We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
NL Down
ENTSO-E data seems fine, so is there a problem with energieopwek.nl?

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `parsers/NL.py`
Content:
```
1 #!/usr/bin/env python3
2 
3 import arrow
4 import math
5 
6 from . import statnett
7 from . import ENTSOE
8 from . import DK
9 import logging
10 import pandas as pd
11 import requests
12 
13 
14 def fetch_production(zone_key='NL', session=None, target_datetime=None,
15                      logger=logging.getLogger(__name__), energieopwek_nl=True):
16     if target_datetime is None:
17         target_datetime = arrow.utcnow()
18     else:
19         target_datetime = arrow.get(target_datetime)
20     r = session or requests.session()
21 
22     consumptions = ENTSOE.fetch_consumption(zone_key=zone_key,
23                                             session=r,
24                                             target_datetime=target_datetime,
25                                             logger=logger)
26     if not consumptions:
27         return
28     for c in consumptions:
29         del c['source']
30     df_consumptions = pd.DataFrame.from_dict(consumptions).set_index(
31         'datetime')
32 
33     # NL has exchanges with BE, DE, NO, GB, DK-DK1
34     exchanges = []
35     for exchange_key in ['BE', 'DE', 'GB']:
36         zone_1, zone_2 = sorted([exchange_key, zone_key])
37         exchange = ENTSOE.fetch_exchange(zone_key1=zone_1,
38                                          zone_key2=zone_2,
39                                          session=r,
40                                          target_datetime=target_datetime,
41                                          logger=logger)
42         if not exchange:
43             return
44         exchanges.extend(exchange or [])
45 
46     # add NO data, fetch once for every hour
47     # This introduces an error, because it doesn't use the average power flow
48     # during the hour, but rather only the value during the first minute of the
49     # hour!
50     zone_1, zone_2 = sorted(['NO', zone_key])
51     exchange_NO = [statnett.fetch_exchange(zone_key1=zone_1, zone_key2=zone_2,
52                                         session=r, target_datetime=dt.datetime,
53                                         logger=logger)
54                 for dt in arrow.Arrow.range(
55             'hour',
56             arrow.get(min([e['datetime']
57                                 for e in exchanges])).replace(minute=0),
58             arrow.get(max([e['datetime']
59                            for e in exchanges])).replace(minute=0))]
60     exchanges.extend(exchange_NO)
61 
62     # add DK1 data (only for dates after operation)
63     if target_datetime > arrow.get('2019-08-24', 'YYYY-MM-DD') :
64         zone_1, zone_2 = sorted(['DK-DK1', zone_key])
65         df_dk = pd.DataFrame(DK.fetch_exchange(zone_key1=zone_1, zone_key2=zone_2,
66                                             session=r, target_datetime=target_datetime,
67                                             logger=logger))
68 
69         # Because other exchanges and consumption data is only available per hour
70         # we floor the timpstamp to hour and group by hour with averaging of netFlow
71         df_dk['datetime'] = df_dk['datetime'].dt.floor('H')
72         exchange_DK = df_dk.groupby(['datetime']).aggregate({'netFlow' : 'mean', 
73             'sortedZoneKeys': 'max', 'source' : 'max'}).reset_index()
74 
75         # because averaging with high precision numbers leads to rounding errors
76         exchange_DK = exchange_DK.round({'netFlow': 3})
77 
78         exchanges.extend(exchange_DK.to_dict(orient='records'))
79 
80     # We want to know the net-imports into NL, so if NL is in zone_1 we need
81     # to flip the direction of the flow. E.g. 100MW for NL->DE means 100MW
82     # export to DE and needs to become -100MW for import to NL.
83     for e in exchanges:
84         if(e['sortedZoneKeys'].startswith('NL->')):
85             e['NL_import'] = -1 * e['netFlow']
86         else:
87             e['NL_import'] = e['netFlow']
88         del e['source']
89         del e['netFlow']
90 
91     df_exchanges = pd.DataFrame.from_dict(exchanges).set_index('datetime')
92     # Sum all exchanges to NL imports
93     df_exchanges = df_exchanges.groupby('datetime').sum()
94 
95     # Fill missing values by propagating the value forward
96     df_consumptions_with_exchanges = df_consumptions.join(df_exchanges).fillna(
97         method='ffill', limit=3)  # Limit to 3 x 15min
98 
99     # Load = Generation + netImports
100     # => Generation = Load - netImports
101     df_total_generations = (df_consumptions_with_exchanges['consumption']
102                             - df_consumptions_with_exchanges['NL_import'])
103 
104     # Fetch all production
105     # The energieopwek_nl parser is backwards compatible with ENTSOE parser.
