qux"
| 0 | 0 | 0 |
1bbc16675a2f5aea15cb82cb075fde95938a9438 | 7,942 | py | Python | deepstreampy/event.py | sapid/deepstreampy-twisted | 78025141bb0ac3aadc248d68f9273bf8993fc3d4 | [
"MIT"
] | 28 | 2016-06-16T08:25:28.000Z | 2022-03-03T06:48:13.000Z | deepstreampy/event.py | sapid/deepstreampy-twisted | 78025141bb0ac3aadc248d68f9273bf8993fc3d4 | [
"MIT"
] | 8 | 2017-05-23T01:38:09.000Z | 2020-03-18T09:21:49.000Z | deepstreampy/event.py | sapid/deepstreampy-twisted | 78025141bb0ac3aadc248d68f9273bf8993fc3d4 | [
"MIT"
] | 11 | 2017-04-25T20:12:24.000Z | 2020-04-21T13:28:32.000Z | """Deepstream event handling."""
from __future__ import absolute_import, division, print_function, with_statement
from __future__ import unicode_literals
from deepstreampy.constants import actions
from deepstreampy.constants import topic as topic_constants
from deepstreampy.constants import event as event_constants
from deepstreampy.message import message_parser
from deepstreampy.message import message_builder
from deepstreampy.utils import Listener
from deepstreampy.utils import AckTimeoutRegistry
from deepstreampy.utils import ResubscribeNotifier
from tornado import concurrent
from pyee import EventEmitter
class EventHandler(object):
"""Handles incoming and outgoing messages related to deepstream events.
"""
def subscribe(self, name, callback):
"""Subscribe to an event.
Adds a callback for both locally emited events as well as events emitted
by other clients.
Args:
name (str): The name of the event.
callback (callable): The function to call when an event is received.
"""
future = None
if not self._emitter.listeners(name):
self._ack_timeout_registry.add(name, actions.SUBSCRIBE)
future = self._connection.send_message(topic_constants.EVENT,
actions.SUBSCRIBE,
[name])
else:
future = concurrent.Future()
future.set_result(None)
self._emitter.on(name, callback)
return future
def unsubscribe(self, name, callback):
"""Unsubscribe from an event.
Removes the callback for the specified event, and notifies the server
of the change.
Args:
name (str): The name of the event
callback (callable): The callback to remove
"""
self._emitter.remove_listener(name, callback)
if not self._emitter.listeners(name):
self._ack_timeout_registry.add(name, actions.UNSUBSCRIBE)
return self._connection.send_message(topic_constants.EVENT,
actions.UNSUBSCRIBE,
[name])
future = concurrent.Future()
future.set_result(None)
return future
def emit(self, name, data):
"""Emit an event locally, and tell the server to broadcast it.
Other connected clients will also receive the event.
Args:
name (str): The name of the event.
data: JSON serializable data to send along with the event.
"""
future = self._connection.send_message(
topic_constants.EVENT, actions.EVENT,
[name, message_builder.typed(data)])
self._emitter.emit(name, data)
return future
def listen(self, pattern, callback):
"""Register as listener for event subscriptions from other clients.
Args:
pattern (str): Regular expression pattern to match subscriptions to
callback (callable): A function that will be called when an event
has been initially subscribed to or is no longer subscribed.
Expects the following arguments:
event_name (str)
is_subscribed (bool)
response (callable, callable)
"""
if (pattern in self._listener and
not self._listener[pattern].destroy_pending):
self._client._on_error(topic_constants.EVENT,
event_constants.LISTENER_EXISTS,
pattern)
future = concurrent.Future()
future.set_result(None)
return future
elif pattern in self._listener:
self._listener[pattern].destroy()
listener = Listener(topic_constants.EVENT,
pattern,
callback,
self._options,
self._client,
self._connection)
self._listener[pattern] = listener
return listener.send_future
def unlisten(self, pattern):
"""Stop listening to the specified pattern.
Remove a previously registered listening pattern. The client will no
longer be listening for active/inactive subscriptions.
Args:
pattern: The regular expression pattern to remove
"""
if pattern not in self._listener:
self._client._on_error(topic_constants.ERROR,
event_constants.NOT_LISTENING,
pattern)
future = concurrent.Future()
future.set_result(None)
return future
listener = self._listener[pattern]
if not listener.destroy_pending:
listener.send_destroy()
else:
self._ack_timeout_registry.add(pattern, actions.UNLISTEN)
listener.destroy()
del self._listener[pattern]
return listener.send_future
| 35.297778 | 80 | 0.568623 | """Deepstream event handling."""
from __future__ import absolute_import, division, print_function, with_statement
from __future__ import unicode_literals
from deepstreampy.constants import actions
from deepstreampy.constants import topic as topic_constants
from deepstreampy.constants import event as event_constants
from deepstreampy.message import message_parser
from deepstreampy.message import message_builder
from deepstreampy.utils import Listener
from deepstreampy.utils import AckTimeoutRegistry
from deepstreampy.utils import ResubscribeNotifier
from tornado import concurrent
from pyee import EventEmitter
class EventHandler(object):
"""Handles incoming and outgoing messages related to deepstream events.
"""
def __init__(self, connection, client, **options):
self._options = options
self._connection = connection
self._client = client
self._emitter = EventEmitter()
self._listener = {}
subscription_timeout = options.get("subscriptionTimeout", 15)
self._ack_timeout_registry = AckTimeoutRegistry(client,
topic_constants.EVENT,
subscription_timeout)
self._resubscribe_notifier = ResubscribeNotifier(client,
self._resubscribe)
def subscribe(self, name, callback):
"""Subscribe to an event.
Adds a callback for both locally emited events as well as events emitted
by other clients.
Args:
name (str): The name of the event.
callback (callable): The function to call when an event is received.
"""
future = None
if not self._emitter.listeners(name):
self._ack_timeout_registry.add(name, actions.SUBSCRIBE)
future = self._connection.send_message(topic_constants.EVENT,
actions.SUBSCRIBE,
[name])
else:
future = concurrent.Future()
future.set_result(None)
self._emitter.on(name, callback)
return future
def unsubscribe(self, name, callback):
"""Unsubscribe from an event.
Removes the callback for the specified event, and notifies the server
of the change.
Args:
name (str): The name of the event
callback (callable): The callback to remove
"""
self._emitter.remove_listener(name, callback)
if not self._emitter.listeners(name):
self._ack_timeout_registry.add(name, actions.UNSUBSCRIBE)
return self._connection.send_message(topic_constants.EVENT,
actions.UNSUBSCRIBE,
[name])
future = concurrent.Future()
future.set_result(None)
return future
def emit(self, name, data):
"""Emit an event locally, and tell the server to broadcast it.
Other connected clients will also receive the event.
Args:
name (str): The name of the event.
data: JSON serializable data to send along with the event.
"""
future = self._connection.send_message(
topic_constants.EVENT, actions.EVENT,
[name, message_builder.typed(data)])
self._emitter.emit(name, data)
return future
def listen(self, pattern, callback):
"""Register as listener for event subscriptions from other clients.
Args:
pattern (str): Regular expression pattern to match subscriptions to
callback (callable): A function that will be called when an event
has been initially subscribed to or is no longer subscribed.
Expects the following arguments:
event_name (str)
is_subscribed (bool)
response (callable, callable)
"""
if (pattern in self._listener and
not self._listener[pattern].destroy_pending):
self._client._on_error(topic_constants.EVENT,
event_constants.LISTENER_EXISTS,
pattern)
future = concurrent.Future()
future.set_result(None)
return future
elif pattern in self._listener:
self._listener[pattern].destroy()
listener = Listener(topic_constants.EVENT,
pattern,
callback,
self._options,
self._client,
self._connection)
self._listener[pattern] = listener
return listener.send_future
def unlisten(self, pattern):
"""Stop listening to the specified pattern.
Remove a previously registered listening pattern. The client will no
longer be listening for active/inactive subscriptions.
Args:
pattern: The regular expression pattern to remove
"""
if pattern not in self._listener:
self._client._on_error(topic_constants.ERROR,
event_constants.NOT_LISTENING,
pattern)
future = concurrent.Future()
future.set_result(None)
return future
listener = self._listener[pattern]
if not listener.destroy_pending:
listener.send_destroy()
else:
self._ack_timeout_registry.add(pattern, actions.UNLISTEN)
listener.destroy()
del self._listener[pattern]
return listener.send_future
def handle(self, message):
action = message['action']
data = message['data']
if action == actions.ACK:
name = message['data'][1]
else:
name = message['data'][0]
if action == actions.EVENT:
if data and len(data) == 2:
self._emitter.emit(
name, message_parser.convert_typed(data[1], self._client))
else:
self._emitter.emit(name)
return
if (action == actions.ACK and data[0] == actions.UNLISTEN and
(name in self._listener) and
self._listener[name].destroy_pending):
self._listener[name].destroy()
del self._listener[name]
return
elif name in self._listener:
self._listener[name]._on_message(message)
return
elif action in (actions.SUBSCRIPTION_FOR_PATTERN_REMOVED,
actions.SUBSCRIPTION_HAS_PROVIDER):
return
if action == actions.ACK:
self._ack_timeout_registry.clear(message)
return
if action == actions.ERROR:
if data[0] == event_constants.MESSAGE_DENIED:
self._ack_timeout_registry.remove(message['data'][1],
message['data'][2])
elif data[0] == event_constants.NOT_SUBSCRIBED:
self._ack_timeout_registry.remove(message['data'][1],
actions.UNSUBSCRIBE)
message['processedError'] = True
self._client._on_error(topic_constants.EVENT, data[0], data[1])
return
self._client._on_error(topic_constants.EVENT,
event_constants.UNSOLICITED_MESSAGE,
name)
def _resubscribe(self):
for event in self._emitter._events:
self._connection.send_message(topic_constants.EVENT,
actions.SUBSCRIBE,
[event])
| 2,690 | 0 | 81 |
0a692b3cd1f1f666974a59c77dda0ffc0636b6d0 | 12,132 | py | Python | research/cv/Yolact++/train.py | mindspore-ai/models | 9127b128e2961fd698977e918861dadfad00a44c | [
"Apache-2.0"
] | 77 | 2021-10-15T08:32:37.000Z | 2022-03-30T13:09:11.000Z | research/cv/Yolact++/train.py | mindspore-ai/models | 9127b128e2961fd698977e918861dadfad00a44c | [
"Apache-2.0"
] | 3 | 2021-10-30T14:44:57.000Z | 2022-02-14T06:57:57.000Z | research/cv/Yolact++/train.py | mindspore-ai/models | 9127b128e2961fd698977e918861dadfad00a44c | [
"Apache-2.0"
] | 24 | 2021-10-15T08:32:45.000Z | 2022-03-24T18:45:20.000Z | # Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
""" Train our Model """
import os
import argparse
import ast
from mindspore.communication.management import get_rank, get_group_size #
import mindspore.common.dtype as mstype
from mindspore import context, Tensor
from mindspore.communication.management import init
from mindspore.train import Model
from mindspore.context import ParallelMode
from mindspore.train.serialization import load_checkpoint, load_param_into_net
import mindspore.nn as nn
from mindspore.common import set_seed
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, TimeMonitor, SummaryCollector, Callback
from mindspore.common import initializer as init_p
from src.loss_monitor import LossMonitor
from src.yolact.layers.modules.loss import MultiBoxLoss
from src.yolact.yolactpp import Yolact
from src.config import yolact_plus_resnet50_config as cfg
from src.dataset import data_to_mindrecord_byte_image, create_yolact_dataset
from src.lr_schedule import dynamic_lr
from src.network_define import WithLossCell
set_seed(1)
parser = argparse.ArgumentParser(description="Yolact++ training")
parser.add_argument("--only_create_dataset", type=ast.literal_eval, default=False, help="If set it true, only create "
"Mindrecord, default is false.")
# Modelarts --run_distribute default is True
parser.add_argument("--run_distribute", type=ast.literal_eval, default=False, help="Run distribute, default is false.")
parser.add_argument("--do_train", type=ast.literal_eval, default=True, help="Do train or not, default is true.")
parser.add_argument("--do_eval", type=ast.literal_eval, default=False, help="Do eval or not, default is false.")
parser.add_argument("--dataset", type=str, default="coco", help="Dataset, default is coco.")
parser.add_argument("--pre_trained", type=str, default=None, help="Pretrain file path.")
parser.add_argument("--device_id", type=int, default=3, help="Device id, default is 0.")
parser.add_argument("--device_num", type=int, default=1, help="Use device nums, default is 1.")
parser.add_argument("--rank_id", type=int, default=0, help="Rank id, default is 0.")
parser.add_argument("--net_ckpt", type=str,
default="/data/yolact/yolact-20_619.ckpt", help="Do")
parser.add_argument("--run_platform", type=str, default="Ascend", choices="Ascend",
help="run platform, only support Ascend.")
parser.add_argument("--distribute", type=ast.literal_eval, default=False, help="Run distribute, default is False.")
parser.add_argument("--train_url", type=str, default="obs://xxx", help="ckpt output dir in obs") # Modelarts
parser.add_argument("--data_url", type=str, default="obs://xxx", help="mindrecord file path.") # Modelarts
parser.add_argument('--is_modelarts', type=str, default="False", help='is train on modelarts')
args_opt = parser.parse_args()
class TransferCallback(Callback):
"""Callback"""
if __name__ == '__main__':
print("Start train for yolact!")
if args_opt.run_platform == "Ascend":
if args_opt.is_modelarts == "True":
import moxing as mox
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=device_id,
max_call_depth=10000)
device_id = int(os.getenv('DEVICE_ID'), 0)
if not args_opt.do_eval and args_opt.run_distribute:
init()
rank = get_rank()
device_num = get_group_size()
context.set_auto_parallel_context(device_num=device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
gradients_mean=True)
else:
rank = 0
device_num = 1
else:
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", max_call_depth=10000,
enable_reduce_precision=True)
if args_opt.distribute:
if os.getenv("DEVICE_ID", "not_set").isdigit():
context.set_context(device_id=int(os.getenv("DEVICE_ID")))
init()
device_num = int(os.getenv("DEVICE_NUM"))
rank = int(os.getenv("RANK_ID"))
rank_size = int(os.getenv("RANK_SIZE"))
context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True,
device_num=device_num)
else:
rank = 0
device_num = 1
context.set_context(device_id=int(args_opt.device_id), save_graphs=True)
else:
raise ValueError("Unsupported platform.")
print("Start create dataset!")
if args_opt.is_modelarts == "True":
ckpt_filename = "resnet50.ckpt"
local_data_url = "/cache/mr/" + str(device_id)
local_pretrained_url = "/cache/weights/"
local_train_url = "/cache/ckpt"
mox.file.make_dirs(local_data_url)
mox.file.make_dirs(local_train_url)
mox.file.make_dirs(local_pretrained_url)
local_pretrained_url = local_pretrained_url + "resnet50.ckpt"
filename = "yolact.mindrecord0"
mox.file.copy_parallel(args_opt.data_url, local_data_url)
if args_opt.pre_trained is not None:
mox.file.copy(args_opt.pre_trained, local_pretrained_url)
local_data_path = os.path.join(local_data_url, filename)
else:
prefix = "yolact.mindrecord"
mindrecord_dir = cfg['mindrecord_dir']
mindrecord_file = os.path.join(mindrecord_dir, prefix + "0")
if rank == 0 and not os.path.exists(mindrecord_file):
if not os.path.isdir(mindrecord_dir):
os.makedirs(mindrecord_dir)
if args_opt.dataset == "coco":
if os.path.isdir(cfg['coco_root']):
print("Create Mindrecord.")
data_to_mindrecord_byte_image("coco", True, prefix)
print("Create Mindrecord Done, at {}".format(mindrecord_dir))
else:
raise Exception("coco_root not exits.")
else:
if os.path.isdir(cfg['IMAGE_DIR']) and os.path.exists(cfg['ANNO_PATH']):
print("Create Mindrecord.")
data_to_mindrecord_byte_image("other", True, prefix)
print("Create Mindrecord Done, at {}".format(mindrecord_dir))
else:
raise Exception("IMAGE_DIR or ANNO_PATH not exits.")
if not args_opt.only_create_dataset:
if args_opt.is_modelarts == "True":
dataset = create_yolact_dataset(local_data_path, batch_size=cfg['batch_size'],
device_num=device_num, rank_id=rank)
else:
dataset = create_yolact_dataset(mindrecord_file, batch_size=cfg['batch_size'],
device_num=device_num, rank_id=rank)
num_steps = dataset.get_dataset_size()
print("pre epoch step num: ", num_steps)
print("Create dataset done!")
net = Yolact()
net = net.set_train()
if args_opt.is_modelarts == "True":
ckpt_file_name = "resnet50.ckpt"
backbone_path = local_pretrained_url
if args_opt.pre_trained is not None:
param_dict = load_checkpoint(backbone_path)
if cfg['pretrain_epoch_size'] == 0:
for item in list(param_dict.keys()):
if not item.startswith('backbone'):
param_dict.pop(item)
load_param_into_net(net, param_dict)
init_weights(net)
if args_opt.is_modelarts == "False":
ckpt_path = args_opt.net_ckpt
if ckpt_path != "":
param_dict = load_checkpoint(ckpt_path)
load_param_into_net(net, param_dict)
loss = MultiBoxLoss(num_classes=cfg['num_classes'], pos_threshold=cfg['positive_iou_threshold'],
neg_threshold=cfg['negative_iou_threshold'], negpos_ratio=cfg['ohem_negpos_ratio'],
batch_size=cfg['batch_size'], num_priors=cfg['num_priors'])
net_with_loss = WithLossCell(net, loss)
lr = Tensor(dynamic_lr(cfg, start_epoch=0, total_epochs=cfg['epoch_size'], steps_each_epoch=num_steps),
mstype.float32)
opt = nn.Momentum(params=net.trainable_params(), learning_rate=cfg['lr'], momentum=cfg['momentum'],
weight_decay=cfg['decay'], loss_scale=cfg['loss_scale'])
# define model
if args_opt.is_modelarts == "True":
model = Model(net_with_loss, optimizer=opt, amp_level='O0')
else:
model = Model(net_with_loss, optimizer=opt, amp_level='O3')
print("============== Starting Training ==============")
time_cb = TimeMonitor(data_size=num_steps)
loss_cb = LossMonitor()
if args_opt.is_modelarts == "True":
summary_collector = SummaryCollector(summary_dir='./summary_dir', collect_freq=100)
else:
summary_collector = SummaryCollector(summary_dir='./summary_dir', collect_freq=10)
cb = [time_cb, loss_cb]
if args_opt.is_modelarts == "True":
if cfg['save_checkpoint']:
ckptconfig = CheckpointConfig(save_checkpoint_steps=cfg['save_checkpoint_epochs'] * num_steps,
keep_checkpoint_max=cfg['keep_checkpoint_max'])
ckpoint_cb = ModelCheckpoint(prefix='yolact', directory=local_train_url, config=ckptconfig)
transferCb = TransferCallback(local_train_url, args_opt.train_url)
if device_id == 0:
cb += [ckpoint_cb, transferCb]
model.train(cfg['epoch_size'], dataset, callbacks=cb, dataset_sink_mode=True)
else:
if cfg['save_checkpoint']:
ckptconfig = CheckpointConfig(save_checkpoint_steps=cfg['save_checkpoint_epochs'] * num_steps,
keep_checkpoint_max=cfg['keep_checkpoint_max'])
save_checkpoint_path = os.path.join(cfg['save_checkpoint_path'], 'ckpt_' + str(rank) + '/')
ckpoint_cb = ModelCheckpoint(prefix='yolact', directory=save_checkpoint_path, config=ckptconfig)
cb += [ckpoint_cb]
model.train(cfg['epoch_size'], dataset, callbacks=cb, dataset_sink_mode=False)
print("============== End Training ==============")
| 49.518367 | 119 | 0.639219 | # Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
""" Train our Model """
import os
import argparse
import ast
from mindspore.communication.management import get_rank, get_group_size #
import mindspore.common.dtype as mstype
from mindspore import context, Tensor
from mindspore.communication.management import init
from mindspore.train import Model
from mindspore.context import ParallelMode
from mindspore.train.serialization import load_checkpoint, load_param_into_net
import mindspore.nn as nn
from mindspore.common import set_seed
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, TimeMonitor, SummaryCollector, Callback
from mindspore.common import initializer as init_p
from src.loss_monitor import LossMonitor
from src.yolact.layers.modules.loss import MultiBoxLoss
from src.yolact.yolactpp import Yolact
from src.config import yolact_plus_resnet50_config as cfg
from src.dataset import data_to_mindrecord_byte_image, create_yolact_dataset
from src.lr_schedule import dynamic_lr
from src.network_define import WithLossCell
set_seed(1)
parser = argparse.ArgumentParser(description="Yolact++ training")
parser.add_argument("--only_create_dataset", type=ast.literal_eval, default=False, help="If set it true, only create "
"Mindrecord, default is false.")
# Modelarts --run_distribute default is True
parser.add_argument("--run_distribute", type=ast.literal_eval, default=False, help="Run distribute, default is false.")
parser.add_argument("--do_train", type=ast.literal_eval, default=True, help="Do train or not, default is true.")
parser.add_argument("--do_eval", type=ast.literal_eval, default=False, help="Do eval or not, default is false.")
parser.add_argument("--dataset", type=str, default="coco", help="Dataset, default is coco.")
parser.add_argument("--pre_trained", type=str, default=None, help="Pretrain file path.")
parser.add_argument("--device_id", type=int, default=3, help="Device id, default is 0.")
parser.add_argument("--device_num", type=int, default=1, help="Use device nums, default is 1.")
parser.add_argument("--rank_id", type=int, default=0, help="Rank id, default is 0.")
parser.add_argument("--net_ckpt", type=str,
default="/data/yolact/yolact-20_619.ckpt", help="Do")
parser.add_argument("--run_platform", type=str, default="Ascend", choices="Ascend",
help="run platform, only support Ascend.")
parser.add_argument("--distribute", type=ast.literal_eval, default=False, help="Run distribute, default is False.")
parser.add_argument("--train_url", type=str, default="obs://xxx", help="ckpt output dir in obs") # Modelarts
parser.add_argument("--data_url", type=str, default="obs://xxx", help="mindrecord file path.") # Modelarts
parser.add_argument('--is_modelarts', type=str, default="False", help='is train on modelarts')
args_opt = parser.parse_args()
class TransferCallback(Callback):
"""Callback"""
def __init__(self, local_train_path, obs_train_path):
super(TransferCallback, self).__init__()
self.local_train_path = local_train_path
self.obs_train_path = obs_train_path
def step_end(self, run_context):
cb_params = run_context.original_args()
current_epoch = cb_params.cur_epoch_num
if current_epoch % 10 == 0 and current_epoch != 0:
mox.file.copy_parallel(self.local_train_path, self.obs_train_path)
def init_weights(module):
for name, cell in module.cells_and_names():
is_conv_layer = isinstance(cell, nn.Conv2d)
if is_conv_layer and "backbone" not in name:
cell.weight.set_data(init_p.initializer('XavierUniform', cell.weight.shape))
if cell.has_bias is True:
cell.bias.set_data(init_p.initializer('zeros', cell.bias.shape))
if __name__ == '__main__':
print("Start train for yolact!")
if args_opt.run_platform == "Ascend":
if args_opt.is_modelarts == "True":
import moxing as mox
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=device_id,
max_call_depth=10000)
device_id = int(os.getenv('DEVICE_ID'), 0)
if not args_opt.do_eval and args_opt.run_distribute:
init()
rank = get_rank()
device_num = get_group_size()
context.set_auto_parallel_context(device_num=device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
gradients_mean=True)
else:
rank = 0
device_num = 1
else:
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", max_call_depth=10000,
enable_reduce_precision=True)
if args_opt.distribute:
if os.getenv("DEVICE_ID", "not_set").isdigit():
context.set_context(device_id=int(os.getenv("DEVICE_ID")))
init()
device_num = int(os.getenv("DEVICE_NUM"))
rank = int(os.getenv("RANK_ID"))
rank_size = int(os.getenv("RANK_SIZE"))
context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True,
device_num=device_num)
else:
rank = 0
device_num = 1
context.set_context(device_id=int(args_opt.device_id), save_graphs=True)
else:
raise ValueError("Unsupported platform.")
print("Start create dataset!")
if args_opt.is_modelarts == "True":
ckpt_filename = "resnet50.ckpt"
local_data_url = "/cache/mr/" + str(device_id)
local_pretrained_url = "/cache/weights/"
local_train_url = "/cache/ckpt"
mox.file.make_dirs(local_data_url)
mox.file.make_dirs(local_train_url)
mox.file.make_dirs(local_pretrained_url)
local_pretrained_url = local_pretrained_url + "resnet50.ckpt"
filename = "yolact.mindrecord0"
mox.file.copy_parallel(args_opt.data_url, local_data_url)
if args_opt.pre_trained is not None:
mox.file.copy(args_opt.pre_trained, local_pretrained_url)
local_data_path = os.path.join(local_data_url, filename)
else:
prefix = "yolact.mindrecord"
mindrecord_dir = cfg['mindrecord_dir']
mindrecord_file = os.path.join(mindrecord_dir, prefix + "0")
if rank == 0 and not os.path.exists(mindrecord_file):
if not os.path.isdir(mindrecord_dir):
os.makedirs(mindrecord_dir)
if args_opt.dataset == "coco":
if os.path.isdir(cfg['coco_root']):
print("Create Mindrecord.")
data_to_mindrecord_byte_image("coco", True, prefix)
print("Create Mindrecord Done, at {}".format(mindrecord_dir))
else:
raise Exception("coco_root not exits.")
else:
if os.path.isdir(cfg['IMAGE_DIR']) and os.path.exists(cfg['ANNO_PATH']):
print("Create Mindrecord.")
data_to_mindrecord_byte_image("other", True, prefix)
print("Create Mindrecord Done, at {}".format(mindrecord_dir))
else:
raise Exception("IMAGE_DIR or ANNO_PATH not exits.")
if not args_opt.only_create_dataset:
if args_opt.is_modelarts == "True":
dataset = create_yolact_dataset(local_data_path, batch_size=cfg['batch_size'],
device_num=device_num, rank_id=rank)
else:
dataset = create_yolact_dataset(mindrecord_file, batch_size=cfg['batch_size'],
device_num=device_num, rank_id=rank)
num_steps = dataset.get_dataset_size()
print("pre epoch step num: ", num_steps)
print("Create dataset done!")
