def get_api(self, start=7, end=30):
now = _utc.now().date()
backward, forward = (now - _td(days=start)), (now + _td(days=end))
return _Agenda.select(
lambda ag: (ag.fecha_con >= backward and ag.fecha_con <= forward
) and (ag.mensaje.tipo >= 1 and ag.mensaje.tipo <= 5)
and not (ag.sms_estado and ag.lmd_estado)).order_by(
lambda ag: (ag.persona.comunidad.nombre, ag.persona.nombres, ag
.persona.apellidos))
def radio_operator(cls):
#return cls.get_api(start=15, end=15).filter(lambda ag: ag.persona.cobertura==2)
now = _utc.now().date()
backward, forward = (now - _td(days=15)), (now + _td(days=15))
return _Agenda.select(lambda ag: ag.persona.cobertura == 2 and (
ag.fecha_con >= backward and ag.fecha_con <= forward) and (
ag.mensaje.tipo >= 1 and ag.mensaje.tipo <= 5
) and not ag.rad_estado).order_by(
lambda ag: (ag.persona.comunidad.nombre, ag.persona.nombres, ag
.persona.apellidos, _desc(ag.fecha_con)))
def _schedule(self):
"""
Initializes scheduler for hci device nodata checking
"""
logger.debug(
f'>>> enter {type(self._scheduler)} device_timeout:{self._device_timeout}'
)
if not self._scheduler:
return
if self._device_timeout:
l_jobid = f'bleak_timeout'
try:
self._scheduler.add_job(
self._do_bleak_timeout,
'interval',
seconds=1,
kwargs={
'jobid': l_jobid,
'reset': self._device_reset
},
id=l_jobid,
replace_existing=True,
max_instances=self.SCHEDULER_MAX_INSTANCES,
coalesce=True,
next_run_time=_dt.now() +
_td(seconds=self._device_timeout))
logger.info(f'>>> jobid:{l_jobid} scheduled')
except:
logger.exception(f'>>> jobid:{l_jobid}')
def _schedule(self, *, scheduler):
logger.debug(f'{self._name} enter {type(scheduler)}')
l_lwt = bool(self._cfg.get('lwt', _def.MQTT_LWT))
l_lwtperiod = int(self._cfg.get('lwtperiod', _def.MQTT_LWTPERIOD))
if l_lwt and l_lwtperiod:
try:
l_jobid = f'{self._name}_publish_lwt'
scheduler.add_job(self._publish_lwt,
'interval',
seconds=l_lwtperiod,
kwargs={
'payload':
self._cfg.get('lwtonline',
_def.MQTT_LWTONLINE)
},
id=l_jobid,
replace_existing=True,
max_instances=self.SCHEDULER_MAX_INSTANCES,
coalesce=True,
next_run_time=_dt.now() +
_td(seconds=l_lwtperiod))
logger.info(
f'{self._name} {l_jobid} scheduled lwtperiod:{l_lwtperiod}'
)
except:
logger.exception(f'*** {self._name}')
def get_forDashboard(cls, days=30, id_red=None, id_mup=None, id_cen=None): time_ago = _utc.now().date() - _td(days=days) query = _newBorn.select(lambda nb: nb.embarazo.parto_inst>=time_ago).order_by(lambda nb: (nb.embarazo.embarazada.comunidad.nombre, nb.embarazo.parto_inst, nb.nombres, nb.apellidos)) if id_red: municipios = _networksCrt.get_byId(id_red).municipios return query.filter(lambda nb: nb.embarazo.embarazada.comunidad.municipio in municipios) elif id_mup: comunidades = _townshipsCrt.get_byId(id_mup).comunidades return query.filter(lambda nb: nb.embarazo.embarazada.comunidad in comunidades) elif id_cen: comunidades = _hospitalsCrt.get_byId(id_cen).comunidades return query.filter(lambda nb: nb.embarazo.embarazada.comunidad in comunidades) else: return query
def get_forDashboard(cls, days=30, id_red=None, id_mup=None, id_cen=None): time_ago = _utc.now().date() - _td(days=days) query = _newBorn.select(lambda nb: nb.embarazo.parto_inst>=time_ago).order_by(lambda nb: (nb.embarazo.embarazada.comunidad.nombre, nb.embarazo.parto_inst, nb.nombres, nb.apellidos)) if id_red: municipios = _networksCrt.get_byId(id_red).municipios return query.filter(lambda nb: nb.embarazo.embarazada.comunidad.municipio in municipios) elif id_mup: comunidades = _townshipsCrt.get_byId(id_mup).comunidades return query.filter(lambda nb: nb.embarazo.embarazada.comunidad in comunidades) elif id_cen: comunidades = _hospitalsCrt.get_byId(id_cen).comunidades return query.filter(lambda nb: nb.embarazo.embarazada.comunidad in comunidades) else: return query
