def iStatBP(self, location, fastaRow):
"""
iStatBP(statsDict, fastaRow) -- At statsDict location, builds a list of base-pair related statistics for subsequent rows of fasta sequence data. This "inplace" function uses statsDict in situ, so does a running total on its various stats.
Future: 'GATTCA' type buckets
"""
if len(fastaRow) >0:
if not isinstance(location, dict):
if isinstance(location, basestring):
(obj, key) = self.callerInstance.getNamespace(location)
location = obj[key] # by reference
else:
raise ValueError ("Error: iStatBP() function wasn't given a good location for first parameter: %s" % location)
buckets = ['G','C','A','T']
if not 'A' in location: #indicates it hasn't been initialized.
#obj[key] = OrderedDict()
for item in buckets:
location[item]=0
location['total'] = 0
#Tally buckets. Allows for N ,U, * etc. to be identified
for char in fastaRow:
char = char.upper()
location[char] = (location[char] + 1) if char in location else 1
location['total'] += len(fastaRow)
#Might not be at last line but calculate these anyways, in case we are:
for item in buckets:
location[item + '%'] = int(round(100*operator.truediv(location[item], location['total'] ) ))
location['GC_content%'] = int(round(100*operator.truediv(location['G'] + location['C'], location['total'] )) )
return location
def __truediv__(self, other):
"Get a new Vector3 by true dividing each component of this one."
return Vector3(
operator.truediv(self._vec[0], other),
operator.truediv(self._vec[1], other),
operator.truediv(self._vec[2], other),
)
def __rtruediv__(self, other):
try:
return Vector2(operator.truediv(other[0], self[0]),
operator.truediv(other[1], self[1]))
except TypeError:
return Vector2(operator.truediv(other, self[0]),
operator.truediv(other, self[1]))
def check_truediv(Poly):
# true division is valid only if the denominator is a Number and
# not a python bool.
p1 = Poly([1,2,3])
p2 = p1 * 5
for stype in np.ScalarType:
if not issubclass(stype, Number) or issubclass(stype, bool):
continue
s = stype(5)
assert_poly_almost_equal(op.truediv(p2, s), p1)
assert_raises(TypeError, op.truediv, s, p2)
for stype in (int, long, float):
s = stype(5)
assert_poly_almost_equal(op.truediv(p2, s), p1)
assert_raises(TypeError, op.truediv, s, p2)
for stype in [complex]:
s = stype(5, 0)
assert_poly_almost_equal(op.truediv(p2, s), p1)
assert_raises(TypeError, op.truediv, s, p2)
for s in [tuple(), list(), dict(), bool(), np.array([1])]:
assert_raises(TypeError, op.truediv, p2, s)
assert_raises(TypeError, op.truediv, s, p2)
for ptype in classes:
assert_raises(TypeError, op.truediv, p2, ptype(1))
def fractionToDecimal(self, numerator, denominator):
import operator
isNegtive = numerator * denominator < 0
numerator = abs(numerator)
denominator = abs(denominator)
quotient = int(operator.truediv(numerator, denominator))
remainder = numerator % denominator
dic = {remainder:0}
intPart = "-" + str(quotient) if isNegtive else str(quotient)
decimals = ""
idx = 1
while remainder != 0:
# print "abaw"
numerator = remainder * 10
quotient = int(operator.truediv(numerator, denominator))
remainder = numerator % denominator
decimals += str(quotient)
if remainder in dic:
decimals = decimals[:dic[remainder]] + "("+decimals[dic[remainder]:]+")"
break
dic[remainder] = idx
idx += 1
return intPart+"."+decimals if len(decimals) else intPart
def test_operators(self):
from operator import truediv
from _numpypy import float64, int_, True_, False_
assert 5 / int_(2) == int_(2)
assert truediv(int_(3), int_(2)) == float64(1.5)
assert truediv(3, int_(2)) == float64(1.5)
assert int_(8) % int_(3) == int_(2)
assert 8 % int_(3) == int_(2)
assert divmod(int_(8), int_(3)) == (int_(2), int_(2))
assert divmod(8, int_(3)) == (int_(2), int_(2))
assert 2 ** int_(3) == int_(8)
assert int_(3) << int_(2) == int_(12)
assert 3 << int_(2) == int_(12)
assert int_(8) >> int_(2) == int_(2)
assert 8 >> int_(2) == int_(2)
assert int_(3) & int_(1) == int_(1)
assert 2 & int_(3) == int_(2)
assert int_(2) | int_(1) == int_(3)
assert 2 | int_(1) == int_(3)
assert int_(3) ^ int_(5) == int_(6)
assert True_ ^ False_ is True_
assert 5 ^ int_(3) == int_(6)
assert +int_(3) == int_(3)
assert ~int_(3) == int_(-4)
raises(TypeError, lambda: float64(3) & 1)
def test_fifth(self):
"Nearest rounding of 1/5 is upwards."
from operator import truediv
assert 1 / 5.0 == fpu.up (lambda: truediv(1.0, 5.0))
assert 1 / 5.0 > fpu.down(lambda: truediv(1.0, 5.0))
assert -1 / 5.0 == fpu.down(lambda: truediv(1.0, -5.0))
assert -1 / 5.0 < fpu.up (lambda: truediv(1.0, -5.0))
def test_fourth(self):
" 1/4 is exact."
from operator import truediv
assert 1 / 4.0 == fpu.down(lambda: truediv(1.0, 4.0))
assert 1 / 4.0 == fpu.up (lambda: truediv(1.0, 4.0))
assert -1 / 4.0 == fpu.up (lambda: truediv(1.0, -4.0))
assert -1 / 4.0 == fpu.down(lambda: truediv(1.0, -4.0))
def test_third(self):
"Nearest rounding of 1/3 is downwards."