net = Yolact()
net = net.set_train()
if args_opt.is_modelarts == "True":
ckpt_file_name = "resnet50.ckpt"
backbone_path = local_pretrained_url
if args_opt.pre_trained is not None:
param_dict = load_checkpoint(backbone_path)
if cfg['pretrain_epoch_size'] == 0:
for item in list(param_dict.keys()):
if not item.startswith('backbone'):
param_dict.pop(item)
load_param_into_net(net, param_dict)
init_weights(net)
if args_opt.is_modelarts == "False":
ckpt_path = args_opt.net_ckpt
if ckpt_path != "":
param_dict = load_checkpoint(ckpt_path)
load_param_into_net(net, param_dict)
loss = MultiBoxLoss(num_classes=cfg['num_classes'], pos_threshold=cfg['positive_iou_threshold'],
neg_threshold=cfg['negative_iou_threshold'], negpos_ratio=cfg['ohem_negpos_ratio'],
batch_size=cfg['batch_size'], num_priors=cfg['num_priors'])
net_with_loss = WithLossCell(net, loss)
lr = Tensor(dynamic_lr(cfg, start_epoch=0, total_epochs=cfg['epoch_size'], steps_each_epoch=num_steps),
mstype.float32)
opt = nn.Momentum(params=net.trainable_params(), learning_rate=cfg['lr'], momentum=cfg['momentum'],
weight_decay=cfg['decay'], loss_scale=cfg['loss_scale'])
# define model
if args_opt.is_modelarts == "True":
model = Model(net_with_loss, optimizer=opt, amp_level='O0')
else:
model = Model(net_with_loss, optimizer=opt, amp_level='O3')
print("============== Starting Training ==============")
time_cb = TimeMonitor(data_size=num_steps)
loss_cb = LossMonitor()
if args_opt.is_modelarts == "True":
summary_collector = SummaryCollector(summary_dir='./summary_dir', collect_freq=100)
else:
summary_collector = SummaryCollector(summary_dir='./summary_dir', collect_freq=10)
cb = [time_cb, loss_cb]
if args_opt.is_modelarts == "True":
if cfg['save_checkpoint']:
ckptconfig = CheckpointConfig(save_checkpoint_steps=cfg['save_checkpoint_epochs'] * num_steps,
keep_checkpoint_max=cfg['keep_checkpoint_max'])
ckpoint_cb = ModelCheckpoint(prefix='yolact', directory=local_train_url, config=ckptconfig)
transferCb = TransferCallback(local_train_url, args_opt.train_url)
if device_id == 0:
cb += [ckpoint_cb, transferCb]
model.train(cfg['epoch_size'], dataset, callbacks=cb, dataset_sink_mode=True)
else:
if cfg['save_checkpoint']:
ckptconfig = CheckpointConfig(save_checkpoint_steps=cfg['save_checkpoint_epochs'] * num_steps,
keep_checkpoint_max=cfg['keep_checkpoint_max'])
save_checkpoint_path = os.path.join(cfg['save_checkpoint_path'], 'ckpt_' + str(rank) + '/')
ckpoint_cb = ModelCheckpoint(prefix='yolact', directory=save_checkpoint_path, config=ckptconfig)
cb += [ckpoint_cb]
model.train(cfg['epoch_size'], dataset, callbacks=cb, dataset_sink_mode=False)
print("============== End Training ==============")
| 788 | 0 | 76 |
d9b0d046b1f7c5af9b84530342ad4cf9beb9cbd8 | 1,481 | py | Python | zksync_sdk/types/signatures.py | zksync-sdk/zksync-python | 740020b6c6b83548cf6cd2ec1b4af94316a74667 | [
"MIT"
] | 22 | 2021-03-05T07:01:05.000Z | 2022-03-26T19:15:19.000Z | zksync_sdk/types/signatures.py | zksync-sdk/zksync-python | 740020b6c6b83548cf6cd2ec1b4af94316a74667 | [
"MIT"
] | 23 | 2021-03-01T06:09:26.000Z | 2022-02-17T21:54:44.000Z | zksync_sdk/types/signatures.py | zksync-sdk/zksync-python | 740020b6c6b83548cf6cd2ec1b4af94316a74667 | [
"MIT"
] | 10 | 2021-03-08T13:43:49.000Z | 2021-08-23T16:18:14.000Z | from dataclasses import dataclass
from enum import Enum
@dataclass
@dataclass
| 25.101695 | 82 | 0.617151 | from dataclasses import dataclass
from enum import Enum
class SignatureType(Enum):
ethereum_signature = "EthereumSignature"
EIP1271_signature = "EIP1271Signature"
@dataclass
class TxEthSignature:
sig_type: SignatureType
signature: str
@classmethod
def from_dict(cls, json: dict):
"""
Only the difference from __init__ that signature is already in hex format
"""
obj = cls(sig_type=SignatureType(json["type"]), signature=b"")
obj.signature = json["signature"]
return obj
def __init__(self, sig_type: SignatureType, signature: bytes):
self.signature = signature.hex()
self.sig_type = sig_type
def dict(self):
return {
"type": self.sig_type.value,
"signature": self.signature
}
@dataclass
class TxSignature:
public_key: str
signature: str
@classmethod
def from_dict(cls, json: dict):
"""
Only the difference from __init__ is that values are already in hex format
"""
obj = cls(public_key=b"", signature=b"")
obj.public_key = json["pubKey"]
obj.signature = json["signature"]
return obj
def __init__(self, public_key: bytes, signature: bytes):
self.public_key = public_key.hex()
self.signature = signature.hex()
def dict(self):
return {
"pubKey": self.public_key,
"signature": self.signature
}
| 444 | 887 | 67 |
ac93c64ddf02c37ad59edc10df03a3fbb2005e63 | 5,860 | py | Python | model/Birdview/test.py | MinesNicaicai/large-scale-pointcloud-matching | cfe140f2be1110ed75b6edd27538021e513a31c9 | [
"MIT"
] | 1 | 2020-11-21T16:39:51.000Z | 2020-11-21T16:39:51.000Z | model/Birdview/test.py | MinesNicaicai/large-scale-pointcloud-matching | cfe140f2be1110ed75b6edd27538021e513a31c9 | [
"MIT"
] | null | null | null | model/Birdview/test.py | MinesNicaicai/large-scale-pointcloud-matching | cfe140f2be1110ed75b6edd27538021e513a31c9 | [
"MIT"
] | 1 | 2020-12-13T14:51:44.000Z | 2020-12-13T14:51:44.000Z | import argparse
import os
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
from PIL import Image
from torchvision.models import resnet18
from dataset import *
from netvlad import EmbedNet
from netvlad import NetVLAD
parser = argparse.ArgumentParser(description='WayzNetVlad')
# parser.add_argument('--mode', type=str, default='train', help='Mode', choices=['train', 'validation'])
# parser.add_argument('--batch_size', type=int, default=2, help='batch_size')
parser.add_argument('--dataset_dir', type=str, default='/media/admini/My_data/0921/dataset_cam4', help='dataset_dir')
parser.add_argument('--num_workers', type=int, default=1, help='num_workers')
# parser.add_argument('--from_scratch', type=bool, default=True, help='from_scratch')
# parser.add_argument('--pretrained_embedding', type=bool, default=True, help='pretrained_embedding')
# parser.add_argument('--num_similar_neg', type=int, default=2, help='num_similar_neg')
# parser.add_argument('--margin', type=float, default=1.0, help='margin')
parser.add_argument('--use_gpu', type=bool, default=True, help='use_gpu')
# parser.add_argument('--learning_rate', type=float, default=0.0005, help='learning_rate')
# parser.add_argument('--positive_radius', type=float, default=0.3, help='positive_search_radius')
# parser.add_argument('--negative_filter_radius', type=float, default=2.0, help='negative_filter_radius')
parser.add_argument('--saved_model_path', type=str, default='saved_model', help='saved_model')
# parser.add_argument('--epochs', type=int, default=10, help='epochs')
parser.add_argument('--load_checkpoints', type=bool, default=True, help='load_checkpoints')
parser.add_argument('--num_clusters', type=int, default=64, help='num_clusters')
# parser.add_argument('--images_dir', type=str, default='/media/admini/My_data/0921/dataset_cam4/images',
# help='images_dir')
parser.add_argument('--queried_image', type=str, default='/media/admini/My_data/0921/dataset_cam4/images/001041.png',
help='queried_image')
parser.add_argument('--generate_database', type=bool, default=False, help='generate_database')
args = parser.parse_args()
if __name__ == '__main__':
test()
| 47.258065 | 123 | 0.690785 | import argparse
import os
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
from PIL import Image
from torchvision.models import resnet18
from dataset import *
from netvlad import EmbedNet
from netvlad import NetVLAD
parser = argparse.ArgumentParser(description='WayzNetVlad')
# parser.add_argument('--mode', type=str, default='train', help='Mode', choices=['train', 'validation'])
# parser.add_argument('--batch_size', type=int, default=2, help='batch_size')
parser.add_argument('--dataset_dir', type=str, default='/media/admini/My_data/0921/dataset_cam4', help='dataset_dir')
parser.add_argument('--num_workers', type=int, default=1, help='num_workers')
# parser.add_argument('--from_scratch', type=bool, default=True, help='from_scratch')
# parser.add_argument('--pretrained_embedding', type=bool, default=True, help='pretrained_embedding')
# parser.add_argument('--num_similar_neg', type=int, default=2, help='num_similar_neg')
# parser.add_argument('--margin', type=float, default=1.0, help='margin')
parser.add_argument('--use_gpu', type=bool, default=True, help='use_gpu')
# parser.add_argument('--learning_rate', type=float, default=0.0005, help='learning_rate')
# parser.add_argument('--positive_radius', type=float, default=0.3, help='positive_search_radius')
# parser.add_argument('--negative_filter_radius', type=float, default=2.0, help='negative_filter_radius')
parser.add_argument('--saved_model_path', type=str, default='saved_model', help='saved_model')
# parser.add_argument('--epochs', type=int, default=10, help='epochs')
parser.add_argument('--load_checkpoints', type=bool, default=True, help='load_checkpoints')
parser.add_argument('--num_clusters', type=int, default=64, help='num_clusters')
# parser.add_argument('--images_dir', type=str, default='/media/admini/My_data/0921/dataset_cam4/images',
# help='images_dir')
parser.add_argument('--queried_image', type=str, default='/media/admini/My_data/0921/dataset_cam4/images/001041.png',
help='queried_image')
parser.add_argument('--generate_database', type=bool, default=False, help='generate_database')
args = parser.parse_args()
def test():
encoder = resnet18()
base_model = nn.Sequential(
encoder.conv1,
encoder.bn1,
encoder.relu,
encoder.maxpool,
encoder.layer1,
encoder.layer2,
encoder.layer3,
encoder.layer4
)
dim = list(base_model.parameters())[-1].shape[0] # last channels (512)
# Define model for embedding
net_vlad = NetVLAD(num_clusters=args.num_clusters, dim=dim, alpha=1.0)
model = EmbedNet(base_model, net_vlad)
# base_model_checkpoint = torch.load(os.path.join(args.saved_model_path, 'base_model.pth.tar'),
# map_location=lambda storage, loc: storage)
# net_vlad_checkpoint = torch.load(os.path.join(args.saved_model_path, 'net_vlad.pth.tar'),
# map_location=lambda storage, loc: storage)
# base_model.load_state_dict(base_model_checkpoint)
# net_vlad.load_state_dict(net_vlad_checkpoint)
model_checkpoint = torch.load(os.path.join(args.saved_model_path, 'model.pth.tar'),
map_location=lambda storage, loc: storage)
model.load_state_dict(model_checkpoint)
print("Loaded model checkpoints from \'{}\'.".format(args.saved_model_path))
# # torch.save(model.state_dict(), os.path.join(args.saved_model_path, 'model.pth.tar'))
#
# image_filenames = [os.path.join(args.images_dir, x) for x in os.listdir(args.images_dir) if
# ImageDatabase.is_image_file(x)]
images_info = make_images_info(args.dataset_dir, with_struct_file=False)
images_dir = os.path.join(args.dataset_dir, 'images')
if args.generate_database:
image_database = ImageDatabase(images_info=images_info, images_dir=images_dir, model=model, generate_database=True)
image_database.export_database('database_0921_cam4.npy')
else:
image_database = ImageDatabase(images_info=images_info, images_dir=images_dir, model=model,
generate_database=False)
image_database.import_database('database_0921_cam4.npy')
queried_image_filename = args.queried_image
queried_image_filenames = [
queried_image_filename
# '/media/admini/My_data/0923/dataset_xiaomi/images/1322.png',
# '/media/admini/My_data/0923/dataset_xiaomi/images/1433.png',
# '/media/admini/My_data/0923/dataset_xiaomi/images/1544.png',
# '/media/admini/My_data/0923/dataset_xiaomi/images/1655.png',
# '/media/admini/My_data/0923/dataset_xiaomi/images/1766.png',
# '/media/admini/My_data/0923/dataset_xiaomi/images/1877.png',
]
plotted_images = []
num_results = 5
for queried_image_filename in queried_image_filenames:
query_results = image_database.query_image(queried_image_filename, num_results=num_results)
# print('query_result: \n{}'.format(query_results))
queried_image = Image.open(queried_image_filename)
# result_image = Image.open(os.path.join(images_dir, query_results[0]['image_file']))
result_images = [Image.open(os.path.join(images_dir, result['image_file'])) for result in query_results]
plotted_images += [queried_image] + result_images
plot_images(plotted_images, num_results+1)
def plot_images(images, cols):
plt.figure()
# plt.subplot(1, 1, 1)
# plt.imshow(images[0])
for i in range(0, len(images)):
# row = i // cols
# col = i % cols
plt.subplot(len(images) // cols, cols, i + 1)
plt.imshow(images[i])
plt.show()
if __name__ == '__main__':
test()
| 3,607 | 0 | 46 |
e0a74c8b5c04dabcccb68f37533b7fb8c88dedad | 5,112 | py | Python | runtool/tests/test_transformations/test_recurse_config.py | arangatang/gluon-ts-tools | 26509b853ddf1039993779f6049eafd4ec434ff7 | [
"Apache-2.0"
] | null | null | null | runtool/tests/test_transformations/test_recurse_config.py | arangatang/gluon-ts-tools | 26509b853ddf1039993779f6049eafd4ec434ff7 | [
"Apache-2.0"
] | null | null | null | runtool/tests/test_transformations/test_recurse_config.py | arangatang/gluon-ts-tools | 26509b853ddf1039993779f6049eafd4ec434ff7 | [
"Apache-2.0"
] | null | null | null | from runtool.recurse_config import (
recursive_apply,
recursive_apply_dict,
recursive_apply_list,
Versions,
)
def transform(node: dict):
"""
Converts node to a version object if the node has a key "versions"
else it multiplies the node by 2 if the node has key "double"
"""
if "version" in node:
return Versions(node["version"])
elif "double" in node:
return 2 * node["double"]
return node
| 27.336898 | 70 | 0.385563 | from runtool.recurse_config import (
recursive_apply,
recursive_apply_dict,
recursive_apply_list,
Versions,
)
def transform(node: dict):
"""
Converts node to a version object if the node has a key "versions"
else it multiplies the node by 2 if the node has key "double"
"""
if "version" in node:
return Versions(node["version"])
elif "double" in node:
return 2 * node["double"]
return node
def compare_recursive_apply(node, expected, fn=transform):
assert recursive_apply(node, fn) == expected
def test_recursive_apply_double_simple():
compare_recursive_apply(
node={"double": 1},
expected=2,
)
def test_recursive_apply_double_nested():
compare_recursive_apply(
node={
"no_double": 2,
"double_this": {"double": 2},
},
expected={"no_double": 2, "double_this": 4},
)
def test_recursive_apply_versions():
compare_recursive_apply(
node={
"my_list": [
{"hello": "there"},
{"a": {"version": [1, 2]}},
]
},
expected=Versions(
[
{"my_list": [{"hello": "there"}, {"a": 1}]},
{"my_list": [{"hello": "there"}, {"a": 2}]},
]
),
)
def test_recursive_apply_trivial():
compare_recursive_apply({}, {})
def test_recursive_apply_merging_versions_simple():
compare_recursive_apply(
node=[Versions([1, 2])],
expected=Versions([[1], [2]]),
fn=lambda x: x,
)
def test_recursive_apply_merging_versions_list():
compare_recursive_apply(
node=[Versions([1, 2]), Versions([3, 4])],
expected=Versions([[1, 3], [1, 4], [2, 3], [2, 4]]),
fn=lambda x: x,
)
def test_recursive_apply_merging_versions_dict():
compare_recursive_apply(
node={"a": Versions([1, 2]), "b": Versions([3, 4])},
expected=Versions(
[
{"a": 1, "b": 3},
{"a": 1, "b": 4},
{"a": 2, "b": 3},
{"a": 2, "b": 4},
]
),
fn=lambda x: x,
)
def test_recursive_apply_merging_versions_list_in_dict():
compare_recursive_apply(
node={
"a": [Versions([1, 2]), Versions([3, 4])],
"b": [Versions([5, 6]), Versions([7, 8])],
},
expected=Versions(
[
{"a": [1, 3], "b": [5, 7]},
{"a": [1, 3], "b": [5, 8]},
{"a": [1, 3], "b": [6, 7]},
{"a": [1, 3], "b": [6, 8]},
{"a": [1, 4], "b": [5, 7]},
{"a": [1, 4], "b": [5, 8]},
{"a": [1, 4], "b": [6, 7]},
{"a": [1, 4], "b": [6, 8]},
{"a": [2, 3], "b": [5, 7]},
{"a": [2, 3], "b": [5, 8]},
{"a": [2, 3], "b": [6, 7]},
{"a": [2, 3], "b": [6, 8]},
{"a": [2, 4], "b": [5, 7]},
{"a": [2, 4], "b": [5, 8]},
{"a": [2, 4], "b": [6, 7]},
{"a": [2, 4], "b": [6, 8]},
]
),
fn=lambda x: x,
)
def test_recursive_apply_merging_versions_list_in_list():
compare_recursive_apply(
node=[
[Versions([1, 2]), Versions([3, 4])],
[Versions([5, 6]), Versions([7, 8])],
],
expected=Versions(
[
[[1, 3], [5, 7]],
[[1, 3], [5, 8]],
[[1, 3], [6, 7]],
[[1, 3], [6, 8]],
[[1, 4], [5, 7]],
[[1, 4], [5, 8]],
[[1, 4], [6, 7]],
[[1, 4], [6, 8]],
[[2, 3], [5, 7]],
[[2, 3], [5, 8]],
[[2, 3], [6, 7]],
[[2, 3], [6, 8]],
[[2, 4], [5, 7]],
[[2, 4], [5, 8]],
[[2, 4], [6, 7]],
[[2, 4], [6, 8]],
]
),
fn=lambda x: x,
)
def test_recursive_apply_merging_versions_dict_in_dict():
compare_recursive_apply(
node={"a": {"b": Versions([1, 2])}, "c": Versions([2, 3])},
expected=Versions(
[
{"a": {"b": 1}, "c": 2},
{"a": {"b": 1}, "c": 3},
{"a": {"b": 2}, "c": 2},
{"a": {"b": 2}, "c": 3},
]
),
fn=lambda x: x,
)
def test_recursive_apply_merging_versions_with_function():
compare_recursive_apply(
node={
"my_list": [
{"hello": "there"},
{"a": {"version": [1, 2]}},
{"b": {"version": [3, 4]}},
]
},
expected=Versions(
[
{"my_list": [{"hello": "there"}, {"a": 1}, {"b": 3}]},
{"my_list": [{"hello": "there"}, {"a": 1}, {"b": 4}]},
{"my_list": [{"hello": "there"}, {"a": 2}, {"b": 3}]},
{"my_list": [{"hello": "there"}, {"a": 2}, {"b": 4}]},
]
),
)
| 4,372 | 0 | 276 |
8b20124b5243f95d96250a4026b4f24c594f8ebb | 2,160 | py | Python | tube/etl/indexers/interpreter.py | chicagopcdc/tube | 547d021fe00aa25995e7e8d2eb2a57f5633395fd | [
"Apache-2.0"
] | null | null | null | tube/etl/indexers/interpreter.py | chicagopcdc/tube | 547d021fe00aa25995e7e8d2eb2a57f5633395fd | [
"Apache-2.0"
] | 2 | 2021-03-24T16:04:22.000Z | 2021-03-30T12:54:07.000Z | tube/etl/indexers/interpreter.py | chicagopcdc/tube | 547d021fe00aa25995e7e8d2eb2a57f5633395fd | [
"Apache-2.0"
] | null | null | null | import yaml
from .aggregation.translator import Translator as AggregatorTranslator
from tube.etl.indexers.injection.translator import Translator as InjectionTranslator
from .base.translator import Translator as BaseTranslator
from tube.utils.dd import init_dictionary
from tube.etl.outputs.es.writer import Writer
| 34.83871 | 84 | 0.668981 | import yaml
from .aggregation.translator import Translator as AggregatorTranslator
from tube.etl.indexers.injection.translator import Translator as InjectionTranslator
from .base.translator import Translator as BaseTranslator
from tube.utils.dd import init_dictionary
from tube.etl.outputs.es.writer import Writer
def create_translators(sc, config):
dictionary, model = init_dictionary(config.DICTIONARY_URL)
mappings = yaml.load(open(config.MAPPING_FILE), Loader=yaml.SafeLoader)
writer = Writer(sc, config)
translators = {}
for m in mappings["mappings"]:
if m["type"] == "aggregator":
translator = AggregatorTranslator(
sc, config.HDFS_DIR, writer, m, model, dictionary
)
elif m["type"] == "collector":
translator = InjectionTranslator(
sc, config.HDFS_DIR, writer, m, model, dictionary
)
else:
translator = BaseTranslator(sc, config.HDFS_DIR, writer)
translators[translator.parser.doc_type] = translator
for translator in list(translators.values()):
translator.update_types()
return translators
def run_transform(translators):
need_to_join = {}
translator_to_translators = {}
for translator in list(translators.values()):
df = translator.translate()
if df is None:
continue
translator.save_to_hadoop(df)
translator.current_step = 1
if len(translator.parser.joining_nodes) > 0:
need_to_join[translator.parser.doc_type] = translator
translator_to_translators[translator.parser.doc_type] = [
j.joining_index for j in translator.parser.joining_nodes
]
for v in list(need_to_join.values()):
df = v.translate_joining_props(translators)
v.save_to_hadoop(df)
v.current_step += 1
for t in list(translators.values()):
df = t.translate_final()
t.write(df)
def get_index_names(config):
stream = open(config.MAPPING_FILE)
mappings = yaml.load(stream, Loader=yaml.SafeLoader)
return [m["name"] for m in mappings["mappings"]]
| 1,774 | 0 | 69 |
07ac0d51e2432251bef3d9eac592921ad7a815a7 | 998 | py | Python | 5-python-dataviz-homework/plotting_charts_with_matplotlib.py | eduardohl/CEBD-1160-winter-2020-code | ffafffff5100b338b7a4842fc05abd7234732d8e | [
"MIT"
] | 1 | 2020-02-11T04:29:14.000Z | 2020-02-11T04:29:14.000Z | 5-python-dataviz-homework/plotting_charts_with_matplotlib.py | eduardohl/CEBD-1160-winter-2020-code | ffafffff5100b338b7a4842fc05abd7234732d8e | [
"MIT"
] | null | null | null | 5-python-dataviz-homework/plotting_charts_with_matplotlib.py | eduardohl/CEBD-1160-winter-2020-code | ffafffff5100b338b7a4842fc05abd7234732d8e | [
"MIT"
] | 5 | 2020-01-18T21:22:04.000Z | 2020-02-27T23:00:07.000Z | # 1 Still using the same DataFrame from the previous exercise insurance.csv plot the chart for charges and save it
# as charges_plot.png
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv('data/insurance.csv', header=0)
plt.plot(df['charges'])
plt.title('Charges Plot')
plt.ylabel('Charges')
plt.savefig('charges_plot.png')
# 2 plot the histogram for bmi and save it as bmi_hist.png
plt.clf()
plt.hist(df['bmi'])
plt.title('BMI Histogram')
plt.ylabel('BMI')
plt.savefig('bmi_hist.png')
# 3 plot the scatterplot for age vs charges and save it as age_charge_scatter.png
plt.clf()
plt.scatter(df['age'], df['charges'])
plt.title('Age vs Charges')
plt.xlabel('Age')
plt.ylabel('Charges')
plt.savefig('age_charge_scatter.png')
# 4 Re-do the previous items, adding the title, x label and y label for each item.
# done above
# 5 Think about the exercise 12 from the previous section. Do the plots match what we saw with the correlation function?
# Yes, charges are going up with age
| 32.193548 | 120 | 0.749499 | # 1 Still using the same DataFrame from the previous exercise insurance.csv plot the chart for charges and save it
# as charges_plot.png
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv('data/insurance.csv', header=0)
plt.plot(df['charges'])
plt.title('Charges Plot')
plt.ylabel('Charges')
plt.savefig('charges_plot.png')
# 2 plot the histogram for bmi and save it as bmi_hist.png
plt.clf()
plt.hist(df['bmi'])
plt.title('BMI Histogram')
plt.ylabel('BMI')
plt.savefig('bmi_hist.png')
# 3 plot the scatterplot for age vs charges and save it as age_charge_scatter.png
plt.clf()
plt.scatter(df['age'], df['charges'])
plt.title('Age vs Charges')
plt.xlabel('Age')
plt.ylabel('Charges')
plt.savefig('age_charge_scatter.png')
# 4 Re-do the previous items, adding the title, x label and y label for each item.
# done above
# 5 Think about the exercise 12 from the previous section. Do the plots match what we saw with the correlation function?
# Yes, charges are going up with age
| 0 | 0 | 0 |
a54c7c2864b2796866b44586d232abc572d83805 | 36,346 | py | Python | bank/database/operate.py | zpf0117b/BankingManagementSystem | 7e6c99e827bb0899d2389e8893311a557c336ea9 | [
"MIT"
] | 3 | 2020-09-03T12:39:21.000Z | 2021-06-16T02:37:54.000Z | bank/database/operate.py | zpf0117b/BankingManagementSystem | 7e6c99e827bb0899d2389e8893311a557c336ea9 | [
"MIT"
] | 4 | 2021-05-15T11:10:18.000Z | 2021-05-15T11:10:26.000Z | bank/database/operate.py | zpf0117b/BankingManagementSystem | 7e6c99e827bb0899d2389e8893311a557c336ea9 | [
"MIT"
] | null | null | null | import sys
from os import path
d = path.dirname(__file__) # 获取当前路径
sys.path.append(d)
from model import BankClient, BankName, Staff, Charge, Cheque, Loan, Lend, Payments, PossessCheque, Saving, PossessSaving, database
from datetime import date, datetime, timedelta
from peewee import DatabaseError, DataError, IntegrityError, InterfaceError, InternalError, NotSupportedError, OperationalError, ProgrammingError, fn, Alias
from random import randint
import pandas as pd
import pandas_profiling as pdp
# return: msg, info
# return msg, info
# return msg, info
# return msg, brief info
| 41.349261 | 313 | 0.615749 | import sys
from os import path
d = path.dirname(__file__) # 获取当前路径
sys.path.append(d)
from model import BankClient, BankName, Staff, Charge, Cheque, Loan, Lend, Payments, PossessCheque, Saving, PossessSaving, database
from datetime import date, datetime, timedelta
from peewee import DatabaseError, DataError, IntegrityError, InterfaceError, InternalError, NotSupportedError, OperationalError, ProgrammingError, fn, Alias
from random import randint
import pandas as pd
import pandas_profiling as pdp
class ClientOperate(object):
# return: msg, info
def __init__(self, branch_name, handle_client):
self.handle_client = handle_client
self.branch_name = branch_name
def get_client_info(self):
try:
# query2 = Charge.select(BankClient.client_id_number, BankClient.client_name, Charge.staff_id_number).join(BankClient,
# on=(Charge.client_id_number == BankClient.client_id_number)).where(Charge.branchbankname == self.branch_name)
query = BankClient.select().where(BankClient.client_id_number == self.handle_client).get()
except Exception as e:
msg1 = repr(e)
client_info = ['' for i in range(8)]
else:
client_info = [query.client_name, query.client_id_number, query.client_contact, query.client_address, query.contactor_name, query.relationship, query.contactor_phone, query.contactor_email]
msg1 = 'success'
finally:
pass
client_info.extend(['','',''])
try:
query2 = Charge.select().where((Charge.client_id_number == self.handle_client) & (Charge.branchbankname == self.branch_name))
except Charge.DoesNotExist as e:
msg2 = 'success'
except Exception as e:
msg2 = repr(e)
else:
for _query2 in query2:
if _query2.loan_or_account == 1:
client_info[10] = _query2.staff_id_number
elif _query2.loan_or_account == 2:
# 00: no account or loan ; 2: account; 1: cheque, 2: saving
if _query2.cheque_or_saving == 1:
client_info[9] = _query2.staff_id_number
elif _query2.cheque_or_saving == 2:
client_info[8] = _query2.staff_id_number
msg2 = 'success'
finally:
pass
msg = 'success' if msg1 == 'success' and msg2 == 'success' else msg1 + msg2
return msg, client_info
def insert_client_info(self, client_name, client_id, client_phone, client_address, contact_name, relationship, contact_phone, contact_email):
# necessary info complete and valid -> add
# you cannot use self.handle_client
if len(client_name) > 20 or '"' in client_name or "'" in client_name or client_id[0:2] != 'ID' or len(client_phone) != 11:
msg = '用户信息不合法'
elif len(contact_phone) != 11 or '@' not in contact_email or '"' in contact_name or "'" in contact_name:
msg = '联系人信息不合法'
else:
try:
client_item = BankClient.create(client_name=client_name, client_id_number=client_id, client_contact=client_phone, client_address=client_address, contactor_name=contact_name, contactor_phone=contact_phone,
contactor_email=contact_email, relationship=relationship)
except Exception as e:
msg = repr(e)
else:
try:
query1 = Staff.select().where(Staff.branch_bank_name == self.branch_name)
except Exception as e:
client_item.delete_instance()
msg = 'no staff, empty bank' + repr(e)
else:
choice_staff = randint(0, query1.count() - 1)
charge_item = Charge.create(branchbankname=self.branch_name, client_id_number=client_id, staff_id_number=query1[choice_staff].staff_id_number,
loan_or_account=0, cheque_or_saving=0)
charge_item.save()
msg = 'success'
finally:
print('insert_client_info: ' + msg)
return msg
def change_client_info(self, client_name, client_phone, client_address, contact_name, relationship, contact_phone, contact_email):
# not none and valid -> change
try:
# print(client_address)
# query2 = Charge.select(BankClient.client_id_number, BankClient.client_name, Charge.staff_id_number).join(BankClient,
# on=(Charge.client_id_number == BankClient.client_id_number)).where(Charge.branchbankname == self.branch_name)
query = BankClient.select().where(BankClient.client_id_number == self.handle_client).get()
except BankClient.DoesNotExist as e:
msg = 'client id not exist'
except Exception as e:
msg = repr(e)
else:
msg = 'success'
# just modify valid info, not raise exception, check whether saved by yourself
if client_name != '' and len(client_name) < 20 and '"' not in client_name and "'" not in client_name:
query.client_name = client_name
try:
query.save()
except Exception as e:
pass
if client_phone != '' and len(client_phone) == 11:
query.client_contact = client_phone
try:
query.save()
except Exception as e:
pass
if client_address != '':
query.client_address = client_address
try:
query.save()
except Exception as e:
pass
if contact_name != '' and '"' not in contact_name and "'" not in contact_name:
query.contactor_name = contact_name
try:
query.save()
except Exception as e:
pass
if relationship != '':
query.relationship = relationship
try:
query.save()
except Exception as e:
pass
if contact_phone != '' and len(contact_phone) == 11:
query.contactor_phone = contact_phone
try:
query.save()
except Exception as e:
pass
if contact_email != '' and '@' in contact_email:
query.contactor_email = contact_email
try:
query.save()
except Exception as e:
pass
finally:
print(msg)
return msg
def delete_client(self):
# you can delete -> delete
try:
query = Charge.select().where((Charge.client_id_number == self.handle_client) & (Charge.branchbankname == self.branch_name))
cursor = database.execute(query)
except Exception as e:
msg = repr(e)
else:
print(cursor.description)
if cursor.description[0][2] is None:
try:
BankClient.delete().where(BankClient.client_id_number == self.handle_client).execute()
except IntegrityError as e:
msg = repr(e)
except Exception as e:
msg = repr(e)
else:
msg = 'success'
finally:
pass
else:
msg = '客户有账户，不允许删除'
finally:
print(msg)
return msg
class AccountOperate(object):
# return msg, info
def __init__(self, branch_name, handle_account):
self.handle_account = handle_account
self.branch_name = branch_name
def get_account_info(self):
# msg, account_info, cheque_or_saving, client_in_account, client_notin_account
try:
# query2 = Charge.select(BankClient.client_id_number, BankClient.client_name, Charge.staff_id_number).join(BankClient,
# on=(Charge.client_id_number == BankClient.client_id_number)).where(Charge.branchbankname == self.branch_name)
query1 = Saving.select().where(Saving.account_number == self.handle_account).get()
except Saving.DoesNotExist as e:
try:
# query2 = Charge.select(BankClient.client_id_number, BankClient.client_name, Charge.staff_id_number).join(BankClient,
# on=(Charge.client_id_number == BankClient.client_id_number)).where(Charge.branchbankname == self.branch_name)
query2 = Cheque.select().where(Cheque.account_number == self.handle_account).get()
except Cheque.DoesNotExist as e:
msg = '账户信息读取失败'
cheque_or_saving = 0
client_in_account = [['', '']]
client_notin_account = [['']]
account_info = ['' for i in range(7)]
except Exception as e:
msg = repr(e)
cheque_or_saving = 0
client_in_account = [['', '']]
client_notin_account = [['']]
account_info = ['' for i in range(7)]
else:
account_info = [query2.account_number, '支票账户', query2.open_date, query2.remaining_sum, query2.overdraft, '', '']
cheque_or_saving = 1
client_in_account = []
client_notin_account = []
# SELECT PossessCheque ...
try:
query3 = PossessCheque.select().where(PossessCheque.account_number == self.handle_account)
except PossessCheque.DoesNotExist as e:
msg = '支票账户信息读取失败'
cheque_or_saving = 0
client_in_account.append(['', ''])
client_notin_account.append([''])
except Exception as e:
msg = repr(e)
cheque_or_saving = 0
client_in_account.append(['', ''])
client_notin_account.append([''])
else:
for _client in query3:
client_in_account.append([_client.client_id_number, _client.last_visit_cheque_date])
try:
query4 = BankClient.select().where(BankClient.client_id_number.not_in([_client.client_id_number for _client in query3]))
except BankClient.DoesNotExist as e:
msg = 'success'
client_notin_account.append([''])
except Exception as e:
msg = repr(e)
client_notin_account.append([''])
else:
for _client2 in query4:
client_notin_account.append([_client2.client_id_number, ''])
msg = 'success'
# SELECT BankClient ... NOT IN
except Exception as e:
msg = repr(e)
cheque_or_saving = 0
client_in_account = [['', '']]
client_notin_account = [['']]
account_info = ['' for i in range(7)]
else:
account_info = [query1.account_number, '储蓄账户', query1.open_date, query1.remaining_sum, '', query1.interest_rate, query1.currency]
cheque_or_saving = -1
client_in_account = []
client_notin_account = []
