Merge pull request #65 from gisce/fix_a5d_b5d_empty_fields

Correcciones en la generación de ficheros A5D y B5D

mesures/a5d.py

@@ -66,10 +66,6 @@ def total(self):
     def ai(self):
         return int(self.file['ai'].sum())
 
-    @property
-    def ae(self):
-        return int(self.file['ae'].sum())
-
     @property
     def cups(self):
         return list(set(self.file['cups']))
@@ -89,7 +85,8 @@ def reader(self, filepath):
             raise Exception("Filepath must be an str or a list")
 
         df = df.groupby(['cups', 'timestamp', 'season', 'factura']).aggregate(
-            {'ai': 'sum', 'ae': 'sum'}
+            {'ai': 'sum', 'ae': 'sum',
+             'r1': 'sum', 'r2': 'sum', 'r3': 'sum', 'r4': 'sum'}
         ).reset_index()
         df['timestamp'] = df['timestamp'].apply(lambda x: x.strftime('%Y/%m/%d %H:%M'))
         for key in ['r1', 'r2', 'r3', 'r4', 'ae', 'method', 'firmeza']:

mesures/b5d.py

@@ -1,5 +1,9 @@
 # -*- coding: utf-8 -*-
 from mesures.a5d import A5D
+from mesures.dates import *
+from mesures.headers import A5D_HEADER as COLUMNS
+from mesures.parsers.dummy_data import DummyCurve
+import pandas as pd
 
 
 class B5D(A5D):
@@ -10,5 +14,37 @@ def __init__(self, data, distributor=None, comer=None, compression='bz2', versio
         :param comer: str comer REE code
         :param compression: 'bz2', 'gz'... OR False otherwise
         """
-        super(B5D, self).__init__(data, distributor=distributor, comer=comer, compression=compression, version=version)
+        if isinstance(data, list):
+            data = DummyCurve(data).curve_data
+        self.columns = COLUMNS
+        self.file = self.reader(data)
+        self.generation_date = datetime.now()
         self.prefix = 'B5D'
+        self.default_compression = compression
+        self.version = version
+        self.distributor = distributor
+        self.comer = comer
+
+    @property
+    def ae(self):
+        return int(self.file['ae'].sum())
+
+    def reader(self, filepath):
+        if isinstance(filepath, str):
+            df = pd.read_csv(
+                filepath, sep=';', names=self.columns
+            )
+        elif isinstance(filepath, list):
+            df = pd.DataFrame(data=filepath)
+        else:
+            raise Exception("Filepath must be an str or a list")
+
+        df = df.groupby(['cups', 'timestamp', 'season', 'factura']).aggregate(
+            {'ai': 'sum', 'ae': 'sum',
+             'r1': 'sum', 'r2': 'sum', 'r3': 'sum', 'r4': 'sum'}
+        ).reset_index()
+        df['timestamp'] = df['timestamp'].apply(lambda x: x.strftime('%Y/%m/%d %H:%M'))
+        for key in ['method', 'firmeza']:
+            df[key] = ''
+        df = df[self.columns]
+        return df

spec/generation_files_spec.py

@@ -904,7 +904,7 @@ def get_sample_cups45_data():
         f1 = f.writer()
         assert isinstance(f1, str)
 
-with fdescription('A P2D'):
+with description('A P2D'):
     with it('is instance of P2D Class'):
         data = SampleData().get_sample_p2d_data()
         f = P2D(data)