Correcciones en la generación de ficheros PMEST en Distribuidora

mesures/parsers/dummy_data.py

@@ -42,7 +42,7 @@ def __init__(self, datas):
                 except ValueError:
                     # str season
                     data['season'] = 0 if data['season'].lower() == 'w' else 1
-            if 'kind_fact' in data:
+            if 'kind_fact' in data and not 'method' in data:
                 try:
                     data['method'] = int(data.pop('kind_fact'))
                 except ValueError:

mesures/pmest.py

@@ -17,6 +17,7 @@ def __init__(self, data, distributor=None, compression='bz2', version=0):
         """
         if isinstance(data, list):
             data = DummyCurve(data).curve_data
+        self.columns = columns
         self.file = self.reader(data)
         self.generation_date = datetime.now()
         self.prefix = 'PMEST'
@@ -93,7 +94,7 @@ def r4(self):
     def reader(self, filepath):
         if isinstance(filepath, str):
             df = pd.read_csv(
-                filepath, sep=';', names=columns
+                filepath, sep=';', names=self.columns
             )
         elif isinstance(filepath, list):
             df = pd.DataFrame(data=filepath)
@@ -120,7 +121,7 @@ def reader(self, filepath):
                 'r4': 'sum',
             }
         ).reset_index()
-        df = df[columns]
+        df = df[self.columns]
         return df
 
     def writer(self):
@@ -138,10 +139,11 @@ def writer(self):
             self.measures_date = di
             dataf = self.file[(self.file['timestamp'] >= di) & (self.file['timestamp'] < df)]
             dataf['timestamp'] = dataf.apply(lambda row: row['timestamp'].strftime('%Y/%m/%d %H'), axis=1)
+            dataf['timestamp'] = dataf['timestamp'].astype(str)
             file_path = os.path.join('/tmp', self.filename)
             kwargs = {'sep': ';',
                       'header': False,
-                      'columns': columns,
+                      'columns': self.columns,
                       'index': False,
                       check_line_terminator_param(): ';\n'
                       }

spec/generation_files_spec.py

@@ -27,6 +27,7 @@
 from mesures.p1d import P1D
 from mesures.p2d import P2D
 from mesures.p5d import P5D
+from mesures.pmest import PMEST
 from mesures.potelectro import POTELECTRO
 from mesures.reobje2 import REOBJE2
 from mesures.reobjecil import REOBJECIL
@@ -237,6 +238,39 @@ def get_sample_f1qh_data():
 
         return data_f1qh
 
+    @staticmethod
+    def get_sample_data_pmest():
+        basic_pmest = {
+            "pm": "DK029141",
+            "tipo_medida": 11,
+            "timestamp": "2024-11-01 01:00:00",
+            "season": 0,
+            "method": 4,
+            "ai": 10,
+            "ae": 11,
+            "r1": 12,
+            "r2": 13,
+            "r3": 14,
+            "r4": 15
+        }
+
+        data_pmest = [basic_pmest.copy()]
+
+        ts = "2024-11-01 01:00:00"
+        for x in range(50):
+            datas = basic_pmest.copy()
+            ts = (datetime.strptime(ts, '%Y-%m-%d %H:%M:%S') + timedelta(hours=1)).strftime('%Y-%m-%d %H:%M:%S')
+            ai = randint(0, 5000)
+            ae = randint(0, 2)
+            r1 = randint(0, 30)
+            r2 = randint(0, 4999)
+            r3 = randint(0, 30)
+            r4 = randint(0, 4999)
+            datas.update({'timestamp': ts, 'ai': ai, 'ae': ae, 'r1': r1, 'r2': r2, 'r3': r3, 'r4': r4})
+            data_pmest.append(datas)
+
+        return data_pmest
+
     @staticmethod
     def get_sample_p5d_data():
         basic_p5d = {
@@ -1606,4 +1640,31 @@ def get_sample_obcups_data():
                     "ES0291000000004444QR1F;2024/10;100;100;;Paga la energia, "
                     "primer aviso.;N;AE\n"
                     )
-        assert f.file[f.columns].to_csv(sep=';', header=None, index=False) == expected
+        assert f.file[f.columns].to_csv(sep=';', header=None, index=False) == expected
+
+with description('An PMEST'):
+    with it('is instance of PMEST Class'):
+        data = SampleData().get_sample_data_pmest()
+        f = PMEST(data)
+        assert isinstance(f, PMEST)
+
+    with it('is a zip of raw files'):
+        data = SampleData().get_sample_data_pmest()
+        f = PMEST(data)
+        res = f.writer()
+        assert zipfile.is_zipfile(res)
+
+    with it('has its class methods'):
+        data = SampleData().get_sample_data_pmest()
+        f = PMEST(data)
+        res = f.writer()
+        assert isinstance(f.total, (int, np.int64))
+        assert f.ai == f.total
+
+    with it('gets expected content'):
+        data = SampleData().get_sample_data_pmest()
+        f = PMEST(data)
+        res = f.writer()
+        # WARNING: timestamp is expressed as "yyyy/mm/dd HH" in file, but in dataframe is still ISO formatted
+        expected = 'DK029141;11;2024-11-01 01:00:00;0;4;10;11;12;13;14;15'
+        assert f.file[f.columns].to_csv(sep=';', header=None, index=False).split('\n')[0] == expected