# SELECT PossessCheque ...
try:
query3 = PossessSaving.select().where(PossessSaving.account_number == self.handle_account)
except PossessSaving.DoesNotExist as e:
msg = '储蓄账户信息读取失败'
cheque_or_saving = 0
client_in_account.append(['', ''])
client_notin_account.append([''])
except Exception as e:
msg = repr(e)
cheque_or_saving = 0
client_in_account.append(['', ''])
client_notin_account.append([''])
else:
for _client in query3:
client_in_account.append([_client.client_id_number, _client.last_visit_saving_date])
try:
query4 = BankClient.select().where(BankClient.client_id_number.not_in([_client.client_id_number for _client in query3]))
except BankClient.DoesNotExist as e:
msg = 'success'
client_notin_account.append([''])
except Exception as e:
msg = repr(e)
client_notin_account.append([''])
else:
for _client in query4:
client_notin_account.append([_client.client_id_number, ''])
msg = 'success'
finally:
print(msg)
print('test',client_in_account, client_notin_account)
return msg, account_info, cheque_or_saving, client_in_account, client_notin_account
def insert_account_info(self, remaining_sum, client_in_account, account_number, open_date, cheque_or_saving, interest_rate, currency, overdraft):
# necessary info complete and valid -> add
# you cannot use self.handle_client
# ignore unnecessary info, opendate = visitdate
# check: one cheque and one saving for one client in one branch
# Cheque/Saving PossessCheque/PossessSaving random: Charge
if cheque_or_saving == 'saving':
if not str(interest_rate).replace('.','0').isdigit() or not str(remaining_sum).replace('.','0').isdigit():
msg = '储蓄账户信息不合法'
else:
# Saving
try:
saving_item = Saving.create(account_number=account_number, branch_bank_name=self.branch_name, currency=currency,
interest_rate=interest_rate, open_date=open_date, remaining_sum=remaining_sum)
except Exception as e:
msg = repr(e)
else:
# PossessSaving
try:
for _client in client_in_account:
possess_item = PossessSaving.create(account_number=account_number, client_id_number=_client, last_visit_saving_date=open_date)
except IntegrityError as e:
saving_item.delete_instance()
msg = '数据插入出错'
print(repr(e))
except Exception as e:
saving_item.delete_instance()
msg = repr(e)
else:
# Charge
try:
possess_charge = {}
for _client2 in client_in_account:
try:
query1 = Charge.select().where((Charge.branchbankname == self.branch_name) & (Charge.client_id_number == _client2) &
(Charge.loan_or_account == 0)).get()
except Exception as e:
saving_item.delete_instance()
msg = repr(e)
else:
# previous account
charge_item = Charge.create(branchbankname=self.branch_name, client_id_number=_client2, staff_id_number=query1.staff_id_number,
loan_or_account=2, cheque_or_saving=2)
possess_charge[_client2] = charge_item
except IntegrityError as e:
for _client3 in possess_charge.keys():
possess_charge[_client3].delete_instance()
saving_item.delete_instance()
msg = '唯一键检查出错'
print(repr(e))
except Exception as e:
for _client4 in possess_charge.keys():
possess_charge[_client4].delete_instance()
saving_item.delete_instance()
msg = repr(e)
print(repr(e))
else:
try:
query3 = BankName.select().where(BankName.branch_bank_name == self.branch_name).get()
query3.assets += float(remaining_sum)
query3.save()
except Exception as e:
for _client4 in possess_charge.keys():
possess_charge[_client4].delete_instance()
saving_item.delete_instance()
msg = repr(e)
print(repr(e))
else:
msg = 'success'
finally:
print('insert_saving_info: ' + msg)
return msg
else:
if not str(overdraft).replace('.','0').isdigit() or not str(remaining_sum).replace('.','0').isdigit():
msg = '支票账户信息不合法'
else:
# Saving
try:
cheque_item = Cheque.create(account_number=account_number, branch_bank_name=self.branch_name, overdraft=overdraft,
open_date=open_date, remaining_sum=remaining_sum)
except Exception as e:
msg = repr(e)
else:
# PossessSaving
try:
for _client in client_in_account:
possess_item = PossessCheque.create(account_number=account_number, client_id_number=_client, last_visit_cheque_date=open_date)
except IntegrityError as e:
cheque_item.delete_instance()
msg = '数据插入出错'
print(repr(e))
except Exception as e:
cheque_item.delete_instance()
msg = repr(e)
else:
# Charge
try:
possess_charge = {}
for _client2 in client_in_account:
try:
query1 = Charge.select().where((Charge.branchbankname == self.branch_name) & (Charge.client_id_number == _client2) &
(Charge.loan_or_account == 0)).get()
except Exception as e:
cheque_item.delete_instance()
msg = repr(e)
else:
charge_item = Charge.create(branchbankname=self.branch_name, client_id_number=_client2, staff_id_number=query1.staff_id_number,
loan_or_account=2, cheque_or_saving=1)
possess_charge[_client2] = charge_item
except IntegrityError as e:
for _client3 in possess_charge.keys():
possess_charge[_client3].delete_instance()
cheque_item.delete_instance()
msg = '唯一键检查出错'
print(repr(e))
except Exception as e:
for _client4 in possess_charge.keys():
possess_charge[_client4].delete_instance()
cheque_item.delete_instance()
msg = repr(e)
else:
try:
query3 = BankName.select().where(BankName.branch_bank_name == self.branch_name).get()
query3.assets += float(remaining_sum)
query3.save()
except Exception as e:
for _client4 in possess_charge.keys():
possess_charge[_client4].delete_instance()
cheque_item.delete_instance()
msg = repr(e)
else:
msg = 'success'
finally:
print('insert_cheque_info: ' + msg)
return msg
def change_account_info(self, remaining_sum, client_in_account, visit_date, interest_rate, overdraft):
# not none and valid -> change
# not none and valid -> change
print( remaining_sum, client_in_account, visit_date, interest_rate, overdraft)
try:
# query2 = Charge.select(BankClient.client_id_number, BankClient.client_name, Charge.staff_id_number).join(BankClient,
# on=(Charge.client_id_number == BankClient.client_id_number)).where(Charge.branchbankname == self.branch_name)
query1 = Saving.select().where(Saving.account_number == self.handle_account).get()
except Saving.DoesNotExist as e:
try:
# query2 = Charge.select(BankClient.client_id_number, BankClient.client_name, Charge.staff_id_number).join(BankClient,
# on=(Charge.client_id_number == BankClient.client_id_number)).where(Charge.branchbankname == self.branch_name)
query2 = Cheque.select().where(Cheque.account_number == self.handle_account).get()
except Exception as e:
msg = repr(e)
else:
msg = 'success'
if str(remaining_sum).replace('.', '0').isdigit() and remaining_sum != '':
diff = query2.remaining_sum - float(remaining_sum)
query2.remaining_sum = float(remaining_sum)
try:
query2.save()
query3 = BankName.select().where(BankName.branch_bank_name == self.branch_name).get()
query3.assets -= float(diff)
query3.save()
except Exception as e:
msg = repr(e)
print(repr(e))
else:
msg = 'success'
if str(overdraft).replace('.','0').isdigit() and overdraft != '':
query2.overdraft = float(overdraft)
try:
query2.save()
except Exception as e:
msg = repr(e)
i = 0
for _client in client_in_account:
if _client != '' and visit_date[i] != '':
try:
query4 = PossessCheque.select().where((PossessCheque.client_id_number == _client) & (PossessCheque.account_number == self.handle_account)).get()
except Exception as e:
pass
else:
query4.last_visit_cheque_date = visit_date[i]
query4.save()
i = i + 1
except Exception as e:
msg = repr(e)
else:
msg = 'success'
# just modify valid info, not raise exception, check whether saved by yourself
if str(remaining_sum).replace('.', '0').isdigit() and remaining_sum != '':
diff = query1.remaining_sum - float(remaining_sum)
query1.remaining_sum = float(remaining_sum)
try:
query1.save()
query3 = BankName.select().where(BankName.branch_bank_name == self.branch_name).get()
query3.assets -= float(diff)
query3.save()
except Exception as e:
msg = repr(e)
print(repr(e))
else:
msg = 'success'
if str(overdraft).replace('.','0').isdigit() and overdraft != '':
query1.overdraft = float(overdraft)
try:
query1.save()
except Exception as e:
msg = repr(e)
i = 0
for _client in client_in_account:
if _client != '' and visit_date[i] != '':
try:
query4 = PossessSaving.select().where((PossessSaving.client_id_number == _client) & (PossessSaving.account_number == self.handle_account)).get()
except Exception as e:
pass
else:
query4.last_visit_saving_date = visit_date[i]
query4.save()
i = i + 1
finally:
print(msg)
return msg
def delete_account(self):
# you can delete -> delete
try:
query1 = Saving.select().where(Saving.account_number == self.handle_account).get()
# cursor1 = database.execute(query1)
except Saving.DoesNotExist as e:
try:
query2 = Cheque.select().where(Cheque.account_number == self.handle_account).get()
# cursor2 = database.execute(query2)
except Exception as e:
msg = repr(e)
else:
try:
# delete cheque
these_client = PossessCheque.select().where(PossessCheque.account_number == self.handle_account)
cursor4 = database.execute(these_client)
try:
query4 = BankName.select().where(BankName.branch_bank_name == self.branch_name).get()
query4.assets -= float(query2.remaining_sum)
query4.save()
except Exception as e:
msg = repr(e)
else:
Cheque.delete().where(Cheque.account_number == self.handle_account).execute()
except Exception as e:
msg = repr(e)
print(msg)
else:
try:
# if no cheque of this client exists, delete from charge
for (account_number, client_id_number, last_visit_cheque_date) in cursor4:
print(account_number, client_id_number, last_visit_cheque_date)
try:
query3 = PossessCheque.select().where((PossessCheque.account_number != self.handle_account) & (PossessCheque.client_id_number == client_id_number)).get()
except PossessCheque.DoesNotExist as e:
try:
q = Charge.delete().where((Charge.client_id_number == client_id_number) & (Charge.branchbankname == self.branch_name) & (Charge.cheque_or_saving == 1)).execute()
except Exception as e:
msg = repr(e)
except Exception as e:
msg = repr(e)
else:
pass
# query = Charge.select().where()
# cursor = database.execute(query)
except Exception as e:
msg = repr(e)
else:
msg = 'success'
except Exception as e:
msg = repr(e)
else:
try:
# delete saving
these_client = PossessSaving.select().where(PossessSaving.account_number == self.handle_account)
cursor4 = database.execute(these_client)
try:
query4 = BankName.select().where(BankName.branch_bank_name == self.branch_name).get()
query4.assets -= float(query1.remaining_sum)
query4.save()
except Exception as e:
msg = repr(e)
else:
Saving.delete().where(Saving.account_number == self.handle_account).execute()
except Exception as e:
msg = repr(e)
else:
try:
# if no cheque of this client exists, delete from charge
for (account_number, client_id_number, last_visit_saving_date) in cursor4:
print(account_number, client_id_number, last_visit_saving_date)
try:
query3 = PossessSaving.select().where((PossessSaving.account_number != self.handle_account) & (PossessSaving.client_id_number == client_id_number)).get()
except PossessSaving.DoesNotExist as e:
try:
q = Charge.delete().where((Charge.client_id_number == client_id_number) & (Charge.branchbankname == self.branch_name) & (Charge.cheque_or_saving == 2)).execute()
except Exception as e:
msg = repr(e)
except Exception as e:
msg = repr(e)
else:
pass
# query = Charge.select().where()
# cursor = database.execute(query)
except Exception as e:
msg = repr(e)
else:
msg = 'success'
finally:
print(msg)
return msg
class LoanOperate(object):
# return msg, info
def __init__(self, branch_name, handle_loan):
self.handle_loan = handle_loan
self.branch_name = branch_name
def get_loan_info(self):
# return: msg, loan_info, client_in_loan, loan_payments
try:
# query2 = Charge.select(BankClient.client_id_number, BankClient.client_name, Charge.staff_id_number).join(BankClient,
# on=(Charge.client_id_number == BankClient.client_id_number)).where(Charge.branchbankname == self.branch_name)
query2 = (Payments.select().where(Payments.loan_number == self.handle_loan))
query = Loan.select().where(Loan.loan_number == self.handle_loan).get()
except Exception as e:
msg = repr(e)
loan_info = ['' for i in range(3)]
client_in_loan = []
loan_payments = ['', '', '']
else:
print(query,query2[:])
loan_info = [query.loan_number, query2.count(), query.issue_overall_amount]
print(loan_info)
msg = 'success'
loan_payments = []
for row in query2:
loan_payments.append([row.pay_date, row.pay_amount, 'NO' if row.pay_date.__ge__(date.today()) else 'YES'])
client_in_loan = []
try:
query3 = (Lend.select().where(Lend.loan_number == self.handle_loan))
except Exception as e:
msg = repr(e)
else:
for row in query3:
client_in_loan.append(row.client_id_number)
finally:
print(msg)
return msg, loan_info, client_in_loan, loan_payments
def insert_loan_info(self, loan_number, overall_issue_amount, overall_issue_time, add_loan_client, payments_date, payments_amount):
# necessary info complete and valid -> add
# you cannot use self.handle_client
# ignore unnecessary info, opendate = visitdate
# check: one cheque and one saving for one client in one branch
# loan lend payments charge
if not overall_issue_time.isdigit():
msg = '贷款信息不符1'
else:
overall_issue_time = int(overall_issue_time)
if overall_issue_time < 1 or not str(overall_issue_amount).replace('.', '0').isdigit() or len(payments_amount) != overall_issue_time or sum([float(payments_amount[i]) for i in range(overall_issue_time)]) != float(overall_issue_amount) or not all([str(x).replace('.', '0').isdigit() for x in payments_amount]):
print(overall_issue_time, str(overall_issue_amount).replace('.', '0').isdigit())
print(len(payments_amount) != overall_issue_time)
print(sum([float(payments_amount[i]) for i in range(overall_issue_time)]) != float(overall_issue_amount) )
print(all([str(x).replace('.', '0').isdigit() for x in payments_amount]))
msg = '贷款信息不符'
else:
issue_now_amount = sum([float(payments_amount[i]) if datetime.date(datetime.strptime(payments_date[i], '%Y-%m-%d')).__le__(date.today()) else 0 for i in range(overall_issue_time)])
try:
loan_item = Loan.create(branch_bank_name=self.branch_name, issue_now_amount=issue_now_amount, issue_overall_amount=float(overall_issue_amount), loan_number=loan_number)
except Exception as e:
msg = repr(e)
else:
# lend
try:
for _client in add_loan_client:
lend_item = Lend.create(loan_number=loan_number, client_id_number=_client)
except IntegrityError as e:
loan_item.delete_instance()
msg = '数据插入出错'
print(repr(e))
except Exception as e:
loan_item.delete_instance()
msg = repr(e)
else:
# payments
try:
for i in range(overall_issue_time):
payments_item = Payments.create(loan_number=loan_number,pay_amount=float(payments_amount[i]),pay_date=payments_date[i])
except Exception as e:
loan_item.delete_instance()
msg = repr(e)
else:
try:
possess_charge = {}
for _client2 in add_loan_client:
try:
query1 = Charge.select().where((Charge.branchbankname == self.branch_name) & (Charge.client_id_number == _client2) &
(Charge.loan_or_account == 0)).get()
except Exception as e:
loan_item.delete_instance()
msg = repr(e)
else:
# previous account
charge_item = Charge.create(branchbankname=self.branch_name, client_id_number=_client2, staff_id_number=query1.staff_id_number,
loan_or_account=1, cheque_or_saving=3)
possess_charge[_client2] = charge_item
except IntegrityError as e:
for _client3 in possess_charge.keys():
possess_charge[_client3].delete_instance()
loan_item.delete_instance()
msg = '唯一键检查出错'
print(repr(e))
except Exception as e:
for _client4 in possess_charge.keys():
possess_charge[_client4].delete_instance()
loan_item.delete_instance()
msg = repr(e)
print(repr(e))
else:
try:
query3 = BankName.select().where(BankName.branch_bank_name == self.branch_name).get()
query3.assets -= issue_now_amount
query3.save()
except Exception as e:
for _client4 in possess_charge.keys():
possess_charge[_client4].delete_instance()
loan_item.delete_instance()
msg = repr(e)
print(repr(e))
else:
msg = 'success'
finally:
print('insert_saving_info: ' + msg)
print(msg)
return msg
def delete_loan(self):
# you can delete -> delete
try:
these_client = Lend.select().where(Lend.loan_number == self.handle_loan)
cursor4 = database.execute(these_client)
this_loan = Loan.select().where(Loan.loan_number == self.handle_loan).get()
except Exception as e:
msg = repr(e)
else:
if this_loan.issue_now_amount == this_loan.issue_overall_amount:
try:
Loan.delete().where(Loan.loan_number == self.handle_loan).execute()
except Exception as e:
msg = repr(e)
else:
msg = 'success'
for (client_id_number, loan_number) in cursor4:
print(client_id_number, loan_number)
# try:
# query3 = Lend.select().where((Lend.loan_number != self.handle_loan) & (Lend.client_id_number == client_id_number)).get()
# except Lend.DoesNotExist as e:
try:
q = Charge.delete().where((Charge.client_id_number == client_id_number) & (Charge.branchbankname == self.branch_name) & (Charge.loan_or_account == 1)).execute()
except Exception as e:
msg = repr(e)
# except Exception as e:
# msg = repr(e)
# else:
# pass
else:
msg = '不允许删除'
finally:
print(msg)
return msg
class Overall(object):
# return msg, brief info
def __init__(self, branch_name):
self.branch_name = branch_name
def get_overall_bank(self):
banks = []
try:
query = BankName.select()
except Exception as e:
msg = repr(e)
else:
for branch in query:
banks.append([branch.branch_bank_name, branch.bank_city, branch.assets])
msg = 'success'
finally:
print(msg)
return msg, banks
def get_overall_client(self):
client = []
try:
query = BankClient.select()
except Exception as e:
msg = repr(e)
else:
for _client in query:
client.append([_client.client_name, _client.client_id_number, _client.contactor_phone])
msg = 'success'
finally:
print(msg)
return msg, client
def get_overall_account(self):
account = []
try:
query1 = Cheque.select()
query2 = Saving.select()
except Exception as e:
msg = repr(e)
else:
for chequeA in query1:
account.append([chequeA.branch_bank_name, chequeA.account_number, '支票'])
for savingA in query2:
account.append([savingA.branch_bank_name, savingA.account_number, '储蓄'])
msg = 'success'
finally:
print(msg)
return msg, account
def get_overall_loan(self):
loan = []
try:
query = Loan.select()
except Exception as e:
msg = repr(e)
else:
for _loan in query:
loan.append(['未开始发放' if _loan.issue_now_amount == 0 else ('发放中' if _loan.issue_now_amount < _loan.issue_overall_amount else '已全部发放'),
_loan.loan_number, _loan.branch_bank_name])
msg = 'success'
finally:
print(msg)
return msg, loan
def generate_dateframe(start_time, branch_name, sta_select):
end_time = date.today()
timetrial = {'M':pd.Series(pd.period_range(start=(datetime.date(datetime.strptime(start_time, '%Y-%m-%d'))-timedelta(days=31)), freq='M', periods=12)),
'Q': pd.Series(pd.period_range(start=(datetime.date(datetime.strptime(start_time, '%Y-%m-%d'))-timedelta(days=91)), freq='Q', periods=12)),
'Y':pd.Series(pd.period_range(start=(datetime.date(datetime.strptime(start_time, '%Y-%m-%d'))-timedelta(days=365)), freq='Y', periods=4))}
# a = .iloc[i].strftime('%Y-%m-%d')
# b = datetime.date(datetime.strptime(a, '%Y-%m-%d'))
# select between
index = timetrial[sta_select]
msg = 'success'
saving_amount = []
cheque_amount = []
loan_amount = []
overall_amount = []
business = []
for i in range(len(index)-1):
time_select = (datetime.date(datetime.strptime(index.iloc[i].strftime('%Y-%m-%d'), '%Y-%m-%d')), datetime.date(datetime.strptime(index.iloc[i + 1].strftime('%Y-%m-%d'), '%Y-%m-%d')))
new_cheque = [0, 0]
new_saving = [0, 0]
new_loan = [0, 0]
try:
query3 = Payments.select().where((Payments.pay_date >= time_select[0]) & (Payments.pay_date < time_select[1]))
except Payments.DoesNotExist as e:
msg = 'success'
except Exception as e:
msg = repr(e)
else:
for _loan in query3:
new_loan[0] += 1
new_loan[1] -= _loan.pay_amount
# business.append(new_cheque[0]+new_saving[0]+new_loan[0])
try:
query1 = Cheque.select().where((Cheque.open_date >= time_select[0]) & (Cheque.open_date < time_select[1]))
except Cheque.DoesNotExist as e:
msg = 'success'
except Exception as e:
msg = repr(e)
else:
for _cheque in query1:
new_cheque[0] += 1
new_cheque[1] += _cheque.remaining_sum
try:
query2 = Saving.select().where((Saving.open_date >= time_select[0]) & (Saving.open_date < time_select[1]))
except Saving.DoesNotExist as e:
msg = 'success'
except Exception as e:
msg = repr(e)
else:
for _saving in query2:
new_saving[0] += 1
new_saving[1] += _saving.remaining_sum
saving_amount.append(new_saving[1])
cheque_amount.append(new_cheque[1])
loan_amount.append(new_loan[1])
overall_amount.append(new_cheque[1] + new_loan[1] + new_saving[1])
business.append(new_cheque[0] + new_loan[0] + new_saving[0])
rep = pd.DataFrame({'PERIOD':index.head(len(index)-1),'SAVING': saving_amount, 'CHEQUE': cheque_amount, 'LOAN': loan_amount, 'ASSETS': overall_amount, 'BUSINESS': business}, index=index.head(len(index)-1))
print(rep)
pdp.ProfileReport(rep).to_file(output_file="./templates/statistic.html")
return msg | 35,451 | 21 | 586 |
581d89ac51782d24c0741d4f545d4bc1a7f38aa1 | 297 | py | Python | tests/settings_test.py | jamesperes/autonomiaBot | bbc90497d29fa16a85477281f87ceaeb7c3e55fc | [
"MIT"
] | 8 | 2018-03-22T21:46:30.000Z | 2021-04-07T07:06:05.000Z | tests/settings_test.py | jamesperes/autonomiaBot | bbc90497d29fa16a85477281f87ceaeb7c3e55fc | [
"MIT"
] | 42 | 2018-03-23T17:20:38.000Z | 2021-06-02T01:19:51.000Z | tests/settings_test.py | jamesperes/autonomiaBot | bbc90497d29fa16a85477281f87ceaeb7c3e55fc | [
"MIT"
] | 3 | 2018-03-26T20:30:53.000Z | 2020-01-18T13:52:32.000Z | # application configuration
from autonomia.settings import * # noqa
DEBUG = True
TESTING = True
LOG_LEVEL = "DEBUG"
WEBHOOK_DOMAIN = "localhost:5000"
WEBHOOK_PATH = "hook"
CHAT_ID = -123456
# Third party configuration
API_TOKEN = "133505823:AAHZFMHno3mzVLErU5b5jJvaeG--qUyLyG0"
REDIS_URL = ""
| 19.8 | 59 | 0.767677 | # application configuration
from autonomia.settings import * # noqa
DEBUG = True
TESTING = True
LOG_LEVEL = "DEBUG"
WEBHOOK_DOMAIN = "localhost:5000"
WEBHOOK_PATH = "hook"
CHAT_ID = -123456
# Third party configuration
API_TOKEN = "133505823:AAHZFMHno3mzVLErU5b5jJvaeG--qUyLyG0"
REDIS_URL = ""
| 0 | 0 | 0 |
af56ccce5a7ef323f67d85aaae8849e66a22a7f8 | 27,895 | py | Python | napalm_yang/models/openconfig/network_instances/network_instance/mpls/lsps/constrained_path/tunnels/tunnel/p2p_tunnel_attributes/p2p_primary_path/p2p_primary_path_/candidate_secondary_paths/candidate_secondary_path/state/__init__.py | ckishimo/napalm-yang | 8f2bd907bd3afcde3c2f8e985192de74748baf6c | [
"Apache-2.0"
] | 64 | 2016-10-20T15:47:18.000Z | 2021-11-11T11:57:32.000Z | napalm_yang/models/openconfig/network_instances/network_instance/mpls/lsps/constrained_path/tunnels/tunnel/p2p_tunnel_attributes/p2p_primary_path/p2p_primary_path_/candidate_secondary_paths/candidate_secondary_path/state/__init__.py | ckishimo/napalm-yang | 8f2bd907bd3afcde3c2f8e985192de74748baf6c | [
"Apache-2.0"
] | 126 | 2016-10-05T10:36:14.000Z | 2019-05-15T08:43:23.000Z | napalm_yang/models/openconfig/network_instances/network_instance/mpls/lsps/constrained_path/tunnels/tunnel/p2p_tunnel_attributes/p2p_primary_path/p2p_primary_path_/candidate_secondary_paths/candidate_secondary_path/state/__init__.py | ckishimo/napalm-yang | 8f2bd907bd3afcde3c2f8e985192de74748baf6c | [
"Apache-2.0"
] | 63 | 2016-11-07T15:23:08.000Z | 2021-09-22T14:41:16.000Z | # -*- coding: utf-8 -*-
from operator import attrgetter
from pyangbind.lib.yangtypes import RestrictedPrecisionDecimalType
from pyangbind.lib.yangtypes import RestrictedClassType
from pyangbind.lib.yangtypes import TypedListType
from pyangbind.lib.yangtypes import YANGBool
from pyangbind.lib.yangtypes import YANGListType
from pyangbind.lib.yangtypes import YANGDynClass
from pyangbind.lib.yangtypes import ReferenceType
from pyangbind.lib.base import PybindBase
from collections import OrderedDict
from decimal import Decimal
from bitarray import bitarray
import six
# PY3 support of some PY2 keywords (needs improved)
if six.PY3:
import builtins as __builtin__
long = int
elif six.PY2:
import __builtin__
class state(PybindBase):
"""
This class was auto-generated by the PythonClass plugin for PYANG
from YANG module openconfig-network-instance - based on the path /network-instances/network-instance/mpls/lsps/constrained-path/tunnels/tunnel/p2p-tunnel-attributes/p2p-primary-path/p2p-primary-path/candidate-secondary-paths/candidate-secondary-path/state. Each member element of
the container is represented as a class variable - with a specific
YANG type.
YANG Description: Operational state parameters relating to the candidate
secondary path
"""
__slots__ = (
"_path_helper", "_extmethods", "__secondary_path", "__priority", "__active"
)
_yang_name = "state"
_pybind_generated_by = "container"
def _get_secondary_path(self):
"""
Getter method for secondary_path, mapped from YANG variable /network_instances/network_instance/mpls/lsps/constrained_path/tunnels/tunnel/p2p_tunnel_attributes/p2p_primary_path/p2p_primary_path/candidate_secondary_paths/candidate_secondary_path/state/secondary_path (leafref)
YANG Description: A reference to the secondary path that should be utilised
when the containing primary path option is in use
"""
return self.__secondary_path
def _set_secondary_path(self, v, load=False):
"""
Setter method for secondary_path, mapped from YANG variable /network_instances/network_instance/mpls/lsps/constrained_path/tunnels/tunnel/p2p_tunnel_attributes/p2p_primary_path/p2p_primary_path/candidate_secondary_paths/candidate_secondary_path/state/secondary_path (leafref)
If this variable is read-only (config: false) in the
source YANG file, then _set_secondary_path is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_secondary_path() directly.
YANG Description: A reference to the secondary path that should be utilised
when the containing primary path option is in use
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(
v,
base=six.text_type,
is_leaf=True,
yang_name="secondary-path",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="leafref",
is_config=False,
)
except (TypeError, ValueError):
raise ValueError(
{
"error-string": """secondary_path must be of a type compatible with leafref""",
"defined-type": "leafref",
"generated-type": """YANGDynClass(base=six.text_type, is_leaf=True, yang_name="secondary-path", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='leafref', is_config=False)""",
}
)
self.__secondary_path = t
if hasattr(self, "_set"):
self._set()
def _get_priority(self):
"""
Getter method for priority, mapped from YANG variable /network_instances/network_instance/mpls/lsps/constrained_path/tunnels/tunnel/p2p_tunnel_attributes/p2p_primary_path/p2p_primary_path/candidate_secondary_paths/candidate_secondary_path/state/priority (uint16)
YANG Description: The priority of the specified secondary path option. Higher
priority options are less preferable - such that a secondary
path reference with a priority of 0 is the most preferred
"""
return self.__priority
def _set_priority(self, v, load=False):
"""
Setter method for priority, mapped from YANG variable /network_instances/network_instance/mpls/lsps/constrained_path/tunnels/tunnel/p2p_tunnel_attributes/p2p_primary_path/p2p_primary_path/candidate_secondary_paths/candidate_secondary_path/state/priority (uint16)
If this variable is read-only (config: false) in the
source YANG file, then _set_priority is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_priority() directly.
YANG Description: The priority of the specified secondary path option. Higher
priority options are less preferable - such that a secondary
path reference with a priority of 0 is the most preferred
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(
v,
base=RestrictedClassType(
base_type=int, restriction_dict={"range": ["0..65535"]}, int_size=16
),
is_leaf=True,
yang_name="priority",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="uint16",
is_config=False,
)
except (TypeError, ValueError):
raise ValueError(
{
"error-string": """priority must be of a type compatible with uint16""",
"defined-type": "uint16",
"generated-type": """YANGDynClass(base=RestrictedClassType(base_type=int, restriction_dict={'range': ['0..65535']},int_size=16), is_leaf=True, yang_name="priority", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='uint16', is_config=False)""",
}
)
self.__priority = t
if hasattr(self, "_set"):
self._set()
def _get_active(self):
"""
Getter method for active, mapped from YANG variable /network_instances/network_instance/mpls/lsps/constrained_path/tunnels/tunnel/p2p_tunnel_attributes/p2p_primary_path/p2p_primary_path/candidate_secondary_paths/candidate_secondary_path/state/active (boolean)
YANG Description: Indicates the current active path option that has
been selected of the candidate secondary paths
"""
return self.__active
def _set_active(self, v, load=False):
"""
Setter method for active, mapped from YANG variable /network_instances/network_instance/mpls/lsps/constrained_path/tunnels/tunnel/p2p_tunnel_attributes/p2p_primary_path/p2p_primary_path/candidate_secondary_paths/candidate_secondary_path/state/active (boolean)
If this variable is read-only (config: false) in the
source YANG file, then _set_active is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_active() directly.
YANG Description: Indicates the current active path option that has
been selected of the candidate secondary paths
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(
v,
base=YANGBool,
is_leaf=True,
yang_name="active",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="boolean",
is_config=False,
)
except (TypeError, ValueError):
raise ValueError(
{
"error-string": """active must be of a type compatible with boolean""",
"defined-type": "boolean",
"generated-type": """YANGDynClass(base=YANGBool, is_leaf=True, yang_name="active", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='boolean', is_config=False)""",
}
)
self.__active = t
if hasattr(self, "_set"):
self._set()
secondary_path = __builtin__.property(_get_secondary_path)
priority = __builtin__.property(_get_priority)
active = __builtin__.property(_get_active)
_pyangbind_elements = OrderedDict(
[("secondary_path", secondary_path), ("priority", priority), ("active", active)]
)
class state(PybindBase):
"""
This class was auto-generated by the PythonClass plugin for PYANG
from YANG module openconfig-network-instance-l2 - based on the path /network-instances/network-instance/mpls/lsps/constrained-path/tunnels/tunnel/p2p-tunnel-attributes/p2p-primary-path/p2p-primary-path/candidate-secondary-paths/candidate-secondary-path/state. Each member element of
the container is represented as a class variable - with a specific
YANG type.
YANG Description: Operational state parameters relating to the candidate
secondary path
"""
__slots__ = (
"_path_helper", "_extmethods", "__secondary_path", "__priority", "__active"
)
_yang_name = "state"
_pybind_generated_by = "container"
def _get_secondary_path(self):
"""
Getter method for secondary_path, mapped from YANG variable /network_instances/network_instance/mpls/lsps/constrained_path/tunnels/tunnel/p2p_tunnel_attributes/p2p_primary_path/p2p_primary_path/candidate_secondary_paths/candidate_secondary_path/state/secondary_path (leafref)
YANG Description: A reference to the secondary path that should be utilised
when the containing primary path option is in use
"""
return self.__secondary_path
def _set_secondary_path(self, v, load=False):
"""
Setter method for secondary_path, mapped from YANG variable /network_instances/network_instance/mpls/lsps/constrained_path/tunnels/tunnel/p2p_tunnel_attributes/p2p_primary_path/p2p_primary_path/candidate_secondary_paths/candidate_secondary_path/state/secondary_path (leafref)
If this variable is read-only (config: false) in the
source YANG file, then _set_secondary_path is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_secondary_path() directly.
YANG Description: A reference to the secondary path that should be utilised
when the containing primary path option is in use
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(
v,
base=six.text_type,
is_leaf=True,
yang_name="secondary-path",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="leafref",
is_config=False,
)
except (TypeError, ValueError):
raise ValueError(
{
"error-string": """secondary_path must be of a type compatible with leafref""",
"defined-type": "leafref",
"generated-type": """YANGDynClass(base=six.text_type, is_leaf=True, yang_name="secondary-path", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='leafref', is_config=False)""",
}
)
self.__secondary_path = t
if hasattr(self, "_set"):
self._set()
def _get_priority(self):
"""
Getter method for priority, mapped from YANG variable /network_instances/network_instance/mpls/lsps/constrained_path/tunnels/tunnel/p2p_tunnel_attributes/p2p_primary_path/p2p_primary_path/candidate_secondary_paths/candidate_secondary_path/state/priority (uint16)
YANG Description: The priority of the specified secondary path option. Higher
priority options are less preferable - such that a secondary
path reference with a priority of 0 is the most preferred
"""
return self.__priority
def _set_priority(self, v, load=False):
"""
Setter method for priority, mapped from YANG variable /network_instances/network_instance/mpls/lsps/constrained_path/tunnels/tunnel/p2p_tunnel_attributes/p2p_primary_path/p2p_primary_path/candidate_secondary_paths/candidate_secondary_path/state/priority (uint16)
If this variable is read-only (config: false) in the
source YANG file, then _set_priority is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_priority() directly.
YANG Description: The priority of the specified secondary path option. Higher
priority options are less preferable - such that a secondary
path reference with a priority of 0 is the most preferred
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(
v,
base=RestrictedClassType(
base_type=int, restriction_dict={"range": ["0..65535"]}, int_size=16
),
is_leaf=True,
yang_name="priority",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="uint16",
is_config=False,
)
except (TypeError, ValueError):
raise ValueError(
{
"error-string": """priority must be of a type compatible with uint16""",
"defined-type": "uint16",
"generated-type": """YANGDynClass(base=RestrictedClassType(base_type=int, restriction_dict={'range': ['0..65535']},int_size=16), is_leaf=True, yang_name="priority", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='uint16', is_config=False)""",
}
)
self.__priority = t
if hasattr(self, "_set"):
self._set()
def _get_active(self):
"""
Getter method for active, mapped from YANG variable /network_instances/network_instance/mpls/lsps/constrained_path/tunnels/tunnel/p2p_tunnel_attributes/p2p_primary_path/p2p_primary_path/candidate_secondary_paths/candidate_secondary_path/state/active (boolean)
YANG Description: Indicates the current active path option that has
been selected of the candidate secondary paths
"""
return self.__active
def _set_active(self, v, load=False):
"""
Setter method for active, mapped from YANG variable /network_instances/network_instance/mpls/lsps/constrained_path/tunnels/tunnel/p2p_tunnel_attributes/p2p_primary_path/p2p_primary_path/candidate_secondary_paths/candidate_secondary_path/state/active (boolean)
If this variable is read-only (config: false) in the
source YANG file, then _set_active is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_active() directly.
YANG Description: Indicates the current active path option that has
been selected of the candidate secondary paths
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(
v,
base=YANGBool,
is_leaf=True,
yang_name="active",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="boolean",
is_config=False,
)
except (TypeError, ValueError):
raise ValueError(
{
"error-string": """active must be of a type compatible with boolean""",
"defined-type": "boolean",
"generated-type": """YANGDynClass(base=YANGBool, is_leaf=True, yang_name="active", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='boolean', is_config=False)""",
}
)
self.__active = t
if hasattr(self, "_set"):
self._set()
secondary_path = __builtin__.property(_get_secondary_path)
priority = __builtin__.property(_get_priority)
active = __builtin__.property(_get_active)
_pyangbind_elements = OrderedDict(
[("secondary_path", secondary_path), ("priority", priority), ("active", active)]
)
| 42.587786 | 423 | 0.624628 | # -*- coding: utf-8 -*-
from operator import attrgetter
from pyangbind.lib.yangtypes import RestrictedPrecisionDecimalType
from pyangbind.lib.yangtypes import RestrictedClassType
from pyangbind.lib.yangtypes import TypedListType
from pyangbind.lib.yangtypes import YANGBool
from pyangbind.lib.yangtypes import YANGListType
from pyangbind.lib.yangtypes import YANGDynClass
from pyangbind.lib.yangtypes import ReferenceType
from pyangbind.lib.base import PybindBase
from collections import OrderedDict
from decimal import Decimal
from bitarray import bitarray
import six
# PY3 support of some PY2 keywords (needs improved)
if six.PY3:
import builtins as __builtin__
long = int
elif six.PY2:
import __builtin__
class state(PybindBase):
"""
This class was auto-generated by the PythonClass plugin for PYANG
from YANG module openconfig-network-instance - based on the path /network-instances/network-instance/mpls/lsps/constrained-path/tunnels/tunnel/p2p-tunnel-attributes/p2p-primary-path/p2p-primary-path/candidate-secondary-paths/candidate-secondary-path/state. Each member element of
the container is represented as a class variable - with a specific
YANG type.
YANG Description: Operational state parameters relating to the candidate
secondary path
"""
__slots__ = (
"_path_helper", "_extmethods", "__secondary_path", "__priority", "__active"
)
_yang_name = "state"
_pybind_generated_by = "container"
def __init__(self, *args, **kwargs):
self._path_helper = False
self._extmethods = False
self.__secondary_path = YANGDynClass(
base=six.text_type,
is_leaf=True,
yang_name="secondary-path",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="leafref",
is_config=False,
)
self.__priority = YANGDynClass(
base=RestrictedClassType(
base_type=int, restriction_dict={"range": ["0..65535"]}, int_size=16
),
is_leaf=True,
yang_name="priority",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="uint16",
is_config=False,
)
self.__active = YANGDynClass(
base=YANGBool,
is_leaf=True,
yang_name="active",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="boolean",
is_config=False,
)
load = kwargs.pop("load", None)
if args:
if len(args) > 1:
raise TypeError("cannot create a YANG container with >1 argument")
all_attr = True
for e in self._pyangbind_elements:
if not hasattr(args[0], e):
all_attr = False
break
if not all_attr:
raise ValueError("Supplied object did not have the correct attributes")
for e in self._pyangbind_elements:
nobj = getattr(args[0], e)
if nobj._changed() is False:
continue
setmethod = getattr(self, "_set_%s" % e)
if load is None:
setmethod(getattr(args[0], e))
else:
setmethod(getattr(args[0], e), load=load)
def _path(self):
if hasattr(self, "_parent"):
return self._parent._path() + [self._yang_name]
else:
return [
"network-instances",
"network-instance",
"mpls",
"lsps",
"constrained-path",
"tunnels",
"tunnel",
"p2p-tunnel-attributes",
"p2p-primary-path",
"p2p-primary-path",
"candidate-secondary-paths",
"candidate-secondary-path",
"state",
]
def _get_secondary_path(self):
"""
Getter method for secondary_path, mapped from YANG variable /network_instances/network_instance/mpls/lsps/constrained_path/tunnels/tunnel/p2p_tunnel_attributes/p2p_primary_path/p2p_primary_path/candidate_secondary_paths/candidate_secondary_path/state/secondary_path (leafref)
YANG Description: A reference to the secondary path that should be utilised
when the containing primary path option is in use
"""
return self.__secondary_path
def _set_secondary_path(self, v, load=False):
"""
Setter method for secondary_path, mapped from YANG variable /network_instances/network_instance/mpls/lsps/constrained_path/tunnels/tunnel/p2p_tunnel_attributes/p2p_primary_path/p2p_primary_path/candidate_secondary_paths/candidate_secondary_path/state/secondary_path (leafref)
If this variable is read-only (config: false) in the
source YANG file, then _set_secondary_path is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_secondary_path() directly.
