in courses/machine_learning/deepdive2/structured/labs/serving/application/lib/pyasn1/codec/ber/encoder.py [0:0]
def encodeValue(self, value, asn1Spec, encodeFun, **options):
if asn1Spec is not None:
value = asn1Spec.clone(value)
if value.isPlusInf:
return (0x40,), False, False
if value.isMinusInf:
return (0x41,), False, False
m, b, e = value
if not m:
return null, False, True
if b == 10:
if LOG:
LOG('encoding REAL into character form')
return str2octs('\x03%dE%s%d' % (m, e == 0 and '+' or '', e)), False, True
elif b == 2:
fo = 0x80 # binary encoding
ms, m, encbase, e = self._chooseEncBase(value)
if ms < 0: # mantissa sign
fo |= 0x40 # sign bit
# exponent & mantissa normalization
if encbase == 2:
while m & 0x1 == 0:
m >>= 1
e += 1
elif encbase == 8:
while m & 0x7 == 0:
m >>= 3
e += 1
fo |= 0x10
else: # encbase = 16
while m & 0xf == 0:
m >>= 4
e += 1
fo |= 0x20
sf = 0 # scale factor
while m & 0x1 == 0:
m >>= 1
sf += 1
if sf > 3:
raise error.PyAsn1Error('Scale factor overflow') # bug if raised
fo |= sf << 2
eo = null
if e == 0 or e == -1:
eo = int2oct(e & 0xff)
else:
while e not in (0, -1):
eo = int2oct(e & 0xff) + eo
e >>= 8
if e == 0 and eo and oct2int(eo[0]) & 0x80:
eo = int2oct(0) + eo
if e == -1 and eo and not (oct2int(eo[0]) & 0x80):
eo = int2oct(0xff) + eo
n = len(eo)
if n > 0xff:
raise error.PyAsn1Error('Real exponent overflow')
if n == 1:
pass
elif n == 2:
fo |= 1
elif n == 3:
fo |= 2
else:
fo |= 3
eo = int2oct(n & 0xff) + eo
po = null
while m:
po = int2oct(m & 0xff) + po
m >>= 8
substrate = int2oct(fo) + eo + po
return substrate, False, True
else:
raise error.PyAsn1Error('Prohibited Real base %s' % b)