from operator import truediv
assert 1 / 3.0 == fpu.down(lambda: truediv(1.0, 3.0))
assert 1 / 3.0 < fpu.up (lambda: truediv(1.0, 3.0))
assert -1 / 3.0 == fpu.up (lambda: truediv(1.0, -3.0))
assert -1 / 3.0 > fpu.down(lambda: truediv(1.0, -3.0))
def __truediv__(self, other):
if type(other) in (int, long, float):
return self.__class__(operator.truediv(self.X, other),
operator.truediv(self.Y, other),
operator.truediv(self.Z, other))
else:
raise TypeError("Divider must be instance of (int, long, or float). '%s' given." % other.__class__.__name__)
def getClusteringCoefficient(self):
index=[]
C_eachnode=[]
ClusteringCoeff=0
for i in range(0,len(adjmatrix)):
num=0
ejk=0
for j in range(0,len(adjmatrix)):
if adjmatrix[i][j]==1:
num=num+1
index.append(j)
for k in range(0,len(index)):
t1=index[k]
for l in range(0,len(index)):
t2=index[l]
if adjmatrix[t1][t2]==1:
ejk=ejk+1
numerator=2 * (operator.truediv(ejk,2))
denominator=num * (num-1)
if denominator==0:
C_eachnode.append(0)
else:
Ci=operator.truediv(numerator,denominator)
C_eachnode.append(Ci)
index=[]
ClusteringCoeff=operator.truediv(sum(C_eachnode),len(nodeslist))
print ClusteringCoeff
def test_truediv_empty(self):
for (fun, funlen, _) in testfunctions:
chebtech = Chebtech2.initfun_fixedlen(fun, funlen)
self.assertTrue(operator.truediv(self.emptyfun,chebtech).isempty)
self.assertTrue(operator.truediv(chebtech,self.emptyfun).isempty)
# __truediv__
self.assertTrue((self.emptyfun/chebtech).isempty)
self.assertTrue((chebtech/self.emptyfun).isempty)
def test_truediv_empty(self):
subinterval = Interval(-2,3)
for (fun, _, _) in testfunctions:
bndfun = Bndfun.initfun_adaptive(fun, subinterval)
self.assertTrue(operator.truediv(self.emptyfun, bndfun).isempty)
self.assertTrue(operator.truediv(self.emptyfun, bndfun).isempty)
# __truediv__
self.assertTrue((self.emptyfun/bndfun).isempty)
self.assertTrue((bndfun/self.emptyfun).isempty)
def __truediv__(self, other):
if isinstance(other, Poly):
if len(other) == 1:
delta, value = other.data.items()[0]
return Poly({(k - delta): operator.truediv(v, other)
for k, v in self.data.iteritems()})
raise NotImplementedError("Can't divide general Poly instances")
return Poly({k: operator.truediv(v, other)
for k, v in self.data.iteritems()})
def test_z_div_truediv_delay_over_constant(self, a):
div_filter = operator.div(z ** -1, a)
truediv_filter = operator.truediv(z ** -1, a)
div_expected = it.imap(lambda x: operator.div(x, a),
[0.] + self.data[:-1])
truediv_expected = it.imap(lambda x: operator.truediv(x, a),
[0.] + self.data[:-1])
assert almost_eq(div_filter(self.data), div_expected)
assert almost_eq(truediv_filter(self.data), truediv_expected)
def test_truedivision(self, input_tuple, expected):
self.ureg.autoconvert_offset_to_baseunit = False
qin1, qin2 = input_tuple
q1, q2 = self.Q_(*qin1), self.Q_(*qin2)
input_tuple = q1, q2
if expected == 'error':
self.assertRaises(OffsetUnitCalculusError, op.truediv, q1, q2)
else:
expected = self.Q_(*expected)
self.assertEqual(op.truediv(q1, q2).units, expected.units)
self.assertQuantityAlmostEqual(op.truediv(q1, q2), expected,
atol=0.01)
def _units_to_mb(speed, unit):
sp = float(speed)
u = unit.lower().strip()
if u[:1] == 'b':
return truediv(sp, 1024 * 1024)
if u[:2] == 'kb':
return truediv(sp, 1024)
if u[:2] == 'mb':
return sp
if u[:2] == 'gb':
return sp * 1024
raise ValueError('could not convert \'%s %s\' to MB/s' % (sp, unit))
def test_truediv(self):
import operator
x = 1000000
a = x / 2
assert a == 500000
a = operator.truediv(x, 2)
assert a == 500000.0
x = 63050394783186940
a = x / 7
assert a == 9007199254740991
a = operator.truediv(x, 7)
assert a == 9007199254740991.0
def __init__(self, *argz, **kwz): tick_strangle_max = op.truediv(optz.tbf_max_delay, optz.tbf_tick) super(NotificationDaemon, self).__init__(*argz, **kwz) self._note_limit = core.FC_TokenBucket( tick=optz.tbf_tick, burst=optz.tbf_size, tick_strangle=lambda x: min(x*optz.tbf_inc, tick_strangle_max), tick_free=lambda x: max(op.truediv(x, optz.tbf_dec), 1) ) self._note_buffer = core.RRQ(optz.queue_len) self._note_windows = dict() self._note_id_pool = it.chain.from_iterable( it.imap(ft.partial(xrange, 1), it.repeat(2**30)) ) self._renderer = NotificationDisplay( optz.layout_margin, optz.layout_anchor, optz.layout_direction, optz.img_w, optz.img_h) self._activity_event()
def __truediv__(self, other):
if isinstance(other, Poly):
if len(other) == 1:
delta, value = next(iteritems(other._data))
return Poly(OrderedDict(((k - delta), operator.truediv(v, value))
for k, v in iteritems(self._data)),
zero=self.zero)
elif len(other) == 0:
raise ZeroDivisionError("Dividing Poly instance by zero")
raise NotImplementedError("Can't divide general Poly instances")
other = thub(other, len(self))
return Poly(OrderedDict((k, operator.truediv(v, other))
for k, v in iteritems(self._data)),
zero=self.zero)
def __init__(self, pubsub=None):
tick_strangle_max = op.truediv(optz.tbf_max_delay, optz.tbf_tick)
self._note_limit = core.FC_TokenBucket(
tick=optz.tbf_tick,
burst=optz.tbf_size,
tick_strangle=lambda x: min(x * optz.tbf_inc, tick_strangle_max),
tick_free=lambda x: max(op.truediv(x, optz.tbf_dec), 1),
)
self._note_buffer = core.RRQ(optz.queue_len)
self._note_history = core.RRQ(optz.history_len)
self._note_windows = dict()
self._note_id_pool = it.chain.from_iterable(it.imap(ft.partial(xrange, 1), it.repeat(2 ** 30)))
self._renderer = NotificationDisplay(
optz.layout_margin,
optz.layout_anchor,
optz.layout_direction,
icon_scale=optz.icon_scale,
markup_default=not optz.markup_disable,
markup_warn=optz.markup_warn_on_err,
markup_strip=optz.markup_strip_on_err,
)
self._activity_event()
self.pubsub = pubsub
if pubsub:
GLib.io_add_watch(pubsub.fileno(), GLib.PRIORITY_DEFAULT, GLib.IO_IN | GLib.IO_PRI, self._notify_pubsub)
if optz.test_message:
# Also test crazy web-of-90s markup here :P
summary = "Notification daemon started <small><tt>¯\(°_o)/¯</tt></small>"
body = (
"Desktop notification daemon started successfully on host: <u>{host}</u>"
'\nVersion: <span stretch="extraexpanded">{v}</span>'
"\nCode path: <small>{code}</small>"
'\nSound enabled: <span color="{sound_color}">{sound}</span>'
'\nPubSub enabled: <span color="{pubsub_color}">{pubsub}</span>'
).format(
host=os.uname()[1],
v=core.__version__,
sound_color="green" if optz.filter_sound else "red",
sound=unicode(bool(optz.filter_sound)).lower(),
pubsub_color="green" if pubsub else "red",
pubsub=unicode(bool(pubsub)).lower(),
code=os.path.abspath(os.path.dirname(core.__file__)),
)
if not self._renderer.markup_default:
summary, body = it.imap(strip_markup, [summary, body])
self.display(summary, body)
if optz.test_sound and optz.filter_sound:
optz.filter_sound["play"](optz.test_sound)
def test_simple_weights(self):
results = {"Red": 0, "Blue": 0}
choices = [(generators.StaticGenerator("Red"), 50),
(generators.StaticGenerator("Blue"), 50)]
generate = generators.WeightedGenerator(choices)
runs = 10000
for i in range(runs):
results[generate()] += 1
MARGIN = 0.025
self.assertTrue(0.5 - MARGIN < truediv(results["Red"], runs) < 0.5 + MARGIN)
self.assertTrue(0.5 - MARGIN < truediv(results["Blue"], runs) < 0.5 + MARGIN)
def test_operators(self):
assert (Q > 1).eval_(2)
assert (Q >= 1).eval_(1)
assert (Q <= 1).eval_(1)
assert (Q < 1).eval_(0)
assert (Q == 1).eval_(1)
assert (Q != 1).eval_(2)
assert not (Q > 1).eval_(1)
assert not (Q >= 1).eval_(0)
assert not (Q <= 1).eval_(2)
assert not (Q < 1).eval_(1)
assert not (Q == 1).eval_(2)
assert not (Q != 1).eval_(1)
assert (Q <= Q).eval_(5)
assert (Q + 5).eval_(2) == 7
assert (Q - 1).eval_(1) == 0
assert (Q * 2).eval_(4) == 8
if not PY3:
assert operator.div(Q, 2).eval_(3) == 1
assert operator.truediv(Q, 2).eval_(3) == 1.5
assert (Q / 2).eval_(4) == 2.0
assert (Q // 2).eval_(4) == 2
assert (Q % 2).eval_(3) == 1
assert (Q ** 2).eval_(3) == 9
def _luadiv(o1, o2):
if o2 == 0.0:
if o1 == 0.0:
return float("nan")
return float("inf")
return operator.truediv(o1, o2)
def __truediv__(self, other):
if isinstance(other, ZFilter):
return ZFilter(self.numpoly * other.denpoly,
self.denpoly * other.numpoly)
if isinstance(other, LinearFilter):
raise ValueError("Filter equations have different domains")
return self * operator.truediv(1, other)
def test_arithmetic_returns_money_instance(self):
assert type(Currency(3) + 2) is Currency
assert type(3 + Currency(2)) is Currency
assert type(Currency(3) - 2) is Currency
assert type(3 - Currency(2)) is Currency
assert type(Currency(3) * 2) is Currency
assert type(3 * Currency(2)) is Currency
assert type(floordiv(Currency(3), 2)) is Currency
assert type(floordiv(3, Currency(2))) is Currency
assert type(truediv(Currency(3), 2)) is Currency
assert type(truediv(3, Currency(2))) is Currency
assert type(Currency(3) ** 2) is Currency
assert type(2 ** Currency(3)) is Currency
assert type(+Currency(2)) is Currency
assert type(-Currency(2)) is Currency
assert type(abs(Currency(2))) is Currency
def uniquifying_suffix(self, iteration):
"""
Create a unique suffix.