YANG Description: A reference to the secondary path that should be utilised
when the containing primary path option is in use
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(
v,
base=six.text_type,
is_leaf=True,
yang_name="secondary-path",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="leafref",
is_config=False,
)
except (TypeError, ValueError):
raise ValueError(
{
"error-string": """secondary_path must be of a type compatible with leafref""",
"defined-type": "leafref",
"generated-type": """YANGDynClass(base=six.text_type, is_leaf=True, yang_name="secondary-path", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='leafref', is_config=False)""",
}
)
self.__secondary_path = t
if hasattr(self, "_set"):
self._set()
def _unset_secondary_path(self):
self.__secondary_path = YANGDynClass(
base=six.text_type,
is_leaf=True,
yang_name="secondary-path",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="leafref",
is_config=False,
)
def _get_priority(self):
"""
Getter method for priority, mapped from YANG variable /network_instances/network_instance/mpls/lsps/constrained_path/tunnels/tunnel/p2p_tunnel_attributes/p2p_primary_path/p2p_primary_path/candidate_secondary_paths/candidate_secondary_path/state/priority (uint16)
YANG Description: The priority of the specified secondary path option. Higher
priority options are less preferable - such that a secondary
path reference with a priority of 0 is the most preferred
"""
return self.__priority
def _set_priority(self, v, load=False):
"""
Setter method for priority, mapped from YANG variable /network_instances/network_instance/mpls/lsps/constrained_path/tunnels/tunnel/p2p_tunnel_attributes/p2p_primary_path/p2p_primary_path/candidate_secondary_paths/candidate_secondary_path/state/priority (uint16)
If this variable is read-only (config: false) in the
source YANG file, then _set_priority is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_priority() directly.
YANG Description: The priority of the specified secondary path option. Higher
priority options are less preferable - such that a secondary
path reference with a priority of 0 is the most preferred
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(
v,
base=RestrictedClassType(
base_type=int, restriction_dict={"range": ["0..65535"]}, int_size=16
),
is_leaf=True,
yang_name="priority",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="uint16",
is_config=False,
)
except (TypeError, ValueError):
raise ValueError(
{
"error-string": """priority must be of a type compatible with uint16""",
"defined-type": "uint16",
"generated-type": """YANGDynClass(base=RestrictedClassType(base_type=int, restriction_dict={'range': ['0..65535']},int_size=16), is_leaf=True, yang_name="priority", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='uint16', is_config=False)""",
}
)
self.__priority = t
if hasattr(self, "_set"):
self._set()
def _unset_priority(self):
self.__priority = YANGDynClass(
base=RestrictedClassType(
base_type=int, restriction_dict={"range": ["0..65535"]}, int_size=16
),
is_leaf=True,
yang_name="priority",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="uint16",
is_config=False,
)
def _get_active(self):
"""
Getter method for active, mapped from YANG variable /network_instances/network_instance/mpls/lsps/constrained_path/tunnels/tunnel/p2p_tunnel_attributes/p2p_primary_path/p2p_primary_path/candidate_secondary_paths/candidate_secondary_path/state/active (boolean)
YANG Description: Indicates the current active path option that has
been selected of the candidate secondary paths
"""
return self.__active
def _set_active(self, v, load=False):
"""
Setter method for active, mapped from YANG variable /network_instances/network_instance/mpls/lsps/constrained_path/tunnels/tunnel/p2p_tunnel_attributes/p2p_primary_path/p2p_primary_path/candidate_secondary_paths/candidate_secondary_path/state/active (boolean)
If this variable is read-only (config: false) in the
source YANG file, then _set_active is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_active() directly.
YANG Description: Indicates the current active path option that has
been selected of the candidate secondary paths
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(
v,
base=YANGBool,
is_leaf=True,
yang_name="active",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="boolean",
is_config=False,
)
except (TypeError, ValueError):
raise ValueError(
{
"error-string": """active must be of a type compatible with boolean""",
"defined-type": "boolean",
"generated-type": """YANGDynClass(base=YANGBool, is_leaf=True, yang_name="active", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='boolean', is_config=False)""",
}
)
self.__active = t
if hasattr(self, "_set"):
self._set()
def _unset_active(self):
self.__active = YANGDynClass(
base=YANGBool,
is_leaf=True,
yang_name="active",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="boolean",
is_config=False,
)
secondary_path = __builtin__.property(_get_secondary_path)
priority = __builtin__.property(_get_priority)
active = __builtin__.property(_get_active)
_pyangbind_elements = OrderedDict(
[("secondary_path", secondary_path), ("priority", priority), ("active", active)]
)
class state(PybindBase):
"""
This class was auto-generated by the PythonClass plugin for PYANG
from YANG module openconfig-network-instance-l2 - based on the path /network-instances/network-instance/mpls/lsps/constrained-path/tunnels/tunnel/p2p-tunnel-attributes/p2p-primary-path/p2p-primary-path/candidate-secondary-paths/candidate-secondary-path/state. Each member element of
the container is represented as a class variable - with a specific
YANG type.
YANG Description: Operational state parameters relating to the candidate
secondary path
"""
__slots__ = (
"_path_helper", "_extmethods", "__secondary_path", "__priority", "__active"
)
_yang_name = "state"
_pybind_generated_by = "container"
def __init__(self, *args, **kwargs):
self._path_helper = False
self._extmethods = False
self.__secondary_path = YANGDynClass(
base=six.text_type,
is_leaf=True,
yang_name="secondary-path",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="leafref",
is_config=False,
)
self.__priority = YANGDynClass(
base=RestrictedClassType(
base_type=int, restriction_dict={"range": ["0..65535"]}, int_size=16
),
is_leaf=True,
yang_name="priority",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="uint16",
is_config=False,
)
self.__active = YANGDynClass(
base=YANGBool,
is_leaf=True,
yang_name="active",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="boolean",
is_config=False,
)
load = kwargs.pop("load", None)
if args:
if len(args) > 1:
raise TypeError("cannot create a YANG container with >1 argument")
all_attr = True
for e in self._pyangbind_elements:
if not hasattr(args[0], e):
all_attr = False
break
if not all_attr:
raise ValueError("Supplied object did not have the correct attributes")
for e in self._pyangbind_elements:
nobj = getattr(args[0], e)
if nobj._changed() is False:
continue
setmethod = getattr(self, "_set_%s" % e)
if load is None:
setmethod(getattr(args[0], e))
else:
setmethod(getattr(args[0], e), load=load)
def _path(self):
if hasattr(self, "_parent"):
return self._parent._path() + [self._yang_name]
else:
return [
"network-instances",
"network-instance",
"mpls",
"lsps",
"constrained-path",
"tunnels",
"tunnel",
"p2p-tunnel-attributes",
"p2p-primary-path",
"p2p-primary-path",
"candidate-secondary-paths",
"candidate-secondary-path",
"state",
]
def _get_secondary_path(self):
"""
Getter method for secondary_path, mapped from YANG variable /network_instances/network_instance/mpls/lsps/constrained_path/tunnels/tunnel/p2p_tunnel_attributes/p2p_primary_path/p2p_primary_path/candidate_secondary_paths/candidate_secondary_path/state/secondary_path (leafref)
YANG Description: A reference to the secondary path that should be utilised
when the containing primary path option is in use
"""
return self.__secondary_path
def _set_secondary_path(self, v, load=False):
"""
Setter method for secondary_path, mapped from YANG variable /network_instances/network_instance/mpls/lsps/constrained_path/tunnels/tunnel/p2p_tunnel_attributes/p2p_primary_path/p2p_primary_path/candidate_secondary_paths/candidate_secondary_path/state/secondary_path (leafref)
If this variable is read-only (config: false) in the
source YANG file, then _set_secondary_path is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_secondary_path() directly.
YANG Description: A reference to the secondary path that should be utilised
when the containing primary path option is in use
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(
v,
base=six.text_type,
is_leaf=True,
yang_name="secondary-path",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="leafref",
is_config=False,
)
except (TypeError, ValueError):
raise ValueError(
{
"error-string": """secondary_path must be of a type compatible with leafref""",
"defined-type": "leafref",
"generated-type": """YANGDynClass(base=six.text_type, is_leaf=True, yang_name="secondary-path", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='leafref', is_config=False)""",
}
)
self.__secondary_path = t
if hasattr(self, "_set"):
self._set()
def _unset_secondary_path(self):
self.__secondary_path = YANGDynClass(
base=six.text_type,
is_leaf=True,
yang_name="secondary-path",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="leafref",
is_config=False,
)
def _get_priority(self):
"""
Getter method for priority, mapped from YANG variable /network_instances/network_instance/mpls/lsps/constrained_path/tunnels/tunnel/p2p_tunnel_attributes/p2p_primary_path/p2p_primary_path/candidate_secondary_paths/candidate_secondary_path/state/priority (uint16)
YANG Description: The priority of the specified secondary path option. Higher
priority options are less preferable - such that a secondary
path reference with a priority of 0 is the most preferred
"""
return self.__priority
def _set_priority(self, v, load=False):
"""
Setter method for priority, mapped from YANG variable /network_instances/network_instance/mpls/lsps/constrained_path/tunnels/tunnel/p2p_tunnel_attributes/p2p_primary_path/p2p_primary_path/candidate_secondary_paths/candidate_secondary_path/state/priority (uint16)
If this variable is read-only (config: false) in the
source YANG file, then _set_priority is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_priority() directly.
YANG Description: The priority of the specified secondary path option. Higher
priority options are less preferable - such that a secondary
path reference with a priority of 0 is the most preferred
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(
v,
base=RestrictedClassType(
base_type=int, restriction_dict={"range": ["0..65535"]}, int_size=16
),
is_leaf=True,
yang_name="priority",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="uint16",
is_config=False,
)
except (TypeError, ValueError):
raise ValueError(
{
"error-string": """priority must be of a type compatible with uint16""",
"defined-type": "uint16",
"generated-type": """YANGDynClass(base=RestrictedClassType(base_type=int, restriction_dict={'range': ['0..65535']},int_size=16), is_leaf=True, yang_name="priority", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='uint16', is_config=False)""",
}
)
self.__priority = t
if hasattr(self, "_set"):
self._set()
def _unset_priority(self):
self.__priority = YANGDynClass(
base=RestrictedClassType(
base_type=int, restriction_dict={"range": ["0..65535"]}, int_size=16
),
is_leaf=True,
yang_name="priority",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="uint16",
is_config=False,
)
def _get_active(self):
"""
Getter method for active, mapped from YANG variable /network_instances/network_instance/mpls/lsps/constrained_path/tunnels/tunnel/p2p_tunnel_attributes/p2p_primary_path/p2p_primary_path/candidate_secondary_paths/candidate_secondary_path/state/active (boolean)
YANG Description: Indicates the current active path option that has
been selected of the candidate secondary paths
"""
return self.__active
def _set_active(self, v, load=False):
"""
Setter method for active, mapped from YANG variable /network_instances/network_instance/mpls/lsps/constrained_path/tunnels/tunnel/p2p_tunnel_attributes/p2p_primary_path/p2p_primary_path/candidate_secondary_paths/candidate_secondary_path/state/active (boolean)
If this variable is read-only (config: false) in the
source YANG file, then _set_active is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_active() directly.
YANG Description: Indicates the current active path option that has
been selected of the candidate secondary paths
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(
v,
base=YANGBool,
is_leaf=True,
yang_name="active",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="boolean",
is_config=False,
)
except (TypeError, ValueError):
raise ValueError(
{
"error-string": """active must be of a type compatible with boolean""",
"defined-type": "boolean",
"generated-type": """YANGDynClass(base=YANGBool, is_leaf=True, yang_name="active", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='boolean', is_config=False)""",
}
)
self.__active = t
if hasattr(self, "_set"):
self._set()
def _unset_active(self):
self.__active = YANGDynClass(
base=YANGBool,
is_leaf=True,
yang_name="active",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="boolean",
is_config=False,
)
secondary_path = __builtin__.property(_get_secondary_path)
priority = __builtin__.property(_get_priority)
active = __builtin__.property(_get_active)
_pyangbind_elements = OrderedDict(
[("secondary_path", secondary_path), ("priority", priority), ("active", active)]
)
| 9,200 | 0 | 270 |
89fc6f1de5be83c46722245878ac20f1102c60e8 | 555 | py | Python | socialite/preprocess/get_max_degree.py | Wangqge/PowerLog_ae | 8546afbcb9a77d516e8c3f0dfbaf2041a4b888f9 | [
"Apache-2.0"
] | null | null | null | socialite/preprocess/get_max_degree.py | Wangqge/PowerLog_ae | 8546afbcb9a77d516e8c3f0dfbaf2041a4b888f9 | [
"Apache-2.0"
] | null | null | null | socialite/preprocess/get_max_degree.py | Wangqge/PowerLog_ae | 8546afbcb9a77d516e8c3f0dfbaf2041a4b888f9 | [
"Apache-2.0"
] | null | null | null | import sys
if len(sys.argv) != 2:
print 'usage: [edge pair input path]'
exit(1)
with open(sys.argv[1], 'r') as fi:
node_dict = {}
for line in fi:
tmp = line.split()
src = int(tmp[0])
if src not in node_dict:
node_dict[src] = 1
else:
node_dict[src] += 1
max_degree = 0
max_node = 0
for node in node_dict:
if node_dict[node] > max_node:
max_degree = node_dict[node]
max_node = node
print max_node
print 'degree ' + str(max_degree)
| 23.125 | 41 | 0.538739 | import sys
if len(sys.argv) != 2:
print 'usage: [edge pair input path]'
exit(1)
with open(sys.argv[1], 'r') as fi:
node_dict = {}
for line in fi:
tmp = line.split()
src = int(tmp[0])
if src not in node_dict:
node_dict[src] = 1
else:
node_dict[src] += 1
max_degree = 0
max_node = 0
for node in node_dict:
if node_dict[node] > max_node:
max_degree = node_dict[node]
max_node = node
print max_node
print 'degree ' + str(max_degree)
| 0 | 0 | 0 |
1658ee280f3440ab4466219888be9de4ead0bb43 | 2,159 | py | Python | derivativetest.py | syangliu/Newton-MR-grad | bf17cac404f0d0c92d5852e265162cc82fe3fefd | [
"Apache-2.0"
] | 1 | 2022-03-31T05:15:31.000Z | 2022-03-31T05:15:31.000Z | derivativetest.py | syangliu/Newton-MR | bf17cac404f0d0c92d5852e265162cc82fe3fefd | [
"Apache-2.0"
] | null | null | null | derivativetest.py | syangliu/Newton-MR | bf17cac404f0d0c92d5852e265162cc82fe3fefd | [
"Apache-2.0"
] | 1 | 2022-03-20T00:11:03.000Z | 2022-03-20T00:11:03.000Z | import numpy as np
import numpy.random as rand
import matplotlib.pyplot as plt
from numpy.linalg import norm
def derivativetest(fun, x0):
"""
Test the gradient and Hessian of a function. A large proportion
parallel in the middle of both plots means accuraccy.
INPUTS:
fun: a function handle that gives f, g, Hv
x0: starting point
OUTPUTS:
derivative test plots
"""
x0 = x0.reshape(len(x0),1)
fun0 = fun(x0)
dx = rand.randn(len(x0),1)
M = 20;
dxs = np.zeros((M,1))
firsterror = np.zeros((M,1))
order1 = np.zeros((M-1,1))
seconderror = np.zeros((M,1))
order2 = np.zeros((M-1,1))
for i in range(M):
x = x0 + dx
fun1 = fun(x)
H0 = Ax(fun0[2],dx)
firsterror[i] = abs(fun1[0] - (fun0[0] + np.dot(
dx.T, fun0[1])))/abs(fun0[0])
seconderror[i] = abs(fun1[0] - (fun0[0] + np.dot(
dx.T, fun0[1]) + 0.5* np.dot(dx.T, H0)))/abs(fun0[0])
print('First Order Error is %8.2e; Second Order Error is %8.2e'% (
firsterror[i], seconderror[i]))
if i > 0:
order1[i-1] = np.log2(firsterror[i-1]/firsterror[i])
order2[i-1] = np.log2(seconderror[i-1]/seconderror[i])
dxs[i] = norm(dx)
dx = dx/2
step = [2**(-i-1) for i in range(M)]
plt.figure(figsize=(12,8))
plt.subplot(221)
plt.loglog(step, abs(firsterror),'b', label = '1st Order Err')
plt.loglog(step, dxs**2,'r', label = 'order')
plt.gca().invert_xaxis()
plt.legend()
plt.subplot(222)
plt.semilogx(step[1:], order1,'b', label = '1st Order')
plt.gca().invert_xaxis()
plt.legend()
plt.subplot(223)
plt.loglog(step, abs(seconderror),'b', label = '2nd Order Err')
plt.loglog(step, dxs**3,'r', label = 'Order')
plt.gca().invert_xaxis()
plt.legend()
plt.subplot(224)
plt.semilogx(step[1:], order2,'b', label = '2nd Order')
plt.gca().invert_xaxis()
plt.legend()
return plt.show()
| 29.175676 | 76 | 0.548865 | import numpy as np
import numpy.random as rand
import matplotlib.pyplot as plt
from numpy.linalg import norm
def derivativetest(fun, x0):
"""
Test the gradient and Hessian of a function. A large proportion
parallel in the middle of both plots means accuraccy.
INPUTS:
fun: a function handle that gives f, g, Hv
x0: starting point
OUTPUTS:
derivative test plots
"""
x0 = x0.reshape(len(x0),1)
fun0 = fun(x0)
dx = rand.randn(len(x0),1)
M = 20;
dxs = np.zeros((M,1))
firsterror = np.zeros((M,1))
order1 = np.zeros((M-1,1))
seconderror = np.zeros((M,1))
order2 = np.zeros((M-1,1))
for i in range(M):
x = x0 + dx
fun1 = fun(x)
H0 = Ax(fun0[2],dx)
firsterror[i] = abs(fun1[0] - (fun0[0] + np.dot(
dx.T, fun0[1])))/abs(fun0[0])
seconderror[i] = abs(fun1[0] - (fun0[0] + np.dot(
dx.T, fun0[1]) + 0.5* np.dot(dx.T, H0)))/abs(fun0[0])
print('First Order Error is %8.2e; Second Order Error is %8.2e'% (
firsterror[i], seconderror[i]))
if i > 0:
order1[i-1] = np.log2(firsterror[i-1]/firsterror[i])
order2[i-1] = np.log2(seconderror[i-1]/seconderror[i])
dxs[i] = norm(dx)
dx = dx/2
step = [2**(-i-1) for i in range(M)]
plt.figure(figsize=(12,8))
plt.subplot(221)
plt.loglog(step, abs(firsterror),'b', label = '1st Order Err')
plt.loglog(step, dxs**2,'r', label = 'order')
plt.gca().invert_xaxis()
plt.legend()
plt.subplot(222)
plt.semilogx(step[1:], order1,'b', label = '1st Order')
plt.gca().invert_xaxis()
plt.legend()
plt.subplot(223)
plt.loglog(step, abs(seconderror),'b', label = '2nd Order Err')
plt.loglog(step, dxs**3,'r', label = 'Order')
plt.gca().invert_xaxis()
plt.legend()
plt.subplot(224)
plt.semilogx(step[1:], order2,'b', label = '2nd Order')
plt.gca().invert_xaxis()
plt.legend()
return plt.show()
def Ax(A, x):
if callable(A):
Ax = A(x)
else:
Ax =A.dot(x)
return Ax | 75 | 0 | 23 |
dc248dbf9ee291025d20284dbe3dde3da67d21fa | 6,222 | py | Python | dt_stripe/migrations/0001_initial.py | itsnamgyu/api-demo | ddf726928bd7f1021143c4dbb530e3017a3edda9 | [
"MIT"
] | 1 | 2019-06-02T08:20:38.000Z | 2019-06-02T08:20:38.000Z | dt_stripe/migrations/0001_initial.py | itsnamgyu/api-demo | ddf726928bd7f1021143c4dbb530e3017a3edda9 | [
"MIT"
] | 12 | 2019-07-21T18:40:35.000Z | 2019-10-05T19:57:49.000Z | dt_stripe/migrations/0001_initial.py | itsnamgyu/django-template | 20f64974e0dda69cf8dcf0dac9e0a309f200fb61 | [
"MIT"
] | null | null | null | # Generated by Django 2.2.4 on 2019-09-23 23:32
from django.db import migrations, models
import django.db.models.deletion
| 69.133333 | 272 | 0.624558 | # Generated by Django 2.2.4 on 2019-09-23 23:32
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='Customer',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('stripe_id', models.CharField(db_index=True, help_text='Id of Stripe Customer', max_length=128, verbose_name='id')),
('name', models.CharField(blank=True, max_length=128, null=True, verbose_name='name')),
('email', models.EmailField(blank=True, max_length=254, null=True, verbose_name='email')),
('description', models.TextField(blank=True, help_text='Description for admins', null=True, verbose_name='description')),
('source_status', models.CharField(choices=[('NA', 'None'), ('FA', 'Source payment failed'), ('AV', 'Source available')], default='NA', max_length=2, verbose_name='source status')),
('default_source_id', models.CharField(max_length=128, null=True, verbose_name='default source id')),
('default_source_brand', models.TextField(null=True, verbose_name='default source brand')),
('default_source_last4', models.CharField(max_length=4, null=True, verbose_name='default source last 4 digits')),
],
),
migrations.CreateModel(
name='Plan',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('stripe_id', models.CharField(db_index=True, help_text='Id of Stripe Plan', max_length=128, verbose_name='id')),
('amount', models.IntegerField(verbose_name='amount')),
('currency', models.CharField(default='usd', max_length=3, verbose_name='currency')),
('interval', models.CharField(choices=[('day', 'Daily'), ('week', 'Weekly'), ('month', 'Monthly'), ('year', 'Yearly')], max_length=16, verbose_name='interval')),
('interval_count', models.IntegerField(default=1, help_text='Number of intervals per billing cycle. I.e., interval=month and interval_count=3 for 3 months.', verbose_name='count')),
('name', models.CharField(help_text='Local name for use by admins', max_length=128, null=True, verbose_name='name')),
('description', models.TextField(blank=True, help_text='Local description for use by admins', null=True, verbose_name='description')),
],
),
migrations.CreateModel(
name='Product',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('stripe_id', models.CharField(db_index=True, help_text='Id of Stripe Product', max_length=128, verbose_name='id')),
('name', models.CharField(max_length=128, verbose_name='name')),
('description', models.TextField(blank=True, help_text="Description to be shown to the user. Only for products of type 'good'.", null=True, verbose_name='description')),
('product_type', models.CharField(choices=[('good', 'Good'), ('service', 'Service')], max_length=16, verbose_name='type')),
],
),
migrations.CreateModel(
name='Subscription',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('stripe_id', models.CharField(db_index=True, help_text='Id of Stripe Subscription', max_length=128, verbose_name='id')),
('status', models.CharField(choices=[('NA', 'No subscription'), ('AV', 'Active'), ('CE', 'Active until end of billing cycle)'), ('CX', 'Canceled due to expiry'), ('CP', 'Canceled due to payment issue')], default='NA', max_length=2, verbose_name='status')),
('customer', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='subscriptions', to='dt_stripe.Customer')),
('plan', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='subscriptions', to='dt_stripe.Plan')),
],
),
migrations.CreateModel(
name='SKU',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('stripe_id', models.CharField(db_index=True, help_text='Id of Stripe SKU', max_length=128, verbose_name='id')),
('price', models.IntegerField(verbose_name='price')),
('currency', models.CharField(default='usd', max_length=3, verbose_name='currency')),
('name', models.CharField(help_text='Local name for use by admins', max_length=128, null=True, verbose_name='name')),
('description', models.TextField(blank=True, help_text='Local description for use by admins', null=True, verbose_name='description')),
('product', models.ForeignKey(on_delete='CASCADE', related_name='skus', to='dt_stripe.Product')),
],
),
migrations.AddField(
model_name='plan',
name='product',
field=models.ForeignKey(on_delete='CASCADE', related_name='plans', to='dt_stripe.Product'),
),
migrations.CreateModel(
name='Order',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('stripe_id', models.CharField(db_index=True, help_text='Id of Stripe Order', max_length=128, verbose_name='id')),
('date_created', models.DateTimeField(auto_now_add=True, verbose_name='date created')),
('customer', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='orders', to='dt_stripe.Customer')),
('sku', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='orders', to='dt_stripe.SKU')),
],
),
]
| 0 | 6,075 | 23 |
ae20cf10f08092916ececf243f8198d8ffd52c36 | 2,915 | py | Python | tests/sett/test_upgrade_crv_setts.py | shuklaayush/badger-system | 1274eadbd0b0f3a02efbf40702719ce1d0a96c44 | [
"MIT"
] | 99 | 2020-12-02T08:40:48.000Z | 2022-03-15T05:21:06.000Z | tests/sett/test_upgrade_crv_setts.py | shuklaayush/badger-system | 1274eadbd0b0f3a02efbf40702719ce1d0a96c44 | [
"MIT"
] | 115 | 2020-12-15T07:15:39.000Z | 2022-03-28T22:21:03.000Z | tests/sett/test_upgrade_crv_setts.py | shuklaayush/badger-system | 1274eadbd0b0f3a02efbf40702719ce1d0a96c44 | [
"MIT"
] | 56 | 2020-12-11T06:50:04.000Z | 2022-02-21T09:17:38.000Z | import pytest
from brownie import Contract, chain, interface
from rich.console import Console
from helpers.sett.SnapshotManager import SnapshotManager
from helpers.sett.simulation.SimulationManager import SimulationManager
from helpers.registry import registry
from helpers.registry.artifacts import artifacts
from helpers.time_utils import days
from helpers.token_utils import distribute_from_whales
from scripts.upgrade.upgrade_crv_setts import (
queue_upgrade_crv_strat,
CRV_SETTS_TO_UPGRADE,
)
from scripts.systems.badger_system import connect_badger
from tests.sett.generic_strategy_tests.strategy_flow import (
assert_deposit_withdraw_single_user_flow,
assert_single_user_harvest_flow,
assert_migrate_single_user,
assert_withdraw_other,
assert_single_user_harvest_flow_remove_fees,
)
from tests.sett.generic_strategy_tests.strategy_permissions import (
assert_strategy_action_permissions,
assert_strategy_config_permissions,
assert_strategy_pausing_permissions,
assert_sett_pausing_permissions,
assert_sett_config_permissions,
assert_controller_permissions,
)
from tests.conftest import badger_single_sett
from config.badger_config import badger_config
console = Console()
@pytest.mark.parametrize(
"settID",
CRV_SETTS_TO_UPGRADE,
)
| 32.388889 | 71 | 0.797942 | import pytest
from brownie import Contract, chain, interface
from rich.console import Console
from helpers.sett.SnapshotManager import SnapshotManager
from helpers.sett.simulation.SimulationManager import SimulationManager
from helpers.registry import registry
from helpers.registry.artifacts import artifacts
from helpers.time_utils import days
from helpers.token_utils import distribute_from_whales
from scripts.upgrade.upgrade_crv_setts import (
queue_upgrade_crv_strat,
CRV_SETTS_TO_UPGRADE,
)
from scripts.systems.badger_system import connect_badger
from tests.sett.generic_strategy_tests.strategy_flow import (
assert_deposit_withdraw_single_user_flow,
assert_single_user_harvest_flow,
assert_migrate_single_user,
assert_withdraw_other,
assert_single_user_harvest_flow_remove_fees,
)
from tests.sett.generic_strategy_tests.strategy_permissions import (
assert_strategy_action_permissions,
assert_strategy_config_permissions,
assert_strategy_pausing_permissions,
assert_sett_pausing_permissions,
assert_sett_config_permissions,
assert_controller_permissions,
)
from tests.conftest import badger_single_sett
from config.badger_config import badger_config
console = Console()
@pytest.mark.parametrize(
"settID",
CRV_SETTS_TO_UPGRADE,
)
def test_simulation_after_upgrade_crv_setts(settID):
# Upgrade crv strategy
badger = connect_badger(badger_config.prod_json)
"""
TODO Get the Implementation before upgrade
"""
txFilename = queue_upgrade_crv_strat(badger, settID)
# Sleep 2 days to pass timelock delay period.
chain.sleep(2 * days(2))
badger.governance_execute_transaction(txFilename)
"""
TODO assert tht implementation has changed
"""
## Object representing the sett we want and the mode we're in
thisSettConfig = {"id": settID, "mode": "test"}
## Get badger so we can get info in sett and strats
badger = badger_single_sett(thisSettConfig)
## We now have the want, we can mint some
deployer = badger.deployer
## Mints token for us
distribute_from_whales(deployer)
snap = SnapshotManager(badger, settID)
simulation = SimulationManager(badger, snap, settID)
simulation.provision()
# Randomize 30 actions.
simulation.randomize(30)
simulation.run()
assert_deposit_withdraw_single_user_flow(thisSettConfig)
assert_single_user_harvest_flow(thisSettConfig)
assert_migrate_single_user(thisSettConfig)
assert_withdraw_other(thisSettConfig)
assert_single_user_harvest_flow_remove_fees(thisSettConfig)
assert_strategy_action_permissions(thisSettConfig)
assert_strategy_config_permissions(thisSettConfig)
assert_strategy_pausing_permissions(thisSettConfig)
assert_sett_pausing_permissions(thisSettConfig)
assert_sett_config_permissions(thisSettConfig)
assert_controller_permissions(thisSettConfig)
| 1,590 | 0 | 22 |
7dff39fe5f92e51a87e2ac41d42efa20ffd5a3e0 | 4,215 | py | Python | wideq/refrigerator.py | davewatson91/wideq | 3746447a1321d4a03e715c110bd8b150256cf6d1 | [
"MIT"
] | 2 | 2021-03-25T22:22:23.000Z | 2021-04-20T05:44:26.000Z | wideq/refrigerator.py | davewatson91/wideq | 3746447a1321d4a03e715c110bd8b150256cf6d1 | [
"MIT"
] | 1 | 2021-04-20T05:43:32.000Z | 2021-04-20T05:46:56.000Z | wideq/refrigerator.py | davewatson91/wideq | 3746447a1321d4a03e715c110bd8b150256cf6d1 | [
"MIT"
] | 3 | 2021-04-20T05:45:25.000Z | 2021-06-04T23:57:17.000Z | import enum
from typing import Optional
from .client import Device
from .util import lookup_enum
class RefrigeratorDevice(Device):
"""A higher-level interface for a refrigerator."""
def set_temp_refrigerator_c(self, temp):
"""Set the refrigerator temperature in Celsius.
"""
value = self.model.enum_value('TempRefrigerator', str(temp))
self._set_control('RETM', value)
def set_temp_freezer_c(self, temp):
"""Set the freezer temperature in Celsius.
"""
value = self.model.enum_value('TempFreezer', str(temp))
self._set_control('REFT', value)
def poll(self) -> Optional['RefrigeratorStatus']:
"""Poll the device's current state.
Monitoring must be started first with `monitor_start`.
:returns: Either a `RefrigeratorStatus` instance or `None` if the
status is not yet available.
"""
# Abort if monitoring has not started yet.
if not hasattr(self, 'mon'):
return None
data = self.mon.poll()
if data:
res = self.model.decode_monitor(data)
return RefrigeratorStatus(self, res)
else:
return None
class RefrigeratorStatus(object):
"""Higher-level information about a refrigerator's current status.
:param refrigerator: The RefrigeratorDevice instance.
:param data: JSON data from the API.
"""
@property
@property
@property
@property
@property
@property
@property
@property
@property
@property
@property
@property
| 29.893617 | 76 | 0.671886 | import enum
from typing import Optional
from .client import Device
from .util import lookup_enum
class IcePlus(enum.Enum):
OFF = "@CP_OFF_EN_W"
ON = "@CP_ON_EN_W"
ICE_PLUS = "@RE_TERM_ICE_PLUS_W"
ICE_PLUS_FREEZE = "@RE_MAIN_SPEED_FREEZE_TERM_W"
ICE_PLUS_OFF = "@CP_TERM_OFF_KO_W"
class FreshAirFilter(enum.Enum):
OFF = "@CP_TERM_OFF_KO_W"
AUTO = "@RE_STATE_FRESH_AIR_FILTER_MODE_AUTO_W"
POWER = "@RE_STATE_FRESH_AIR_FILTER_MODE_POWER_W"
REPLACE_FILTER = "@RE_STATE_REPLACE_FILTER_W"
SMARTCARE_ON = "@RE_STATE_SMART_SMART_CARE_ON"
SMARTCARE_OFF = "@RE_STATE_SMART_SMART_CARE_OFF"
SMARTCARE_WAIT = "@RE_STATE_SMART_SMART_CARE_WAIT"
EMPTY = ""
class SmartSavingMode(enum.Enum):
OFF = "@CP_TERM_USE_NOT_W"
NIGHT = "@RE_SMARTSAVING_MODE_NIGHT_W"
CUSTOM = "@RE_SMARTSAVING_MODE_CUSTOM_W"
SMART_GRID_OFF = "@CP_OFF_EN_W"
SMART_GRID_DEMAND_RESPONSE = "@RE_TERM_DEMAND_RESPONSE_FUNCTIONALITY_W"
SMART_GRID_CUSTOM = "@RE_TERM_DELAY_DEFROST_CAPABILITY_W"
EMPTY = ""
class RefrigeratorDevice(Device):
"""A higher-level interface for a refrigerator."""
def set_temp_refrigerator_c(self, temp):
"""Set the refrigerator temperature in Celsius.
"""
value = self.model.enum_value('TempRefrigerator', str(temp))
self._set_control('RETM', value)
def set_temp_freezer_c(self, temp):
"""Set the freezer temperature in Celsius.
"""
value = self.model.enum_value('TempFreezer', str(temp))
self._set_control('REFT', value)
def poll(self) -> Optional['RefrigeratorStatus']:
"""Poll the device's current state.
Monitoring must be started first with `monitor_start`.
:returns: Either a `RefrigeratorStatus` instance or `None` if the
status is not yet available.
"""
# Abort if monitoring has not started yet.
if not hasattr(self, 'mon'):
return None
data = self.mon.poll()
if data:
res = self.model.decode_monitor(data)
return RefrigeratorStatus(self, res)
else:
return None
class RefrigeratorStatus(object):
"""Higher-level information about a refrigerator's current status.
:param refrigerator: The RefrigeratorDevice instance.
:param data: JSON data from the API.
