From d2da018dc9891af0acfa577ea7e30aef7f60c1f1 Mon Sep 17 00:00:00 2001
From: Darren Boss <darren.boss@gov.bc.ca>
Date: Tue, 31 Oct 2023 16:08:08 -0700
Subject: [PATCH] Fix off by one day error Whitespace

---
 api/app/db/crud/observations.py            |  4 ++--
 api/app/weather_models/machine_learning.py | 19 +++++++++----------
 2 files changed, 11 insertions(+), 12 deletions(-)

diff --git a/api/app/db/crud/observations.py b/api/app/db/crud/observations.py
index 00b79b352..f3ee6eb0e 100644
--- a/api/app/db/crud/observations.py
+++ b/api/app/db/crud/observations.py
@@ -90,7 +90,7 @@ def get_accumulated_precip_by_24h_interval(session: Session, station_code: int,
     2023-11-01 20:00:00
     2023-11-02 20:00:00
     2023-11-03 20:00:00
-
+    
     We then join the HourlyActuals table so that we can sum hourly precip in a 24 hour period. The join is based on the weather_date field in the HourlyActuals table being in a 24 hour range using this odd looking syntax:
 
     weather_date <@ tstzrange(day, day + '24 hours', '(]')
@@ -113,7 +113,7 @@ def get_accumulated_precip_by_24h_interval(session: Session, station_code: int,
         LEFT JOIN
             hourly_actuals
         ON 
-            weather_date <@ tstzrange(day, day - '24 hours', '(]')
+            weather_date <@ tstzrange(day - INTERVAL '24 hours', day, '(]')
         WHERE
             station_code = {}
         GROUP BY
diff --git a/api/app/weather_models/machine_learning.py b/api/app/weather_models/machine_learning.py
index d96277fb9..3cb079503 100644
--- a/api/app/weather_models/machine_learning.py
+++ b/api/app/weather_models/machine_learning.py
@@ -89,7 +89,6 @@ def __init__(self,
         # NOTE: This could be an environment variable.
         self.max_days_to_learn = MAX_DAYS_TO_LEARN
 
-
     def _add_sample_to_collection(self,
                                   prediction: ModelRunPrediction,
                                   actual: HourlyActual,
@@ -142,15 +141,8 @@ def _collect_data(self, start_date: datetime):
                 prev_prediction = prediction
             prev_actual = actual
         return sample_collection
-    
-    def learn(self):
-        # Calculate the date to start learning from.
-        start_date =  self.max_learn_date - \
-            timedelta(days=self.max_days_to_learn)
-        self.learn_models(start_date)
-        self.learn_precip_model(start_date)
 
-    def learn_models(self, start_date: datetime):
+    def _learn_models(self, start_date: datetime):
         """ Collect data and perform linear regression.
         """
         # collect data
@@ -175,7 +167,7 @@ def learn_models(self, start_date: datetime):
         self.regression_models_v2.collect_data(query)
         self.regression_models_v2.train()
         
-    def learn_precip_model(self, start_date):
+    def _learn_precip_model(self, start_date):
         """ Collect precip data and perform linear regression.
         """
         # Precip is based on 24 hour periods at 20:00 hours UTC. Use the start_date
@@ -193,6 +185,13 @@ def learn_precip_model(self, start_date):
         self.regression_models_v2.add_precip_samples(actual_daily_precip, predicted_daily_precip)
         self.regression_models_v2.train_precip()
 
+    def learn(self):
+        # Calculate the date to start learning from.
+        start_date = self.max_learn_date - \
+            timedelta(days=self.max_days_to_learn)
+        self._learn_models(start_date)
+        self._learn_precip_model(start_date) 
+
     def predict_temperature(self, model_temperature: float, timestamp: datetime):
         """ Predict the bias adjusted temperature for a given point in time, given a corresponding model
         temperature.