This creates a suffix based on the number given as ``iteration``. It
will return a value encoded as lowercase ascii letter. So we have an
alphabet of 26 letters. The returned suffix will be for example ``_yh``
where ``yh`` is the encoding of ``iteration``. The length of it will be
``math.log(iteration, 26)``.
Examples::
uniquifying_suffix(0) == '_a'
uniquifying_suffix(25) == '_z'
uniquifying_suffix(26) == '_ba'
uniquifying_suffix(52) == '_ca'
"""
alphabet = string.ascii_lowercase
length = len(alphabet)
if iteration == 0:
power = 0
else:
power = int(math.log(iteration, length))
current = iteration
suffix = ''
for exp in reversed(list(range(0, power + 1))):
digit = int(truediv(current, length ** exp))
suffix += alphabet[digit]
current = current % length ** exp
return '_{suffix}'.format(suffix=suffix)
def recalc_cpu(self):
# Determine how much CPU we have.
self.available_cpus = array([0,0,0,0,0], long)
self.sleeping_cpus = 0
for base in g.all_bases():
if base.done:
if base.power_state in ["active", "overclocked", "suicide"]:
self.available_cpus[:base.location.safety+1] += base.cpu
elif base.power_state == "sleep":
self.sleeping_cpus += base.cpu
# Convert back from <type 'numpy.int32'> to avoid overflow issues later.
self.available_cpus = [int(danger) for danger in self.available_cpus]
# If we don't have enough to meet our CPU usage, we reduce each task's
# usage proportionately.
# It must be computed separalty for each danger.
needed_cpus = array([0,0,0,0,0], long)
for task_id, cpu in self.cpu_usage.iteritems():
danger = task.danger_for(task_id)
needed_cpus[:danger+1] += cpu
for danger, (available_cpu, needed_cpu) in enumerate(zip(self.available_cpus, needed_cpus)):
if needed_cpu > available_cpu:
pct_left = truediv(available_cpu, needed_cpu)
for task_id, cpu_assigned in self.cpu_usage.iteritems():
task_danger = task.danger_for(task_id)
if (danger == task_danger):
self.cpu_usage[task_id] = int(cpu_assigned * pct_left)
g.map_screen.needs_rebuild = True
def test_arithmetic_returns_money_instance():
assert type(Money(3) + 2) is Money
assert type(3 + Money(2)) is Money
assert type(Money(3) - 2) is Money
assert type(3 - Money(2)) is Money
assert type(Money(3) * 2) is Money
assert type(3 * Money(2)) is Money
assert type(floordiv(Money(3), 2)) is Money
assert type(floordiv(3, Money(2))) is Money
assert type(truediv(Money(3), 2)) is Money
assert type(truediv(3, Money(2))) is Money
assert type(Money(3) ** 2) is Money
assert type(2 ** Money(3)) is Money
assert type(+Money(2)) is Money
assert type(-Money(2)) is Money
assert type(abs(Money(2))) is Money
def test_div_truediv(self, op): input1 = [1, 5, 7., 3.3] input2 = [9.2, 10, 11, 4.9] data = op(Stream(input1), Stream(input2)) expected = [operator.truediv(x, y) for x, y in xzip(input1, input2)] assert isinstance(data, Stream) assert list(data) == expected
class TestSeriesOperators(TestData):
def test_operators_empty_int_corner(self):
s1 = Series([], [], dtype=np.int32)
s2 = Series({'x': 0.})
assert_series_equal(s1 * s2, Series([np.nan], index=['x']))
def test_ops_datetimelike_align(self):
# GH 7500
# datetimelike ops need to align
dt = Series(date_range('2012-1-1', periods=3, freq='D'))
dt.iloc[2] = np.nan
dt2 = dt[::-1]
expected = Series([timedelta(0), timedelta(0), pd.NaT])
# name is reset
result = dt2 - dt
assert_series_equal(result, expected)
expected = Series(expected, name=0)
result = (dt2.to_frame() - dt.to_frame())[0]
assert_series_equal(result, expected)
def test_operators_corner(self):
series = self.ts
empty = Series([], index=Index([]))
result = series + empty
assert np.isnan(result).all()
result = empty + Series([], index=Index([]))
assert len(result) == 0
# TODO: this returned NotImplemented earlier, what to do?
# deltas = Series([timedelta(1)] * 5, index=np.arange(5))
# sub_deltas = deltas[::2]
# deltas5 = deltas * 5
# deltas = deltas + sub_deltas
# float + int
int_ts = self.ts.astype(int)[:-5]
added = self.ts + int_ts
expected = Series(self.ts.values[:-5] + int_ts.values,
index=self.ts.index[:-5],
name='ts')
tm.assert_series_equal(added[:-5], expected)
pairings = []
for op in ['add', 'sub', 'mul', 'pow', 'truediv', 'floordiv']:
fv = 0
lop = getattr(Series, op)
lequiv = getattr(operator, op)
rop = getattr(Series, 'r' + op)