"""
def __init__(self, refrigerator: RefrigeratorDevice, data: dict):
self.refrigerator = refrigerator
self.data = data
@property
def temp_refrigerator_c(self):
temp = lookup_enum('TempRefrigerator', self.data, self.refrigerator)
return int(temp)
@property
def temp_freezer_c(self):
temp = lookup_enum('TempFreezer', self.data, self.refrigerator)
return int(temp)
@property
def ice_plus_status(self):
status = lookup_enum('IcePlus', self.data, self.refrigerator)
return IcePlus(status)
@property
def fresh_air_filter_status(self):
status = lookup_enum('FreshAirFilter', self.data, self.refrigerator)
return FreshAirFilter(status)
@property
def energy_saving_mode(self):
mode = lookup_enum('SmartSavingMode', self.data, self.refrigerator)
return SmartSavingMode(mode)
@property
def door_opened(self):
state = lookup_enum('DoorOpenState', self.data, self.refrigerator)
return state == "OPEN"
@property
def temp_unit(self):
return lookup_enum('TempUnit', self.data, self.refrigerator)
@property
def energy_saving_enabled(self):
mode = lookup_enum(
'SmartSavingModeStatus', self.data, self.refrigerator
)
return mode == 'ON'
@property
def locked(self):
status = lookup_enum('LockingStatus', self.data, self.refrigerator)
return status == "LOCK"
@property
def active_saving_status(self):
return self.data['ActiveSavingStatus']
@property
def eco_enabled(self):
eco = lookup_enum('EcoFriendly', self.data, self.refrigerator)
return eco == "@CP_ON_EN_W"
@property
def water_filter_used_month(self):
return self.data['WaterFilterUsedMonth']
| 1,326 | 872 | 408 |
75331b011bd2fbc98c3034f1d5e7ec1e561ea62b | 780 | py | Python | ursinanetworking/examples/QTChat/server.py | nutanstrek/UrsinaNetworking | f505f073f9a25f9f79fa0a1c059aeed6661377e0 | [
"MIT"
] | 38 | 2021-04-06T05:07:24.000Z | 2022-03-29T01:44:10.000Z | ursinanetworking/examples/QTChat/server.py | nutanstrek/UrsinaNetworking | f505f073f9a25f9f79fa0a1c059aeed6661377e0 | [
"MIT"
] | 3 | 2021-04-11T02:24:19.000Z | 2022-03-29T03:34:19.000Z | ursinanetworking/examples/QTChat/server.py | nutanstrek/UrsinaNetworking | f505f073f9a25f9f79fa0a1c059aeed6661377e0 | [
"MIT"
] | 6 | 2021-04-06T12:01:25.000Z | 2022-03-28T20:12:10.000Z | from ursinanetworking import *
Server = UrsinaNetworkingServer("localhost", 25565)
@Server.event
@Server.event
while True:
Server.process_net_events() | 30 | 85 | 0.694872 | from ursinanetworking import *
Server = UrsinaNetworkingServer("localhost", 25565)
def broadcast_message(message, author):
Server.broadcast("receive_message", {"message" : message, "author" : author})
@Server.event
def onClientConnected(client):
client.registered = False
#broadcast_message(f"{client} connected", "system")
@Server.event
def send_message(client, datas):
if not client.registered:
client.registered = True
client.name = datas
client.send_message("clear_chat", "")
client.send_message("get_identity", {"id" : client.id, "name" : client.name})
broadcast_message(f"{datas} à rejoint le chat !", "système")
else:
broadcast_message(datas, client.name)
while True:
Server.process_net_events() | 557 | 0 | 67 |
43e6bb65360a0bc2d24721dd4aa6582d161eb065 | 2,575 | py | Python | src/day8.py | chao-mu/aoc2021 | 9cb0590a8de100f260a78f22e50d00d6acb13ae4 | [
"CC0-1.0"
] | null | null | null | src/day8.py | chao-mu/aoc2021 | 9cb0590a8de100f260a78f22e50d00d6acb13ae4 | [
"CC0-1.0"
] | null | null | null | src/day8.py | chao-mu/aoc2021 | 9cb0590a8de100f260a78f22e50d00d6acb13ae4 | [
"CC0-1.0"
] | null | null | null | #!/usr/bin/env python3
from aoc2021.util import print_solutions, import_strs
from math import inf
from itertools import permutations
import copy
Numbers = {
"abcefg": 0,
"cf": 1,
"acdeg": 2,
"acdfg": 3,
"bcdf": 4,
"abdfg": 5,
"abdefg": 6,
"acf": 7,
"abcdefg": 8,
"abcdfg": 9
}
if __name__ == "__main__":
main()
| 22.198276 | 76 | 0.553398 | #!/usr/bin/env python3
from aoc2021.util import print_solutions, import_strs
from math import inf
from itertools import permutations
import copy
def part_1(clock):
uniqs = 0
for face in clock:
_, signals = face
for signal in signals:
if len(signal) in [2, 4, 3, 7]:
uniqs += 1
return uniqs
Numbers = {
"abcefg": 0,
"cf": 1,
"acdeg": 2,
"acdfg": 3,
"bcdf": 4,
"abdfg": 5,
"abdefg": 6,
"acf": 7,
"abcdefg": 8,
"abcdfg": 9
}
def part_2(clock):
total = 0
for face in clock:
patterns, signals = face
assignments = backward_search({c: "*" for c in "abcdefg"}, patterns)
number = ""
for s in signals:
correct_s = "".join(sorted(assignments[c] for c in s))
number += str(Numbers[correct_s])
total += int(number)
return total
def backward_search(assignments, patterns):
unassigned = None
domain = set("abcdefg")
for s, e in assignments.items():
if e == "*":
unassigned = s
else:
domain.remove(e)
if unassigned is None:
return assignments
for d in domain:
local = copy.deepcopy(assignments)
local[unassigned] = d
if consistent(local, patterns):
local = backward_search(local, patterns)
if local is not None:
return local
return None
def consistent(assignments, patterns):
for pattern in patterns:
new_str = "".join(assignments[s] for s in pattern)
candidates = [s for s in Numbers if len(s) == len(new_str)]
candidate_found = False
for candidate in candidates:
matched = True
for idx, char in enumerate(new_str):
if char != "*" and char not in candidate:
matched = False
break
if matched:
candidate_found = True
if not candidate_found:
return False
return True
def import_clock(path):
clock = []
lines = import_strs(path)
for line in lines:
patterns, signals = line.strip().split(" | ")
clock.append([patterns.split(" "), signals.split(" ")])
return clock
def main():
print_solutions(
["resources/day8-test.txt", "resources/day8.txt"],
import_clock,
part_1
)
print_solutions(
["resources/day8-test.txt", "resources/day8.txt"],
import_clock,
part_2
)
if __name__ == "__main__":
main()
| 2,076 | 0 | 138 |
4344fa9db3e3548e4b2450324536e75bc3e05d90 | 1,660 | py | Python | CUT/experiments/__init__.py | Theomat/colorization-av-enseirb-2020 | c54c2388ea39a62289fa2f1c51b4757bf55d3c4f | [
"Apache-2.0"
] | 1,422 | 2020-07-31T00:31:19.000Z | 2022-03-31T11:35:26.000Z | CUT/experiments/__init__.py | Theomat/colorization-av-enseirb-2020 | c54c2388ea39a62289fa2f1c51b4757bf55d3c4f | [
"Apache-2.0"
] | 123 | 2020-07-31T04:16:03.000Z | 2022-03-21T14:02:20.000Z | CUT/experiments/__init__.py | Theomat/colorization-av-enseirb-2020 | c54c2388ea39a62289fa2f1c51b4757bf55d3c4f | [
"Apache-2.0"
] | 324 | 2020-07-31T00:40:11.000Z | 2022-03-31T10:01:10.000Z | import os
import importlib
if __name__ == "__main__":
import sys
import pickle
assert len(sys.argv) >= 3
name = sys.argv[1]
Launcher = find_launcher_using_name(name)
cache = "/tmp/tmux_launcher/{}".format(name)
if os.path.isfile(cache):
instance = pickle.load(open(cache, 'r'))
else:
instance = Launcher()
cmd = sys.argv[2]
if cmd == "launch":
instance.launch()
elif cmd == "stop":
instance.stop()
elif cmd == "send":
expid = int(sys.argv[3])
cmd = int(sys.argv[4])
instance.send_command(expid, cmd)
os.makedirs("/tmp/tmux_launcher/", exist_ok=True)
pickle.dump(instance, open(cache, 'w'))
| 30.181818 | 80 | 0.633735 | import os
import importlib
def find_launcher_using_name(launcher_name):
# cur_dir = os.path.dirname(os.path.abspath(__file__))
# pythonfiles = glob.glob(cur_dir + '/**/*.py')
launcher_filename = "experiments.{}_launcher".format(launcher_name)
launcherlib = importlib.import_module(launcher_filename)
# In the file, the class called LauncherNameLauncher() will
# be instantiated. It has to be a subclass of BaseLauncher,
# and it is case-insensitive.
launcher = None
target_launcher_name = launcher_name.replace('_', '') + 'launcher'
for name, cls in launcherlib.__dict__.items():
if name.lower() == target_launcher_name.lower():
launcher = cls
if launcher is None:
raise ValueError("In %s.py, there should be a subclass of BaseLauncher "
"with class name that matches %s in lowercase." %
(launcher_filename, target_launcher_name))
return launcher
if __name__ == "__main__":
import sys
import pickle
assert len(sys.argv) >= 3
name = sys.argv[1]
Launcher = find_launcher_using_name(name)
cache = "/tmp/tmux_launcher/{}".format(name)
if os.path.isfile(cache):
instance = pickle.load(open(cache, 'r'))
else:
instance = Launcher()
cmd = sys.argv[2]
if cmd == "launch":
instance.launch()
elif cmd == "stop":
instance.stop()
elif cmd == "send":
expid = int(sys.argv[3])
cmd = int(sys.argv[4])
instance.send_command(expid, cmd)
os.makedirs("/tmp/tmux_launcher/", exist_ok=True)
pickle.dump(instance, open(cache, 'w'))
| 927 | 0 | 23 |
f2c84250d49b0b78d8283962f1d175aafa4b43ad | 2,366 | py | Python | tools/gyp_jc3_handling_editor.py | xforce/jc3-handling-editor | 48485e91ef3ce0e2849ae5f5aae1ef40f989c4c6 | [
"MIT"
] | 4 | 2016-11-25T12:45:35.000Z | 2020-02-20T21:55:49.000Z | tools/gyp_jc3_handling_editor.py | xforce/jc3-handling-editor | 48485e91ef3ce0e2849ae5f5aae1ef40f989c4c6 | [
"MIT"
] | 6 | 2016-11-21T19:32:47.000Z | 2017-03-21T17:18:43.000Z | tools/gyp_jc3_handling_editor.py | xforce/jc3-handling-editor | 48485e91ef3ce0e2849ae5f5aae1ef40f989c4c6 | [
"MIT"
] | 2 | 2016-11-20T21:05:41.000Z | 2022-01-31T02:19:12.000Z | #!/usr/bin/env python
import glob
import os
import shlex
import sys
import platform
script_dir = os.path.dirname(__file__)
jc3_handling_editor_root = os.path.normpath(os.path.join(script_dir, os.pardir))
sys.path.insert(0, os.path.abspath(os.path.join(jc3_handling_editor_root, 'tools')))
sys.path.insert(0, os.path.join(jc3_handling_editor_root, 'tools', 'gyp', 'pylib'))
import gyp
if __name__ == '__main__':
args = sys.argv[1:]
# GYP bug.
if sys.platform == 'win32':
args.append(os.path.join(jc3_handling_editor_root, 'jc3_handling_editor.gyp'))
standalone_fn = os.path.join(jc3_handling_editor_root, 'standalone.gypi')
toolchain_fn = os.path.join(jc3_handling_editor_root, 'toolchain.gypi')
common_fn = os.path.join(jc3_handling_editor_root, 'common.gypi')
options_fn = os.path.join(jc3_handling_editor_root, 'config.gypi')
else:
args.append(os.path.join(os.path.abspath(jc3_handling_editor_root), 'jc3_handling_editor.gyp'))
standalone_fn = os.path.join(os.path.abspath(jc3_handling_editor_root), 'standalone.gypi')
toolchain_fn = os.path.join(os.path.abspath(jc3_handling_editor_root), 'toolchain.gypi')
common_fn = os.path.join(os.path.abspath(jc3_handling_editor_root), 'common.gypi')
options_fn = os.path.join(os.path.abspath(jc3_handling_editor_root), 'config.gypi')
if os.path.exists(standalone_fn):
args.extend(['-I', standalone_fn])
if os.path.exists(toolchain_fn):
args.extend(['-I', toolchain_fn])
if os.path.exists(common_fn):
args.extend(['-I', common_fn])
if os.path.exists(options_fn):
args.extend(['-I', options_fn])
args.append('--depth=' + jc3_handling_editor_root)
#args.append('-Dcomponent=shared_library')
#args.append('-Dlibrary=shared_library')
gyp_args = list(args)
print os.environ.get('GYP_GENERATORS')
gyp_generators = os.environ.get('GYP_GENERATORS')
#if platform.system() == 'Linux' and gyp_generators != 'ninja':
# --generator-output defines where the Makefile goes.
gyp_args.append('--generator-output=out')
# -Goutput_dir defines where the build output goes, relative to the
# Makefile. Set it to . so that the build output doesn't end up in out/out.
gyp_args.append('-Goutput_dir=.')
run_gyp(gyp_args)
| 35.313433 | 99 | 0.724852 | #!/usr/bin/env python
import glob
import os
import shlex
import sys
import platform
script_dir = os.path.dirname(__file__)
jc3_handling_editor_root = os.path.normpath(os.path.join(script_dir, os.pardir))
sys.path.insert(0, os.path.abspath(os.path.join(jc3_handling_editor_root, 'tools')))
sys.path.insert(0, os.path.join(jc3_handling_editor_root, 'tools', 'gyp', 'pylib'))
import gyp
def run_gyp(args):
rc = gyp.main(args)
if rc != 0:
print 'Error running GYP'
sys.exit(rc)
if __name__ == '__main__':
args = sys.argv[1:]
# GYP bug.
if sys.platform == 'win32':
args.append(os.path.join(jc3_handling_editor_root, 'jc3_handling_editor.gyp'))
standalone_fn = os.path.join(jc3_handling_editor_root, 'standalone.gypi')
toolchain_fn = os.path.join(jc3_handling_editor_root, 'toolchain.gypi')
common_fn = os.path.join(jc3_handling_editor_root, 'common.gypi')
options_fn = os.path.join(jc3_handling_editor_root, 'config.gypi')
else:
args.append(os.path.join(os.path.abspath(jc3_handling_editor_root), 'jc3_handling_editor.gyp'))
standalone_fn = os.path.join(os.path.abspath(jc3_handling_editor_root), 'standalone.gypi')
toolchain_fn = os.path.join(os.path.abspath(jc3_handling_editor_root), 'toolchain.gypi')
common_fn = os.path.join(os.path.abspath(jc3_handling_editor_root), 'common.gypi')
options_fn = os.path.join(os.path.abspath(jc3_handling_editor_root), 'config.gypi')
if os.path.exists(standalone_fn):
args.extend(['-I', standalone_fn])
if os.path.exists(toolchain_fn):
args.extend(['-I', toolchain_fn])
if os.path.exists(common_fn):
args.extend(['-I', common_fn])
if os.path.exists(options_fn):
args.extend(['-I', options_fn])
args.append('--depth=' + jc3_handling_editor_root)
#args.append('-Dcomponent=shared_library')
#args.append('-Dlibrary=shared_library')
gyp_args = list(args)
print os.environ.get('GYP_GENERATORS')
gyp_generators = os.environ.get('GYP_GENERATORS')
#if platform.system() == 'Linux' and gyp_generators != 'ninja':
# --generator-output defines where the Makefile goes.
gyp_args.append('--generator-output=out')
# -Goutput_dir defines where the build output goes, relative to the
# Makefile. Set it to . so that the build output doesn't end up in out/out.
gyp_args.append('-Goutput_dir=.')
run_gyp(gyp_args)
| 80 | 0 | 23 |
04f5b502729ac86c25e910f36933883cca287f3f | 627 | py | Python | sliceprovider/ResourceManagement.py | fernnf/idc | 6abf0e55467ab2b2cd3a22b869b3e0698ebfc5d2 | [
"Apache-2.0"
] | null | null | null | sliceprovider/ResourceManagement.py | fernnf/idc | 6abf0e55467ab2b2cd3a22b869b3e0698ebfc5d2 | [
"Apache-2.0"
] | 3 | 2018-08-16T14:35:11.000Z | 2018-08-16T14:52:12.000Z | sliceprovider/ResourceManagement.py | fernnf/idc | 6abf0e55467ab2b2cd3a22b869b3e0698ebfc5d2 | [
"Apache-2.0"
] | null | null | null | """
Res. & VM Mgmt. component of the Infrastructure Management & Abstraction (IMA)
Interacts with Docker to replicate the container from origin to destiny
"""
from docker import APIClient
| 27.26087 | 78 | 0.740032 | """
Res. & VM Mgmt. component of the Infrastructure Management & Abstraction (IMA)
Interacts with Docker to replicate the container from origin to destiny
"""
from docker import APIClient
def replicateContainer(containerID, orig_url, dest_url):
orig_client = APIClient(base_url=orig_url, tlf=False)
dest_client = APIClient(base_url=dest_url, tls=False)
image = orig_client.get_image(containerID)
data=None
for chunck in image:
if data == None:
data = chunck
else:
data += chunck
dest_client.load_image(data)
container = dest_client.create_container(containerID, name=containerID)
| 416 | 0 | 23 |
1207bd1d9849c7d09ef1136111f4fbe44b5966be | 3,921 | py | Python | src/parser.py | zhexiongliu/Nasdaq-ITCH-5.0-VWAP | aa78482171d2172d4feeb826b78e00dee195c181 | [
"MIT"
] | 9 | 2019-06-04T14:08:17.000Z | 2021-11-24T12:02:57.000Z | src/parser.py | zhexiongliu/Nasdaq-ITCH-5.0-VWAP | aa78482171d2172d4feeb826b78e00dee195c181 | [
"MIT"
] | null | null | null | src/parser.py | zhexiongliu/Nasdaq-ITCH-5.0-VWAP | aa78482171d2172d4feeb826b78e00dee195c181 | [
"MIT"
] | 2 | 2019-10-06T01:47:26.000Z | 2022-01-10T13:20:58.000Z | import gzip
import struct
import datetime
import pandas as pd
import os
if __name__ == '__main__':
bin_data = gzip.open(os.path.join('..', 'res', '08302019.NASDAQ_ITCH50.gz'), 'rb')
msg_header = bin_data.read(1)
itch = ITCH()
while msg_header:
if msg_header == "S":
message = itch.get_binary(11)
elif msg_header == "R":
message = itch.get_binary(38)
elif msg_header == "H":
message = itch.get_binary(24)
elif msg_header == "Y":
message = itch.get_binary(19)
elif msg_header == "L":
message = itch.get_binary(25)
elif msg_header == "V":
message = itch.get_binary(34)
elif msg_header == "W":
message = itch.get_binary(11)
elif msg_header == "K":
message = itch.get_binary(27)
elif msg_header == "A":
message = itch.get_binary(35)
elif msg_header == "F":
message = itch.get_binary(39)
elif msg_header == "E":
message = itch.get_binary(30)
elif msg_header == "C":
message = itch.get_binary(35)
elif msg_header == "X":
message = itch.get_binary(22)
elif msg_header == "D":
message = itch.get_binary(18)
elif msg_header == "U":
message = itch.get_binary(34)
elif msg_header == "P":
message = itch.get_binary(43)
itch.get_vwap(message)
elif msg_header == "Q":
message = itch.get_binary(39)
elif msg_header == "B":
message = itch.get_binary(18)
elif msg_header == "I":
message = itch.get_binary(49)
elif msg_header == "N":
message = itch.get_binary(19)
msg_header = bin_data.read(1)
bin_data.close()
| 29.261194 | 115 | 0.530477 | import gzip
import struct
import datetime
import pandas as pd
import os
class ITCH():
def __init__(self):
self.temp = []
self.flag = None
if not os.path.exists(os.path.join('.', 'output')):
os.makedirs(os.path.join('.', 'output'))
def get_binary(self, size):
read = bin_data.read(size)
return read
def convert_time(self, stamp):
time = datetime.datetime.fromtimestamp(stamp / 1e9)
time = time.strftime('%H:%M:%S')
return time
def cal_vwap(self, df):
df['amount'] = df['price'] * df['volume']
df['time'] = pd.to_datetime(df['time'])
df = df.groupby([df['time'].dt.hour, df['symbol']])['amount', 'volume'].sum()
df['vwap'] = df['amount'] / df['volume']
df['vwap'] = df['vwap'].round(2)
df = df.reset_index()
df['time'] = df.apply(lambda x: str(x['time']) + ':00:00', axis=1)
df = df[['time', 'symbol', 'vwap']]
return df
def get_vwap(self, message):
parsed_data, hour = self.trade_message(message)
if self.flag is None:
self.flag = hour
if self.flag != hour:
df = pd.DataFrame(self.temp, columns=['time', 'symbol', 'price', 'volume'])
result = self.cal_vwap(df)
result.to_csv(os.path.join('..', 'output', str(self.flag) + '.txt'), sep=' ', index=False)
print(result)
self.temp = []
self.flag = hour
self.temp.append(parsed_data)
def trade_message(self, msg):
msg_type = 'P'
temp = struct.unpack('>4s6sQcI8cIQ', msg)
new_msg = struct.pack('>s4s2s6sQsI8sIQ', msg_type, temp[0], '\x00\x00', temp[1], temp[2], temp[3], temp[4],
''.join(list(temp[5:13])), temp[13], temp[14])
value = struct.unpack('>sHHQQsI8sIQ', new_msg)
value = list(value)
value[3] = self.convert_time(value[3])
value[7] = value[7].strip()
value[8] = float(value[8])
value[8] = value[8] / 10000
return [value[3], value[7], value[8], value[6]], value[3].split(':')[0]
if __name__ == '__main__':
bin_data = gzip.open(os.path.join('..', 'res', '08302019.NASDAQ_ITCH50.gz'), 'rb')
msg_header = bin_data.read(1)
itch = ITCH()
while msg_header:
if msg_header == "S":
message = itch.get_binary(11)
elif msg_header == "R":
message = itch.get_binary(38)
elif msg_header == "H":
message = itch.get_binary(24)
elif msg_header == "Y":
message = itch.get_binary(19)
elif msg_header == "L":
message = itch.get_binary(25)
elif msg_header == "V":
message = itch.get_binary(34)
elif msg_header == "W":
message = itch.get_binary(11)
elif msg_header == "K":
message = itch.get_binary(27)
elif msg_header == "A":
message = itch.get_binary(35)
elif msg_header == "F":
message = itch.get_binary(39)
elif msg_header == "E":
message = itch.get_binary(30)
elif msg_header == "C":
message = itch.get_binary(35)
elif msg_header == "X":
message = itch.get_binary(22)
elif msg_header == "D":
message = itch.get_binary(18)
elif msg_header == "U":
message = itch.get_binary(34)
elif msg_header == "P":
message = itch.get_binary(43)
itch.get_vwap(message)
elif msg_header == "Q":
message = itch.get_binary(39)
elif msg_header == "B":
message = itch.get_binary(18)
elif msg_header == "I":
message = itch.get_binary(49)
elif msg_header == "N":
message = itch.get_binary(19)
msg_header = bin_data.read(1)
bin_data.close()
| 1,886 | -8 | 185 |
c5ab311027e4d350a3a36cc33e8cee4864e34548 | 198 | py | Python | 4.py | lzbferrari/ljb_python | e545891c707e412bb12601c94b456be721c85573 | [
"MIT"
] | null | null | null | 4.py | lzbferrari/ljb_python | e545891c707e412bb12601c94b456be721c85573 | [
"MIT"
] | null | null | null | 4.py | lzbferrari/ljb_python | e545891c707e412bb12601c94b456be721c85573 | [
"MIT"
] | null | null | null | '''
创造一个变量他等于5如果这个变量小于10会打印我打得过那些忍者，小于30会打印有点难，不过我能应付如果小于50会打印太多了
'''
ninjas = 5
if ninjas < 10:
print('我打得过那些忍者')
elif ninjas < 30:
print('有点难，不过我能应付')
elif ninjas < 50:
print('太多了')
| 15.230769 | 61 | 0.681818 | '''
创造一个变量他等于5如果这个变量小于10会打印我打得过那些忍者，小于30会打印有点难，不过我能应付如果小于50会打印太多了
'''
ninjas = 5
if ninjas < 10:
print('我打得过那些忍者')
elif ninjas < 30:
print('有点难，不过我能应付')
elif ninjas < 50:
print('太多了')
| 0 | 0 | 0 |
0d8cdabb6bfa49bc5a270e7bc3fa38275087f891 | 1,273 | py | Python | tests/ut/python/parallel/test_auto_parallel_inference.py | doc22940/mindspore | 21bcdcd8adb97b9171b2822a7ed2c4c138c99607 | [
"Apache-2.0"
] | 1 | 2020-05-13T11:31:21.000Z | 2020-05-13T11:31:21.000Z | tests/ut/python/parallel/test_auto_parallel_inference.py | doc22940/mindspore | 21bcdcd8adb97b9171b2822a7ed2c4c138c99607 | [
"Apache-2.0"
] | null | null | null | tests/ut/python/parallel/test_auto_parallel_inference.py | doc22940/mindspore | 21bcdcd8adb97b9171b2822a7ed2c4c138c99607 | [
"Apache-2.0"
] | null | null | null | import numpy as np
import mindspore.nn as nn
from mindspore import Tensor, context
from mindspore.ops import operations as P
from mindspore.nn import WithLossCell, TrainOneStepCell
from mindspore.nn import Momentum
from mindspore.parallel._cost_model_context import set_cost_model_context | 34.405405 | 88 | 0.728987 | import numpy as np
import mindspore.nn as nn
from mindspore import Tensor, context
from mindspore.ops import operations as P
from mindspore.nn import WithLossCell, TrainOneStepCell
from mindspore.nn import Momentum
from mindspore.parallel._cost_model_context import set_cost_model_context
class Net(nn.Cell):
def __init__(self, input_ch, out_ch):
super(Net, self).__init__()
self.dense = nn.Dense(input_ch, out_ch)
self.relu = P.ReLU()
def construct(self, x):
x = self.dense(x)
x = self.relu(x)
return x
def test_inference_phase():
context.set_auto_parallel_context(device_num=8, global_rank=0)
context.set_auto_parallel_context(parallel_mode="auto_parallel")
set_cost_model_context(run_phase=1)
net = Net(512, 128)
predict = Tensor(np.ones([64, 512]).astype(np.float32) * 0.001)
label = Tensor(np.ones([64, 128]).astype(np.float32))
loss = nn.SoftmaxCrossEntropyWithLogits()
optimizer = Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9)
net_with_loss = WithLossCell(net, loss)
train_network = TrainOneStepCell(net_with_loss, optimizer)
train_network.set_train()
train_network.set_auto_parallel()
output = train_network(predict, label) | 887 | -2 | 99 |
f9da18cdb4b8afbbf3b00ddbb3ca9a4c8a07f66c | 2,043 | py | Python | agua_amiga/gui/streak_window.py | mpnordland/agua_amiga | abbfaceea6aa90332a7782c2ea6d01f1fec1d41a | [
"MIT"
] | 2 | 2022-01-04T10:35:24.000Z | 2022-02-02T21:51:25.000Z | agua_amiga/gui/streak_window.py | mpnordland/agua_amiga | abbfaceea6aa90332a7782c2ea6d01f1fec1d41a | [
"MIT"
] | null | null | null | agua_amiga/gui/streak_window.py | mpnordland/agua_amiga | abbfaceea6aa90332a7782c2ea6d01f1fec1d41a | [
"MIT"
] | null | null | null | from gi.repository import GLib, Gio, Gtk
from datetime import datetime, timedelta, date
from agua_amiga.datastore import Datastore, convert_from_display_to_mL, convert_from_mL_to_display
@Gtk.Template.from_file("ui_definitions/StreakWindow.glade") | 38.54717 | 160 | 0.657367 | from gi.repository import GLib, Gio, Gtk
from datetime import datetime, timedelta, date
from agua_amiga.datastore import Datastore, convert_from_display_to_mL, convert_from_mL_to_display
@Gtk.Template.from_file("ui_definitions/StreakWindow.glade")
class StreakWindow(Gtk.Window):
__gtype_name__ = "StreakWindow"
calendar_streaks: Gtk.Button = Gtk.Template.Child()
def __init__(self, *args, **kwargs) -> None:
assert 'datastore' in kwargs.keys()
self.datastore: Datastore = kwargs['datastore']
del kwargs['datastore']
super().__init__(*args, **kwargs)
self.update_marked_days()
def update_marked_days(self):
self.calendar_streaks.clear_marks()
month_start = date(self.calendar_streaks.get_property('year'), self.calendar_streaks.get_property('month') + 1, 1)
if month_start.month < 12:
month_end = month_start.replace(month=month_start.month + 1) - timedelta(days=1)
else:
month_end = date(month_start.year + 1, 1, 1)
display_units = self.datastore.get_display_units()
days = self.datastore.get_days_drunk_water(month_start, month_end)
if days is not None:
water_data = dict(days)
for day in days:
parsed_day = date.fromisoformat(day[0])
self.calendar_streaks.mark_day(parsed_day.day)
def detail_func(widget, year, month, day):
try:
day_volume_drunk = water_data[f"{year}-{month+1:2}-{day:2}"]
return f"<span size=\"medium\" line_height=\"2\">{convert_from_mL_to_display(day_volume_drunk, display_units)} {display_units.value}</span>"
except Exception as e:
return f"<span size=\"medium\" line_height=\"2\">0 {display_units.value}</span>"
self.calendar_streaks.set_detail_func(detail_func)
@Gtk.Template.Callback()
def calendar_streaks_month_changed_cb(self, widget, **_kwargs):
self.update_marked_days() | 1,555 | 217 | 22 |
1102cf09a961527fe7f8f6eccb81022c752e0bfc | 957 | py | Python | subset.py | zygmuntz/phraug2 | ef2a6d42008b31024407208d2b9798ddddc2b7a5 | [
"BSD-2-Clause"
] | 190 | 2015-01-14T15:32:30.000Z | 2022-02-17T08:18:27.000Z | subset.py | zygmuntz/phraug2 | ef2a6d42008b31024407208d2b9798ddddc2b7a5 | [
"BSD-2-Clause"
] | 6 | 2015-04-24T17:07:08.000Z | 2020-01-13T10:07:37.000Z | subset.py | zygmuntz/phraug2 | ef2a6d42008b31024407208d2b9798ddddc2b7a5 | [
"BSD-2-Clause"
] | 69 | 2015-01-25T19:49:42.000Z | 2021-02-10T05:24:34.000Z | 'Save a subset of lines from an input file; start at offset and count n lines'
'default 100 lines starting from 0'
import sys
import argparse
parser = argparse.ArgumentParser( description = "Save a subset of lines from the input file to the output file" )
parser.add_argument( "input_file", help = "path to input file" )
parser.add_argument( "output_file", help = "path to output file" )
parser.add_argument( "-o", "--offset", help = "line number to start from, default 0", type = int, default = 0 )
parser.add_argument( "-l", "--lines", help = "number of lines to write, default 100", type = int, default = 100 )
args = parser.parse_args()
try:
lines = int( sys.argv[4] )
except IndexError:
lines = 100
i = open( args.input_file )
o = open( args.output_file, 'wb' )
offset = args.offset
count = 0
for line in i:
if offset > 0:
offset -= 1
continue
o.write( line )
count += 1
if count >= args.lines:
break
| 19.9375 | 113 | 0.668757 | 'Save a subset of lines from an input file; start at offset and count n lines'
'default 100 lines starting from 0'
import sys
import argparse
parser = argparse.ArgumentParser( description = "Save a subset of lines from the input file to the output file" )
parser.add_argument( "input_file", help = "path to input file" )
parser.add_argument( "output_file", help = "path to output file" )
parser.add_argument( "-o", "--offset", help = "line number to start from, default 0", type = int, default = 0 )
parser.add_argument( "-l", "--lines", help = "number of lines to write, default 100", type = int, default = 100 )
args = parser.parse_args()
try:
lines = int( sys.argv[4] )
except IndexError:
lines = 100
i = open( args.input_file )
o = open( args.output_file, 'wb' )
offset = args.offset
count = 0
for line in i:
if offset > 0:
offset -= 1
continue
o.write( line )
count += 1
if count >= args.lines:
break
| 0 | 0 | 0 |
6e26e1f0af0b391c2d44a64eeef08c7927ec934b | 13,401 | py | Python | pygw2/core/models/character.py | Natsku123/pygw2 | c8e579c07f4d33c7afadc8dee510be0a5e71d16e | [
"MIT"
] | 1 | 2020-01-19T21:27:53.000Z | 2020-01-19T21:27:53.000Z | pygw2/core/models/character.py | Natsku123/pygw2 | c8e579c07f4d33c7afadc8dee510be0a5e71d16e | [
"MIT"
] | 1 | 2021-12-09T21:18:13.000Z | 2021-12-09T21:18:13.000Z | pygw2/core/models/character.py | Natsku123/pygw2 | c8e579c07f4d33c7afadc8dee510be0a5e71d16e | [
"MIT"
] | null | null | null | import datetime
from typing import Optional, List, Union, TYPE_CHECKING
from pygw2.core.enums import *
from pygw2.utils import LazyLoader, BaseModel
if TYPE_CHECKING:
from pygw2.core.models.pvp import PvPEquipment
from pygw2.core.models.sab import SAB
from pygw2.core.models.guild import Guild
from pygw2.core.models.misc import Title, Color
from pygw2.core.models.backstory import BiographyAnswer
from pygw2.core.models.items import Item
from pygw2.core.models.general import Skin, ItemStat