def save(self, persona, mensaje, fecha_con=None, days=7): try: with _db_session: if fecha_con is None: now = _utc.now().date().isoformat() ag = _Agenda(persona=persona, mensaje=mensaje, fecha_msj=now, fecha_con=now, enviado=True) else: fecha_msj = (fecha_con - _td(days=days)).isoformat() fecha_con = fecha_con.isoformat() ag = _Agenda(persona=persona, mensaje=mensaje, fecha_msj=fecha_msj, fecha_con=fecha_con) _commit() return True if ag else False except Exception, e: print e return False
def _schedule(self, *, scheduler):
logger.debug(f'{self._name} enter {type(scheduler)}')
if self._write_lastdata_int:
try:
l_jobid = f'{self._name}_lastdata'
scheduler.add_job(
self._check_lastdata,
'interval',
seconds = 1,
id = l_jobid,
replace_existing = True,
max_instances = self.SCHEDULER_MAX_INSTANCES,
coalesce = True,
next_run_time = _dt.now()+_td(seconds=_def.RUUVI_WRITE_LASTDATA_DELAY)
)
logger.info(f'{self._name} {l_jobid} scheduled')
except:
logger.exception(f'*** {self._name}')
def fix_mountain_time(monkey_session):
"""
Hack to determine if we need to adjust our datetime objects for the time
difference between Boulder and G'burg
"""
def currenttz():
if time.daylight:
return _tz(_td(seconds=-time.altzone), time.tzname[1])
else: # pragma: no cover
return _tz(_td(seconds=-time.timezone), time.tzname[0])
tz_string = currenttz().tzname(_dt.now())
# if timezone is MST or MDT, we're 2 hours behind, so we need to adjust
# datetime objects to match file store
if tz_string in ['MST', 'MDT']:
# get current timezone, and adjust tz_offset as needed
monkey_session.setattr(nexusLIMS.utils, "tz_offset", _td(hours=-2))
monkey_session.setenv('ignore_mib', 'True')
monkey_session.setenv('is_mountain_time', 'True')
def get_regions_parallel(positions, genome_file, base=0, count=7):
"""Return a list of regions surrounding a position.
Will loop through each chromosome and search all positions in that
chromosome in one batch. Lookup is serial per chromosome.
Args:
positions (dict): Dictionary of {chrom->positons}
genome_file (str): Location of a genome fasta file or directory of
files. If directory, file names must be
<chrom_name>.fa[.gz]. Gzipped OK.
base (int): Either 0 or 1, base of positions in your list
count (int): Distance + and - the position to extract
Returns:
dict: {chrom->{postion->sequence}}
"""
outs = []
for chrom in positions.keys():
if os.path.isdir(genome_file):
fa_file = get_fasta_file(genome_file, chrom)
if not os.path.isfile(fa_file):
raise FileNotFoundError('{} not found.'.format(genome_file))
mins = int(len(positions[chrom]) / 2000) + 60
time = str(_td(minutes=mins))
outs.append(
fyrd.submit(
get_regions,
({
chrom: positions[chrom]
}, fa_file, base, count),
cores=1,
mem='6GB',
time=time,
))
final = {}
for out in outs:
final.update(out.get())
return final
def save(self, persona, mensaje, fecha_con=None, days=0):
try:
with _db_session:
if fecha_con is None:
now = _utc.now().date().isoformat()
ag = _Agenda(persona=persona,
mensaje=mensaje,
fecha_msj=now,
fecha_con=now,
sms_estado=True)
else:
fecha_msj = (fecha_con - _td(days=days)).isoformat()
fecha_con = fecha_con.isoformat()
ag = _Agenda(persona=persona,
mensaje=mensaje,
fecha_msj=fecha_msj,
fecha_con=fecha_con)
_commit()
return True if ag else False
except Exception, e:
print e
return False
def _schedule(self, *, scheduler):
logger.debug(f'{self._name} enter {type(scheduler)}')
try:
l_jobid = f'{self._name}_do_connect'
scheduler.add_job(self._do_connect,
'interval',
seconds=self._cfg.get(
'supervision_interval',
_def.INFLUX_SUPERVISION_INTERVAL),
kwargs={
'cfg': self._cfg,
'jobid': l_jobid
},
id=l_jobid,
replace_existing=True,
max_instances=self.SCHEDULER_MAX_INSTANCES,
coalesce=True,
next_run_time=_dt.now() +
_td(seconds=self.INFLUX_CONNECT_DELAY))
except:
logger.exception(f'*** {self._name}')
def get_regions_parallel(positions, genome_file, base=0, count=7):
"""Return a list of regions surrounding a position.
Will loop through each chromosome and search all positions in that
chromosome in one batch. Lookup is serial per chromosome.