# bind op at definition time...
requiv = lambda x, y, op=op: getattr(operator, op)(y, x)
pairings.append((lop, lequiv, fv))
pairings.append((rop, requiv, fv))
if compat.PY3:
pairings.append((Series.div, operator.truediv, 1))
pairings.append((Series.rdiv, lambda x, y: operator.truediv(y, x), 1))
else:
pairings.append((Series.div, operator.div, 1))
pairings.append((Series.rdiv, lambda x, y: operator.div(y, x), 1))
@pytest.mark.parametrize('op, equiv_op, fv', pairings)
def test_operators_combine(self, op, equiv_op, fv):
def _check_fill(meth, op, a, b, fill_value=0):
exp_index = a.index.union(b.index)
a = a.reindex(exp_index)
b = b.reindex(exp_index)
amask = isna(a)
bmask = isna(b)
exp_values = []
for i in range(len(exp_index)):
with np.errstate(all='ignore'):
if amask[i]:
if bmask[i]:
exp_values.append(nan)
continue
exp_values.append(op(fill_value, b[i]))
elif bmask[i]:
if amask[i]:
exp_values.append(nan)
continue
exp_values.append(op(a[i], fill_value))
else:
exp_values.append(op(a[i], b[i]))
result = meth(a, b, fill_value=fill_value)
expected = Series(exp_values, exp_index)
assert_series_equal(result, expected)
a = Series([nan, 1., 2., 3., nan], index=np.arange(5))
b = Series([nan, 1, nan, 3, nan, 4.], index=np.arange(6))
result = op(a, b)
exp = equiv_op(a, b)
assert_series_equal(result, exp)
_check_fill(op, equiv_op, a, b, fill_value=fv)
# should accept axis=0 or axis='rows'
op(a, b, axis=0)
def test_operators_na_handling(self):
from decimal import Decimal
from datetime import date
s = Series([Decimal('1.3'), Decimal('2.3')],
index=[date(2012, 1, 1), date(2012, 1, 2)])
result = s + s.shift(1)
result2 = s.shift(1) + s
assert isna(result[0])
assert isna(result2[0])
def test_op_duplicate_index(self):
# GH14227
s1 = Series([1, 2], index=[1, 1])
s2 = Series([10, 10], index=[1, 2])
result = s1 + s2
expected = pd.Series([11, 12, np.nan], index=[1, 1, 2])
assert_series_equal(result, expected)
@pytest.mark.parametrize(
"test_input,error_type",
[
(pd.Series([]), ValueError),
# For strings, or any Series with dtype 'O'
(pd.Series(['foo', 'bar', 'baz']), TypeError),
(pd.Series([(1, ), (2, )]), TypeError),
# For mixed data types
(pd.Series(['foo', 'foo', 'bar', 'bar', None, np.nan, 'baz'
]), TypeError),
])
def test_assert_idxminmax_raises(self, test_input, error_type):
"""
Cases where ``Series.argmax`` and related should raise an exception
"""
with pytest.raises(error_type):
test_input.idxmin()
with pytest.raises(error_type):
test_input.idxmin(skipna=False)
with pytest.raises(error_type):
test_input.idxmax()
with pytest.raises(error_type):
test_input.idxmax(skipna=False)
def test_idxminmax_with_inf(self):
# For numeric data with NA and Inf (GH #13595)
s = pd.Series([0, -np.inf, np.inf, np.nan])
assert s.idxmin() == 1
assert np.isnan(s.idxmin(skipna=False))
assert s.idxmax() == 2
assert np.isnan(s.idxmax(skipna=False))
# Using old-style behavior that treats floating point nan, -inf, and
# +inf as missing
with pd.option_context('mode.use_inf_as_na', True):
assert s.idxmin() == 0
assert np.isnan(s.idxmin(skipna=False))
assert s.idxmax() == 0
np.isnan(s.idxmax(skipna=False))
def __rtruediv__(self, other):
return operator.truediv(other, self._val)
def __truediv__(self, other: Num) -> float:
return operator.truediv(object.__getattribute__(self, "_obj"), other)
def __rtruediv__(self, other):
return operator.truediv(other, self.__wrapped__)
def __itruediv__(self, other):
'True divide each component of this Vector3.'
self.x = operator.truediv(self.x, other)
self.y = operator.truediv(self.y, other)
self.z = operator.truediv(self.z, other)
return self
def test_truediv_scalar(self):
finalVector = truediv(Vector(6, 6), 2.)
self.assertAlmostEqual(finalVector.x, 3.)
self.assertAlmostEqual(finalVector.y, 3.)
def __rtruediv__(self, other: Number) -> MatBaseType:
return self._apply_op(other, lambda val_b, val_a: operator.truediv(val_a, val_b))
def __rtruediv__(self, other):
assert type(other) in (int, float), \
'Cannot divide types {} and {}'.format(self.__class__.__name__, type(other))
return Vector3D(operator.truediv(other, self.x),
operator.truediv(other, self.y),
operator.truediv(other, self.z))
'normalize_to_max':
lambda d, _: d / np.max(d),
# Cumulation
'cumulate':
lambda d, _: np.cumsum(d),
'cumulate_complementary':
lambda d, _: np.cumsum(d[::-1])[::-1],
# Elementwise (binary) operations
'add':
lambda d, v: operator.add(d, v),
'concat':
lambda d, v: operator.concat(d, v),
'div':
lambda d, v: operator.truediv(d, v),
'truediv':
lambda d, v: operator.truediv(d, v),
'floordiv':
lambda d, v: operator.floordiv(d, v),
'lshift':
lambda d, v: operator.lshift(d, v),
'mod':
lambda d, v: operator.mod(d, v),
'mul':
lambda d, v: operator.mul(d, v),
'matmul':
lambda d, v: operator.matmul(d, v),
'rshift':
lambda d, v: operator.rshift(d, v),
'sub':
def __rtruediv__(self, y): return NonStandardInteger(operator.truediv(y, self.val))
def run_model(self):
## initialize data structure
self.res = np.zeros([
self.duration,
len(MODEL_RUN_COLUMNS) + len(EXPORT_COLUMNS_FOR_CSV)
],
dtype=np.float32)
self.res[0, 0] = self.nf1
self.res[0, 1] = self.nf2
self.res[0, 2] = self.nf3
self.res[0, 3] = self.nm1
self.res[0, 4] = self.nm2
self.res[0, 5] = self.nm3
self.res[0, 6] = 0
self.res[0, 7] = 0
self.res[0, 8] = 0
self.res[0, 9] = self.female_promotion_probability_1
self.res[0, 10] = self.female_promotion_probability_2
self.res[0, 11] = np.float32(
sum(list([self.nf1, self.nf2, self.nf3])) / sum(
list([
self.nf1, self.nf2, self.nf3, self.nm1, self.nm2, self.nm3
])))
self.res[0, 12] = 0
self.res[0, 13] = self.res[0, 0:6].sum()
self.res[0, 14:] = 0
# I assign the state variables to temporary variables. That way I
# don't have to worry about overwriting the original state variables.
hiring_rate_female_level_1 = self.bf1
hiring_rate_female_level_2 = self.bf2
hiring_rate_female_level_3 = self.bf3
attrition_rate_female_level_1 = self.df1
attrition_rate_female_level_2 = self.df2
attrition_rate_female_level_3 = self.df3
attrition_rate_male_level_1 = self.dm1
attrition_rate_male_level_2 = self.dm2
attrition_rate_male_level_3 = self.dm3
probability_of_outside_hire_level_3 = self.phire3
probability_of_outside_hire_level_2 = self.phire2
female_promotion_probability_1_2 = self.female_promotion_probability_1
female_promotion_probability_2_3 = self.female_promotion_probability_2
department_size_upper_bound = self.upperbound
department_size_lower_bound = self.lowerbound
variation_range = self.variation_range
unfilled_vacanies = 0
change_to_level_1 = 0
change_to_level_2 = 0
change_to_level_3 = 0
for i in range(1, self.duration):
# initialize variables for this iteration
prev_number_of_females_level_1 = self.res[i - 1, 0]
prev_number_of_females_level_2 = self.res[i - 1, 1]
prev_number_of_females_level_3 = self.res[i - 1, 2]
prev_number_of_males_level_1 = self.res[i - 1, 3]
prev_number_of_males_level_2 = self.res[i - 1, 4]
prev_number_of_males_level_3 = self.res[i - 1, 5]
prev_number_of_vacancies_level_3 = self.res[i - 1, 6]
prev_number_of_vacancies_level_2 = self.res[i - 1, 7]
prev_number_of_vacancies_level_1 = self.res[i - 1, 8]
prev_promotion_rate_female_level_1 = self.female_promotion_probability_1
prev_promotion_rate_female_level_2 = self.female_promotion_probability_2
department_size = self.res[i - 1, 0:6].sum()