from pygw2.core.models.wvw import WvWAbility
| 23.428322 | 85 | 0.676367 | import datetime
from typing import Optional, List, Union, TYPE_CHECKING
from pygw2.core.enums import *
from pygw2.utils import LazyLoader, BaseModel
if TYPE_CHECKING:
from pygw2.core.models.pvp import PvPEquipment
from pygw2.core.models.sab import SAB
from pygw2.core.models.guild import Guild
from pygw2.core.models.misc import Title, Color
from pygw2.core.models.backstory import BiographyAnswer
from pygw2.core.models.items import Item
from pygw2.core.models.general import Skin, ItemStat
from pygw2.core.models.wvw import WvWAbility
class Crafting(BaseModel):
discipline: Discipline
rating: int
active: bool
class Attributes(BaseModel):
Power: Optional[int] = 0
Precision: Optional[int] = 0
Toughness: Optional[int] = 0
Vitality: Optional[int] = 0
ConditionDamage: Optional[int] = 0
ConditionDuration: Optional[int] = 0
Healing: Optional[int] = 0
BoonDuration: Optional[int] = 0
class Stats(BaseModel):
id: int
values_: LazyLoader
@property
def values(self) -> "ItemStat":
return self.values_()
attributes: Attributes
class Equipment(BaseModel):
id: int
item_: LazyLoader
tabs: Optional[List[int]]
@property
def item(self) -> "Item":
return self.item_()
slot: Optional[EquipmentSlot]
location: EquipmentLocation
infusions_: Optional[LazyLoader]
@property
def infusions(self) -> List["Item"]:
return self.infusions_() if self.infusions_ is not None else None
upgrades_: Optional[LazyLoader]
@property
def upgrades(self) -> List["Item"]:
return self.upgrades_() if self.upgrades_ is not None else None
skin_: Optional[LazyLoader]
@property
def skin(self) -> List["Skin"]:
return self.skin_() if self.skin_ is not None else None
stats: Optional[Stats] = None
binding: Optional[Binding] = None
charges: Optional[int] = None
bound_to: Optional[str] = None
dyes_: Optional[LazyLoader]
@property
def dyes(self) -> List["Color"]:
return self.dyes_() if self.dyes_ is not None else None
class ItemInventory(BaseModel):
id: int
item_: LazyLoader
@property
def item(self) -> "Item":
return self.item_()
count: int
infusions_: Optional[LazyLoader]
@property
def infusions(self) -> List["Item"]:
return self.infusions_() if self.infusions_ is not None else None
upgrades_: Optional[LazyLoader]
@property
def upgrades(self) -> List["Item"]:
return self.upgrades_() if self.upgrades_ is not None else None
skin_: Optional[LazyLoader]
@property
def skin(self) -> List["Skin"]:
return self.skin_() if self.skin_ is not None else None
stats: Optional[Stats] = None
binding: Optional[Binding] = None
bound_to: Optional[str] = None
class Bag(BaseModel):
id: int
item_: LazyLoader
@property
def item(self) -> "Item":
return self.item_()
size: int
inventory: List[Union[ItemInventory, None]]
class SkillsBase(BaseModel):
heal_: Optional[LazyLoader]
@property
def heal(self) -> "Skill":
return self.heal_() if self.heal_ else None
utilities_: Optional[LazyLoader]
@property
def utilities(self) -> List["Skill"]:
return self.utilities_() if self.utilities_ else None
elite_: Optional[LazyLoader]
@property
def elite(self):
return self.elite_() if self.elite_ else None
legends_: Optional[LazyLoader]
@property
def legends(self):
return self.legends_() if self.legends_ is not None else None
class SkillTree(BaseModel):
id: int # TODO compared against the training section for each /v2/professions
spent: int
done: bool
class Skills(BaseModel):
pve: SkillsBase
pvp: SkillsBase
wvw: SkillsBase
class SpecializationBase(BaseModel):
id: Optional[int]
specialization_: Optional[LazyLoader]
@property
def specialization(self) -> "Specialization":
return self.specialization_() if self.specialization_ else None
traits_: Optional[LazyLoader]
@property
def traits(self) -> List["Trait"]:
return self.traits_() if self.traits_ else None
class Specializations(BaseModel):
pve: SpecializationBase
pvp: SpecializationBase
wvw: SpecializationBase
class CharacterCore(BaseModel):
name: str
race: Races
gender: Gender
profession: Professions
level: int
guild_: Optional[LazyLoader] = None
@property
def guild(self) -> "Guild":
return self.guild_() if self.guild_ is not None else None
age: int
created: datetime.datetime
deaths: int
title_: Optional[LazyLoader] = None
@property
def title(self) -> "Title":
return self.title_() if self.title_ is not None else None
class Build(BaseModel):
name: str
profession: Professions
specializations: List[SpecializationBase]
skills: SkillsBase
aquatic_skills: SkillsBase
class BuildTab(BaseModel):
tab: int
is_active: bool
build: Build
class EquipmentTab(BaseModel):
tab: int
name: str
is_active: bool
equipment: List["Equipment"]
equipment_pvp: "PvPEquipment"
class Character(BaseModel):
name: str
race: Races
gender: Gender
profession: Professions
level: int
build_tabs_unlocked: int
active_build_tab: int
equipment_tabs_unlocked: int
active_equipment_tab: int
guild_: Optional[LazyLoader] = None
@property
def guild(self) -> "Guild":
return self.guild_() if self.guild_ is not None else None
age: int
created: datetime.datetime
deaths: int
title_: Optional[LazyLoader] = None
@property
def title(self) -> "Title":
return self.title_() if self.title_ is not None else None
backstory_: LazyLoader
@property
def backstory(self) -> List["BiographyAnswer"]:
return self.backstory_()
crafting: List["Crafting"]
equipment: List["Equipment"]
build_tabs: List["BuildTab"]
equipment_tabs: List["EquipmentTab"]
heropoints_: LazyLoader
@property
def heropoints(self) -> List["str"]: # TODO replace with proper lookup
return self.heropoints_()
bags: List[Optional[Bag]] = []
@property
def inventory(self) -> List[Optional[Bag]]:
return self.bags
training: List[SkillTree]
sab_: LazyLoader
@property
def sab(self) -> "SAB":
return self.sab_()
wvw_abilities: List["CharacterWvWAbility"]
flags: List[CharacterFlag] = []
class CharacterWvWAbility(BaseModel):
id: int
ability_: LazyLoader
@property
def ability(self) -> "WvWAbility":
return self.ability_()
rank: int
class ProfessionTrainingTrack(BaseModel):
cost: int = 0
type: ProfessionTrainingTrackType
skill_id: Optional[int]
skill_: Optional[LazyLoader]
@property
def skill(self) -> Optional["Skill"]:
return self.skill_() if self.skill_ is not None else None
trait_id: Optional[int]
trait_: Optional[LazyLoader]
@property
def trait(self) -> Optional["Trait"]:
return self.trait_() if self.trait_ is not None else None
class ProfessionTraining(BaseModel):
id: int
skill_: LazyLoader
@property
def skill(self) -> "Skill":
return self.skill_()
specialization_: LazyLoader
@property
def specialization(self) -> "Specialization":
return self.specialization_()
category: ProfessionTrainingCategory
name: str
track: List[ProfessionTrainingTrack]
class WeaponSkill(BaseModel):
id: int
skill_: LazyLoader
@property
def skill(self) -> "Skill":
return self.skill_()
slot: SkillSlot
offhand: Optional[str]
attunement: Optional[str]
source: Optional[str]
class ProfessionWeapon(BaseModel):
flag: Optional[List[ProfessionWeaponFlag]]
specialization_: Optional[LazyLoader]
@property
def specialization(self) -> Optional["Specialization"]:
return self.specialization_() if self.specialization_ is not None else None
skills: List[WeaponSkill]
class ProfessionWeapons(BaseModel):
Axe: Optional["ProfessionWeapon"]
Dagger: Optional["ProfessionWeapon"]
Mace: Optional["ProfessionWeapon"]
Pistol: Optional["ProfessionWeapon"]
Sword: Optional["ProfessionWeapon"]
Scepter: Optional["ProfessionWeapon"]
Focus: Optional["ProfessionWeapon"]
Shield: Optional["ProfessionWeapon"]
Torch: Optional["ProfessionWeapon"]
Warhorn: Optional["ProfessionWeapon"]
Greatsword: Optional["ProfessionWeapon"]
Hammer: Optional["ProfessionWeapon"]
Longbow: Optional["ProfessionWeapon"]
Rifle: Optional["ProfessionWeapon"]
Shortbow: Optional["ProfessionWeapon"]
Staff: Optional["ProfessionWeapon"]
Speargun: Optional["ProfessionWeapon"]
Spear: Optional["ProfessionWeapon"]
Trident: Optional["ProfessionWeapon"]
class Profession(BaseModel):
id: str = ""
name: str = ""
icon: str = ""
icon_big: str = ""
specializations_: LazyLoader
@property
def specializations(self) -> List["Specialization"]:
return self.specializations_()
training: List[ProfessionTraining]
weapons: ProfessionWeapons
class Race(BaseModel):
id: str
skills_: LazyLoader
@property
def skills(self) -> List["Skill"]:
return self.skills_()
class Specialization(BaseModel):
id: int
name: str
profession: str
elite: bool = False
icon: str
background: str
minor_traits_: LazyLoader
@property
def minor_traits(self) -> List["Trait"]:
return self.minor_traits_()
major_traits_: LazyLoader
@property
def major_traits(self) -> List["Trait"]:
return self.major_traits_()
class SkillFactPrefix(BaseModel):
text: str
icon: str
status: str
description: str
class SkillFact(BaseModel):
text: Optional[str]
icon: Optional[str]
type: SkillFactType
value: Optional[Union[int, bool]]
target: Optional[str]
status: Optional[str]
description: Optional[str]
apply_count: Optional[int]
duration: Optional[int]
field_type: Optional[ComboFieldType]
finisher_type: Optional[ComboFinisherType]
percent: Optional[int]
hit_count: Optional[int]
dmg_multiplier: Optional[int]
distance: Optional[int]
prefix: Optional[SkillFactPrefix]
class SkillTraitedFact(SkillFact):
requires_trait_: LazyLoader
@property
def requires_trait(self) -> "Trait":
return self.requires_trait_()
overrides: Optional[int] # TODO resolve from facts
class Skill(BaseModel):
id: int
name: str
description: Optional[str]
icon: str
chat_link: str
type: Optional[SkillType]
weapon_type: Optional[WeaponType]
professions: List[Professions]
slot: SkillSlot
facts: Optional[List[SkillFact]]
traited_facts: Optional[List[SkillTraitedFact]]
categories: Optional[List[SkillCategories]]
attunement: Optional[Attunement]
cost: Optional[int]
dual_wield: Optional[str]
flip_skill_: Optional[LazyLoader]
@property
def flip_skill(self) -> Optional["Skill"]:
return self.flip_skill_() if self.flip_skill_ is not None else None
initiative: Optional[int]
next_chain_: Optional[LazyLoader]
@property
def next_chain(self) -> Optional["Skill"]:
return self.next_chain_() if self.next_chain_ is not None else None
prev_chain_: Optional[LazyLoader]
@property
def prev_chain(self) -> Optional["Skill"]:
return self.prev_chain_ if self.prev_chain_ is not None else None
transform_skills_: Optional[LazyLoader]
@property
def transform_skills(self) -> Optional[List["Skill"]]:
return self.transform_skills_ if self.transform_skills_ is not None else None
bundle_skills_: Optional[LazyLoader]
@property
def bundle_skills(self) -> Optional[List["Skill"]]:
return self.bundle_skills_ if self.bundle_skills_ is not None else None
toolbelt_skill_: Optional[LazyLoader]
@property
def toolbelt_skill(self) -> Optional["Skill"]:
return self.toolbelt_skill_ if self.toolbelt_skill_ is not None else None
class TraitSkill(BaseModel):
id: int
name: str
description: str
icon: str
facts: Optional[List[SkillFact]]
traited_facts: Optional[List[SkillTraitedFact]]
class Trait(BaseModel):
id: int
name: str
icon: str
description: str
specialization_: LazyLoader
@property
def specialization(self) -> "Specialization":
return self.specialization_()
tier: TraitTier
slot: TraitSlot
facts: Optional[List[SkillFact]]
traited_facts: Optional[List[SkillTraitedFact]]
skills: Optional[List[TraitSkill]]
class Legend(BaseModel):
id: str = ""
swap_: LazyLoader
@property
def swap(self) -> "Skill":
return self.swap_()
heal_: LazyLoader
@property
def heal(self) -> "Skill":
return self.heal_()
elite_: LazyLoader
@property
def elite(self) -> "Skill":
return self.elite_()
utilities_: LazyLoader
@property
def utilities(self) -> List["Skill"]:
return self.utilities_()
| 3,203 | 8,859 | 736 |
531964ba161ada42348b3db5388f68060006c2ec | 2,423 | py | Python | plotter.py | BartlomiejF/ParHumidSensor | 4782a0ae646c13d037fbe9bceda2d6ec5c440b72 | [
"MIT"
] | null | null | null | plotter.py | BartlomiejF/ParHumidSensor | 4782a0ae646c13d037fbe9bceda2d6ec5c440b72 | [
"MIT"
] | null | null | null | plotter.py | BartlomiejF/ParHumidSensor | 4782a0ae646c13d037fbe9bceda2d6ec5c440b72 | [
"MIT"
] | null | null | null | import pandas as pd
import matplotlib.pyplot as plt
from io import BytesIO
import base64
| 41.775862 | 96 | 0.666116 | import pandas as pd
import matplotlib.pyplot as plt
from io import BytesIO
import base64
def plot(airqualjson=None, temphumidjson=None):
# carnb_data = pd.DataFrame.from_dict(carnbjson)
figdata_airqual_png = None
figdata_temphumid_png = None
if airqualjson is not None:
airqual_data = pd.DataFrame.from_dict(airqualjson)
airqual_time = airqual_data["date_time"].astype("datetime64")
fig_airqual, ax_airqual = plt.subplots(1, 2, figsize=(10, 5))
ax_airqual[0].plot(airqual_time, airqual_data["pm2_5"])
ax_airqual[0].axhline(20, ls="--", c="r")
ax_airqual[0].axhspan(0, 20, 0, 1, color="g", alpha=0.2)
ax_airqual[0].set_title("pm 2.5")
axymax = airqual_data["pm2_5"].max()
axymax = axymax*1.1
if axymax > 20:
ax_airqual[0].axhspan(20, axymax, 0, 1, color="r", alpha=0.2)
ax_airqual[1].plot(airqual_time, airqual_data["pm10"])
ax_airqual[1].axhline(50, ls="--", c="r")
ax_airqual[1].axhspan(0, 50, 0, 1, color="g", alpha=0.2)
ax_airqual[1].set_title("pm 10")
ax1ymax = airqual_data["pm10"].max()
ax1ymax = ax1ymax*1.1
if ax1ymax > 50:
ax_airqual[1].axhspan(50, ax1ymax, 0, 1, color="r", alpha=0.2)
figfile_airqual = BytesIO()
fig_airqual.savefig(figfile_airqual, format='png')
figfile_airqual.seek(0) # rewind to beginning of file
figdata_airqual_png = figfile_airqual.getvalue() # extract string (stream of bytes)
figdata_airqual_png = base64.b64encode(figdata_airqual_png)
if temphumidjson is not None:
temphumid_data = pd.DataFrame.from_dict(temphumidjson)
temphumid_time = temphumid_data["date_time"].astype("datetime64")
fig_temphumid, ax_temphumid = plt.subplots(1, 2, figsize=(10, 5))
ax_temphumid[0].plot(temphumid_time, temphumid_data["temperature"])
ax_temphumid[1].plot(temphumid_time, temphumid_data["humidity"])
figfile_temphumid = BytesIO()
fig_temphumid.savefig(figfile_temphumid, format='png')
figfile_temphumid.seek(0) # rewind to beginning of file
figdata_temphumid_png = figfile_temphumid.getvalue() # extract string (stream of bytes)
figdata_temphumid_png = base64.b64encode(figdata_temphumid_png)
return {"airqual": figdata_airqual_png,
"temphumid": figdata_temphumid_png,}
| 2,310 | 0 | 23 |
0c09238973033b093fa0969c6f3f3f36233309e5 | 1,906 | py | Python | src/sentry/api/bases/team.py | augustand/sentry | 9e5e0b6744d878d623f483661e40c49bb902047c | [
"BSD-3-Clause"
] | 1 | 2021-08-10T06:07:13.000Z | 2021-08-10T06:07:13.000Z | src/sentry/api/bases/team.py | augustand/sentry | 9e5e0b6744d878d623f483661e40c49bb902047c | [
"BSD-3-Clause"
] | 5 | 2019-12-28T18:13:59.000Z | 2022-03-02T04:32:45.000Z | src/sentry/api/bases/team.py | augustand/sentry | 9e5e0b6744d878d623f483661e40c49bb902047c | [
"BSD-3-Clause"
] | 1 | 2017-04-08T04:09:18.000Z | 2017-04-08T04:09:18.000Z | from __future__ import absolute_import
from sentry.api.base import Endpoint
from sentry.api.exceptions import ResourceDoesNotExist
from sentry.app import raven
from sentry.models import Team, TeamStatus
from sentry.models.apikey import ROOT_KEY
from .organization import OrganizationPermission
| 31.245902 | 83 | 0.643757 | from __future__ import absolute_import
from sentry.api.base import Endpoint
from sentry.api.exceptions import ResourceDoesNotExist
from sentry.app import raven
from sentry.models import Team, TeamStatus
from sentry.models.apikey import ROOT_KEY
from .organization import OrganizationPermission
class TeamPermission(OrganizationPermission):
scope_map = {
'GET': ['team:read', 'team:write', 'team:delete'],
'POST': ['team:write', 'team:delete'],
'PUT': ['team:write', 'team:delete'],
'DELETE': ['team:delete'],
}
def has_object_permission(self, request, view, team):
result = super(TeamPermission, self).has_object_permission(
request, view, team.organization)
if not result:
return result
if not (request.user and request.user.is_authenticated()) and request.auth:
if request.auth is ROOT_KEY:
return True
return request.auth.organization_id == team.organization.id
allowed_scopes = set(self.scope_map.get(request.method, []))
return any(
request.access.has_team_scope(team, s)
for s in allowed_scopes,
)
class TeamEndpoint(Endpoint):
permission_classes = (TeamPermission,)
def convert_args(self, request, organization_slug, team_slug, *args, **kwargs):
try:
team = Team.objects.filter(
organization__slug=organization_slug,
slug=team_slug,
).select_related('organization').get()
except Team.DoesNotExist:
raise ResourceDoesNotExist
if team.status != TeamStatus.VISIBLE:
raise ResourceDoesNotExist
self.check_object_permissions(request, team)
raven.tags_context({
'organization': team.organization_id,
})
kwargs['team'] = team
return (args, kwargs)
| 1,222 | 340 | 46 |
8e5dd8d936892226fc66c24989ea52dcf046dedb | 663 | py | Python | PyNEng/To-Do Task 5.2b.py | fugrusha/education | 89712a93e0727e94543cb252482920a503135c7f | [
"MIT"
] | null | null | null | PyNEng/To-Do Task 5.2b.py | fugrusha/education | 89712a93e0727e94543cb252482920a503135c7f | [
"MIT"
] | null | null | null | PyNEng/To-Do Task 5.2b.py | fugrusha/education | 89712a93e0727e94543cb252482920a503135c7f | [
"MIT"
] | null | null | null | # Задание 5.2b
london_co = {
'r1' : {
'location': '21 New Globe Walk',
'vendor': 'Cisco',
'model': '4451',
'ios': '15.4',
'ip': '10.255.0.1'
},
'r2' : {
'location': '21 New Globe Walk',
'vendor': 'Cisco',
'model': '4451',
'ios': '15.4',
'ip': '10.255.0.2'
},
'sw1' : {
'location': '21 New Globe Walk',
'vendor': 'Cisco',
'model': '3850',
'ios': '3.6.XE',
'ip': '10.255.0.101',
'vlans': '10,20,30',
'routing': True
}
}
device = input('Enter divece name: ')
key = dict(device).keys()
param = input('Enter parameter name {}: '.format(key))
print(london_co[device][param]) | 20.090909 | 54 | 0.493213 | # Задание 5.2b
london_co = {
'r1' : {
'location': '21 New Globe Walk',
'vendor': 'Cisco',
'model': '4451',
'ios': '15.4',
'ip': '10.255.0.1'
},
'r2' : {
'location': '21 New Globe Walk',
'vendor': 'Cisco',
'model': '4451',
'ios': '15.4',
'ip': '10.255.0.2'
},
'sw1' : {
'location': '21 New Globe Walk',
'vendor': 'Cisco',
'model': '3850',
'ios': '3.6.XE',
'ip': '10.255.0.101',
'vlans': '10,20,30',
'routing': True
}
}
device = input('Enter divece name: ')
key = dict(device).keys()
param = input('Enter parameter name {}: '.format(key))
print(london_co[device][param]) | 0 | 0 | 0 |
86bc01aa6a0d9b9a74b765460b3330e59f59daef | 1,857 | py | Python | src/snovault/elasticsearch/searches/interfaces.py | caseylitton/snovault | cfaace963bc9b1ca87bca4739b380c559026bcfb | [
"MIT"
] | null | null | null | src/snovault/elasticsearch/searches/interfaces.py | caseylitton/snovault | cfaace963bc9b1ca87bca4739b380c559026bcfb | [
"MIT"
] | null | null | null | src/snovault/elasticsearch/searches/interfaces.py | caseylitton/snovault | cfaace963bc9b1ca87bca4739b380c559026bcfb | [
"MIT"
] | null | null | null | ADVANCED_QUERY_KEY = 'advancedQuery'
ALL = 'all'
AND = 'AND'
AND_JOIN = ' AND '
AND_NOT_JOIN = ' AND NOT '
APPENDED = 'appended'
APPLICATION_JSON = 'application/json'
ASC = 'asc'
AT_ID = '@id'
AT_CONTEXT = '@context'
AT_TYPE = '@type'
AUDIT = 'audit'
AUDIT_TITLE = 'Audit'
BOOL = 'bool'
BOOST_VALUES = 'boost_values'
BUCKETS = 'buckets'
CLEAR_FILTERS = 'clear_filters'
COLLECTION_NAME = 'item_type'
COLUMNS = 'columns'
DASH = '-'
DEBUG_KEY = 'debug'
DESC = 'desc'
DOC_COUNT = 'doc_count'
EMBEDDED = 'embedded.'
EMBEDDED_FRAME = 'embedded'
EMBEDDED_TYPE = 'embedded.@type'
EXCLUDE = 'exclude'
EXISTS = 'exists'
FACETS = 'facets'
FIELD_KEY = 'field'
FILTERS = 'filters'
FRAME_KEY = 'frame'
FROM_KEY = 'from'
GRAPH = '@graph'
GROUP_BY = 'group_by'
GROUP_SUBMITTER = 'group.submitter'
HITS = 'hits'
ITEM = 'Item'
JS_IS_EQUAL = 'isEqual'
JS_TRUE = 'true'
JS_FALSE = 'false'
JSONLD_CONTEXT = 'jsonld_context'
KEY = 'key'
LIMIT_KEY = 'limit'
LENGTH = 'length'
LONG = 'long'
MATRIX = 'matrix'
MATRIX_TITLE = 'Matrix'
MODE_KEY = 'mode'
NO = 'no'
NO_LIMIT = 999999
NO_RESULTS_FOUND = 'No results found'
NON_SORTABLE = 'non_sortable'
NOT_JOIN = ' NOT '
NOT_FLAG = '!'
NOTIFICATION = 'notification'
OBJECT_FRAME = 'object'
ORDER = 'order'
PERIOD = '.'
PICKER = 'picker'
PRINCIPALS_ALLOWED_VIEW = 'principals_allowed.view'
PROPERTIES = 'properties'
QUERY_STRING = 'query_string'
RAW_QUERY = 'raw_query'
REMOVE = 'remove'
REPORT_TITLE = 'Report'
SEARCH_AUDIT = 'search_audit'
SEARCH_BASE = 'search_base'
SEARCH_PATH = '/search/'
SEARCH_TITLE = 'Search'
SEARCH_TERM_KEY = 'searchTerm'
SIMPLE_QUERY_STRING = 'simple_query_string'
SORT_KEY = 'sort'
_SOURCE = '_source'
SUCCESS = 'Success'
SUMMARY_TITLE = 'Summary'
SUMMARY = 'summary'
TITLE = 'title'
TERM = 'term'
TERMS = 'terms'
TOTAL = 'total'
TYPES = 'types'
TYPE_KEY = 'type'
WILDCARD = '*'
X = 'x'
Y = 'y'
YES = 'yes'
| 21.102273 | 51 | 0.706516 | ADVANCED_QUERY_KEY = 'advancedQuery'
ALL = 'all'
AND = 'AND'
AND_JOIN = ' AND '
AND_NOT_JOIN = ' AND NOT '
APPENDED = 'appended'
APPLICATION_JSON = 'application/json'
ASC = 'asc'
AT_ID = '@id'
AT_CONTEXT = '@context'
AT_TYPE = '@type'
AUDIT = 'audit'
AUDIT_TITLE = 'Audit'
BOOL = 'bool'
BOOST_VALUES = 'boost_values'
BUCKETS = 'buckets'
CLEAR_FILTERS = 'clear_filters'
COLLECTION_NAME = 'item_type'
COLUMNS = 'columns'
DASH = '-'
DEBUG_KEY = 'debug'
DESC = 'desc'
DOC_COUNT = 'doc_count'
EMBEDDED = 'embedded.'
EMBEDDED_FRAME = 'embedded'
EMBEDDED_TYPE = 'embedded.@type'
EXCLUDE = 'exclude'
EXISTS = 'exists'
FACETS = 'facets'
FIELD_KEY = 'field'
FILTERS = 'filters'
FRAME_KEY = 'frame'
FROM_KEY = 'from'
GRAPH = '@graph'
GROUP_BY = 'group_by'
GROUP_SUBMITTER = 'group.submitter'
HITS = 'hits'
ITEM = 'Item'
JS_IS_EQUAL = 'isEqual'
JS_TRUE = 'true'
JS_FALSE = 'false'
JSONLD_CONTEXT = 'jsonld_context'
KEY = 'key'
LIMIT_KEY = 'limit'
LENGTH = 'length'
LONG = 'long'
MATRIX = 'matrix'
MATRIX_TITLE = 'Matrix'
MODE_KEY = 'mode'
NO = 'no'
NO_LIMIT = 999999
NO_RESULTS_FOUND = 'No results found'
NON_SORTABLE = 'non_sortable'
NOT_JOIN = ' NOT '
NOT_FLAG = '!'
NOTIFICATION = 'notification'
OBJECT_FRAME = 'object'
ORDER = 'order'
PERIOD = '.'
PICKER = 'picker'
PRINCIPALS_ALLOWED_VIEW = 'principals_allowed.view'
PROPERTIES = 'properties'
QUERY_STRING = 'query_string'
RAW_QUERY = 'raw_query'
REMOVE = 'remove'
REPORT_TITLE = 'Report'
SEARCH_AUDIT = 'search_audit'
SEARCH_BASE = 'search_base'
SEARCH_PATH = '/search/'
SEARCH_TITLE = 'Search'
SEARCH_TERM_KEY = 'searchTerm'
SIMPLE_QUERY_STRING = 'simple_query_string'
SORT_KEY = 'sort'
_SOURCE = '_source'
SUCCESS = 'Success'
SUMMARY_TITLE = 'Summary'
SUMMARY = 'summary'
TITLE = 'title'
TERM = 'term'
TERMS = 'terms'
TOTAL = 'total'
TYPES = 'types'
TYPE_KEY = 'type'
WILDCARD = '*'
X = 'x'
Y = 'y'
YES = 'yes'
| 0 | 0 | 0 |
bbed43c689132df7b14e284f2ed94a474f88d84a | 399 | py | Python | tests/conftest.py | dajor/youtube2 | 390a701c0de3cb7dd6240a775b980edda150c376 | [
"MIT"
] | null | null | null | tests/conftest.py | dajor/youtube2 | 390a701c0de3cb7dd6240a775b980edda150c376 | [
"MIT"
] | null | null | null | tests/conftest.py | dajor/youtube2 | 390a701c0de3cb7dd6240a775b980edda150c376 | [
"MIT"
] | null | null | null | import pytest
from app.app import create_app
@pytest.fixture()
@pytest.fixture()
@pytest.fixture()
| 13.3 | 37 | 0.606516 | import pytest
from app.app import create_app
@pytest.fixture()
def app():
app = create_app()
app.config.update(
{
"TESTING": True,
}
)
# other setup can go here
yield app
# clean up / reset resources here
@pytest.fixture()
def client(app):
return app.test_client()
@pytest.fixture()
def runner(app):
return app.test_cli_runner()
| 227 | 0 | 66 |
6c171d09be3d70b2660fd3b47e32ad03e08cc1fc | 29,122 | py | Python | src/charm.py | stgraber/charm-lxd | 48c4b56a50902f9dcaebe6d3f80911927bab09ab | [
"Apache-2.0"
] | null | null | null | src/charm.py | stgraber/charm-lxd | 48c4b56a50902f9dcaebe6d3f80911927bab09ab | [
"Apache-2.0"
] | null | null | null | src/charm.py | stgraber/charm-lxd | 48c4b56a50902f9dcaebe6d3f80911927bab09ab | [
"Apache-2.0"
] | null | null | null | #!/usr/bin/env python3
"""LXD charm."""
import logging
import os
import shutil
import subprocess
import tarfile
import tempfile
from urllib.error import HTTPError, URLError
from urllib.request import urlopen
from ops.charm import (
ActionEvent,
CharmBase,
ConfigChangedEvent,
InstallEvent,
StartEvent,
)
from ops.framework import StoredState
from ops.main import main
from ops.model import ActiveStatus, BlockedStatus, MaintenanceStatus, ModelError
logger = logging.getLogger(__name__)
SYSCTL_CONFIGS = {
"fs.aio-max-nr": 524288,
"fs.inotify.max_queued_events": 1048576,
"fs.inotify.max_user_instances": 1048576,
"fs.inotify.max_user_watches": 1048576,
"kernel.dmesg_restrict": 1,
"kernel.keys.maxbytes": 2000000,
"kernel.keys.maxkeys": 2000,
"net.core.bpf_jit_limit": 3000000000,
"net.ipv4.neigh.default.gc_thresh3": 8192,
"net.ipv6.neigh.default.gc_thresh3": 8192,
"vm.max_map_count": 262144,
}
SYSTEMD_TMPFILES_CONFIGS = [
"z /proc/sched_debug 0400 - - -",
"z /sys/kernel/slab 0700 - - -",
]
REBOOT_REQUIRED_FILE = "/run/lxd-reboot-required"
class LxdCharm(CharmBase):
"""LXD charm class."""
_stored = StoredState()
def __init__(self, *args):
"""Initialize charm's variables."""
super().__init__(*args)
# Initialize the persistent storage if needed
self._stored.set_default(
addresses={},
config={},
lxd_binary_path=None,
lxd_initialized=False,
lxd_installed=False,
lxd_snap_path=None,
reboot_required=False,
)
# Action event handlers
self.framework.observe(
self.on.add_trusted_client_action, self._on_action_add_trusted_client
)
self.framework.observe(self.on.debug_action, self._on_action_debug)
self.framework.observe(
self.on.show_pending_config_action, self._on_action_show_pending_config
)
# Main event handlers
self.framework.observe(self.on.install, self._on_charm_install)
self.framework.observe(self.on.config_changed, self._on_charm_config_changed)
self.framework.observe(self.on.start, self._on_charm_start)
self.framework.observe(self.on.upgrade_charm, self._on_charm_upgrade)
def _on_action_add_trusted_client(self, event: ActionEvent) -> None:
"""Add a client certificate to the trusted list."""
name = event.params.get("name", "unknown")
cert = event.params.get("cert")
cert_url = event.params.get("cert-url")
projects = event.params.get("projects")
if not cert and not cert_url:
msg = "One of cert or cert-url parameter needs to be provided."
event.fail(msg)
logger.error(msg)
return
if cert:
# The received PEM needs to be mangled to be able to split()
# on spaces without breaking the "-----BEGIN CERTIFICATE-----"
# and "-----END CERTIFICATE-----" lines
cert = (
"\n".join(cert.replace(" CERTIFICATE", "CERTIFICATE", 2).split())
.replace("CERTIFICATE", " CERTIFICATE", 2)
.encode()
)
# Ignore the cert-url param if a cert was provided
cert_url = None
if cert_url and not (cert_url.startswith("http://") or cert_url.startswith("https://")):
msg = 'The cert-url parameter needs to start with "http://" or "https://".'
event.fail(msg)
logger.error(msg)
return
if cert_url:
try:
response = urlopen(cert_url)
except HTTPError as e:
msg = f"The server couldn't fulfill the request. Error code: {e.code}"
event.fail(msg)
logger.error(msg)
return
except URLError as e:
msg = f"We failed to reach a server. Reason: {e.reason}"
event.fail(msg)
logger.error(msg)
return
else:
cert = response.read()
if not cert:
msg = "Invalid/empty certificate provided/retrieved."
event.fail(msg)
logger.error(msg)
return
cmd = ["lxc", "config", "trust", "add", "-", "--name", name]
if projects:
cmd += ["--restricted", "--projects", projects]
try:
subprocess.run(cmd, input=cert, check=True)
except subprocess.CalledProcessError as e:
msg = f'Failed to run "{e.cmd}": {e.returncode}'
event.fail(msg)
logger.error(msg)
raise RuntimeError
event.set_results({"result": "the client certificate is now trusted"})
def _on_action_debug(self, event: ActionEvent) -> None:
"""Collect information for a bug report."""
try:
b = subprocess.run(["lxd.buginfo"], check=True)
except subprocess.CalledProcessError as e:
msg = f'Failed to run "{e.cmd}": {e.returncode}'
event.fail(msg)
logger.error(msg)
raise RuntimeError
event.set_results({"buginfo": b.stdout})
logger.debug("lxd.buginfo called successfully")
def _on_action_show_pending_config(self, event: ActionEvent) -> None:
"""Show the currently pending configuration changes (queued for after the reboot)."""
event.set_results({"pending": self.config_changed()})
def _on_charm_config_changed(self, event: ConfigChangedEvent) -> None:
"""React to configuration changes.
Some configuration items can be set only once
while others are changable, sometimes requiring
a service reload or even a machine reboot.