106     # Because of data quality issues we switch to using energieopwek, but if
107     # data quality of ENTSOE improves we can switch back to using a single
108     # source.
109     productions_ENTSOE = ENTSOE.fetch_production(zone_key=zone_key, session=r,
110                             target_datetime=target_datetime, logger=logger)
111     if energieopwek_nl:
112         productions_eopwek = fetch_production_energieopwek_nl(session=r,
113                             target_datetime=target_datetime, logger=logger)
114         # For every production value we look up the corresponding ENTSOE
115         # values and copy the nuclear, gas, coal, biomass and unknown production. 
116         productions = []
117         for p in productions_eopwek:
118             entsoe_value = next((pe for pe in productions_ENTSOE
119                 if pe["datetime"] == p["datetime"]), None)
120             if entsoe_value:
121                 p["production"]["nuclear"] = entsoe_value["production"]["nuclear"]
122                 p["production"]["gas"] = entsoe_value["production"]["gas"]
123                 p["production"]["coal"] = entsoe_value["production"]["coal"]
124                 p["production"]["biomass"] = entsoe_value["production"]["biomass"]
125                 p["production"]["unknown"] = entsoe_value["production"]["unknown"]
126                 productions.append(p)
127     else:
128         productions = productions_ENTSOE
129     if not productions:
130         return
131 
132     # Flatten production dictionaries (we ignore storage)
133     for p in productions:
134         # if for some reason theré's no unknown value
135         if not 'unknown' in p['production'] or p['production']['unknown'] == None:
136             p['production']['unknown'] = 0
137 
138         Z = sum([x or 0 for x in p['production'].values()])
139         # Only calculate the difference if the datetime exists
140         # If total ENTSOE reported production (Z) is less than total generation
141         # (calculated from consumption and imports), then there must be some
142         # unknown production missing, so we add the difference.
143         # The difference can actually be negative, because consumption is based
144         # on TSO network load, but locally generated electricity may never leave
145         # the DSO network and be substantial (e.g. Solar).
146         if p['datetime'] in df_total_generations and Z < df_total_generations[p['datetime']]:
147             p['production']['unknown'] = round((
148                     df_total_generations[p['datetime']] - Z + p['production']['unknown']), 3)
149 
150     # Filter invalid
151     # We should probably add logging to this
152     return [p for p in productions if p['production']['unknown'] > 0]
153 
154 
155 def fetch_production_energieopwek_nl(session=None, target_datetime=None,
156                                      logger=logging.getLogger(__name__)) -> list:
157     if target_datetime is None:
158         target_datetime = arrow.utcnow()
159 
160     # Get production values for target and target-1 day
161     df_current = get_production_data_energieopwek(
162                             target_datetime, session=session)
163     df_previous = get_production_data_energieopwek(
164                             target_datetime.shift(days=-1), session=session)
165 
166     # Concat them, oldest first to keep chronological order intact
167     df = pd.concat([df_previous, df_current])
168 
169     output = []
170     base_time = arrow.get(target_datetime.date(), 'Europe/Paris').shift(days=-1).to('utc')
171 
172     for i, prod in enumerate(df.to_dict(orient='records')):
173         output.append(
174             {
175                 'zoneKey': 'NL',
176                 'datetime': base_time.shift(minutes=i*15).datetime,
177                 'production': prod,
178                 'source': 'energieopwek.nl, entsoe.eu'
179             }
180         )
181     return output
182 
183 def get_production_data_energieopwek(date, session=None):
184     r = session or requests.session()
185 
186     # The API returns values per day from local time midnight until the last
187     # round 10 minutes if the requested date is today or for the entire day if
188     # it's in the past. 'sid' can be anything.