Args:
positions (dict): Dictionary of {chrom->positons}
genome_file (str): Location of a genome fasta file or directory of
files. If directory, file names must be
<chrom_name>.fa[.gz]. Gzipped OK.
base (int): Either 0 or 1, base of positions in your list
count (int): Distance + and - the position to extract
Returns:
dict: {chrom->{postion->sequence}}
"""
outs = []
for chrom in positions.keys():
if os.path.isdir(genome_file):
fa_file = get_fasta_file(genome_file, chrom)
if not os.path.isfile(fa_file):
raise FileNotFoundError('{} not found.'.format(genome_file))
mins = int(len(positions[chrom])/2000)+60
time = str(_td(minutes=mins))
outs.append(
fyrd.submit(
get_regions,
({chrom: positions[chrom]}, fa_file, base, count),
cores=1, mem='6GB', time=time,
)
)
final = {}
for out in outs:
final.update(out.get())
return final
def dst(self, dt):
return _td()
hooks_dir = _parser.get('socrates', 'hooks_dir',
fallback=SOCRATES_DIR + os.sep + 'hooks')
scripts_dir = _parser.get('socrates', 'scripts_dir',
fallback=SOCRATES_DIR + os.sep + 'scripts')
static_dir = _parser.get('socrates', 'static_dir',
fallback=SOCRATES_DIR + os.sep + 'static')
dropbox_dir = _parser.get('socrates', 'dropbox_dir',
fallback=SOCRATES_DIR + os.sep + 'dropbox')
criteria_dir = _parser.get('socrates', 'criteria_dir',
fallback=SOCRATES_DIR + os.sep + 'criteria')
from datetime import timedelta as _td
if _parser.has_option('socrates', 'grace_period'):
_grace_str = _parser.get('socrates', 'grace_period')
grace_period = _td(seconds=int(_grace_str))
else:
grace_period = _td(seconds=0)
_f = False
if not os.path.isdir(hooks_dir):
_f = True
util.error("hooks directory does not exist or cannot be accessed")
if not os.path.isdir(scripts_dir):
_f = True
util.error("scripts directory does not exist or cannot be accessed")
if not os.path.isdir(static_dir):
def get_allChildrens(cls, days=30): time_ago = _utc.now().date() - _td(days=days) return _newBorn.select(lambda nb: nb.embarazo.parto_inst>=time_ago)
def metrics(returns,
benchmark=None,
rf=0.,
display=True,
mode='basic',
sep=False,
compounded=True,
periods_per_year=252,
prepare_returns=True,
match_dates=False,
**kwargs):
win_year, _ = _get_trading_periods(periods_per_year)
if benchmark is not None \
and isinstance(benchmark, _pd.DataFrame) and len(benchmark.columns) > 1:
raise ValueError("`benchmark` must be a pandas Series, "
"but a multi-column DataFrame was passed")
blank = ['']
if isinstance(returns, _pd.DataFrame):
if len(returns.columns) > 1:
raise ValueError(
"`returns` needs to be a Pandas Series or one column DataFrame. multi colums DataFrame was passed"
)
returns = returns[returns.columns[0]]
if prepare_returns:
returns = _utils._prepare_returns(returns)
df = _pd.DataFrame({"returns": returns})
if benchmark is not None:
blank = ['', '']
benchmark = _utils._prepare_benchmark(benchmark, returns.index, rf)
if match_dates is True:
returns, benchmark = _match_dates(returns, benchmark)
df["returns"] = returns
df["benchmark"] = benchmark
df = df.fillna(0)
# pct multiplier
pct = 100 if display or "internal" in kwargs else 1
if kwargs.get("as_pct", False):
pct = 100
# return df
dd = _calc_dd(df,
display=(display or "internal" in kwargs),
as_pct=kwargs.get("as_pct", False))
metrics = _pd.DataFrame()
s_start = {'returns': df['returns'].index.strftime('%Y-%m-%d')[0]}
s_end = {'returns': df['returns'].index.strftime('%Y-%m-%d')[-1]}
s_rf = {'returns': rf}
if "benchmark" in df:
s_start['benchmark'] = df['benchmark'].index.strftime('%Y-%m-%d')[0]
s_end['benchmark'] = df['benchmark'].index.strftime('%Y-%m-%d')[-1]
s_rf['benchmark'] = rf
metrics['Start Period'] = _pd.Series(s_start)
metrics['End Period'] = _pd.Series(s_end)
metrics['Risk-Free Rate %'] = _pd.Series(s_rf)