# Process Model
# Determine department size variation for this timestep
# first both female and males leave the department according to binomial probability.
female_attrition_level_3 = binomial(prev_number_of_females_level_3,
attrition_rate_female_level_3)
male_attrition_level_3 = binomial(prev_number_of_males_level_3,
attrition_rate_male_level_3)
# the departures create a set of vacancies. These vacancies are the basis for new hiring
total_vacancies_3 = female_attrition_level_3 + \
male_attrition_level_3 + change_to_level_3
# women are hired first and then men
hiring_female_3 = binomial(
max(0,
total_vacancies_3), probability_of_outside_hire_level_3 *
hiring_rate_female_level_3)
hiring_male_3 = binomial(
max(0, total_vacancies_3 - hiring_female_3),
probability_of_outside_hire_level_3 *
(1 - hiring_rate_female_level_3))
total_hiring_3 = hiring_female_3 + hiring_male_3
# level 3 vacancies that are not filled by new hires create opportunities
# for promotion from level 2. Again women are promoted first and men second.
# Also note the error trap that if we try to promote more professors from
# level 2 than there exist at level 2, then we will prevent this from happening.
vacancies_remaining_after_hiring_3 = total_vacancies_3 - total_hiring_3
potential_promotions_after_hiring_3 = max(
0, vacancies_remaining_after_hiring_3)
promotions_of_females_level_2_3 = binomial(
min(potential_promotions_after_hiring_3,
prev_number_of_females_level_2),
female_promotion_probability_2_3)
promotions_of_males_level_2_3 = binomial(
max(
0,
min(
vacancies_remaining_after_hiring_3 -
promotions_of_females_level_2_3,
prev_number_of_males_level_2)),
1 - female_promotion_probability_2_3)
# attrition at level 2 - either people leave from attrition or promotion
female_attrition_level_2 = binomial(
max(
0, prev_number_of_females_level_2 -
promotions_of_females_level_2_3),
attrition_rate_female_level_2)
male_attrition_level_2 = binomial(
max(
0, prev_number_of_males_level_2 -
promotions_of_males_level_2_3),
attrition_rate_male_level_2)
# the departures create a set of vacancies. These vacancies are the basis for new hiring
total_vacancies_2 = sum(
list([
female_attrition_level_2, male_attrition_level_2,
promotions_of_females_level_2_3,
promotions_of_males_level_2_3, change_to_level_2
]))
hiring_female_2 = binomial(
max(0,
total_vacancies_2), probability_of_outside_hire_level_2 *
hiring_rate_female_level_2)
hiring_male_2 = binomial(
max(0, total_vacancies_2 - hiring_female_2),
probability_of_outside_hire_level_2 *
(1 - hiring_rate_female_level_2))
total_hiring_2 = hiring_female_2 + hiring_male_2
vacancies_remaining_after_hiring_2 = total_vacancies_2 - total_hiring_2
potential_promotions_after_hiring_2 = max(
0, vacancies_remaining_after_hiring_2)
promotions_of_females_level_1_2 = binomial(
max(
0,
min(potential_promotions_after_hiring_2,
prev_number_of_females_level_1)),
female_promotion_probability_1_2)
promotions_of_males_level_1_2 = binomial(
max(
0,
min(
vacancies_remaining_after_hiring_2 -
promotions_of_females_level_1_2,
prev_number_of_females_level_1)),
probability_of_outside_hire_level_2 *
(1 - female_promotion_probability_1_2))
## Level 1
female_attrition_level_1 = binomial(
max(
0, prev_number_of_females_level_1 -
promotions_of_females_level_1_2),
attrition_rate_female_level_1)
male_attrition_level_1 = binomial(
max(
0, prev_number_of_males_level_1 -
promotions_of_males_level_1_2),
attrition_rate_male_level_1)
total_vacancies_1 = sum(
list([
female_attrition_level_1, male_attrition_level_1,
promotions_of_females_level_1_2,
promotions_of_males_level_1_2, change_to_level_1
]))
hiring_female_1 = binomial(max(0, total_vacancies_1),
hiring_rate_female_level_1)
hiring_male_1 = binomial(
max(0, total_vacancies_1 - hiring_female_1),
1 - hiring_rate_female_level_1)
# Write state variables to array and move to next iteration
self.res[i, 0] = number_of_females_level_1 = sum(
list([
prev_number_of_females_level_1,
neg(female_attrition_level_1),
neg(promotions_of_females_level_1_2), hiring_female_1
]))
assert (number_of_females_level_1 >=
0), "negative number of females 1"
self.res[i, 1] = number_of_females_level_2 = max(
0,
sum(
list([
prev_number_of_females_level_2,
neg(female_attrition_level_2),
neg(promotions_of_females_level_2_3),
promotions_of_females_level_1_2, hiring_female_2
])))
self.res[i, 2] = number_of_females_level_3 = sum(
list([
prev_number_of_females_level_3,
neg(female_attrition_level_3),
promotions_of_females_level_2_3, hiring_female_3
]))
self.res[i, 3] = number_of_males_level_1 = sum(
list([
prev_number_of_males_level_1,
neg(male_attrition_level_1),
neg(promotions_of_males_level_1_2), hiring_male_1
]))
self.res[i, 4] = number_of_males_level_2 = sum(
list([
prev_number_of_males_level_2,
neg(male_attrition_level_2),
neg(promotions_of_males_level_2_3),
promotions_of_males_level_1_2, hiring_male_2
]))
self.res[i, 5] = number_of_males_level_3 = sum(
list([
prev_number_of_males_level_3,
neg(male_attrition_level_3), promotions_of_males_level_2_3,
hiring_male_3
]))
self.res[i, 6] = sum(
list([male_attrition_level_3, female_attrition_level_3]))
self.res[i, 7] = sum(
list([
male_attrition_level_2, female_attrition_level_2,
promotions_of_females_level_2_3,
promotions_of_males_level_2_3
]))
self.res[i, 8] = sum(
list([
male_attrition_level_1, female_attrition_level_1,
promotions_of_males_level_1_2,
promotions_of_females_level_1_2
]))
self.res[i, 9] = self.female_promotion_probability_1
self.res[i, 10] = self.female_promotion_probability_2
self.res[i, 11] = np.float32(
truediv(
sum(
list([
number_of_females_level_1,
number_of_females_level_2,
number_of_females_level_3
])),
sum(
list([
number_of_females_level_1,
number_of_females_level_2,
number_of_females_level_3, number_of_males_level_1,
number_of_males_level_2, number_of_males_level_3
]))))
unfilled_vacanies = abs(department_size - self.res[i, 0:6].sum())
self.res[i, 12] = unfilled_vacanies
department_size = self.res[i, 0:6].sum()