189     url = 'http://energieopwek.nl/jsonData.php?sid=2ecde3&Day=%s' % date.format('YYYY-MM-DD')
190     response = r.get(url)
191     obj = response.json()
192     production_input = obj['TenMin']['Country']
193 
194     # extract the power values in kW from the different production types
195     # we only need column 0, 1 and 3 contain energy sum values
196     df_solar =    pd.DataFrame(production_input['Solar'])       .drop(['1','3'], axis=1).astype(int).rename(columns={"0" : "solar"})
197     df_offshore = pd.DataFrame(production_input['WindOffshore']).drop(['1','3'], axis=1).astype(int)
198     df_onshore =  pd.DataFrame(production_input['Wind'])        .drop(['1','3'], axis=1).astype(int)
199 
200     # We don't differentiate between onshore and offshore wind so we sum them
201     # toghether and build a single data frame with named columns
202     df_wind = df_onshore.add(df_offshore).rename(columns={"0": "wind"})
203     df = pd.concat([df_solar, df_wind], axis=1)
204 
205     # resample from 10min resolution to 15min resolution to align with ENTSOE data
206     # we duplicate every row and then group them per 3 and take the mean
207     df = pd.concat([df]*2).sort_index(axis=0).reset_index(drop=True).groupby(by=lambda x : math.floor(x/3)).mean()
208 
209     # Convert kW to MW with kW resolution
210     df = df.apply(lambda x: round(x / 1000, 3))
211 
212     return df
213 
214 if __name__ == '__main__':
215     print(fetch_production())
216
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/parsers/NL.py b/parsers/NL.py
--- a/parsers/NL.py
+++ b/parsers/NL.py
@@ -12,7 +12,7 @@
 
 
 def fetch_production(zone_key='NL', session=None, target_datetime=None,
-                     logger=logging.getLogger(__name__), energieopwek_nl=True):
+                     logger=logging.getLogger(__name__), energieopwek_nl=False):
     if target_datetime is None:
         target_datetime = arrow.utcnow()
     else:

{"golden_diff": "diff --git a/parsers/NL.py b/parsers/NL.py\n--- a/parsers/NL.py\n+++ b/parsers/NL.py\n@@ -12,7 +12,7 @@\n \n \n def fetch_production(zone_key='NL', session=None, target_datetime=None,\n-                     logger=logging.getLogger(__name__), energieopwek_nl=True):\n+                     logger=logging.getLogger(__name__), energieopwek_nl=False):\n     if target_datetime is None:\n         target_datetime = arrow.utcnow()\n     else:\n", "issue": "NL Down\nENTSO-E data seems fine, so is there a problem with energieopwek.nl?\n", "before_files": [{"content": "#!/usr/bin/env python3\n\nimport arrow\nimport math\n\nfrom . import statnett\nfrom . import ENTSOE\nfrom . import DK\nimport logging\nimport pandas as pd\nimport requests\n\n\ndef fetch_production(zone_key='NL', session=None, target_datetime=None,\n                     logger=logging.getLogger(__name__), energieopwek_nl=True):\n    if target_datetime is None:\n        target_datetime = arrow.utcnow()\n    else:\n        target_datetime = arrow.get(target_datetime)\n    r = session or requests.session()\n\n    consumptions = ENTSOE.fetch_consumption(zone_key=zone_key,\n                                            session=r,\n                                            target_datetime=target_datetime,\n                                            logger=logger)\n    if not consumptions:\n        return\n    for c in consumptions:\n        del c['source']\n    df_consumptions = pd.DataFrame.from_dict(consumptions).set_index(\n        'datetime')\n\n    # NL has exchanges with BE, DE, NO, GB, DK-DK1\n    exchanges = []\n    for exchange_key in ['BE', 'DE', 'GB']:\n        zone_1, zone_2 = sorted([exchange_key, zone_key])\n        exchange = ENTSOE.fetch_exchange(zone_key1=zone_1,\n                                         zone_key2=zone_2,\n                                         session=r,\n                                         target_datetime=target_datetime,\n                                         logger=logger)\n        if not exchange:\n            return\n        exchanges.extend(exchange or [])\n\n    # add NO data, fetch once for every hour\n    # This introduces an error, because it doesn't use the average power flow\n    # during the hour, but rather only the value during the first minute of the\n    # hour!