metrics['Time in Market %'] = _stats.exposure(df,
prepare_returns=False) * pct
metrics['~'] = blank
if compounded:
metrics['Cumulative Return %'] = (_stats.comp(df) * pct).map(
'{:,.2f}'.format)
else:
metrics['Total Return %'] = (df.sum() * pct).map('{:,.2f}'.format)
metrics['CAGR﹪%'] = _stats.cagr(df, rf, compounded) * pct
metrics['~~~~~~~~~~~~~~'] = blank
metrics['Sharpe'] = _stats.sharpe(df, rf, win_year, True)
if mode.lower() == 'full':
metrics['Smart Sharpe'] = _stats.smart_sharpe(df, rf, win_year, True)
metrics['Sortino'] = _stats.sortino(df, rf, win_year, True)
if mode.lower() == 'full':
metrics['Smart Sortino'] = _stats.smart_sortino(df, rf, win_year, True)
metrics['Sortino/√2'] = metrics['Sortino'] / _sqrt(2)
if mode.lower() == 'full':
metrics['Smart Sortino/√2'] = metrics['Smart Sortino'] / _sqrt(2)
metrics['Omega'] = _stats.omega(df, rf, 0., win_year)
metrics['~~~~~~~~'] = blank
metrics['Max Drawdown %'] = blank
metrics['Longest DD Days'] = blank
if mode.lower() == 'full':
ret_vol = _stats.volatility(
df['returns'], win_year, True, prepare_returns=False) * pct
if "benchmark" in df:
bench_vol = _stats.volatility(
df['benchmark'], win_year, True, prepare_returns=False) * pct
metrics['Volatility (ann.) %'] = [ret_vol, bench_vol]
metrics['R^2'] = _stats.r_squared(df['returns'],
df['benchmark'],
prepare_returns=False)
else:
metrics['Volatility (ann.) %'] = [ret_vol]
metrics['Calmar'] = _stats.calmar(df, prepare_returns=False)
metrics['Skew'] = _stats.skew(df, prepare_returns=False)
metrics['Kurtosis'] = _stats.kurtosis(df, prepare_returns=False)
metrics['~~~~~~~~~~'] = blank
metrics['Expected Daily %%'] = _stats.expected_return(
df, prepare_returns=False) * pct
metrics['Expected Monthly %%'] = _stats.expected_return(
df, aggregate='M', prepare_returns=False) * pct
metrics['Expected Yearly %%'] = _stats.expected_return(
df, aggregate='A', prepare_returns=False) * pct
metrics['Kelly Criterion %'] = _stats.kelly_criterion(
df, prepare_returns=False) * pct
metrics['Risk of Ruin %'] = _stats.risk_of_ruin(df,
prepare_returns=False)
metrics['Daily Value-at-Risk %'] = -abs(
_stats.var(df, prepare_returns=False) * pct)
metrics['Expected Shortfall (cVaR) %'] = -abs(
_stats.cvar(df, prepare_returns=False) * pct)
metrics['~~~~~~'] = blank
metrics['Gain/Pain Ratio'] = _stats.gain_to_pain_ratio(df, rf)
metrics['Gain/Pain (1M)'] = _stats.gain_to_pain_ratio(df, rf, "M")
# if mode.lower() == 'full':
# metrics['GPR (3M)'] = _stats.gain_to_pain_ratio(df, rf, "Q")
# metrics['GPR (6M)'] = _stats.gain_to_pain_ratio(df, rf, "2Q")
# metrics['GPR (1Y)'] = _stats.gain_to_pain_ratio(df, rf, "A")
metrics['~~~~~~~'] = blank
metrics['Payoff Ratio'] = _stats.payoff_ratio(df, prepare_returns=False)
metrics['Profit Factor'] = _stats.profit_factor(df, prepare_returns=False)
metrics['Common Sense Ratio'] = _stats.common_sense_ratio(
df, prepare_returns=False)
metrics['CPC Index'] = _stats.cpc_index(df, prepare_returns=False)
metrics['Tail Ratio'] = _stats.tail_ratio(df, prepare_returns=False)
metrics['Outlier Win Ratio'] = _stats.outlier_win_ratio(
df, prepare_returns=False)
metrics['Outlier Loss Ratio'] = _stats.outlier_loss_ratio(
df, prepare_returns=False)
# returns
metrics['~~'] = blank
comp_func = _stats.comp if compounded else _np.sum
today = df.index[-1] # _dt.today()
metrics['MTD %'] = comp_func(
df[df.index >= _dt(today.year, today.month, 1)]) * pct
d = today - _td(3 * 365 / 12)
metrics['3M %'] = comp_func(
df[df.index >= _dt(d.year, d.month, d.day)]) * pct
d = today - _td(6 * 365 / 12)
metrics['6M %'] = comp_func(
df[df.index >= _dt(d.year, d.month, d.day)]) * pct
metrics['YTD %'] = comp_func(df[df.index >= _dt(today.year, 1, 1)]) * pct
d = today - _td(12 * 365 / 12)
metrics['1Y %'] = comp_func(
