def mean(pmf: PMF) -> num:
fs = pmf >> names
ws = pmf >> values
try:
return np.average(fs, weights=ws) >> to(_, pyfloat)
except TypeError:
fs, ws = list([fs, ws] >> zip) >> select >> (
lambda fv: not isinstance(fv[0], str)) >> zip
return np.average(fs, weights=ws) >> to(_, pyfloat)
def testBTFn():
fn = (tSally + null) ^ (tFred * tJoe)
rep = [tSally, null] \
>> sortUsing >> (lambda x: x.id) \
>> each >> (lambda x: x >> to(_, pystr)) \
>> joinAll(_, ' + ')
repr(fn) >> check >> equal >> f'(({rep}) -> (fred * joe))'
assert isinstance(fn, BTFn)
def rep1(pad: cluedo_pad, handId) -> display_table:
cfg = tvstruct(nameWidth=17,
cellWidth=12,
hasWidth=2,
noCountWidth=5,
suggestCountWidth=3,
likeWidth=7)
handIds = (pad >> values >> first >> keys >> to(_, pylist) |
(N**pystr)[tvseq]) >> drop >> TBI
titlesPadding = ' ' * (cfg.nameWidth + cfg.hasWidth + cfg.noCountWidth +
cfg.suggestCountWidth + cfg.likeWidth)
tTitle = [
titlesPadding +
(handIds >> each >>
((lambda hId:
('Me' if hId == handId else hId) >> padWith(_, left=cfg.cellWidth))
| pystr ^ pystr) >> joinAll)
] | display_table
tNames = (people, weapons, rooms) \
>> interleave >> ['----'] \
>> each >> (lambda c: c >> padWith(_, left=cfg.nameWidth)) | display_table
# show TBI, countHands - noCount, sum priors, sum suggests
stats = genStats(pad, handId)
cHands = handIds >> count # i.e. the number of players in the game, the number of hands less the TBI hand
tSummary = (people, weapons, rooms) \
>> each >> (lambda g: g
>> each >> (lambda c: c
>> ppCardSummary(_, handId, pad, stats, cfg, cHands)
)
) \
>> interleave >> [''] \
>> each >> (lambda c: c >> padWith(_, left=cfg.nameWidth)) \
| display_table
tHands = (people, weapons, rooms) \
>> each >> (lambda g: g
>> each >> (lambda c: handIds \
>> each >> ((lambda hId: \
pad[c][hId] >> ppCell(_, stats[c], cfg)
) | pystr^pystr)
>> joinAll
)
) \
>> interleave >> [''] \
>> each >> (lambda c: c >> padWith(_, left=cfg.nameWidth)) \
| display_table
return tTitle >> join >> (tNames >> hjoin >> tSummary >> hjoin >> tHands
| display_table)
def testDrop():
card = pystr['card']
cards = (N**card)[tvseq].setPP('deck').setConstructor(tvseq)
Gr = 'Green'
Mu = 'Mustard'
Or = 'Orchid'
Pe = 'Peacock'
Pl = 'Plum'
Sc = 'Scarlet'
people = [Gr, Mu, Or, Pe, Pl, Sc] >> to(_, cards)
people >> drop >> [0, 5] >> _v >> check >> equal >> [Mu, Or, Pe, Pl]
def test():
renames = {
'Date': 'date',
'1 Mo': '_1m',
'2 Mo': '_2m',
'3 Mo': '_3m',
'6 Mo': '_6m',
'1 Yr': '_1y',
'2 Yr': '_2y',
'3 Yr': '_3y',
'5 Yr': '_5y',
'7 Yr': '_7y',
'10 Yr': '_10y',
'20 Yr': '_20y',
'30 Yr': '_30y'
}
conversions = dict(
date=vec.to(_, pydate, 'MM/DD/YY'),
_1m=vec.to(_, pyfloat),
_2m=vec.to(_, pyfloat),
_3m=vec.to(_, pyfloat),
_6m=vec.to(_, pyfloat),
_1y=vec.to(_, pyfloat),
_2y=vec.to(_, pyfloat),
_3y=vec.to(_, pyfloat),
_5y=vec.to(_, pyfloat),
_7y=vec.to(_, pyfloat),
_10y=vec.to(_, pyfloat),
_20y=vec.to(_, pyfloat),
_30y=vec.to(_, pyfloat),
)
filename = 'us yields.csv'
ytms = (path + '/' + filename) >> fromCsv(_, renames, conversions)
# take logs
ytms2 = ytms \
>> names >> drop >> ['date', '_2m'] \
>> inject(_, agg(ytms), _) >> (lambda prior, name:
prior >> atPut(_,
'log' >> join >> (name >> drop >> 1),
prior >> at(_, name) >> vec.log
)
)
# select the desired date range
d1 = '2021.01.01' >> to(_, pydate, 'YYYY.MM.DD')
d2 = '2021.04.01' >> to(_, pydate, 'YYYY.MM.DD')
usD1ToD2 = ytms2 >> select >> (lambda r: d1 <= r.date and r.date < d2)
# diff and calc covariance matrices
usDiffs = usD1ToD2 \
>> names >> drop >> 'date' \
>> inject(_, agg(), _) >> (lambda p, f:
p >> atPut(_, f, usD1ToD2 >> at(_, f) >> vec.diff)
)
t1 = usDiffs >> names
t2 = t1 >> select >> startsWith(_, '_')
usDiffCov = usDiffs \
>> take >> (usDiffs >> names >> select >> startsWith(_, '_')) \
>> to(_, tvarray) >> stats.core.cov
usLogDiffCov = usDiffs \
>> take >> (usDiffs >> names >> select >> startsWith(_, 'log')) \
>> to(_, tvarray) >> stats.core.cov
usDiffCov >> check >> sameShape >> usLogDiffCov
usDiffCov >> check >> different >> usLogDiffCov
def dateAsDayString(d):
return d >> day >> to(_, pystr) >> pad(_, right=3)
def test_repr_or_str():
[Fred(1)] >> to(_, str) >> assertEquals >> '[rep(1)]'
def test_misc():
[[1, 2], [3, 5]] >> to(_,matrix[tvarray]) >> shape >> check >> equal >> (2,2)
adhoc(a=1,b=2,c=3) >> drop >> ['a', 'b'] >> to(_,pydict) >> check >> equal >> dict(c=3)
[dict(a=1)] >> at(_, 0) >> at(_, "a") >> check >> equal >> 1
dict(b=1, a=2) >> keys >> to(_, pylist) >> sort