def main():
"""Create analysis with crude data in the analysis collection."""
for cow in mongo.cows():
type_ = TYPES['identity']
label = LABELS['prods']
parents = []
settings = {'cow': cow}
if mongo.is_analysis(type_, label, parents, settings):
print('The document must be unique: {}'.format(cow))
else:
mongo.db.analysis.insert_one({
'type': type_,
'label': label,
'parents': parents,
'settings': settings,
'data': mongo.prods(cow),
})
def main():
"""Complete the crudedata collection with missing dates."""
for cow in mongo.cows():
print("Completing cow {}...".format(cow))
dates = mongo.dates(cow)
missing = get_missing_dates(dates)
if missing:
for lact in reversed(mongo.lacts(cow)):
# The last date and day in database for this lactation
last_date, last_day = get_last(cow, lact)
# We start at the last day of the lactation then add the
# missing lines till we reach the first day of the lactation
while last_day > 1:
last_day -= 1
last_date -= dt.timedelta(1)
if last_date in missing:
# If day = 1, we'll have a problem of unique key in
# the second loop
missing.remove(last_date)
insert(cow, last_date, lact, last_day)
last_date, last_day = get_last(cow, lact)
# We start at the last day of the lactation then add the
# missing lines till we reach the next lactation or the end of
# the data if this lactation is the last one
while last_date + dt.timedelta(1) in missing:
last_day += 1
last_date += dt.timedelta(1)
insert(cow, last_date, lact, last_day)
def main():
"""Complete the crudedata collection with missing dates."""
for cow in mongo.cows():
print("Completing cow {}...".format(cow))
dates = mongo.dates(cow)
missing = get_missing_dates(dates)
if missing:
for lact in reversed(mongo.lacts(cow)):
# The last date and day in database for this lactation
last_date, last_day = get_last(cow, lact)
# We start at the last day of the lactation then add the
# missing lines till we reach the first day of the lactation
while last_day > 1:
last_day -= 1
last_date -= dt.timedelta(1)
if last_date in missing:
# If day = 1, we'll have a problem of unique key in
# the second loop
missing.remove(last_date)
insert(cow, last_date, lact, last_day)
last_date, last_day = get_last(cow, lact)
# We start at the last day of the lactation then add the
# missing lines till we reach the next lactation or the end of
# the data if this lactation is the last one
while last_date + dt.timedelta(1) in missing:
last_day += 1
last_date += dt.timedelta(1)
insert(cow, last_date, lact, last_day)
def main():
"""Launch the analysis."""
for cow in mongo.cows():
# Crude
dta = mongo.identity(cow, LABELS['prods'])
_id = dta['_id']
views.Crude(_id).render()
acf_ids = analysis.acf({
LABELS['values']: {},
LABELS['confint']: {'alpha': 0.05},
}, {
'data': _id,
})
views.ACF('production', 'ACF', [acf_ids]).render()
pacf_ids = analysis.pacf({
LABELS['values']: {},
LABELS['confint']: {'alpha': 0.05},
}, {
'data': _id,
})
views.PACF('production', 'PACF', [pacf_ids]).render()
# Smoothing
smooth_ids = []
steps = [7, 30]
for step in steps:
id_ = smoothing(_id, step)[LABELS['values']]
smooth_ids.append(id_)
# R: TODO
if step == 7:
sdta = mongo.data(id_)
views.Smoothing('production', 'Smoothed production',
smooth_ids).render()
# Differencing
diff_ids = []
acf_ids = []
pacf_ids = []
degrees = [1]
for degree in degrees:
id_ = differencing(_id, degree)[LABELS['values']]
diff_ids.append(id_)
acf_ids.append(analysis.acf({
LABELS['values']: {},
LABELS['confint']: {'alpha': 0.05},
}, {
'data': id_,
}))
pacf_ids.append(analysis.pacf({
LABELS['values']: {},
LABELS['confint']: {'alpha': 0.05},
}, {
'data': id_,
}))
views.Differencing('production', 'Differenced production',
diff_ids).render()
views.ACF(join('production', 'differenced'), 'ACF', acf_ids).render()
views.PACF(join('production', 'differenced'), 'PACF', pacf_ids).render()
def main():
"""Launch the analysis."""
for cow in mongo.cows():
# Crude
dta = mongo.identity(cow, LABELS['prods'])
_id = dta['_id']
views.Crude(_id).render()
acf_ids = analysis.acf(
{
LABELS['values']: {},
LABELS['confint']: {
'alpha': 0.05
},
}, {
'data': _id,
})
views.ACF('production', 'ACF', [acf_ids]).render()
pacf_ids = analysis.pacf(
{
LABELS['values']: {},
LABELS['confint']: {
'alpha': 0.05
},
}, {
'data': _id,
})
views.PACF('production', 'PACF', [pacf_ids]).render()
# Smoothing
smooth_ids = []
steps = [7, 30]
for step in steps:
id_ = smoothing(_id, step)[LABELS['values']]
smooth_ids.append(id_)
# R: TODO
if step == 7:
sdta = mongo.data(id_)
views.Smoothing('production', 'Smoothed production',
smooth_ids).render()
# Differencing
diff_ids = []
acf_ids = []
pacf_ids = []
degrees = [1]
for degree in degrees:
id_ = differencing(_id, degree)[LABELS['values']]
diff_ids.append(id_)
acf_ids.append(
analysis.acf(
{
LABELS['values']: {},
LABELS['confint']: {
'alpha': 0.05
},
}, {
'data': id_,
}))
pacf_ids.append(
analysis.pacf(
{
LABELS['values']: {},
LABELS['confint']: {
'alpha': 0.05
},
}, {
'data': id_,
}))
views.Differencing('production', 'Differenced production',
diff_ids).render()
views.ACF(join('production', 'differenced'), 'ACF', acf_ids).render()
views.PACF(join('production', 'differenced'), 'PACF',
pacf_ids).render()