\n    zone_1, zone_2 = sorted(['NO', zone_key])\n    exchange_NO = [statnett.fetch_exchange(zone_key1=zone_1, zone_key2=zone_2,\n                                        session=r, target_datetime=dt.datetime,\n                                        logger=logger)\n                for dt in arrow.Arrow.range(\n            'hour',\n            arrow.get(min([e['datetime']\n                                for e in exchanges])).replace(minute=0),\n            arrow.get(max([e['datetime']\n                           for e in exchanges])).replace(minute=0))]\n    exchanges.extend(exchange_NO)\n\n    # add DK1 data (only for dates after operation)\n    if target_datetime > arrow.get('2019-08-24', 'YYYY-MM-DD') :\n        zone_1, zone_2 = sorted(['DK-DK1', zone_key])\n        df_dk = pd.DataFrame(DK.fetch_exchange(zone_key1=zone_1, zone_key2=zone_2,\n                                            session=r, target_datetime=target_datetime,\n                                            logger=logger))\n\n        # Because other exchanges and consumption data is only available per hour\n        # we floor the timpstamp to hour and group by hour with averaging of netFlow\n        df_dk['datetime'] = df_dk['datetime'].dt.floor('H')\n        exchange_DK = df_dk.groupby(['datetime']).aggregate({'netFlow' : 'mean', \n            'sortedZoneKeys': 'max', 'source' : 'max'}).reset_index()\n\n        # because averaging with high precision numbers leads to rounding errors\n        exchange_DK = exchange_DK.round({'netFlow': 3})\n\n        exchanges.extend(exchange_DK.to_dict(orient='records'))\n\n    # We want to know the net-imports into NL, so if NL is in zone_1 we need\n    # to flip the direction of the flow. E.g. 100MW for NL->DE means 100MW\n    # export to DE and needs to become -100MW for import to NL.\n    for e in exchanges:\n        if(e['sortedZoneKeys'].startswith('NL->')):\n            e['NL_import'] = -1 * e['netFlow']\n        else:\n            e['NL_import'] = e['netFlow']\n        del e['source']\n        del e['netFlow']\n\n    df_exchanges = pd.DataFrame.from_dict(exchanges).set_index('datetime')\n    # Sum all exchanges to NL imports\n    df_exchanges = df_exchanges.groupby('datetime').sum()\n\n    # Fill missing values by propagating the value forward\n    df_consumptions_with_exchanges = df_consumptions.join(df_exchanges).fillna(\n        method='ffill', limit=3)  # Limit to 3 x 15min\n\n    # Load = Generation + netImports\n    # => Generation = Load - netImports\n    df_total_generations = (df_consumptions_with_exchanges['consumption']\n                            - df_consumptions_with_exchanges['NL_import'])\n\n    # Fetch all production\n    # The energieopwek_nl parser is backwards compatible with ENTSOE parser.\n    # Because of data quality issues we switch to using energieopwek, but if\n    # data quality of ENTSOE improves we can switch back to using a single\n    # source.\n    productions_ENTSOE = ENTSOE.fetch_production(zone_key=zone_key, session=r,\n                            target_datetime=target_datetime, logger=logger)\n    if energieopwek_nl:\n        productions_eopwek = fetch_production_energieopwek_nl(session=r,\n                            target_datetime=target_datetime, logger=logger)\n        # For every production value we look up the corresponding ENTSOE\n        # values and copy the nuclear, gas, coal, biomass and unknown production. \n        productions = []\n        for p in productions_eopwek:\n            entsoe_value = next((pe for pe in productions_ENTSOE\n                if pe[\"datetime\"] == p[\"datetime\"]), None)\n            if entsoe_value:\n                p[\"production\"][\"nuclear\"] = entsoe_value[\"production\"][\"nuclear\"]\n                p[\"production\"][\"gas\"] = entsoe_value[\"production\"][\"gas\"]\n                p[\"production\"][\"coal\"] = entsoe_value[\"production\"][\"coal\"]\n                p[\"production\"][\"biomass\"] = entsoe_value[\"production\"][\"biomass\"]\n                p[\"production\"][\"unknown\"] = entsoe_value[\"production\"][\"unknown\"]\n                productions.append(p)\n    else:\n        productions = productions_ENTSOE\n    if not productions:\n        return\n\n    # Flatten production dictionaries (we ignore storage)\n    for p in productions:\n        # if for some reason ther\u00e9's no unknown value\n        if not 'unknown' in p['production'] or p['production']['unknown'] == None:\n            p['production']['unknown'] = 0\n\n        Z = sum([x or 0 for x in p['production'].values()])\n        # Only calculate the difference if the datetime exists\n        # If total ENTSOE reported production (Z) is less than total generation\n        # (calculated from consumption and imports), then there must be some\n        # unknown production missing, so we add the difference.\n        # The difference can actually be negative, because consumption is based\n        # on TSO network load, but locally generated electricity may never leave\n        # the DSO network and be substantial (e.g. Solar).