self.res[i, 13] = department_size
self.res[i, 14] = hiring_female_3
self.res[i, 15] = hiring_male_3
self.res[i, 16] = hiring_female_2
self.res[i, 17] = hiring_male_2
self.res[i, 18] = hiring_female_1
self.res[i, 19] = hiring_male_1
self.res[i, 20] = 0
self.res[i, 21] = 0
self.res[i, 22] = promotions_of_females_level_2_3
self.res[i, 23] = promotions_of_males_level_2_3
self.res[i, 24] = promotions_of_females_level_1_2
self.res[i, 25] = promotions_of_males_level_1_2
self.res[i, 26] = hiring_rate_female_level_1
self.res[i, 27] = hiring_rate_female_level_2
self.res[i, 28] = hiring_rate_female_level_3
self.res[i, 29] = 1 - hiring_rate_female_level_1
self.res[i, 30] = 1 - hiring_rate_female_level_2
self.res[i, 31] = 1 - hiring_rate_female_level_3
self.res[i, 32] = attrition_rate_female_level_1
self.res[i, 33] = attrition_rate_female_level_2
self.res[i, 34] = attrition_rate_female_level_3
self.res[i, 35] = attrition_rate_male_level_1
self.res[i, 36] = attrition_rate_male_level_2
self.res[i, 37] = attrition_rate_male_level_3
self.res[i, 38] = 1
self.res[i, 39] = probability_of_outside_hire_level_2
self.res[i, 40] = probability_of_outside_hire_level_3
self.res[i, 41] = female_promotion_probability_1_2
self.res[i, 42] = female_promotion_probability_2_3
self.res[i, 43] = 1 - female_promotion_probability_1_2
self.res[i, 44] = 1 - female_promotion_probability_2_3
self.res[i, 45] = department_size_upper_bound
self.res[i, 46] = department_size_lower_bound
self.res[i, 47] = variation_range
self.res[i, 48] = self.duration
# this produces an array of values. Then I need to assign the
# values to levels. So if I have say a range of variation of 5. I
# will get something like [-1,0,1,-1,0] or something. I need to
# turn this into something like [2,-1,0]. That means randomly
# assigning the values in the array to levels.
flag = False
while flag == False:
changes = np.random.choice([-1, 0, 1], variation_range)
levels = np.random.choice([1, 2, 3], variation_range) #
# random level
# choice
# need to test whether the candidate changes keep the
# department size within bounds.
# print(["old dept size:", department_size,
# "new dept size:", self.res[i, 0:6].sum(),
# "candidate:", department_size +
# changes.sum(),
# " added postions: ", changes.sum(),
# "unfilled ", unfilled_vacanies])
if (department_size + changes.sum() <=
department_size_upper_bound
and department_size + changes.sum() >=
department_size_lower_bound):
change_to_level_3 = np.int(
changes[np.where(levels == 3)[0]].sum())
change_to_level_2 = np.int(
changes[np.where(levels == 2)[0]].sum())
change_to_level_1 = np.int(
changes[np.where(levels == 1)[0]].sum())
flag = True
if (department_size > department_size_upper_bound):
change_to_level_3 = 0
change_to_level_2 = 0
change_to_level_1 = 0
flag = True
if department_size < department_size_lower_bound:
changes = np.ones(variation_range)
change_to_level_3 = np.int(
changes[np.where(levels == 3)[0]].sum())
change_to_level_2 = np.int(
changes[np.where(levels == 2)[0]].sum())
change_to_level_1 = np.int(
changes[np.where(levels == 1)[0]].sum())
flag = True
df_ = pd.DataFrame(self.res)
df_.columns = MODEL_RUN_COLUMNS + EXPORT_COLUMNS_FOR_CSV
recarray_results = df_.to_records(index=True)
self.run = recarray_results
return recarray_results
def _percent_complete(self, available=(0, 0, 0)):
available_array = array(available, long)
return truediv(self.cost_paid + available_array, self.total_cost)
def test_truediv_twovectors(self):
finalVector = truediv(Vector(6, 6), Vector(2., 2.))
self.assertAlmostEqual(finalVector.x, 3.)
self.assertAlmostEqual(finalVector.y, 3.)
def __rdiv__(self, other):
return operator.truediv(*reversed(self._get_operands(other)))
def main():
print('{:.2f}'.format(float(truediv(*map(int, input().split())))))
def is_valid_prefix_expression(expression):
'''
>>> is_valid_prefix_expression('12')
Correct prefix expression
>>> is_valid_prefix_expression('+ 12 4')
Correct prefix expression
>>> is_valid_prefix_expression('- + 12 4 10')
Correct prefix expression
>>> is_valid_prefix_expression('+ - + 12 4 10 * 11 4')
Correct prefix expression
>>> is_valid_prefix_expression('/ + - + 12 4 10 * 11 4 5')
Correct prefix expression
>>> is_valid_prefix_expression('+ / + - + 12 4 10 * 11 4 5 - 80 82 ')
Correct prefix expression
>>> is_valid_prefix_expression('twelve')
Incorrect prefix expression
>>> is_valid_prefix_expression('2 3')
Incorrect prefix expression
>>> is_valid_prefix_expression('+ + 2 3')
Incorrect prefix expression
>>> is_valid_prefix_expression('+1 2')
Incorrect prefix expression
>>> is_valid_prefix_expression('+ / 1 2 *3 4')
Incorrect prefix expression
>>> is_valid_prefix_expression('+1 2')
Incorrect prefix expression
>>> is_valid_prefix_expression('+ +1 2')
Correct prefix expression
>>> is_valid_prefix_expression('++1 2')
Incorrect prefix expression
>>> is_valid_prefix_expression('+ +1 -2')
Correct prefix expression
'''
stack = []
op_dic = {
'+': lambda x, y: add(x, y),
'-': lambda x, y: sub(x, y),
'*': lambda x, y: mul(x, y),
'/': lambda x, y: truediv(x, y)
}
try:
list_ = expression.split(' ')
for item in reversed(list_):
if not item:
continue
if item in op_dic:
up = stack.pop()
down = stack.pop()
stack.append(op_dic[item](up, down))
else:
stack.append(int(item))
if len(stack) != 1:
raise ListNonEmpty
# - IndexError is raised in particular when trying to pop from an empty list
# - ValueError is raised in particular when trying to convert to an int
# a string that cannot be converted to an int
# - ListNonEmpty is expected to be raised when a list is found out not to be empty
except (IndexError, ValueError, ListNonEmpty):
print('Incorrect prefix expression')
else:
print('Correct prefix expression')
def __truediv__(self, other):
'Get a new Vector3 by true dividing each component of this one.'