"""
logger.info("Updating charm config")
# Confirm that the config is valid
if not self.config_is_valid():
return
# Get all the configs that changed
changed = self.config_changed()
if not changed:
logger.debug("No configuration changes to apply")
return
# Check if the required reboot occurred and clear the flag if yes
if os.path.exists(REBOOT_REQUIRED_FILE):
self.unit_blocked("Reboot required, deferring config change")
event.defer()
return
# Check if any required reboot was done
self.system_clear_reboot_required()
# Apply all the configs that changed
try:
if "snap-channel" in changed:
self.snap_install_lxd()
elif "sysctl-tuning" in changed:
self.kernel_sysctl()
elif "kernel-hardening" in changed:
self.kernel_hardening()
elif [k for k in changed if k.startswith("snap-config-")]:
self.snap_config_set()
except RuntimeError:
msg = "Failed to apply some configuration change(s): %s" % ", ".join(changed)
self.unit_blocked(msg)
event.defer()
return
# If some changes needed a reboot to take effect, enter blocked status
if self._stored.reboot_required:
self.unit_blocked("Machine reboot required")
return
# All done
self.unit_active("Configuration change(s) applied successfully")
def config_changed(self) -> dict:
"""Figure out what changed."""
new_config = self.config
old_config = self._stored.config
apply_config = {}
for k, v in new_config.items():
if k not in old_config:
apply_config[k] = v
elif v != old_config[k]:
apply_config[k] = v
return apply_config
def config_is_valid(self) -> bool:
"""Validate the config."""
if "local" in self.model.storages and len(self.model.storages["local"]) > 1:
self.unit_blocked("LXD charm only supports a single storage volume")
return False
config_changed = self.config_changed()
# If nothing changed and we were blocked due to a lxd- key
# change (post-init), we can assume the change was reverted thus unblocking us
if (
not config_changed
and isinstance(self.unit.status, BlockedStatus)
and "Can't modify lxd- keys after initialization:" in str(self.unit.status)
):
self.unit_active("Unblocking as the lxd- keys were reset to their initial values")
for k in config_changed:
if k.startswith("lxd-") and self._stored.lxd_initialized:
self.unit_blocked(f"Can't modify lxd- keys after initialization: {k}")
return False
return True
def juju_set_proxy(self) -> None:
"""Apply proxy config."""
juju_proxy = "/etc/juju-proxy.conf"
if not os.path.exists(juju_proxy):
logger.debug("No proxy config from Juju.")
return
http_proxy = None
https_proxy = None
no_proxy = None
with open(juju_proxy, encoding="UTF-8") as f:
for line in f.read().splitlines():
# Only consider lines exporting variables
if not line.startswith("export "):
continue
# Parse export lines
try:
# Strip "export " prefix and split variable/value
k, v = line.replace("export ", "", 1).split("=", 1)
except (IndexError, ValueError):
continue
if k == "HTTP_PROXY":
http_proxy = v
elif k == "HTTPS_PROXY":
https_proxy = v
elif k == "NO_PROXY":
no_proxy = v
try:
if http_proxy:
logger.debug(f"Configuring core.proxy_http={http_proxy}")
subprocess.run(["lxc", "config", "set", "core.proxy_http", http_proxy], check=True)
if https_proxy:
logger.debug(f"Configuring core.proxy_https={https_proxy}")
subprocess.run(
["lxc", "config", "set", "core.proxy_https", https_proxy],
check=True,
)
if no_proxy:
logger.debug(f"Configuring core.proxy_ignore_hosts={no_proxy}")
subprocess.run(
["lxc", "config", "set", "core.proxy_ignore_hosts", no_proxy],
check=True,
)
except subprocess.CalledProcessError as e:
self.unit_blocked(f'Failed to run "{e.cmd}": {e.returncode}')
raise RuntimeError
def kernel_sysctl(self) -> None:
"""Apply sysctl tuning keys."""
logger.debug("Applying sysctl tuning")
sysctl_file = "/etc/sysctl.d/60-lxd.conf"
config = self.config["sysctl-tuning"]
if config:
self.unit_maintenance(f"Applying sysctl config file: {sysctl_file}")
with open(sysctl_file, "w", encoding="UTF-8") as f:
for k, v in SYSCTL_CONFIGS.items():
f.write(f"{k} = {v}\n")
try:
subprocess.run(["sysctl", "--quiet", "--load", sysctl_file], check=True)
except subprocess.CalledProcessError as e:
self.unit_blocked(f'Failed to run "{e.cmd}": {e.returncode}')
raise RuntimeError
elif os.path.exists(sysctl_file):
self.unit_maintenance(f"Removing sysctl config file: {sysctl_file}")
os.remove(sysctl_file)
# Persist the configuration
self._stored.config["sysctl-tuning"] = config
def kernel_hardening(self) -> None:
"""Apply kernel hardening systemd tmpfiles."""
logger.debug("Applying kernel hardening")
systemd_tmpfiles = "/etc/tmpfiles.d/lxd.conf"
config = self.config["kernel-hardening"]
if config:
self.unit_maintenance(f"Applying kernel hardening config file: {systemd_tmpfiles}")
with open(systemd_tmpfiles, "w", encoding="UTF-8") as f:
f.write("\n".join(SYSTEMD_TMPFILES_CONFIGS) + "\n")
try:
subprocess.run(["systemd-tmpfiles", "--create"], check=True)
except subprocess.CalledProcessError as e:
self.unit_blocked(f'Failed to run "{e.cmd}": {e.returncode}')
raise RuntimeError
elif os.path.exists(systemd_tmpfiles):
self.unit_maintenance(f"Removing kernel hardening config file: {systemd_tmpfiles}")
os.remove(systemd_tmpfiles)
# Persist the configuration
self._stored.config["kernel-hardening"] = config
def lxd_init(self) -> None:
"""Apply initial configuration of LXD."""
self.unit_maintenance("Initializing LXD in standalone mode")
preseed = self.config.get("lxd-preseed")
if preseed:
self.unit_maintenance("Applying LXD preseed")
try:
# NOTE: When preseeding, no further configuration is applied.
subprocess.run(["lxd", "init", "--preseed"], check=True, input=preseed.encode())
except subprocess.CalledProcessError as e:
self.unit_blocked(f'Failed to run "{e.cmd}": {e.returncode}')
raise RuntimeError
else:
self.unit_maintenance("Performing initial configuration")
try:
# Configure the storage
if "local" in self.model.storages and len(self.model.storages["local"]) == 1:
src = f"source={self.model.storages['local'][0].location}"
self.unit_maintenance(f"Configuring external storage pool (zfs, {src})")
subprocess.run(["lxc", "storage", "create", "local", "zfs", src], check=True)
else:
self.unit_maintenance("Configuring local storage pool (dir)")
subprocess.run(["lxc", "storage", "create", "local", "dir"], check=True)
subprocess.run(
[
"lxc",
"profile",
"device",
"add",
"default",
"root",
"disk",
"pool=local",
"path=/",
],
check=True,
)
# Configure the network
self.unit_maintenance("Configuring network bridge (lxdbr0)")
subprocess.run(["lxc", "network", "create", "lxdbr0"], check=True)
subprocess.run(
[
"lxc",
"profile",
"device",
"add",
"default",
"eth0",
"nic",
"network=lxdbr0",
"name=eth0",
],
check=True,
)
except subprocess.CalledProcessError as e:
self.unit_blocked(f'Failed to run "{e.cmd}": {e.returncode}')
raise RuntimeError
# Initial configuration of core.proxy_* keys
self.juju_set_proxy()
# Done with the initialization
self._stored.config["lxd-preseed"] = preseed
# Flag any `lxd-*` keys not handled, there should be none
for k in self.config_changed():
if k.startswith("lxd-"):
logger.error(f"lxd_init did not handle the key config named: {k}")
def lxd_is_active(self) -> bool:
"""Indicate if the lxd daemon is active."""
c = subprocess.run(
["systemctl", "is-active", "--quiet", "snap.lxd.daemon.service"],
check=False,
)
return c.returncode == 0
def lxd_reload(self) -> None:
"""Reload the lxd daemon."""
self.unit_maintenance("Reloading LXD")
try:
# Avoid occasional race during startup where a reload could cause a failure
subprocess.run(["lxd", "waitready", "--timeout=30"], check=False)
# Start a monitor process and wait for it to exit due to the service
# reloading and the old lxd process closing the monitor's socket.
mon = subprocess.Popen(
["lxc", "monitor", "--type=nonexistent"], stderr=subprocess.DEVNULL
)
subprocess.run(["systemctl", "reload", "snap.lxd.daemon.service"], check=True)
mon.wait(timeout=600)
except subprocess.TimeoutExpired:
if not mon.returncode:
mon.kill()
self.unit_maintenance("Timeout while reloading the LXD service")
raise RuntimeError
except subprocess.CalledProcessError as e:
self.unit_blocked(f'Failed to run "{e.cmd}": {e.returncode}')
raise RuntimeError
def resource_sideload(self) -> None:
"""Sideload resources."""
# Multi-arch support
arch = os.uname().machine
if arch == "x86_64":
possible_archs = ["x86_64", "amd64"]
else:
possible_archs = [arch]
# LXD snap
lxd_snap_resource = None
fname_suffix = ".snap"
try:
# Note: self._stored can only store simple data types (int/float/dict/list/etc)
lxd_snap_resource = str(self.model.resources.fetch("lxd-snap"))
except ModelError:
pass
tmp_dir = None
if lxd_snap_resource and tarfile.is_tarfile(lxd_snap_resource):
logger.debug(f"{lxd_snap_resource} is a tarball; unpacking")
tmp_dir = tempfile.mkdtemp()
tarball = tarfile.open(lxd_snap_resource)
valid_names = {f"lxd_{x}{fname_suffix}" for x in possible_archs}
for f in valid_names.intersection(tarball.getnames()):
tarball.extract(f, path=tmp_dir)
logger.debug(f"{f} was extracted from the tarball")
self._stored.lxd_snap_path = f"{tmp_dir}/{f}"
break
else:
logger.debug("Missing arch specific snap from tarball.")
else:
self._stored.lxd_snap_path = lxd_snap_resource
if self._stored.lxd_snap_path:
self.snap_sideload_lxd()
if tmp_dir:
os.remove(self._stored.lxd_snap_path)
os.rmdir(tmp_dir)
# LXD binary
lxd_binary_resource = None
fname_suffix = ""
try:
# Note: self._stored can only store simple data types (int/float/dict/list/etc)
lxd_binary_resource = str(self.model.resources.fetch("lxd-binary"))
except ModelError:
pass
tmp_dir = None
if lxd_binary_resource and tarfile.is_tarfile(lxd_binary_resource):
logger.debug(f"{lxd_binary_resource} is a tarball; unpacking")
tmp_dir = tempfile.mkdtemp()
tarball = tarfile.open(lxd_binary_resource)
valid_names = {f"lxd_{x}{fname_suffix}" for x in possible_archs}
for f in valid_names.intersection(tarball.getnames()):
tarball.extract(f, path=tmp_dir)
logger.debug(f"{f} was extracted from the tarball")
self._stored.lxd_binary_path = f"{tmp_dir}/{f}"
break
else:
logger.debug("Missing arch specific binary from tarball.")
else:
self._stored.lxd_binary_path = lxd_binary_resource
if self._stored.lxd_binary_path:
self.snap_sideload_lxd_binary()
if tmp_dir:
os.remove(self._stored.lxd_binary_path)
os.rmdir(tmp_dir)
def snap_config_set(self) -> None:
"""Apply snap set to LXD."""
logger.debug("Applying snap set lxd")
# Get all the `snap-config-*` configs to set
snap_set = {k: v for k, v in self.config_changed().items() if k.startswith("snap-config-")}
if not snap_set:
return
# Apply the changes
snap_set_list = []
reboot_needed = False
for k, v in snap_set.items():
# Convert Juju config to "snap set" syntax
if v is None:
snap_value = ""
elif isinstance(v, bool):
snap_value = str(v).lower()
else:
snap_value = v
snap_key = k.replace("snap-config-", "", 1).replace("-", ".")
# Set the snap config
snap_set_list.append("%s=%s" % (snap_key, snap_value))
# Keys that require a reboot
if k.startswith("snap-config-lxcfs-"):
# `snap-config-lxcfs-*` cannot be applied live
reboot_needed = True
continue
self.unit_maintenance("Setting snap configuration(s): " + ", ".join(snap_set_list))
try:
subprocess.run(["snap", "set", "lxd"] + snap_set_list, check=True)
except subprocess.CalledProcessError as e:
self.unit_blocked(f'Failed to run "{e.cmd}": {e.returncode}')
raise RuntimeError
# If "snap set lxd" was successful: save all the k/v applied
for k, v in snap_set.items():
self._stored.config[k] = v
if not self.lxd_is_active():
logger.debug("Skipping LXD reload (service inactive)")
return
self.lxd_reload()
if reboot_needed:
self.system_set_reboot_required()
def snap_install_lxd(self) -> None:
"""Install LXD from snap."""
channel = self.config["snap-channel"]
if channel:
channel_name = channel
else:
channel_name = "latest/stable"
self.unit_maintenance(f"Installing LXD snap (channel={channel_name})")
try:
subprocess.run(["snap", "install", "lxd", f"--channel={channel}"], check=True)
subprocess.run(["snap", "refresh", "lxd", f"--channel={channel}"], check=True)
if os.path.exists("/var/lib/lxd"):
subprocess.run(["lxd.migrate", "-yes"], check=True)
except subprocess.CalledProcessError as e:
self.unit_blocked(f'Failed to run "{e.cmd}": {e.returncode}')
raise RuntimeError
# Done with the snap installation
self._stored.config["snap-channel"] = channel
def snap_sideload_lxd(self) -> None:
"""Sideload LXD snap resource."""
logger.debug("Applying LXD snap sideload changes")
# A 0 byte file will unload the resource
if os.path.getsize(self._stored.lxd_snap_path) == 0:
logger.debug("Reverting to LXD snap from snapstore")
channel = self._stored.config["snap-channel"]
cmd = ["snap", "refresh", "lxd", f"--channel={channel}", "--amend"]
alias = None
enable = None
else:
logger.debug("Sideloading LXD snap")
cmd = ["snap", "install", "--dangerous", self._stored.lxd_snap_path]
# Since the sideloaded snap doesn't have an assertion, some things need
# to be done manually
alias = ["snap", "alias", "lxd.lxc", "lxc"]
enable = ["systemctl", "enable", "--now", "snap.lxd.daemon.unix.socket"]
try:
subprocess.run(cmd, check=True)
if alias:
subprocess.run(alias, check=True)
if enable:
subprocess.run(enable, check=True)
except subprocess.CalledProcessError as e:
self.unit_blocked(f'Failed to run "{e.cmd}": {e.returncode}')
raise RuntimeError
def snap_sideload_lxd_binary(self) -> None:
"""Sideload LXD binary resource."""
logger.debug("Applying LXD binary sideload changes")
lxd_debug = "/var/snap/lxd/common/lxd.debug"
# A 0 byte file will unload the resource
if os.path.getsize(self._stored.lxd_binary_path) == 0:
logger.debug("Unloading sideloaded LXD binary")
if os.path.exists(lxd_debug):
os.remove(lxd_debug)
else:
logger.debug("Sideloading LXD binary")
# Avoid "Text file busy" error
if os.path.exists(lxd_debug):
logger.debug("Removing old sideloaded LXD binary")
os.remove(lxd_debug)
shutil.copyfile(self._stored.lxd_binary_path, lxd_debug)
os.chmod(lxd_debug, 0o755)
self.lxd_reload()
def system_clear_reboot_required(self) -> None:
"""Clear the reboot_required flag if a reboot occurred."""
# If the required reboot occurred so let's clear the flag
if self._stored.reboot_required and not os.path.exists(REBOOT_REQUIRED_FILE):
self._stored.reboot_required = False
logger.debug("Required reboot done")
def system_set_reboot_required(self) -> None:
"""Indicate that a reboot is required to reach a clean state."""
# Touch a flag file indicating that a reboot is required.
try:
open(REBOOT_REQUIRED_FILE, "a").close()
self._stored.reboot_required = True
except OSError:
logger.warning(f"Failed to create: {REBOOT_REQUIRED_FILE}")
def unit_active(self, msg: str = "") -> None:
"""Set the unit's status to active and log the provided message, if any."""
self.unit.status = ActiveStatus()
if msg:
logger.debug(msg)
def unit_blocked(self, msg: str) -> None:
"""Set the unit's status to blocked and log the provided message."""
self.unit.status = BlockedStatus(msg)
logger.error(msg)
def unit_maintenance(self, msg: str) -> None:
"""Set the unit's status to maintenance and log the provided message."""
self.unit.status = MaintenanceStatus(msg)
logger.info(msg)
if __name__ == "__main__":
main(LxdCharm)
| 37.576774 | 99 | 0.571836 | #!/usr/bin/env python3
"""LXD charm."""
import logging
import os
import shutil
import subprocess
import tarfile
import tempfile
from urllib.error import HTTPError, URLError
from urllib.request import urlopen
from ops.charm import (
ActionEvent,
CharmBase,
ConfigChangedEvent,
InstallEvent,
StartEvent,
)
from ops.framework import StoredState
from ops.main import main
from ops.model import ActiveStatus, BlockedStatus, MaintenanceStatus, ModelError
logger = logging.getLogger(__name__)
SYSCTL_CONFIGS = {
"fs.aio-max-nr": 524288,
"fs.inotify.max_queued_events": 1048576,
"fs.inotify.max_user_instances": 1048576,
"fs.inotify.max_user_watches": 1048576,
"kernel.dmesg_restrict": 1,
"kernel.keys.maxbytes": 2000000,
"kernel.keys.maxkeys": 2000,
"net.core.bpf_jit_limit": 3000000000,
"net.ipv4.neigh.default.gc_thresh3": 8192,
"net.ipv6.neigh.default.gc_thresh3": 8192,
"vm.max_map_count": 262144,
}
SYSTEMD_TMPFILES_CONFIGS = [
"z /proc/sched_debug 0400 - - -",
"z /sys/kernel/slab 0700 - - -",
]
REBOOT_REQUIRED_FILE = "/run/lxd-reboot-required"
class LxdCharm(CharmBase):
"""LXD charm class."""
_stored = StoredState()
def __init__(self, *args):
"""Initialize charm's variables."""
super().__init__(*args)
# Initialize the persistent storage if needed
self._stored.set_default(
addresses={},
config={},
lxd_binary_path=None,
lxd_initialized=False,
lxd_installed=False,
lxd_snap_path=None,
reboot_required=False,
)
# Action event handlers
self.framework.observe(
self.on.add_trusted_client_action, self._on_action_add_trusted_client
)
self.framework.observe(self.on.debug_action, self._on_action_debug)
self.framework.observe(
self.on.show_pending_config_action, self._on_action_show_pending_config
)
# Main event handlers
self.framework.observe(self.on.install, self._on_charm_install)
self.framework.observe(self.on.config_changed, self._on_charm_config_changed)
self.framework.observe(self.on.start, self._on_charm_start)
self.framework.observe(self.on.upgrade_charm, self._on_charm_upgrade)
def _on_action_add_trusted_client(self, event: ActionEvent) -> None:
"""Add a client certificate to the trusted list."""
name = event.params.get("name", "unknown")
cert = event.params.get("cert")
cert_url = event.params.get("cert-url")
projects = event.params.get("projects")
if not cert and not cert_url:
msg = "One of cert or cert-url parameter needs to be provided."
event.fail(msg)
logger.error(msg)
return
if cert:
# The received PEM needs to be mangled to be able to split()
# on spaces without breaking the "-----BEGIN CERTIFICATE-----"
# and "-----END CERTIFICATE-----" lines
cert = (
"\n".join(cert.replace(" CERTIFICATE", "CERTIFICATE", 2).split())
.replace("CERTIFICATE", " CERTIFICATE", 2)
.encode()
)
# Ignore the cert-url param if a cert was provided
cert_url = None
if cert_url and not (cert_url.startswith("http://") or cert_url.startswith("https://")):
msg = 'The cert-url parameter needs to start with "http://" or "https://".'
event.fail(msg)
logger.error(msg)
return
if cert_url:
try:
response = urlopen(cert_url)
except HTTPError as e:
msg = f"The server couldn't fulfill the request. Error code: {e.code}"
event.fail(msg)
logger.error(msg)
return
except URLError as e:
msg = f"We failed to reach a server. Reason: {e.reason}"
event.fail(msg)
logger.error(msg)
return
else:
cert = response.read()
if not cert:
msg = "Invalid/empty certificate provided/retrieved."
event.fail(msg)
logger.error(msg)
return
cmd = ["lxc", "config", "trust", "add", "-", "--name", name]
if projects:
cmd += ["--restricted", "--projects", projects]
try:
subprocess.run(cmd, input=cert, check=True)
except subprocess.CalledProcessError as e:
msg = f'Failed to run "{e.cmd}": {e.returncode}'
event.fail(msg)
logger.error(msg)
raise RuntimeError
event.set_results({"result": "the client certificate is now trusted"})
def _on_action_debug(self, event: ActionEvent) -> None:
"""Collect information for a bug report."""
try:
b = subprocess.run(["lxd.buginfo"], check=True)
except subprocess.CalledProcessError as e:
msg = f'Failed to run "{e.cmd}": {e.returncode}'
event.fail(msg)
logger.error(msg)
raise RuntimeError
event.set_results({"buginfo": b.stdout})
logger.debug("lxd.buginfo called successfully")
def _on_action_show_pending_config(self, event: ActionEvent) -> None:
"""Show the currently pending configuration changes (queued for after the reboot)."""
event.set_results({"pending": self.config_changed()})
def _on_charm_install(self, event: InstallEvent) -> None:
logger.info("Installing the LXD charm")
# Confirm that the config is valid
if not self.config_is_valid():
return
# Install LXD itself
try:
self.snap_install_lxd()
self._stored.lxd_installed = True
logger.info("LXD installed successfully")
except RuntimeError:
logger.error("Failed to install LXD")
event.defer()
return
# Apply various configs
self.snap_config_set()
self.kernel_sysctl()
self.kernel_hardening()
# Initial configuration
try:
self.lxd_init()
self._stored.lxd_initialized = True
logger.info("LXD initialized successfully")
except RuntimeError:
logger.error("Failed to initialize LXD")
event.defer()
return
# Apply sideloaded resources attached at deploy time
self.resource_sideload()
# All done
self.unit_active()
def _on_charm_config_changed(self, event: ConfigChangedEvent) -> None:
"""React to configuration changes.
Some configuration items can be set only once
while others are changable, sometimes requiring
a service reload or even a machine reboot.
"""
logger.info("Updating charm config")
# Confirm that the config is valid
if not self.config_is_valid():
return
# Get all the configs that changed
changed = self.config_changed()
if not changed:
logger.debug("No configuration changes to apply")
return
# Check if the required reboot occurred and clear the flag if yes
if os.path.exists(REBOOT_REQUIRED_FILE):
self.unit_blocked("Reboot required, deferring config change")
event.defer()
return
# Check if any required reboot was done
self.system_clear_reboot_required()
# Apply all the configs that changed
try:
if "snap-channel" in changed:
self.snap_install_lxd()
elif "sysctl-tuning" in changed:
self.kernel_sysctl()
elif "kernel-hardening" in changed:
self.kernel_hardening()
elif [k for k in changed if k.startswith("snap-config-")]:
self.snap_config_set()
except RuntimeError:
msg = "Failed to apply some configuration change(s): %s" % ", ".join(changed)
self.unit_blocked(msg)
event.defer()
return
# If some changes needed a reboot to take effect, enter blocked status
if self._stored.reboot_required:
self.unit_blocked("Machine reboot required")
return
# All done
self.unit_active("Configuration change(s) applied successfully")
def _on_charm_start(self, event: StartEvent) -> None:
logger.info("Starting the LXD charm")
if not self._stored.lxd_initialized:
logger.debug("LXD is not initialized yet, not starting the charm")
return
# Check if any required reboot was done
self.system_clear_reboot_required()
if not self._stored.reboot_required and isinstance(self.unit.status, BlockedStatus):
self.unit_active("Pending configuration changes were applied during the last reboot")
# Apply pending config changes (those were likely queued up while the unit was
# down/rebooting)
if self.config_changed():
logger.debug("Pending config changes detected")
self._on_charm_config_changed(event)
def _on_charm_upgrade(self, _):
logger.info("Upgrading the LXD charm")
# Nothing to do if LXD is not initialized yet
if not self._stored.lxd_initialized:
logger.info("Nothing to upgrade as LXD is not initialized yet")
return
# If LXD was initialized and new `lxd-*` keys are introduced on
# upgrades, those will need to be taken as-is (with a warning) because
# they would otherwise cause failures during the config-changed event
# following the upgrade-charm event.
changed = self.config_changed()
for k, v in changed.items():
if k.startswith("lxd-"):
logger.warning(
f'The new "{k}" key won\'t be applied to existing units '
"as their LXD is already initialized"
)
self._stored.config[k] = v
# Apply sideloaded resources attached after deployment
self.resource_sideload()
def config_changed(self) -> dict:
"""Figure out what changed."""
new_config = self.config
old_config = self._stored.config
apply_config = {}
for k, v in new_config.items():
if k not in old_config:
apply_config[k] = v
elif v != old_config[k]:
apply_config[k] = v
return apply_config
def config_is_valid(self) -> bool:
"""Validate the config."""
if "local" in self.model.storages and len(self.model.storages["local"]) > 1:
self.unit_blocked("LXD charm only supports a single storage volume")
return False
config_changed = self.config_changed()
# If nothing changed and we were blocked due to a lxd- key
# change (post-init), we can assume the change was reverted thus unblocking us
if (
not config_changed
and isinstance(self.unit.status, BlockedStatus)
and "Can't modify lxd- keys after initialization:" in str(self.unit.status)
):
self.unit_active("Unblocking as the lxd- keys were reset to their initial values")
for k in config_changed:
if k.startswith("lxd-") and self._stored.lxd_initialized:
self.unit_blocked(f"Can't modify lxd- keys after initialization: {k}")
return False
return True
def juju_set_proxy(self) -> None:
"""Apply proxy config."""
juju_proxy = "/etc/juju-proxy.conf"
if not os.path.exists(juju_proxy):
logger.debug("No proxy config from Juju.")
return
http_proxy = None
https_proxy = None
no_proxy = None
with open(juju_proxy, encoding="UTF-8") as f:
for line in f.read().splitlines():
# Only consider lines exporting variables
if not line.startswith("export "):
continue
# Parse export lines
try:
# Strip "export " prefix and split variable/value
k, v = line.replace("export ", "", 1).split("=", 1)
except (IndexError, ValueError):
continue
if k == "HTTP_PROXY":
http_proxy = v
elif k == "HTTPS_PROXY":
https_proxy = v
elif k == "NO_PROXY":
no_proxy = v
try:
if http_proxy:
logger.debug(f"Configuring core.proxy_http={http_proxy}")
subprocess.run(["lxc", "config", "set", "core.proxy_http", http_proxy], check=True)
if https_proxy:
logger.debug(f"Configuring core.proxy_https={https_proxy}")
subprocess.run(
["lxc", "config", "set", "core.proxy_https", https_proxy],
check=True,
)
if no_proxy:
logger.debug(f"Configuring core.proxy_ignore_hosts={no_proxy}")
subprocess.run(
["lxc", "config", "set", "core.proxy_ignore_hosts", no_proxy],
check=True,
)
except subprocess.CalledProcessError as e:
self.unit_blocked(f'Failed to run "{e.cmd}": {e.returncode}')
raise RuntimeError
def kernel_sysctl(self) -> None:
"""Apply sysctl tuning keys."""
logger.debug("Applying sysctl tuning")
sysctl_file = "/etc/sysctl.d/60-lxd.conf"
config = self.config["sysctl-tuning"]
if config:
self.unit_maintenance(f"Applying sysctl config file: {sysctl_file}")
with open(sysctl_file, "w", encoding="UTF-8") as f:
for k, v in SYSCTL_CONFIGS.items():
f.write(f"{k} = {v}\n")
try:
subprocess.run(["sysctl", "--quiet", "--load", sysctl_file], check=True)
except subprocess.CalledProcessError as e:
self.unit_blocked(f'Failed to run "{e.cmd}": {e.returncode}')
raise RuntimeError
elif os.path.exists(sysctl_file):
self.unit_maintenance(f"Removing sysctl config file: {sysctl_file}")
os.remove(sysctl_file)
# Persist the configuration
self._stored.config["sysctl-tuning"] = config
def kernel_hardening(self) -> None:
"""Apply kernel hardening systemd tmpfiles."""
logger.debug("Applying kernel hardening")
systemd_tmpfiles = "/etc/tmpfiles.d/lxd.conf"
config = self.config["kernel-hardening"]
if config:
self.unit_maintenance(f"Applying kernel hardening config file: {systemd_tmpfiles}")
with open(systemd_tmpfiles, "w", encoding="UTF-8") as f:
f.write("\n".join(SYSTEMD_TMPFILES_CONFIGS) + "\n")
try:
subprocess.run(["systemd-tmpfiles", "--create"], check=True)
except subprocess.CalledProcessError as e:
self.unit_blocked(f'Failed to run "{e.cmd}": {e.returncode}')
raise RuntimeError
elif os.path.exists(systemd_tmpfiles):
self.unit_maintenance(f"Removing kernel hardening config file: {systemd_tmpfiles}")
os.remove(systemd_tmpfiles)
# Persist the configuration
self._stored.config["kernel-hardening"] = config
def lxd_init(self) -> None:
"""Apply initial configuration of LXD."""
self.unit_maintenance("Initializing LXD in standalone mode")
preseed = self.config.get("lxd-preseed")
if preseed:
self.unit_maintenance("Applying LXD preseed")
try:
# NOTE: When preseeding, no further configuration is applied.
subprocess.run(["lxd", "init", "--preseed"], check=True, input=preseed.encode())
except subprocess.CalledProcessError as e:
self.unit_blocked(f'Failed to run "{e.cmd}": {e.returncode}')
raise RuntimeError
else:
self.unit_maintenance("Performing initial configuration")
try:
# Configure the storage
if "local" in self.model.storages and len(self.model.storages["local"]) == 1:
src = f"source={self.model.storages['local'][0].location}"
self.unit_maintenance(f"Configuring external storage pool (zfs, {src})")
subprocess.run(["lxc", "storage", "create", "local", "zfs", src], check=True)
else:
self.unit_maintenance("Configuring local storage pool (dir)")
subprocess.run(["lxc", "storage", "create", "local", "dir"], check=True)
subprocess.run(
[
"lxc",
"profile",
"device",
"add",
"default",
"root",
"disk",
"pool=local",
"path=/",
],
check=True,
)
# Configure the network
self.unit_maintenance("Configuring network bridge (lxdbr0)")
subprocess.run(["lxc", "network", "create", "lxdbr0"], check=True)
subprocess.run(
[
"lxc",
"profile",
"device",
"add",
"default",
"eth0",
"nic",
"network=lxdbr0",
"name=eth0",
],
check=True,
)
except subprocess.CalledProcessError as e:
self.unit_blocked(f'Failed to run "{e.cmd}": {e.returncode}')
raise RuntimeError
# Initial configuration of core.proxy_* keys
self.juju_set_proxy()
# Done with the initialization
self._stored.config["lxd-preseed"] = preseed
# Flag any `lxd-*` keys not handled, there should be none
for k in self.config_changed():
if k.startswith("lxd-"):
logger.error(f"lxd_init did not handle the key config named: {k}")
def lxd_is_active(self) -> bool:
"""Indicate if the lxd daemon is active."""
c = subprocess.run(
["systemctl", "is-active", "--quiet", "snap.lxd.daemon.service"],
check=False,
)
return c.returncode == 0
def lxd_reload(self) -> None:
"""Reload the lxd daemon."""
self.unit_maintenance("Reloading LXD")
try:
# Avoid occasional race during startup where a reload could cause a failure
subprocess.run(["lxd", "waitready", "--timeout=30"], check=False)
# Start a monitor process and wait for it to exit due to the service
# reloading and the old lxd process closing the monitor's socket.
mon = subprocess.Popen(
["lxc", "monitor", "--type=nonexistent"], stderr=subprocess.DEVNULL
)
subprocess.run(["systemctl", "reload", "snap.lxd.daemon.service"], check=True)
mon.wait(timeout=600)
except subprocess.TimeoutExpired:
if not mon.returncode:
mon.kill()
self.unit_maintenance("Timeout while reloading the LXD service")
raise RuntimeError
except subprocess.CalledProcessError as e:
self.unit_blocked(f'Failed to run "{e.cmd}": {e.returncode}')
raise RuntimeError
def resource_sideload(self) -> None:
"""Sideload resources."""
# Multi-arch support
arch = os.uname().machine
if arch == "x86_64":
possible_archs = ["x86_64", "amd64"]
else:
possible_archs = [arch]
# LXD snap
lxd_snap_resource = None
fname_suffix = ".snap"
try:
# Note: self._stored can only store simple data types (int/float/dict/list/etc)
lxd_snap_resource = str(self.model.resources.fetch("lxd-snap"))
except ModelError:
pass
tmp_dir = None
if lxd_snap_resource and tarfile.is_tarfile(lxd_snap_resource):
logger.debug(f"{lxd_snap_resource} is a tarball; unpacking")
tmp_dir = tempfile.mkdtemp()
tarball = tarfile.open(lxd_snap_resource)
valid_names = {f"lxd_{x}{fname_suffix}" for x in possible_archs}
for f in valid_names.intersection(tarball.getnames()):
tarball.extract(f, path=tmp_dir)
logger.debug(f"{f} was extracted from the tarball")
self._stored.lxd_snap_path = f"{tmp_dir}/{f}"
break
else:
logger.debug("Missing arch specific snap from tarball.")
else:
self._stored.lxd_snap_path = lxd_snap_resource
if self._stored.lxd_snap_path:
self.snap_sideload_lxd()
if tmp_dir:
os.remove(self._stored.lxd_snap_path)
os.rmdir(tmp_dir)
# LXD binary
lxd_binary_resource = None
fname_suffix = ""
try:
# Note: self._stored can only store simple data types (int/float/dict/list/etc)
lxd_binary_resource = str(self.model.resources.fetch("lxd-binary"))
except ModelError:
pass
tmp_dir = None
if lxd_binary_resource and tarfile.is_tarfile(lxd_binary_resource):
logger.debug(f"{lxd_binary_resource} is a tarball; unpacking")
tmp_dir = tempfile.mkdtemp()
tarball = tarfile.open(lxd_binary_resource)
valid_names = {f"lxd_{x}{fname_suffix}" for x in possible_archs}
for f in valid_names.intersection(tarball.getnames()):
tarball.extract(f, path=tmp_dir)
logger.debug(f"{f} was extracted from the tarball")
self._stored.lxd_binary_path = f"{tmp_dir}/{f}"
break
else:
logger.debug("Missing arch specific binary from tarball.")
else:
self._stored.lxd_binary_path = lxd_binary_resource
if self._stored.lxd_binary_path:
self.snap_sideload_lxd_binary()
if tmp_dir:
os.remove(self._stored.lxd_binary_path)
os.rmdir(tmp_dir)
def snap_config_set(self) -> None:
"""Apply snap set to LXD."""
logger.debug("Applying snap set lxd")
# Get all the `snap-config-*` configs to set
snap_set = {k: v for k, v in self.config_changed().items() if k.startswith("snap-config-")}
if not snap_set:
return
# Apply the changes
snap_set_list = []
reboot_needed = False
for k, v in snap_set.items():
# Convert Juju config to "snap set" syntax
if v is None:
snap_value = ""
elif isinstance(v, bool):
snap_value = str(v).lower()
else:
snap_value = v
snap_key = k.replace("snap-config-", "", 1).replace("-", ".")
