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Merge branch 'feature/yearly-report2' into test-prod
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@jhagberg
jhagberg committed Dec 28, 2024
2 parents 06aaef5 + 1e615e6 commit 1894b4b
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Showing 7 changed files with 522 additions and 175 deletions.
89 changes: 87 additions & 2 deletions app/utils/data_access.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -724,7 +724,92 @@ def get_herd(herd_id, user_uuid=None):
if data["genebank"] not in user.accessible_genebanks:
return None

data["individuals"] = [i.as_dict() for i in herd.individuals]
# Get the latest herd tracking entries for each individual
latest_tracking_subq = (
HerdTracking
.select(
HerdTracking.individual,
fn.MAX(HerdTracking.herd_tracking_date).alias('max_date')
)
.group_by(HerdTracking.individual)
.alias('latest_tracking')
)

# Join with the actual tracking entries to get the herd information
latest_herd = (
HerdTracking
.select(
HerdTracking.individual,
HerdTracking.herd,
HerdTracking.herd_tracking_date
)
.join(
latest_tracking_subq,
on=(
(HerdTracking.individual == latest_tracking_subq.c.individual_id) &
(HerdTracking.herd_tracking_date == latest_tracking_subq.c.max_date)
)
)
.alias('latest_herd')
)

# Get individuals with all required data
individuals = (Individual
.select(Individual)
.join(latest_herd, on=(Individual.id == latest_herd.c.individual_id))
.where(latest_herd.c.herd_id == herd.id)
.distinct())

# Create a mapping of individual numbers to their data to deduplicate
individual_map = {}
for individual in individuals:
if individual.number not in individual_map:
individual_map[individual.number] = individual

# Convert to list and sort by number
unique_individuals = sorted(individual_map.values(), key=lambda x: x.number)

# Prefetch related data
weights = Weight.select().where(Weight.individual.in_([i.id for i in unique_individuals]))
bodyfats = Bodyfat.select().where(Bodyfat.individual.in_([i.id for i in unique_individuals]))

# Create a mapping of individual IDs to their weights and bodyfats
weight_map = {}
bodyfat_map = {}

for weight in weights:
if weight.individual_id not in weight_map:
weight_map[weight.individual_id] = []
weight_map[weight.individual_id].append(weight)

for bodyfat in bodyfats:
if bodyfat.individual_id not in bodyfat_map:
bodyfat_map[bodyfat.individual_id] = []
bodyfat_map[bodyfat.individual_id].append(bodyfat)

# Get all herd tracking entries for each individual
tracking_map = {}
for tracking in (HerdTracking
.select(HerdTracking, Herd)
.join(Herd)
.where(HerdTracking.individual.in_([i.id for i in unique_individuals]))
.order_by(HerdTracking.herd_tracking_date.desc())):
if tracking.individual_id not in tracking_map:
tracking_map[tracking.individual_id] = []
tracking_map[tracking.individual_id].append({
'herd_id': tracking.herd.id,
'herd': tracking.herd.herd,
'herd_name': tracking.herd.herd_name,
'date': tracking.herd_tracking_date.strftime("%Y-%m-%d") if tracking.herd_tracking_date else None
})

# Attach the data to each individual
for individual in unique_individuals:
individual.weight_set = weight_map.get(individual.id, [])
individual.bodyfat_set = bodyfat_map.get(individual.id, [])
individual.herd_tracking_set = tracking_map.get(individual.id, [])

data["individuals"] = [i.as_dict() for i in unique_individuals]
return data
except DoesNotExist:
return data
Expand Down Expand Up @@ -807,7 +892,7 @@ def save_yearly_report(herd_id, form_data, user):

try:
report_round = YearlyReportRound.get_by_id(report_round_id)
if not report_round.is_active:
if not (report_round.is_active or report_round.manually_activated):
return {
"status": "error",
"message": "Report round is not active",
Expand Down
148 changes: 109 additions & 39 deletions app/utils/database.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -231,29 +231,95 @@ def individuals(self):
Returns a list of all individuals in the herd.
"""

# Rank all herdtracking values by individual and date
current_herd = HerdTracking.select(
HerdTracking.herd.alias("herd"),
HerdTracking.individual.alias("i_id"),
fn.RANK()
.over(
order_by=[HerdTracking.herd_tracking_date.desc()],
partition_by=[HerdTracking.individual],
# Get the latest herd tracking entries for each individual
latest_tracking_subq = (
HerdTracking
.select(
HerdTracking.individual,
fn.MAX(HerdTracking.herd_tracking_date).alias('max_date')
)
.alias("rank"),
.group_by(HerdTracking.individual)
.alias('latest_tracking')
)