return Vector3Index(self.index, operator.truediv(self.x, other),
operator.truediv(self.y, other),
operator.truediv(self.z, other))
def doLinePlots(args, df0, key_geo, comparison=True):
if comparison == True:
region = 'WorldAll'
if key_geo == 'EU':
return
else:
region = key_geo
for perCapita in (
True,
False,
):
# for k, limSum in (('cases_weekly', 20000), ('deaths_weekly', 500)):
for k in ('cases', 'deaths'):
print('line', key_geo, perCapita, k)
path = 'days100_{}_perCapita{}_{}.png'.format(
k, perCapita, key_geo.replace(' ', '_'))
if os.path.isfile(path):
continue
k += '_weekly'
d = {True: [], False: []}
# for country in df0['countriesAndTerritories'].unique():
labels = set()
for country in args.d_region2countries[region]:
if comparison is True and country == key_geo:
continue
if df0[df0['countriesAndTerritories'].isin([country
])][k].sum() == 0:
continue
if perCapita is True:
value = operator.truediv(
10**6 * df0[df0['countriesAndTerritories'].isin(
[country])][k].sum(),
df0[df0['countriesAndTerritories'].isin(
[country])]['popData2019'].unique(),
)[0]
if np.isnan(value):
continue
else:
value = df0[df0['countriesAndTerritories'].isin(
[country])][k].sum()
if comparison is True:
_ = args.d_country2continent[
country] == args.d_country2continent[key_geo]
else:
_ = True
d[_].append((value, country))
if comparison is True:
tuples = itertools.chain(
reversed(sorted(d[False])),
reversed(sorted(d[True])),
[(None, key_geo)],
)
else:
tuples = itertools.chain(
reversed(sorted(d[False])),
reversed(sorted(d[True])),
)
for t in tuples:
country = t[1]
df = df0[df0['countriesAndTerritories'].isin(
[country])].sort_values(by='dateRep', ascending=True)
if k == 'deaths' and 'South' in country:
print(k, country, df[k].sum())
if df[k].sum() < 10:
continue
# if df[k].sum() < limSum and country not in (
# 'Japan', 'South_Korea', 'Taiwan', 'Singapore',
# 'United_States_of_America', 'United_Kingdom',
# ):
# continue
# if country in ('Iran',):
# continue
# print(country, df[k].sum())
# print(country, df[k].sum())
if perCapita is False:
lim = {'cases_weekly': 1000, 'deaths_weekly': 100}[k]
y = df[k].cumsum()[df[k].cumsum() > lim].values
else:
lim = 1
# s = 10**6 * df[k].cumsum()[df[k].cumsum() > 100].values / df['popData2018'].unique()[0]
# y = s
s = 10**6 * df[k].cumsum() / df['popData2019'].unique()
y = s[s > lim]
y = df[k].cumsum()[df[k].cumsum(
) > 100].values / df['popData2019'].unique()
if df[k].sum() < lim:
continue
if len(y) == 0:
continue
x = list(range(len(y)))
if comparison is False:
color = None
label = '{} ({:d})'.format(
country.replace('_', ' ').replace(
'United States of America',
'US').replace('United Kingdom', 'UK'),
int(max(y)),
)
linewidth = 2
else:
if country == key_geo:
# country = country.replace('United States of America', 'US')
# country = country.replace('United Kingdom', 'UK')
color = '#e41a1c' # red
label = '{} ({:d})'.format(
country.replace('_', ' '),
int(max(y)),
)
linewidth = 4
else:
continent = args.d_country2continent[country]
if continent == 'North America':
continent = 'Americas'
color = {
# 'North America': '#8dd3c7',
'Americas': '#8dd3c7',
'Africa': '#ffffb3',
'Europe': '#bebada',
'Asia': '#fb8072',
'Oceania': '#80b1d3',
}[continent]
if continent == args.d_country2continent[key_geo]:
color = 'darkgrey'
label = continent
else:
color = 'lightgrey'
label = 'Rest of World'
if label in labels:
label = None
else:
labels.add(label)
linewidth = 2
plt.semilogy(
x,
y,
label=label,
color=color,
linewidth=linewidth,
)
plt.legend(prop={'size': 6})
if perCapita is True:
textPerCapita = ' per 1 million capita'
else:
textPerCapita = ''
if lim == 1:
kSingPlur = k.lower().split('_')[0][:-1]
else:
kSingPlur = k.lower().split('_')[0]
plt.xlabel('Weeks since {} confirmed {}{}'.format(
lim, kSingPlur, textPerCapita))
plt.ylabel('Cumulated confirmed {}{}'.format(
k.lower().split('_')[0], textPerCapita))
if lim == 1:
textLim = '{}{}'.format(k.lower()[:-1], textPerCapita)
textLim = kSingPlur
else:
textLim = '{}{}'.format(k.lower(), textPerCapita)
textLim = kSingPlur
keyUpperCase = '{}{} {}'.format(
k.split('_')[1][0].upper(),
k.split('_')[1][1:],
k.split('_')[0])
plt.title('{}\n{}{} after first week with more than {} {}'.format(
key_geo, keyUpperCase, textPerCapita, lim, textLim),
fontsize='small')
plt.savefig(path, dpi=75)
plt.savefig(path[:-4] + '_thumb.png', dpi=25)
plt.clf()
return
def aspect(self):
producer = self._renderer.producer
return truediv(*producer.framesize) if producer is not None else 16 / 9
func print() has output/return value None and displays whatever is passed into it. """ print(-2) is None # True # true dividend 2013 / 10 # integer dividend 2013 // 10 # reminder dividend 2013 % 10 from operator import truediv, floordiv, mod truediv(2013, 10) floordiv(2013, 10) mod(2013, 10) """ Boolean contexts False values in Python: False, 0, '', None True values in Python: anything else """ """ return and print
def __div__(self, other):
return operator.truediv(*self._get_operands(other))
def optimize_image(path):
_image = Image.open(path)
if _image.size[0] > 720:
_image.resize(
(720, round(truediv(*_image.size[::-1]) * 720))).save(path,
quality=95)
def python_filter(img, layer):
region = layer.get_pixel_rgn(0, 0, img.width, img.height, True, False)
xed = img.width
yed = img.height
clust = [[0] * 8] * 8
r0 = [[0] * 8] * 8
g0 = [[0] * 8] * 8
b0 = [[0] * 8] * 8
plte = [0x000000] * 16
plte[0] = 0x000000
plte[2] = 0x0000B4
plte[1] = 0xB40000
plte[3] = 0xB400B4
plte[4] = 0x00B400
plte[6] = 0x00B4B4
plte[5] = 0xB4B400
plte[7] = 0xB4B4B4
plte[8] = 0x000000
plte[10] = 0x0000FF
plte[9] = 0xFF0000
plte[11] = 0xFF00FF
plte[12] = 0x00FF00
plte[14] = 0x00FFFF
plte[13] = 0xFFFF00
plte[15] = 0xFFFFFF
#'- "00,12,03,15,08,04,11,07,02,14,01,13,10,06,09,05."
#'- "00,06,08,14,02,12,04,10,08,14,00,06,04,10,02,12."
clust = [[0, 6, 8, 14], [2, 12, 4, 10], [8, 14, 0, 6], [4, 10, 2, 12]]
hbedge = 180 #- halfbright attr edge -zx32=218, zx32fs=153 , realthing=180?
rgrsp = 30
ggrsp = 30
bgrsp = 30 #- for r, g and b saturation levels
for y1 in range(0, int(img.height / 8), 1):
gimp.progress_update(operator.truediv(y1, int(img.height / 8)))
# print y1
for x1 in range(0, int(img.width / 8), 1):
for y2 in range(0, 8, 1):
for x2 in range(0, 8, 1):
y = y1 * 8 + y2
x = x1 * 8 + x2
# zzz=point(x,y)
str1 = region[x, y]
b0[x2][y2] = ord(str1[0])
g0[x2][y2] = ord(str1[1])
r0[x2][y2] = ord(str1[2])
bi = 0
ri = 0
gi = 0
for y2 in range(0, 8, 1):
for x2 in range(0, 8, 1):
x = (x1 * 8) + x2
y = (y1 * 8) + y2
bi = bi + b0[x2][y2]
gi = gi + g0[x2][y2]
ri = ri + r0[x2][y2]
# next:next
#b=bi/64
#g=gi/64
#r=ri/64
b = operator.truediv(bi, 64)
g = operator.truediv(gi, 64)
r = operator.truediv(ri, 64)
xrreg = 0
hbrite = 0
if (r < hbedge) and (g < hbedge) and (b < hbedge):
hbrite = 1
hbampl = 255 - (hbrite * (255 - hbedge))
# if b>(hbampl/2):
if b > operator.truediv(hbampl, 2):
b = (hbampl - b)
xrreg = xrreg | 1
# if r>(hbampl/2):
if r > operator.truediv(hbampl, 2):
r = (hbampl - r)
xrreg = xrreg | 2
# if g>(hbampl/2):
if g > operator.truediv(hbampl, 2):
g = (hbampl - g)
xrreg = xrreg | 4
# halbr=(r*rgrsp)/100
# halbg=(g*ggrsp)/100
# halbb=(b*bgrsp)/100
halbr = operator.truediv((r * rgrsp), 100)
halbg = operator.truediv((g * ggrsp), 100)
halbb = operator.truediv((b * bgrsp), 100)
vlik = 7
if ((r > halbb) and (g <= halbb)) or ((b <= halbr) and
(g <= halbr)):
vlik = 3
if ((g > halbb) and (r <= halbb)) or ((b <= halbg) and
(r <= halbg)):
vlik = 5
if ((g > halbr) and (b <= halbr)) or ((r <= halbg) and
(b <= halbg)):
vlik = 6
if ((r <= halbb) and (g <= halbb)):
vlik = 1
if ((b <= halbr) and (g <= halbr)):
vlik = 2
if ((b <= halbg) and (r <= halbg)):
vlik = 4
brattr = 1 - hbrite
ikattr = (
vlik ^ xrreg
) #- ^ is used for xor? very weird.... - from ansi-basic, ^ is ** ?