df[df.index >= _dt(d.year, d.month, d.day)]) * pct
d = today - _td(3 * 365)
metrics['3Y (ann.) %'] = _stats.cagr(
df[df.index >= _dt(d.year, d.month, d.day)], 0., compounded) * pct
d = today - _td(5 * 365)
metrics['5Y (ann.) %'] = _stats.cagr(
df[df.index >= _dt(d.year, d.month, d.day)], 0., compounded) * pct
d = today - _td(10 * 365)
metrics['10Y (ann.) %'] = _stats.cagr(
df[df.index >= _dt(d.year, d.month, d.day)], 0., compounded) * pct
metrics['All-time (ann.) %'] = _stats.cagr(df, 0., compounded) * pct
# best/worst
if mode.lower() == 'full':
metrics['~~~'] = blank
metrics['Best Day %'] = _stats.best(df, prepare_returns=False) * pct
metrics['Worst Day %'] = _stats.worst(df, prepare_returns=False) * pct
metrics['Best Month %'] = _stats.best(
df, aggregate='M', prepare_returns=False) * pct
metrics['Worst Month %'] = _stats.worst(
df, aggregate='M', prepare_returns=False) * pct
metrics['Best Year %'] = _stats.best(
df, aggregate='A', prepare_returns=False) * pct
metrics['Worst Year %'] = _stats.worst(
df, aggregate='A', prepare_returns=False) * pct
# dd
metrics['~~~~'] = blank
for ix, row in dd.iterrows():
metrics[ix] = row
metrics['Recovery Factor'] = _stats.recovery_factor(df)
metrics['Ulcer Index'] = _stats.ulcer_index(df)
metrics['Serenity Index'] = _stats.serenity_index(df, rf)
# win rate
if mode.lower() == 'full':
metrics['~~~~~'] = blank
metrics['Avg. Up Month %'] = _stats.avg_win(
df, aggregate='M', prepare_returns=False) * pct
metrics['Avg. Down Month %'] = _stats.avg_loss(
df, aggregate='M', prepare_returns=False) * pct
metrics['Win Days %%'] = _stats.win_rate(df,
prepare_returns=False) * pct
metrics['Win Month %%'] = _stats.win_rate(
df, aggregate='M', prepare_returns=False) * pct
metrics['Win Quarter %%'] = _stats.win_rate(
df, aggregate='Q', prepare_returns=False) * pct
metrics['Win Year %%'] = _stats.win_rate(
df, aggregate='A', prepare_returns=False) * pct
if "benchmark" in df:
metrics['~~~~~~~'] = blank
greeks = _stats.greeks(df['returns'],
df['benchmark'],
win_year,
prepare_returns=False)
metrics['Beta'] = [str(round(greeks['beta'], 2)), '-']
metrics['Alpha'] = [str(round(greeks['alpha'], 2)), '-']
# prepare for display
for col in metrics.columns:
try:
metrics[col] = metrics[col].astype(float).round(2)
if display or "internal" in kwargs:
metrics[col] = metrics[col].astype(str)
except Exception:
pass
if (display or "internal" in kwargs) and "%" in col:
metrics[col] = metrics[col] + '%'
try:
metrics['Longest DD Days'] = _pd.to_numeric(
metrics['Longest DD Days']).astype('int')
metrics['Avg. Drawdown Days'] = _pd.to_numeric(
metrics['Avg. Drawdown Days']).astype('int')
if display or "internal" in kwargs:
metrics['Longest DD Days'] = metrics['Longest DD Days'].astype(str)
metrics['Avg. Drawdown Days'] = metrics[
'Avg. Drawdown Days'].astype(str)
except Exception:
metrics['Longest DD Days'] = '-'
metrics['Avg. Drawdown Days'] = '-'
if display or "internal" in kwargs:
metrics['Longest DD Days'] = '-'
metrics['Avg. Drawdown Days'] = '-'
metrics.columns = [
col if '~' not in col else '' for col in metrics.columns
]
metrics.columns = [
col[:-1] if '%' in col else col for col in metrics.columns
]
metrics = metrics.T
if "benchmark" in df:
metrics.columns = ['Strategy', 'Benchmark']
else:
metrics.columns = ['Strategy']
if display:
print(_tabulate(metrics, headers="keys", tablefmt='simple'))
return None
if not sep:
metrics = metrics[metrics.index != '']
return metrics
def utcoffset(self, dt): return _td(hours=-7)
def get_dinucleotides_parallel(positions, genome_file, base=0, return_as='list'):
"""Return a list of all + and - strand dinucleotides around each position.
Will loop through each chromosome and search all positions in that
chromosome in one batch. Lookup is parallel per chromosome.