# Set the snap config
snap_set_list.append("%s=%s" % (snap_key, snap_value))
# Keys that require a reboot
if k.startswith("snap-config-lxcfs-"):
# `snap-config-lxcfs-*` cannot be applied live
reboot_needed = True
continue
self.unit_maintenance("Setting snap configuration(s): " + ", ".join(snap_set_list))
try:
subprocess.run(["snap", "set", "lxd"] + snap_set_list, check=True)
except subprocess.CalledProcessError as e:
self.unit_blocked(f'Failed to run "{e.cmd}": {e.returncode}')
raise RuntimeError
# If "snap set lxd" was successful: save all the k/v applied
for k, v in snap_set.items():
self._stored.config[k] = v
if not self.lxd_is_active():
logger.debug("Skipping LXD reload (service inactive)")
return
self.lxd_reload()
if reboot_needed:
self.system_set_reboot_required()
def snap_install_lxd(self) -> None:
"""Install LXD from snap."""
channel = self.config["snap-channel"]
if channel:
channel_name = channel
else:
channel_name = "latest/stable"
self.unit_maintenance(f"Installing LXD snap (channel={channel_name})")
try:
subprocess.run(["snap", "install", "lxd", f"--channel={channel}"], check=True)
subprocess.run(["snap", "refresh", "lxd", f"--channel={channel}"], check=True)
if os.path.exists("/var/lib/lxd"):
subprocess.run(["lxd.migrate", "-yes"], check=True)
except subprocess.CalledProcessError as e:
self.unit_blocked(f'Failed to run "{e.cmd}": {e.returncode}')
raise RuntimeError
# Done with the snap installation
self._stored.config["snap-channel"] = channel
def snap_sideload_lxd(self) -> None:
"""Sideload LXD snap resource."""
logger.debug("Applying LXD snap sideload changes")
# A 0 byte file will unload the resource
if os.path.getsize(self._stored.lxd_snap_path) == 0:
logger.debug("Reverting to LXD snap from snapstore")
channel = self._stored.config["snap-channel"]
cmd = ["snap", "refresh", "lxd", f"--channel={channel}", "--amend"]
alias = None
enable = None
else:
logger.debug("Sideloading LXD snap")
cmd = ["snap", "install", "--dangerous", self._stored.lxd_snap_path]
# Since the sideloaded snap doesn't have an assertion, some things need
# to be done manually
alias = ["snap", "alias", "lxd.lxc", "lxc"]
enable = ["systemctl", "enable", "--now", "snap.lxd.daemon.unix.socket"]
try:
subprocess.run(cmd, check=True)
if alias:
subprocess.run(alias, check=True)
if enable:
subprocess.run(enable, check=True)
except subprocess.CalledProcessError as e:
self.unit_blocked(f'Failed to run "{e.cmd}": {e.returncode}')
raise RuntimeError
def snap_sideload_lxd_binary(self) -> None:
"""Sideload LXD binary resource."""
logger.debug("Applying LXD binary sideload changes")
lxd_debug = "/var/snap/lxd/common/lxd.debug"
# A 0 byte file will unload the resource
if os.path.getsize(self._stored.lxd_binary_path) == 0:
logger.debug("Unloading sideloaded LXD binary")
if os.path.exists(lxd_debug):
os.remove(lxd_debug)
else:
logger.debug("Sideloading LXD binary")
# Avoid "Text file busy" error
if os.path.exists(lxd_debug):
logger.debug("Removing old sideloaded LXD binary")
os.remove(lxd_debug)
shutil.copyfile(self._stored.lxd_binary_path, lxd_debug)
os.chmod(lxd_debug, 0o755)
self.lxd_reload()
def system_clear_reboot_required(self) -> None:
"""Clear the reboot_required flag if a reboot occurred."""
# If the required reboot occurred so let's clear the flag
if self._stored.reboot_required and not os.path.exists(REBOOT_REQUIRED_FILE):
self._stored.reboot_required = False
logger.debug("Required reboot done")
def system_set_reboot_required(self) -> None:
"""Indicate that a reboot is required to reach a clean state."""
# Touch a flag file indicating that a reboot is required.
try:
open(REBOOT_REQUIRED_FILE, "a").close()
self._stored.reboot_required = True
except OSError:
logger.warning(f"Failed to create: {REBOOT_REQUIRED_FILE}")
def unit_active(self, msg: str = "") -> None:
"""Set the unit's status to active and log the provided message, if any."""
self.unit.status = ActiveStatus()
if msg:
logger.debug(msg)
def unit_blocked(self, msg: str) -> None:
"""Set the unit's status to blocked and log the provided message."""
self.unit.status = BlockedStatus(msg)
logger.error(msg)
def unit_maintenance(self, msg: str) -> None:
"""Set the unit's status to maintenance and log the provided message."""
self.unit.status = MaintenanceStatus(msg)
logger.info(msg)
if __name__ == "__main__":
main(LxdCharm)
| 2,791 | 0 | 81 |
e78f3c1f96b6fb488ed74ffb65acfec6dce420a4 | 5,332 | py | Python | textube.py | mmjakub/textube | 118db7a6734e4484ce9d33916322a912277086ac | [
"BSD-2-Clause"
] | null | null | null | textube.py | mmjakub/textube | 118db7a6734e4484ce9d33916322a912277086ac | [
"BSD-2-Clause"
] | null | null | null | textube.py | mmjakub/textube | 118db7a6734e4484ce9d33916322a912277086ac | [
"BSD-2-Clause"
] | null | null | null | #!/usr/bin/env python3.9
import base64
import html
from http.client import HTTPSConnection
import json
import threading
import urllib.parse
if __name__ == "__main__":
import argparse
import concurrent.futures as cofu
import logging
import pathlib
import sys
parser = argparse.ArgumentParser(
description='Download subtitles for a youtube channel/playlist')
parser.add_argument('-v', '--verbose', action='store_true')
parser.add_argument('-q', '--quiet', action='store_true')
parser.add_argument('-l', '--list', action='store_true',
help='only list target IDs')
parser.add_argument('-o', '--output-dir', type=pathlib.Path, default='.', metavar='path',
help='output destination, otherwise current directory')
parser.add_argument('-c', '--connections', type=int, default=10, metavar='count',
help='number of parallel connections to use')
parser.add_argument('ids', nargs='+', metavar='ID',
help='channel or playlist id')
args = parser.parse_args()
logger = logging.getLogger('root')
logger.setLevel(logging.DEBUG if args.verbose else logging.ERROR if args.quiet else logging.INFO)
ch = logging.StreamHandler()
ch.setLevel(logging.DEBUG)
logger.addHandler(ch)
logger.debug(f'saving subtitles in: {args.output_dir.resolve()}')
logger.debug(f'using {args.connections} connections')
logger.debug(f'getting videos from: {" ".join(args.ids)}')
ids = (e for plId in args.ids for e in fetch_items(plId, selector=lambda e: e['videoId']))
with cofu.ThreadPoolExecutor(args.connections, initializer=ConnectionManager.threadInit) as executor:
future_id = {executor.submit(get_captions_from_config, vid) : vid for vid in ids}
for future in cofu.as_completed(future_id):
videoId = future_id[future]
text = future.result()
if text is not None:
logger.debug(f'saving subtitles for {videoId}')
with open(args.output_dir / videoId, 'w') as f:
f.write(text)
else:
logger.warning(f'no subs for {videoId}')
| 37.027778 | 111 | 0.67123 | #!/usr/bin/env python3.9
import base64
import html
from http.client import HTTPSConnection
import json
import threading
import urllib.parse
class ConnectionManager():
_thread_local = threading.local()
_thread_local.gapis = HTTPSConnection('www.googleapis.com')
_thread_local.yt = HTTPSConnection('www.youtube-nocookie.com')
@classmethod
def threadInit(cls):
cls._thread_local.gapis = HTTPSConnection('www.googleapis.com')
cls._thread_local.yt = HTTPSConnection('www.youtube-nocookie.com')
@classmethod
def closeAll(cls):
cls._thread_local.gapis.close()
cls._thread_local.yt.close()
@classmethod
def yt(cls):
return cls._thread_local.yt
@classmethod
def gapis(cls):
return cls._thread_local.gapis
def yt_api_get(endpoint, **params):
params.update(key='AIzaSyAa8yy0GdcGPHdtD083HiGGx_S0vMPScDM')
gapis = ConnectionManager.gapis()
gapis.request('GET', f'/youtube/v3/{endpoint}?{"&".join(f"{k}={v}" for k,v in params.items())}', headers={
'X-Origin': 'https://explorer.apis.google.com',
'X-Referer': 'https://explorer.apis.google.com'})
return json.loads(gapis.getresponse().read())
def itube_api(endpoint, **kwargs):
data = {'context': {'client': {'clientName': 'WEB', 'clientVersion': '2.00000101'}}}
data.update(kwargs)
yt = ConnectionManager.yt()
yt.request('POST',
f'https://www.youtube-nocookie.com/youtubei/v1/{endpoint}?key=AIzaSyAO_FJ2SlqU8Q4STEHLGCilw_Y9_11qcW8',
body=json.dumps(data))
return json.loads(yt.getresponse().read())
def yt_get(url):
yt = ConnectionManager.yt()
yt.request('GET', url)
body = str(yt.getresponse().read())
return html.unescape(html.unescape(body))
def channel2uploads(id):
return 'UU' + id[2:] if id.startswith('UC') else id
def flatten_cue(cue):
cue = cue['transcriptCueGroupRenderer']
return cue['formattedStartOffset']['simpleText'],\
cue['cues'][0]['transcriptCueRenderer']['cue']['simpleText']
def fetch_videoIds(playlistId):
return list(fetch_items(playlistId, selector=lambda e: e['videoId']))
def fetch_items(playlistId, part='contentDetails', selector=lambda e: e, token=None):
params = {} if token is None else {'pageToken': token}
data = yt_api_get('playlistItems', part=part, maxResults=50,
playlistId=playlistId, **params)
yield from (selector(e[part]) for e in data['items'])
if (token := data.get('nextPageToken')) is not None:
yield from fetch_items(playlistId, part, selector, token)
def get_transcript(videoId):
params = urllib.parse.quote(base64.b64encode(bytes([10,11]) + videoId.encode()))
r_json = itube_api('get_transcript', params=params)
try:
cues = r_json['actions'][0]['updateEngagementPanelAction']['content']\
['transcriptRenderer']['body']['transcriptBodyRenderer']['cueGroups']
except (KeyError):
return []
return list(map(flatten_cue, cues))
def get_captions_from_config(videoId, fmt=None):
params = {} if fmt is None else {'fmt': fmt}
config = itube_api('player', videoId=videoId)
try:
return yt_get(config['captions']['playerCaptionsTracklistRenderer']['captionTracks'][0]['baseUrl'])
except KeyError:
return None
if __name__ == "__main__":
import argparse
import concurrent.futures as cofu
import logging
import pathlib
import sys
parser = argparse.ArgumentParser(
description='Download subtitles for a youtube channel/playlist')
parser.add_argument('-v', '--verbose', action='store_true')
parser.add_argument('-q', '--quiet', action='store_true')
parser.add_argument('-l', '--list', action='store_true',
help='only list target IDs')
parser.add_argument('-o', '--output-dir', type=pathlib.Path, default='.', metavar='path',
help='output destination, otherwise current directory')
parser.add_argument('-c', '--connections', type=int, default=10, metavar='count',
help='number of parallel connections to use')
parser.add_argument('ids', nargs='+', metavar='ID',
help='channel or playlist id')
args = parser.parse_args()
logger = logging.getLogger('root')
logger.setLevel(logging.DEBUG if args.verbose else logging.ERROR if args.quiet else logging.INFO)
ch = logging.StreamHandler()
ch.setLevel(logging.DEBUG)
logger.addHandler(ch)
logger.debug(f'saving subtitles in: {args.output_dir.resolve()}')
logger.debug(f'using {args.connections} connections')
logger.debug(f'getting videos from: {" ".join(args.ids)}')
ids = (e for plId in args.ids for e in fetch_items(plId, selector=lambda e: e['videoId']))
with cofu.ThreadPoolExecutor(args.connections, initializer=ConnectionManager.threadInit) as executor:
future_id = {executor.submit(get_captions_from_config, vid) : vid for vid in ids}
for future in cofu.as_completed(future_id):
videoId = future_id[future]
text = future.result()
if text is not None:
logger.debug(f'saving subtitles for {videoId}')
with open(args.output_dir / videoId, 'w') as f:
f.write(text)
else:
logger.warning(f'no subs for {videoId}')
| 2,587 | 350 | 234 |
fd6f0427dc3f1f96809c38df7ca11b783c62bcca | 6,777 | py | Python | main.py | codewithlennylen/GAS-LAWS | 1420d2e68d029d2fc137cc909b5398e67be4f0c6 | [
"MIT"
] | null | null | null | main.py | codewithlennylen/GAS-LAWS | 1420d2e68d029d2fc137cc909b5398e67be4f0c6 | [
"MIT"
] | null | null | null | main.py | codewithlennylen/GAS-LAWS | 1420d2e68d029d2fc137cc909b5398e67be4f0c6 | [
"MIT"
] | null | null | null | import pygame
import random
pygame.init()
WIDTH, HEIGHT = 960, 700
SCREEN = pygame.display.set_mode((WIDTH, HEIGHT))
WHITE = (255, 255, 255)
BLACK = (0, 0, 0)
RED = (255, 0, 0)
BLUE = (0, 0, 255)
CLOCK = pygame.time.Clock()
FPS = 30
particles_list = []
container = Container(500, 500)
generate_particles(container, 10)
main_font = pygame.font.SysFont('comicsans', 50)
lost_font = pygame.font.SysFont('comicsans', 60)
# Draw text
lives_label = main_font.render(f"Which Law ? ", 1, BLUE)
level_label = main_font.render(f"Level", 1, BLUE)
done = False
while not done:
for event in pygame.event.get():
if event.type == pygame.QUIT:
done = True
mouse_pos = pygame.mouse.get_pos()
mouse_press = pygame.mouse.get_pressed()
keys = pygame.key.get_pressed()
SCREEN.fill(WHITE)
container.draw()
SCREEN.blit(lives_label, (10, 10))
SCREEN.blit(level_label, (WIDTH-level_label.get_width() - 10, 10))
for p in particles_list:
p.draw()
p.move()
p.collide()
# p1.draw()
# p1.move()
# p1.collide()
# p2.draw()
# p2.move()
# p2.collide()
# if keys[pygame.K_SPACE]:
# p1.move()
# pygame.draw.rect(SCREEN, BLACK, pygame.Rect(
# 10, HEIGHT//2 - 125, 300, 250), 5)
# pygame.draw.rect(SCREEN, BLACK, pygame.Rect(
# 330, HEIGHT//2 - 125, 300, 250), 5)
# pygame.draw.rect(SCREEN, BLACK, pygame.Rect(
# 650, HEIGHT//2 - 125, 300, 250), 5)
pygame.display.update()
CLOCK.tick(FPS)
| 35.668421 | 147 | 0.614284 | import pygame
import random
pygame.init()
WIDTH, HEIGHT = 960, 700
SCREEN = pygame.display.set_mode((WIDTH, HEIGHT))
WHITE = (255, 255, 255)
BLACK = (0, 0, 0)
RED = (255, 0, 0)
BLUE = (0, 0, 255)
CLOCK = pygame.time.Clock()
FPS = 30
particles_list = []
class Container(object):
def __init__(self, width=600, height=600):
self.color = BLACK
self.width = width
self.height = height
self.pos_x = ((WIDTH // 2) - (self.width // 2))
self.pos_y = ((HEIGHT // 2) - (self.height // 2))
def draw(self):
pygame.draw.rect(SCREEN, self.color, pygame.Rect(
self.pos_x, self.pos_y, self.width, self.height), 5)
class GasParticle():
def __init__(self, boundary):
"""Initialize the Object/Instance
Args:
boundary (List): This is the boundary that defines the container.
(Within which the particles are held and collide against)
Pos_X, Pos_Y, Width, Height
"""
self.radius = 10 # Particle Radius
self.velocity_range = [8, 9, 10]
self.velocity = random.choice(self.velocity_range)
# True = Positive && False = Negative
self.x_direction = random.choice([True, False])
# True = Positive && False = Negative
self.y_direction = random.choice([True, False])
self.color = RED
# List of defined Particles for particle-collision
self.gas_particles = particles_list
self.boundary = boundary
self.pos_x = random.randrange(
self.boundary[0] + self.radius, (self.boundary[0] + self.boundary[2]) - self.radius)
self.pos_y = random.randrange(
self.boundary[1] + self.radius, (self.boundary[1] + self.boundary[3]) - self.radius)
self.center = [self.pos_x, self.pos_y]
def move(self):
"""Movement of the Particle. Rudimentary-Not as random as it should be
We are simply changing direction to a predefined direction- PHYSICS-ELEMENT IS MISSING (VECTORS)
"""
if self.x_direction == True:
self.center[0] += self.velocity
elif self.x_direction == False:
self.center[0] -= self.velocity
if self.y_direction == True:
self.center[1] += self.velocity
elif self.y_direction == False:
self.center[1] -= self.velocity
def collide_with_container(self):
"""With the boundaries of the bounding box defined. We can easily create/simulate collisions with the container
"""
if self.center[0] + self.radius >= self.boundary[2] + self.boundary[0]:
self.x_direction = False
elif self.center[0] - self.radius < self.boundary[0]:
self.x_direction = True
if self.center[1] + self.radius >= self.boundary[3] + self.boundary[1]:
self.y_direction = False
elif self.center[1] - self.radius < self.boundary[1]:
self.y_direction = True
def collide_with_particle(self):
"""This handles the collision of this.particle with other particles.
BUG : Still not working. All particles assemble to one corner upon collision.
"""
for gas_particle in self.gas_particles:
if self.center[0] + self.radius > gas_particle.center[0] - gas_particle.radius:
if self.center[1] > gas_particle.center[1] - gas_particle.radius and self.center[1] < gas_particle.center[1] + gas_particle.radius:
self.x_direction = False
elif self.center[0] - self.radius < gas_particle.center[0] + gas_particle.radius:
if self.center[1] > gas_particle.center[1] - gas_particle.radius and self.center[1] < gas_particle.center[1] + gas_particle.radius:
self.x_direction = True
if self.center[1] + self.radius > gas_particle.center[1] - gas_particle.radius:
if self.center[0] > gas_particle.center[0] - gas_particle.radius and self.center[0] < gas_particle.center[0] + gas_particle.radius:
self.y_direction = False
elif self.center[1] - self.radius < gas_particle.center[1] + gas_particle.radius:
if self.center[0] > gas_particle.center[0] - gas_particle.radius and self.center[0] < gas_particle.center[0] + gas_particle.radius:
self.y_direction = True
# for gas_particle in self.gas_particles:
# if self.center[0] + self.radius >= gas_particle.center[0] - gas_particle.radius:
# self.x_direction = False
# elif self.center[0] - self.radius <= gas_particle.center[0] + gas_particle.radius:
# self.x_direction = True
# if self.center[1] + self.radius >= gas_particle.center[1] - gas_particle.radius:
# self.y_direction = False
# elif self.center[1] - self.radius <= gas_particle.center[1] + gas_particle.radius:
# self.y_direction = True
def collide(self):
# self.collide_with_particle()
self.collide_with_container()
def draw(self):
"""Draw/render the object onto the pygame.Surface(SCREEN)
"""
pygame.draw.circle(SCREEN, self.color, self.center, self.radius)
def generate_particles(container, n):
global particles_list
for _ in range(n):
p = GasParticle([container.pos_x, container.pos_y, container.width, container.height])
particles_list.append(p)
container = Container(500, 500)
generate_particles(container, 10)
main_font = pygame.font.SysFont('comicsans', 50)
lost_font = pygame.font.SysFont('comicsans', 60)
# Draw text
lives_label = main_font.render(f"Which Law ? ", 1, BLUE)
level_label = main_font.render(f"Level", 1, BLUE)
done = False
while not done:
for event in pygame.event.get():
if event.type == pygame.QUIT:
done = True
mouse_pos = pygame.mouse.get_pos()
mouse_press = pygame.mouse.get_pressed()
keys = pygame.key.get_pressed()
SCREEN.fill(WHITE)
container.draw()
SCREEN.blit(lives_label, (10, 10))
SCREEN.blit(level_label, (WIDTH-level_label.get_width() - 10, 10))
for p in particles_list:
p.draw()
p.move()
p.collide()
# p1.draw()
# p1.move()
# p1.collide()
# p2.draw()
# p2.move()
# p2.collide()
# if keys[pygame.K_SPACE]:
# p1.move()
# pygame.draw.rect(SCREEN, BLACK, pygame.Rect(
# 10, HEIGHT//2 - 125, 300, 250), 5)
# pygame.draw.rect(SCREEN, BLACK, pygame.Rect(
# 330, HEIGHT//2 - 125, 300, 250), 5)
# pygame.draw.rect(SCREEN, BLACK, pygame.Rect(
# 650, HEIGHT//2 - 125, 300, 250), 5)
pygame.display.update()
CLOCK.tick(FPS)
| 603 | 4,514 | 122 |
3363492ff5f446b8195aa3dfd8957d55562fe848 | 4,835 | py | Python | submit_scripts_and_plotting/paper_plots/make_interpretation_plots.py | apizzuto/v2_alert_stacking_FRA | 6b5bd3f6cd1b5e472797a81a389a30198c64cd9f | [
"MIT"
] | null | null | null | submit_scripts_and_plotting/paper_plots/make_interpretation_plots.py | apizzuto/v2_alert_stacking_FRA | 6b5bd3f6cd1b5e472797a81a389a30198c64cd9f | [
"MIT"
] | 4 | 2021-01-08T20:14:23.000Z | 2021-03-25T21:05:15.000Z | submit_scripts_and_plotting/paper_plots/make_interpretation_plots.py | apizzuto/v2_alert_stacking_FRA | 6b5bd3f6cd1b5e472797a81a389a30198c64cd9f | [
"MIT"
] | null | null | null | '''Script to make all interpretation based plots
for the alert followup fast response paper'''
import os
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import scipy.stats as st
from francis import utils
from results_utils import *
from francis.universe.universe_plotter import UniversePlotter
mpl.style.use(utils.initialize_mpl_style())
base_output = os.path.abspath(utils.get_francis_path() + '../figures/paper_plots/') + '/'
palette = sns.light_palette((210, 90, 60), input="husl", n_colors=12)
if __name__ == "__main__":
unis, binom_scans = get_universe_dict()
binomial_scan_plots(unis, binom_scans)
binomial_distribution_plots(unis, binom_scans)
sensitivity_and_upper_limits(unis, binom_scans) | 37.192308 | 96 | 0.583247 | '''Script to make all interpretation based plots
for the alert followup fast response paper'''
import os
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import scipy.stats as st
from francis import utils
from results_utils import *
from francis.universe.universe_plotter import UniversePlotter
mpl.style.use(utils.initialize_mpl_style())
base_output = os.path.abspath(utils.get_francis_path() + '../figures/paper_plots/') + '/'
palette = sns.light_palette((210, 90, 60), input="husl", n_colors=12)
def get_universe_dict():
print("Initializing all of the results from three different time scales")
unis = dict()
binom_scans = dict()
for delta_t in [1000., 172800., None]:
uni = UniversePlotter(delta_t, 9.6, 'SC', 'MD2014SFR', save=False)
pp, tmps = uni.get_overall_background_p(with_tmps=True)
truth, pvals, arr = get_true_binomial_p(delta_t=delta_t)
ppost = np.count_nonzero(uni.stacked_p <= truth) / float(len(uni.stacked_p))
unis[delta_t] = uni
binom_scans[delta_t] = {
'truth': truth,
'pvals': pvals,
'arr': arr,
'tmps': tmps,
'p_post': ppost
}
if delta_t is None:
print('\t- Done with time integrated initialization')
else:
print(f'\t- Done with time window {delta_t:.1e}')
return unis, binom_scans
def binomial_scan_plots(unis, binom_scans):
print('Making binomial scan plots . . . ')
for delta_t in [1000., 172800., None]:
uni = unis[delta_t]
scan = binom_scans[delta_t]
fig, ax = plt.subplots(dpi=200)
plt.plot(
np.r_[:len(scan['arr'])] + 1,
scan['arr'],
color=palette[-1],
lw=2.,
zorder=20
)
thresh_ps = st.norm.sf([0., 1., 2., 3.])
percents = np.percentile(uni.stacked_p, thresh_ps * 100.)
for perc, lab in zip(percents, ['Median', r'$+1\sigma$', r'$+2\sigma$', r'$+3\sigma$']):
plt.axhline(perc, ls='-.', color = sns.xkcd_rgb['dodger blue'], lw=0.85, alpha=0.8)
plt.text(160, perc*1.1, lab, color = sns.xkcd_rgb['dodger blue'], alpha=0.8)
plt.yscale('log')
plt.xlabel('Number of sources')
plt.ylabel('Binomial p-value')
plt.ylim(1.1e-5, 1.2e0)
delta_t_str = 'steady' if delta_t is None else f'delta_t_{delta_t:.0e}'.replace('+', '')
for ftype in ['.png', '.pdf']:
plt.savefig(
base_output + f'binomial_scan_{delta_t_str}{ftype}',
bbox_inches='tight'
)
plt.close()
print('\t- Done with binomial scan plots')
def binomial_distribution_plots(unis, binom_scans):
print('Making binomial distribution plots')
for delta_t in [1000., 172800., None]:
uni = unis[delta_t]
scan = binom_scans[delta_t]
uni.plot_background_binomial_p()
plt.axvline(scan['truth'], color='k', zorder=20, lw=2.)
plt.text(3e-4, 1e2, f"p-val: {scan['p_post']:.3f}")
plt.xlim(plt.xlim()[1], plt.xlim()[0])
delta_t_str = 'steady' if delta_t is None else f'delta_t_{delta_t:.0e}'.replace('+', '')
for ftype in ['.png', '.pdf']:
plt.savefig(
base_output + f'binomial_distribution_{delta_t_str}{ftype}',
bbox_inches='tight'
)
plt.close()
print('\t- Done with binomial distribution plots')
def sensitivity_and_upper_limits(unis, binom_scans):
print('Calculating upper limits and making plots')
for delta_t in [1000., 172800., None]:
uni = unis[delta_t]
scan = binom_scans[delta_t]
delta_t_str = 'steady' if delta_t is None else f'delta_t_{delta_t:.0e}'.replace('+', '')
uni.brazil_bands(rotated=True)
for ftype in ['.png', '.pdf']:
plt.savefig(
base_output + f'sensitivity_rotated_{delta_t_str}{ftype}',
bbox_inches='tight'
)
plt.close()
for rotated in [True, False]:
uni.brazil_bands(
rotated=rotated,
with_result=True,
result=scan['truth']
)
rotated_str = '_rotated' if rotated else ''
for ftype in ['.png', '.pdf']:
plt.savefig(
base_output + f'upper_limit{rotated_str}_{delta_t_str}{ftype}',
bbox_inches='tight'
)
plt.close()
print('\t- Done with upper limit plots')
if __name__ == "__main__":
unis, binom_scans = get_universe_dict()
binomial_scan_plots(unis, binom_scans)
binomial_distribution_plots(unis, binom_scans)
sensitivity_and_upper_limits(unis, binom_scans) | 3,990 | 0 | 100 |
b4ffc6d69f9d120135e6f21d3579c97210bec686 | 957 | py | Python | tests/test_igit.py | jmosbacher/igit | 62b613fd5bed28b603160dc998c02106ee4fdef0 | [
"Apache-2.0"
] | null | null | null | tests/test_igit.py | jmosbacher/igit | 62b613fd5bed28b603160dc998c02106ee4fdef0 | [
"Apache-2.0"
] | 107 | 2021-06-28T02:10:11.000Z | 2022-03-30T02:38:03.000Z | tests/test_igit.py | jmosbacher/igit | 62b613fd5bed28b603160dc998c02106ee4fdef0 | [
"Apache-2.0"
] | null | null | null | #!/usr/bin/env python
"""Tests for `igit` package."""
# pylint: disable=redefined-outer-name
import random
import pytest
import igit
@pytest.fixture(scope="module")
| 22.255814 | 62 | 0.654127 | #!/usr/bin/env python
"""Tests for `igit` package."""
# pylint: disable=redefined-outer-name
import random
import pytest
import igit
@pytest.fixture(scope="module")
def memory_repo():
return igit.init("memory://igit_test")
def test_interval_tree():
tree = igit.IntervalTree()
tree[1, 10] = 9
assert tree[5] == 9
tree[5, 20] = 11
assert tree[15] == 11
assert tree[10] == 11
assert tree[2] == 9
def test_label_tree():
setting2 = igit.LabelTree()
setting2["subsetting1"] = 1
assert setting2["subsetting1"] == 1
setting2["subsetting2"] = 9.9
assert setting2["subsetting2"] == 9.9
setting2["subsetting3"] = "text"
assert setting2["subsetting3"] == "text"
def test_commit(memory_repo):
tree = igit.LabelTree()
tree['test'] = 'string'
memory_repo.add(label_tree=tree)
ref = memory_repo.commit(f"commit {random.randint(1,10)}")
assert isinstance(ref, igit.models.CommitRef)
| 693 | 0 | 91 |
79a11c886a6c2abb67ccf6ef4d9891b92e62ae1b | 1,191 | py | Python | projects/nncrystal/tools/export_dataset.py | nncrystals/detectron2 | ea5dcbbb135a62807cacded5a381253d1f605427 | [
"Apache-2.0"
] | null | null | null | projects/nncrystal/tools/export_dataset.py | nncrystals/detectron2 | ea5dcbbb135a62807cacded5a381253d1f605427 | [
"Apache-2.0"
] | null | null | null | projects/nncrystal/tools/export_dataset.py | nncrystals/detectron2 | ea5dcbbb135a62807cacded5a381253d1f605427 | [
"Apache-2.0"
] | null | null | null | import argparse
import json
import os
from xml.etree import ElementTree
from cvat.api import CVATAPI
from cvat.argument import cvat_args
from cvat.cvat_xml import cvat_xml_to_coco
if __name__ == '__main__':
parser = argparse.ArgumentParser()
cvat_args(parser)
parser.add_argument("--job_id", action="append", type=int, required=True)
parser.add_argument("--output_dir", default="./datasets")
args = parser.parse_args()
api = CVATAPI(args.cvat_host)
api.login(args.cvat_username, args.cvat_password)
for job_id in args.job_id:
job = api.get_job(job_id).json()
try:
task_id = job["task_id"]
except:
raise RuntimeError(job)
data = api.export_data(task_id, format="CVAT XML 1.1 for images").content
root = ElementTree.fromstring(data)
result = cvat_xml_to_coco(root, ignore_crowded=True,
occluded_as_crowded=True,
ignore_attributes=True
)
with open(os.path.join(args.output_dir, f"export_job_{job_id}.coco.json"), "w") as f:
json.dump(result, f)
| 34.028571 | 93 | 0.619647 | import argparse
import json
import os
from xml.etree import ElementTree
from cvat.api import CVATAPI
from cvat.argument import cvat_args
from cvat.cvat_xml import cvat_xml_to_coco
if __name__ == '__main__':
parser = argparse.ArgumentParser()
cvat_args(parser)
parser.add_argument("--job_id", action="append", type=int, required=True)
parser.add_argument("--output_dir", default="./datasets")
args = parser.parse_args()
api = CVATAPI(args.cvat_host)
api.login(args.cvat_username, args.cvat_password)
for job_id in args.job_id:
job = api.get_job(job_id).json()
try:
task_id = job["task_id"]
except:
raise RuntimeError(job)
data = api.export_data(task_id, format="CVAT XML 1.1 for images").content
root = ElementTree.fromstring(data)
result = cvat_xml_to_coco(root, ignore_crowded=True,
occluded_as_crowded=True,
ignore_attributes=True
)
with open(os.path.join(args.output_dir, f"export_job_{job_id}.coco.json"), "w") as f:
json.dump(result, f)
| 0 | 0 | 0 |
5522a16200a742cf8ad364ad9d24d6f9e876d0e7 | 1,533 | py | Python | apps/user/views.py | Agrimonia/Mobileshop | 1922572c7652aa85f4676a66fc49e456f560d120 | [
"MIT"
] | 4 | 2017-12-17T13:41:31.000Z | 2018-09-22T06:17:14.000Z | apps/user/views.py | Agrimonia/Mobileshop | 1922572c7652aa85f4676a66fc49e456f560d120 | [
"MIT"
] | 2 | 2017-12-16T08:30:13.000Z | 2020-01-09T03:21:43.000Z | apps/user/views.py | Agrimonia/Mobileshop | 1922572c7652aa85f4676a66fc49e456f560d120 | [
"MIT"
] | 1 | 2017-12-04T13:55:12.000Z | 2017-12-04T13:55:12.000Z | # from django.shortcuts import render
import json
from rest_framework import status
from rest_framework.views import APIView
from rest_framework.response import Response
from rest_framework.permissions import AllowAny
from backend.helpers import InputErrorMessage, JSONResponse
from .models import UserSerialiser, User
# Create your views here. | 36.5 | 114 | 0.668624 | # from django.shortcuts import render
import json
from rest_framework import status
from rest_framework.views import APIView
from rest_framework.response import Response
from rest_framework.permissions import AllowAny
from backend.helpers import InputErrorMessage, JSONResponse
from .models import UserSerialiser, User
# Create your views here.
class UserShow(APIView):
def get(self, request, format=None):
serializer = UserSerialiser(request.user)
return JSONResponse(serializer.data)
class Register(APIView):
permission_classes = (AllowAny, )
def post(self, request):
try:
data = json.loads(request.body.decode('utf-8'))
except json.JSONDecodeError:
return InputErrorMessage("Invalid JSON body")
if "username" not in data:
return InputErrorMessage("username not provide.")
if User.objects.filter(username=data["username"]).exists():
return InputErrorMessage("username is used.")
if "email" not in data:
return InputErrorMessage("email not provide.")
if User.objects.filter(email=data["email"]).exists():
return InputErrorMessage("email is used.")
if "password" not in data:
return InputErrorMessage("password not provide.")
user = User.objects.create_user(username=data["username"], email=data["email"], password=data["password"])
user.save()
return JSONResponse({
"code": 200,
"message": "OK",
}) | 1,040 | 75 | 71 |