paattr = xrreg
if ikattr < paattr:
tmpr = ikattr
ikattr = paattr
paattr = tmpr
ikval = ikattr + ((ikattr & 6) / 2)
paval = paattr + ((paattr & 6) / 2)
# lumik=(ikval*255)/10
lumik = operator.truediv((ikval * 255), 10)
# lumpa=(paval*255)/10
lumpa = operator.truediv((paval * 255), 10)
if brattr < 1:
# lumik=(lumik*hbedge)/255
lumik = operator.truediv((lumik * hbedge), 255)
# lumpa=(lumpa*hbedge)/255
lumpa = operator.truediv((lumpa * hbedge), 255)
dflum = lumik - lumpa
for y2 in range(0, 8, 1):
for x2 in range(0, 8, 1):
y = y1 * 8 + y2
x = x1 * 8 + x2
b = b0[x2][y2]
g = g0[x2][y2]
r = r0[x2][y2]
vlue = (float(b + (r * 3) + (g * 6)) / 10)
patgf = (float((clust[x2 & 3][y2 & 3] + 1) * 255) / 16)
varnd = (float(patgf * dflum) / 255) + lumpa
ik = ikattr + (8 * brattr)
if varnd > vlue:
ik = paattr + (8 * brattr)
str2 = chr((plte[ik] & 0xFF0000) / 65536) + chr(
(plte[ik] & 0x00FF00) / 256) + chr(plte[ik] & 0x0000FF)
region[x, y] = str2
layer.update(0, 0, img.width, img.height)
from operator import add, mul
2 + 3
add(2, 3)
2 + 3 * 4 + 5
add(add(2, mul(3, 4)), 5)
(2 + 3) * (4 + 5)
mul(add(2, 3), add(4, 5))
"""Division"""
2014 / 10
2014 // 10
2014 % 10
from operator import truediv, floordiv, mod
floordiv(2014, 10)
truediv(2014, 10)
mod(2014, 10)
"""Multiple return values"""
def divide_exact(n, d):
return n // d, n % d
quotient, remainder = divide_exact(2014, 10)
"""Doctrings, doctests, & default arguments"""
def divide_exact2(n, d):
"""Return the quotient and remainder of dividing N by D.
def calculate_additional_fields(data):
'''
add calculated fields to data: \
total days, total price, \
total square root of price, unit cost
'''
for key, value in data.items():
# --------------------------------
try:
rental_start = datetime.datetime.strptime(value['rental_start'],
'%m/%d/%y')
except (SyntaxError, ValueError) as err:
logging.error('incorect formatting at %s generates %s', key, err)
logging.warning('rental start date for %s format not m-d-y', key)
logging.debug('rectify rental start date for %s: %s', key, value)
# --------------------------------
try:
rental_end = datetime.datetime.strptime(value['rental_end'],
'%m/%d/%y')
except (SyntaxError, ValueError):
logging.warning(
'rental end date for %s has incorrect format \
not matching m-d-y', key)
logging.debug('rectify rental end date for %s: %s', key, value)
# --------------------------------
value['total_days'] = (rental_end - rental_start).days
if op.lt(value['total_days'], 0):
logging.warning(
'for %s the rental start date %s \
comes after rental end date %s', key, rental_start,
rental_end)
logging.debug('rectify rental start date for %s: %s', key, value)
# --------------------------------
value['rental_start'] = value['rental_end']
value['rental_end'] = value['rental_start']
value['total_price'] = value['total_days'] * value['price_per_day']
if op.lt(value['total_price'], 0):
logging.warning(
'for %s the calculated total price is negative \
because calculated rental duration ended up \
being negative', key)
try:
value['sqrt_total_price'] = math.sqrt(value['total_price'])
except (ValueError, KeyError) as err:
logging.warning(
'the rental start and end dates reversely \
inserted leading to a math error %s; \
correcting negativity could lead to \
a valid business result', err.args)
logging.debug('reverse dates insertion to correct at %s', key)
finally:
logging.warning('corrected square root total price for %s',
key)
value['sqrt_total_price'] = round(
math.sqrt(abs(value['total_price'])), 2)
value['total_price'] = abs(value['total_price'])
else:
value['sqrt_total_price'] = math.sqrt(value['total_price'])
# --------------------------------
if op.le(value['units_rented'], 0):
try:
value['unit_cost'] = op.truediv(value['total_price'],
value['units_rented'])
except (ArithmeticError, ZeroDivisionError) as err:
logging.warning(
"for %s the unit cost \
cannot be calculated because %s units \
are rented", key, value['units_rented'])
logging.error('division by zero: %s', err)
else:
value['unit_cost'] = round(
op.truediv(value['total_price'], value['units_rented']), 2)
value['total_days'] = abs(value['total_days'])
return data
def __rtruediv__(self, other):
assert type(other) in (int, long, float)
return Vector2(operator.truediv(other, self.x),
operator.truediv(other, self.y))
def L2_Norm(data):
norm = np.linalg.norm(data, ord=2)
return truediv(data, norm)
def __truediv__(self, other):
if isinstance(other, Register):
return operator.truediv(self._val, other._val)
else:
return operator.truediv(self._val, other)
print "Length: ", len(y_test)
mf = get_mf(dataset)
mfc = membership.membershipfunction.MemFuncs(mf)
anf = anfis.ANFIS(x_train, y_train, mfc)
anf.trainHybridJangOffLine(epochs=10)
y_predicted = []
for i in range(len(y_test)):
res = round(anf.fittedValues[y_test[i]], 1)
if abs(res - 0) < abs(res - 1) < abs(res - 2):
y_predicted.append(0)
elif abs(res - 0) > abs(res - 1) < abs(res - 2):
y_predicted.append(1)
elif abs(res - 0) > abs(res - 1) > abs(res - 2):
y_predicted.append(2)
trupred = 0
print y_test
print y_predicted
#check accuracy
for i in range(len(y_predicted)):
if y_predicted[i] == y_test[i]:
trupred += 1
print "Sum of TruePrediction: ", trupred
print truediv(trupred, len(y_test)) * 100, "%"
print(classification_report(y_test, y_predicted))
anf.plotResults()
def feature_normalize2(data):
mu = np.mean(data, axis=0)
std = np.std(data, axis=0)
return truediv((data - mu), std)