Args:
positions (dict): Dictionary of {chrom->positons}
genome_file (str): Location of a genome fasta file or directory of
files. If directory, file names must be
<chrom_name>.fa[.gz]. Gzipped OK. Directory is
preferred in parallel mode.
base (int): Either 0 or 1, base of positions in your list
return_as (str): dict: Return a dictionary of:
{chrom->{postion->{'ref': str, '+': tuple, '-': tuple}}}
list: just returns two lists with no positions.
df: return DataFrame
Returns:
(list, list): + strand dinucleotides, - strand dinucleotides. Returns
a dict or instead if requested through return_as.
"""
outs = []
for chrom in positions.keys():
if os.path.isdir(genome_file):
fa_file = get_fasta_file(genome_file, chrom)
if not os.path.isfile(fa_file):
raise FileNotFoundError('{} not found.'.format(genome_file))
mins = int(len(positions[chrom])/2000)+45
time = str(_td(minutes=mins))
outs.append(
fyrd.submit(
get_dinucleotides,
({chrom: positions[chrom]}, fa_file, base, return_as),
cores=1, mem='6GB', time=time,
)
)
if return_as == 'df':
final = []
elif return_as == 'dict':
final = {}
else:
final = ([], [])
fyrd.wait(outs)
print('Getting results')
for out in outs:
res = out.get()
if return_as == 'df':
if isinstance(res, dict):
res = dict_to_df(res, base)
final.append(res)
elif return_as == 'dict':
final.update(res)
else:
plus, minus = res
final[0] += plus
final[1] += minus
if return_as == 'df':
print('Joining dataframe')
final = pd.concat(final)
return final
def get_api(self, start=7, end=30): now = _utc.now().date() backward, forward = (now - _td(days=start)), (now + _td(days=end)) return _Agenda.select(lambda ag: (ag.fecha_con>=backward and ag.fecha_con<=forward) and (ag.mensaje.tipo>=1 and ag.mensaje.tipo<=5) and not(ag.sms_estado and ag.lmd_estado)).order_by(lambda ag: (ag.persona.comunidad.nombre, ag.persona.nombres, ag.persona.apellidos))
def utcoffset(self, dt):
return _td(hours=-7)
def get_all(cls): backward = _utc.now().date() - _td(days=30) return _Agenda.select(lambda ag: ag.fecha_con>=backward).order_by(lambda ag: (ag.persona.comunidad.nombre, ag.persona.nombres, ag.persona.apellidos, _desc(ag.fecha_con)))
def dst(self, dt): return _td()
def currenttz():
if time.daylight:
return _tz(_td(seconds=-time.altzone), time.tzname[1])
else: # pragma: no cover
return _tz(_td(seconds=-time.timezone), time.tzname[0])
def test_timedelta_assignment(self, Sample): fuzz = _td(seconds=5) delta = _td(days=2, hours=12) now = _dt.utcnow() instance = Sample(delta) assert abs(instance.field.replace(tzinfo=None) - (now + delta)) < fuzz
def get_allChildrens(cls, days=30): time_ago = _utc.now().date() - _td(days=days) return _newBorn.select(lambda nb: nb.embarazo.parto_inst>=time_ago)
def get_all(cls):
backward = _utc.now().date() - _td(days=30)
return _Agenda.select(lambda ag: ag.fecha_con >= backward).order_by(
lambda ag: (ag.persona.comunidad.nombre, ag.persona.nombres, ag.
persona.apellidos, _desc(ag.fecha_con)))
def metrics(returns,
benchmark=None,
rf=0.,
display=True,
mode='basic',
sep=False,
**kwargs):
if isinstance(returns, _pd.DataFrame) and len(returns.columns) > 1:
raise ValueError("`returns` must be a pandas Series, "
"but a multi-column DataFrame was passed")
if benchmark is not None:
if isinstance(returns, _pd.DataFrame) and len(returns.columns) > 1:
raise ValueError("`benchmark` must be a pandas Series, "
"but a multi-column DataFrame was passed")
blank = ['']
df = _pd.DataFrame({"returns": _utils._prepare_returns(returns, rf)})
if benchmark is not None:
blank = ['', '']
df["benchmark"] = _utils._prepare_benchmark(benchmark, returns.index,
rf)
df = df.dropna()
# pct multiplier
pct = 100 if display or "internal" in kwargs else 1
# return df
dd = _calc_dd(df, display=(display or "internal" in kwargs))
metrics = _pd.DataFrame()
s_start = {'returns': df['returns'].index.strftime('%Y-%m-%d')[0]}
s_end = {'returns': df['returns'].index.strftime('%Y-%m-%d')[-1]}