# Select all the individuals in the current herd
i_query = (
Individual.select()
.join(current_herd, on=(Individual.id == current_herd.c.i_id))
.where(current_herd.c.rank == 1)
.where(current_herd.c.herd == self.id)
# Join with the actual tracking entries to get the herd information
latest_herd = (
HerdTracking
.select(
HerdTracking.individual,
HerdTracking.herd,
HerdTracking.herd_tracking_date
)
.join(
latest_tracking_subq,
on=(
(HerdTracking.individual == latest_tracking_subq.c.individual_id) &
(HerdTracking.herd_tracking_date == latest_tracking_subq.c.max_date)
)
)
.alias('latest_herd')
)

# return as a list
# pylint: disable=unnecessary-comprehension
return [i for i in i_query]
# Get all herd tracking entries for each individual
all_tracking = (
HerdTracking
.select(
HerdTracking.individual,
HerdTracking.herd,
HerdTracking.herd_tracking_date
)
.order_by(HerdTracking.herd_tracking_date.desc())
.alias('all_tracking')
)

# Get individuals with all required data
individuals = (Individual
.select(Individual, latest_herd.c.herd_tracking_date)
.join(latest_herd, on=(Individual.id == latest_herd.c.individual_id))
.where(latest_herd.c.herd_id == self.id)
.distinct())

# Prefetch related data
weights = Weight.select().where(Weight.individual.in_([i.id for i in individuals]))
bodyfats = Bodyfat.select().where(Bodyfat.individual.in_([i.id for i in individuals]))

# Create a mapping of individual IDs to their weights and bodyfats
weight_map = {}
bodyfat_map = {}

for weight in weights:
if weight.individual_id not in weight_map:
weight_map[weight.individual_id] = []
weight_map[weight.individual_id].append(weight)

for bodyfat in bodyfats:
if bodyfat.individual_id not in bodyfat_map:
bodyfat_map[bodyfat.individual_id] = []
bodyfat_map[bodyfat.individual_id].append(bodyfat)

# Get all herd tracking entries for each individual
tracking_map = {}
for tracking in (HerdTracking
.select(HerdTracking, Herd)
.join(Herd)
.where(HerdTracking.individual.in_([i.id for i in individuals]))
.order_by(HerdTracking.herd_tracking_date.desc())):
if tracking.individual_id not in tracking_map:
tracking_map[tracking.individual_id] = []
tracking_map[tracking.individual_id].append({
'herd_id': tracking.herd.id,
'herd': tracking.herd.herd,
'herd_name': tracking.herd.herd_name,
'date': tracking.herd_tracking_date.strftime("%Y-%m-%d") if tracking.herd_tracking_date else None
})

# Attach the data to each individual
for individual in individuals:
individual.weight_set = weight_map.get(individual.id, [])
individual.bodyfat_set = bodyfat_map.get(individual.id, [])
individual.herd_tracking_set = tracking_map.get(individual.id, [])

return [i for i in individuals]

def short_info(self):
"""
Expand Down Expand Up @@ -725,28 +791,32 @@ def as_dict(self):
for b in self.bodyfat_set
]

ht_history = (
HerdTracking.select()
.where(HerdTracking.individual == self.id)
.order_by(HerdTracking.herd_tracking_date.desc())
)
# Use prefetched herd tracking data if available, otherwise query it
if hasattr(self, 'herd_tracking_set'):
data["herd_tracking"] = self.herd_tracking_set
else:
ht_history = (
HerdTracking.select()
.where(HerdTracking.individual == self.id)
.order_by(HerdTracking.herd_tracking_date.desc())
)

try:
data["herd_tracking"] = [
{
"herd_id": h.herd.id,
"herd": h.herd.herd,
"herd_name": h.herd.herd_name,
"date": (
h.herd_tracking_date.strftime("%Y-%m-%d")
if h.herd_tracking_date
else None
),
}
for h in ht_history
]
except DoesNotExist:
data["herd_tracking"] = []
try:
data["herd_tracking"] = [
{
"herd_id": h.herd.id,
"herd": h.herd.herd,
"herd_name": h.herd.herd_name,
"date": (
h.herd_tracking_date.strftime("%Y-%m-%d")
if h.herd_tracking_date
else None
),
}
for h in ht_history
]
except DoesNotExist:
data["herd_tracking"] = []

return data

Expand Down
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