Load pickle; reorganise some files

py/clustering.py

@@ -12,57 +12,27 @@ import seaborn as sns
 # 
 # qparams = ['%%1117', '20/04/2017']
 
-#query = """
-#SELECT read_date, period, AVG(kwh_tot) AS average
-#FROM public.coup_tall_april
-#GROUP BY read_date, period
-#ORDER BY read_date, period;
-#"""
-#
-#qparams = []
-#
-#df = getQuery(query, qparams)
-#
-#print(df.info())
-#
-#sns.set()
-#
-##sns.lineplot(x = 'read_time', y = 'kwh_tot', hue = 'icp_id', data = df)
-#sns.lineplot(x = 'period', y = 'average', hue = 'read_date', data = df)
-#
-#plt.show()
+# query = """
+# SELECT read_date, period, AVG(kwh_tot) AS average
+# FROM public.coup_tall_april
+# GROUP BY read_date, period
+# ORDER BY read_date, period;
+# """
+# 
+# qparams = []
+# 
+# df = getQuery(query, qparams)
+# 
+# print(df.info())
+# 
+# sns.set()
+# 
+# #sns.lineplot(x = 'read_time', y = 'kwh_tot', hue = 'icp_id', data = df)
+# sns.lineplot(x = 'period', y = 'average', hue = 'read_date', data = df)
+# 
+# plt.show()
 
-query = """
-SELECT comb.icp_id, comb.read_time, COALESCE(kwh_tot, 0) AS kwh_tot
-FROM
-(
-    SELECT read_time, icp_id
-    FROM
-    (
-        SELECT read_time 
-        FROM GENERATE_SERIES('2017-04-01 00:30:00'::timestamp, '2017-05-01 00:00:00'::timestamp, 
-            '30 minutes'::interval) read_time
-    ) AS tsdata CROSS JOIN
-    (
-        SELECT *
-        FROM
-        (
-            SELECT icp_id, COUNT(DISTINCT read_date) AS data_days 
-            FROM coup_prd.coupdatamaster
-            WHERE read_date >= to_date('01/01/2017','dd/mm/yyyy')
-                AND read_date <  to_date('01/01/2018','dd/mm/yyyy')
-                AND content_code = 'UN'
-            GROUP BY icp_id
-        ) AS cir 
-        WHERE data_days >= 360
-    ) AS qual_icp
-) AS comb
-LEFT JOIN
-(
-    SELECT *, read_date + CONCAT(period / 2, ':', period %% 2 * 30, ':00')::time AS read_time
-    FROM public.coup_tall_april
-) AS tall_timestamp 
-ON comb.read_time = tall_timestamp.read_time AND comb.icp_id = tall_timestamp.icp_id;
-"""
+df = p.read_pickle('../data/April.pkl')
 
-pickleQuery(query, "../data/April.pkl")
+print(df)
+print(df.info())

py/downkwh.py

@@ -0,0 +1,39 @@
+from util import getQuery, pickleQuery
+import pandas as p
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+query = """
+SELECT comb.icp_id, comb.read_time, COALESCE(kwh_tot, 0) AS kwh_tot
+FROM
+(
+    SELECT read_time, icp_id
+    FROM
+    (
+        SELECT read_time 
+        FROM GENERATE_SERIES('2017-04-01 00:30:00'::timestamp, '2017-05-01 00:00:00'::timestamp, 
+            '30 minutes'::interval) read_time
+    ) AS tsdata CROSS JOIN
+    (
+        SELECT *
+        FROM
+        (
+            SELECT icp_id, COUNT(DISTINCT read_date) AS data_days 
+            FROM coup_prd.coupdatamaster
+            WHERE read_date >= to_date('01/01/2017','dd/mm/yyyy')
+                AND read_date <  to_date('01/01/2018','dd/mm/yyyy')
+                AND content_code = 'UN'
+            GROUP BY icp_id
+        ) AS cir 
+        WHERE data_days >= 360
+    ) AS qual_icp
+) AS comb
+LEFT JOIN
+(
+    SELECT *, read_date + CONCAT(period / 2, ':', period %% 2 * 30, ':00')::time AS read_time
+    FROM public.coup_tall_april
+) AS tall_timestamp 
+ON comb.read_time = tall_timestamp.read_time AND comb.icp_id = tall_timestamp.icp_id;
+"""
+
+pickleQuery(query, "../data/April.pkl")