s_rf = {'returns': rf}
if "benchmark" in df:
s_start['benchmark'] = df['benchmark'].index.strftime('%Y-%m-%d')[0]
s_end['benchmark'] = df['benchmark'].index.strftime('%Y-%m-%d')[-1]
s_rf['benchmark'] = rf
metrics['Start Period'] = _pd.Series(s_start)
metrics['End Period'] = _pd.Series(s_end)
metrics['Risk-free rate %'] = _pd.Series(s_rf)
metrics['Exposure %%'] = _stats.exposure(df) * pct
metrics['~'] = blank
metrics['Cumulative Return %'] = _stats.comp(df) * pct
metrics['CAGR%%'] = _stats.cagr(df, rf) * pct
metrics['Sharpe'] = _stats.sharpe(df, rf)
metrics['Sortino'] = _stats.sortino(df, rf)
metrics['Max Drawdown %'] = blank
metrics['Longest DD Days'] = blank
if mode.lower() == 'full':
ret_vol = _stats.volatility(df['returns']) * pct
if "benchmark" in df:
bench_vol = _stats.volatility(df['benchmark']) * pct
metrics['Volatility (ann.) %'] = [ret_vol, bench_vol]
metrics['R^2'] = _stats.r_squared(df['returns'], df['benchmark'])
else:
metrics['Volatility (ann.) %'] = [ret_vol]
metrics['Calmar'] = _stats.calmar(df)
metrics['Skew'] = _stats.skew(df)
metrics['Kurtosis'] = _stats.kurtosis(df)
if mode.lower() == 'full':
metrics['~~~~~~~~~~'] = blank
metrics['Expected Daily %%'] = _stats.expected_return(df) * pct
metrics['Expected Monthly %%'] = _stats.expected_return(
df, aggregate='M') * pct
metrics['Expected Yearly %%'] = _stats.expected_return(
df, aggregate='A') * pct
metrics['Kelly Criterion %'] = _stats.kelly_criterion(df) * pct
metrics['Risk of Ruin %'] = _stats.risk_of_ruin(df)
metrics['Daily Value-at-Risk %'] = -abs(_stats.var(df) * pct)
metrics['Expected Shortfall (cVaR) %'] = -abs(_stats.cvar(df) * pct)
metrics['~~~~~~'] = blank
metrics['Payoff Ratio'] = _stats.payoff_ratio(df)
metrics['Profit Factor'] = _stats.profit_factor(df)
metrics['Common Sense Ratio'] = _stats.common_sense_ratio(df)
metrics['CPC Index'] = _stats.cpc_index(df)
metrics['Tail Ratio'] = _stats.tail_ratio(df)
metrics['Outlier Win Ratio'] = _stats.outlier_win_ratio(df)
metrics['Outlier Loss Ratio'] = _stats.outlier_loss_ratio(df)
# returns
metrics['~~'] = blank
today = _dt.today()
metrics['MTD %'] = _stats.comp(
df[df.index >= _dt(today.year, today.month, 1)]) * pct
d = today - _td(3 * 365 / 12)
metrics['3M %'] = _stats.comp(
df[df.index >= _dt(d.year, d.month, d.day)]) * pct
d = today - _td(6 * 365 / 12)
metrics['6M %'] = _stats.comp(
df[df.index >= _dt(d.year, d.month, d.day)]) * pct
metrics['YTD %'] = _stats.comp(df[df.index >= _dt(today.year, 1, 1)]) * pct
d = today - _td(12 * 365 / 12)
metrics['1Y %'] = _stats.comp(
df[df.index >= _dt(d.year, d.month, d.day)]) * pct
metrics['3Y (ann.) %'] = _stats.cagr(
df[df.index >= _dt(today.year - 3, today.month, today.day)]) * pct
metrics['5Y (ann.) %'] = _stats.cagr(
df[df.index >= _dt(today.year - 5, today.month, today.day)]) * pct
metrics['10Y (ann.) %'] = _stats.cagr(
df[df.index >= _dt(today.year - 10, today.month, today.day)]) * pct
metrics['All-time (ann.) %'] = _stats.cagr(df) * pct
# best/worst
if mode.lower() == 'full':
metrics['~~~'] = blank
metrics['Best Day %'] = _stats.best(df) * pct
metrics['Worst Day %'] = _stats.worst(df) * pct
metrics['Best Month %'] = _stats.best(df, aggregate='M') * pct
metrics['Worst Month %'] = _stats.worst(df, aggregate='M') * pct
metrics['Best Year %'] = _stats.best(df, aggregate='A') * pct
metrics['Worst Year %'] = _stats.worst(df, aggregate='A') * pct
# dd
metrics['~~~~'] = blank
for ix, row in dd.iterrows():
metrics[ix] = row
metrics['Recovery Factor'] = _stats.recovery_factor(df)
metrics['Ulcer Index'] = _stats.ulcer_index(df, rf)
# win rate
if mode.lower() == 'full':
metrics['~~~~~'] = blank
metrics['Avg. Up Month %'] = _stats.avg_win(df, aggregate='M') * pct
metrics['Avg. Down Month %'] = _stats.avg_loss(df, aggregate='M') * pct
metrics['Win Days %%'] = _stats.win_rate(df) * pct
metrics['Win Month %%'] = _stats.win_rate(df, aggregate='M') * pct
metrics['Win Quarter %%'] = _stats.win_rate(df, aggregate='Q') * pct
metrics['Win Year %%'] = _stats.win_rate(df, aggregate='A') * pct
if mode.lower() == "full" and "benchmark" in df:
metrics['~~~~~~~'] = blank
greeks = _stats.greeks(df['returns'], df['benchmark'])
metrics['Beta'] = [str(round(greeks['beta'], 2)), '-']
metrics['Alpha'] = [str(round(greeks['alpha'], 2)), '-']
# prepare for display
for col in metrics.columns:
try:
metrics[col] = metrics[col].astype(float).round(2)
if display or "internal" in kwargs:
metrics[col] = metrics[col].astype(str)
except Exception:
pass
if (display or "internal" in kwargs) and "%" in col:
metrics[col] = metrics[col] + '%'
metrics['Longest DD Days'] = _pd.to_numeric(
metrics['Longest DD Days']).astype('int')
metrics['Avg. Drawdown Days'] = _pd.to_numeric(
metrics['Avg. Drawdown Days']).astype('int')
if display or "internal" in kwargs:
metrics['Longest DD Days'] = metrics['Longest DD Days'].astype(str)
metrics['Avg. Drawdown Days'] = metrics[
'Avg. Drawdown Days'].astype(str)
metrics.columns = [
col if '~' not in col else '' for col in metrics.columns
]
metrics.columns = [
col[:-1] if '%' in col else col for col in metrics.columns
]
metrics = metrics.T
if "benchmark" in df:
metrics.columns = ['Strategy', 'Benchmark']
else:
metrics.columns = ['Strategy']
if display:
print(_tabulate(metrics, headers="keys", tablefmt='simple'))
return
if not sep:
metrics = metrics[metrics.index != '']
return metrics
def radio_operator(cls): #return cls.get_api(start=15, end=15).filter(lambda ag: ag.persona.cobertura==2) now = _utc.now().date() backward, forward = (now - _td(days=15)), (now + _td(days=15)) return _Agenda.select(lambda ag: ag.persona.cobertura==2 and (ag.fecha_con>=backward and ag.fecha_con<=forward) and (ag.mensaje.tipo>=1 and ag.mensaje.tipo<=5) and not ag.rad_estado).order_by(lambda ag: (ag.persona.comunidad.nombre, ag.persona.nombres, ag.persona.apellidos, _desc(ag.fecha_con)))
def get_dinucleotides_parallel(positions,
genome_file,
base=0,
return_as='list'):
"""Return a list of all + and - strand dinucleotides around each position.
Will loop through each chromosome and search all positions in that
chromosome in one batch. Lookup is parallel per chromosome.
Args:
positions (dict): Dictionary of {chrom->positons}
genome_file (str): Location of a genome fasta file or directory of
files. If directory, file names must be
<chrom_name>.fa[.gz]. Gzipped OK. Directory is
preferred in parallel mode.
base (int): Either 0 or 1, base of positions in your list
return_as (str): dict: Return a dictionary of:
{chrom->{postion->{'ref': str, '+': tuple, '-': tuple}}}
list: just returns two lists with no positions.
df: return DataFrame
Returns:
(list, list): + strand dinucleotides, - strand dinucleotides. Returns
a dict or instead if requested through return_as.
"""
outs = []
for chrom in positions.keys():
if os.path.isdir(genome_file):
fa_file = get_fasta_file(genome_file, chrom)
if not os.path.isfile(fa_file):
raise FileNotFoundError('{} not found.'.format(genome_file))
mins = int(len(positions[chrom]) / 2000) + 45
time = str(_td(minutes=mins))
outs.append(
fyrd.submit(
get_dinucleotides,
({
chrom: positions[chrom]
}, fa_file, base, return_as),
cores=1,
mem='6GB',
time=time,
))
if return_as == 'df':
final = []
elif return_as == 'dict':
final = {}
else:
final = ([], [])
fyrd.wait(outs)
print('Getting results')
for out in outs:
res = out.get()
if return_as == 'df':
if isinstance(res, dict):
res = dict_to_df(res, base)
final.append(res)
elif return_as == 'dict':
final.update(res)
else:
plus, minus = res
final[0] += plus
final[1] += minus
if return_as == 'df':
print('Joining dataframe')
final = pd.concat(final)
return final
def test_timedelta_assignment(self, Sample):
fuzz = _td(seconds=5)
delta = _td(days=2, hours=12)
now = _dt.utcnow()
instance = Sample(delta)
assert abs(instance.field.replace(tzinfo=None) - (now + delta)) < fuzz