\n        if p['datetime'] in df_total_generations and Z < df_total_generations[p['datetime']]:\n            p['production']['unknown'] = round((\n                    df_total_generations[p['datetime']] - Z + p['production']['unknown']), 3)\n\n    # Filter invalid\n    # We should probably add logging to this\n    return [p for p in productions if p['production']['unknown'] > 0]\n\n\ndef fetch_production_energieopwek_nl(session=None, target_datetime=None,\n                                     logger=logging.getLogger(__name__)) -> list:\n    if target_datetime is None:\n        target_datetime = arrow.utcnow()\n\n    # Get production values for target and target-1 day\n    df_current = get_production_data_energieopwek(\n                            target_datetime, session=session)\n    df_previous = get_production_data_energieopwek(\n                            target_datetime.shift(days=-1), session=session)\n\n    # Concat them, oldest first to keep chronological order intact\n    df = pd.concat([df_previous, df_current])\n\n    output = []\n    base_time = arrow.get(target_datetime.date(), 'Europe/Paris').shift(days=-1).to('utc')\n\n    for i, prod in enumerate(df.to_dict(orient='records')):\n        output.append(\n            {\n                'zoneKey': 'NL',\n                'datetime': base_time.shift(minutes=i*15).datetime,\n                'production': prod,\n                'source': 'energieopwek.nl, entsoe.eu'\n            }\n        )\n    return output\n\ndef get_production_data_energieopwek(date, session=None):\n    r = session or requests.session()\n\n    # The API returns values per day from local time midnight until the last\n    # round 10 minutes if the requested date is today or for the entire day if\n    # it's in the past. 'sid' can be anything.\n    url = 'http://energieopwek.nl/jsonData.php?sid=2ecde3&Day=%s' % date.format('YYYY-MM-DD')\n    response = r.get(url)\n    obj = response.json()\n    production_input = obj['TenMin']['Country']\n\n    # extract the power values in kW from the different production types\n    # we only need column 0, 1 and 3 contain energy sum values\n    df_solar =    pd.DataFrame(production_input['Solar'])       .drop(['1','3'], axis=1).astype(int).rename(columns={\"0\" : \"solar\"})\n    df_offshore = pd.DataFrame(production_input['WindOffshore']).drop(['1','3'], axis=1).astype(int)\n    df_onshore =  pd.DataFrame(production_input['Wind'])        .drop(['1','3'], axis=1).astype(int)\n\n    # We don't differentiate between onshore and offshore wind so we sum them\n    # toghether and build a single data frame with named columns\n    df_wind = df_onshore.add(df_offshore).rename(columns={\"0\": \"wind\"})\n    df = pd.concat([df_solar, df_wind], axis=1)\n\n    # resample from 10min resolution to 15min resolution to align with ENTSOE data\n    # we duplicate every row and then group them per 3 and take the mean\n    df = pd.concat([df]*2).sort_index(axis=0).reset_index(drop=True).groupby(by=lambda x : math.floor(x/3)).mean()\n\n    # Convert kW to MW with kW resolution\n    df = df.apply(lambda x: round(x / 1000, 3))\n\n    return df\n\nif __name__ == '__main__':\n    print(fetch_production())\n", "path": "parsers/NL.py"}], "after_files": [{"content": "#!/usr/bin/env python3\n\nimport arrow\nimport math\n\nfrom . import statnett\nfrom . import ENTSOE\nfrom . import DK\nimport logging\nimport pandas as pd\nimport requests\n\n\ndef fetch_production(zone_key='NL', session=None, target_datetime=None,\n                     logger=logging.getLogger(__name__), energieopwek_nl=False):\n    if target_datetime is None:\n        target_datetime = arrow.utcnow()\n    else:\n        target_datetime = arrow.get(target_datetime)\n    r = session or requests.session()\n\n    consumptions = ENTSOE.fetch_consumption(zone_key=zone_key,\n                                            session=r,\n                                            target_datetime=target_datetime,\n                                            logger=logger)\n    if not consumptions:\n        return\n    for c in consumptions:\n        del c['source']\n    df_consumptions = pd.DataFrame.from_dict(consumptions).set_index(\n        'datetime')\n\n    # NL has exchanges with BE, DE, NO, GB, DK-DK1\n    exchanges = []\n    for exchange_key in ['BE', 'DE', 'GB']:\n        zone_1, zone_2 = sorted([exchange_key, zone_key])\n        exchange = ENTSOE.fetch_exchange(zone_key1=zone_1,\n                                         zone_key2=zone_2,\n                                         session=r,\n                                         target_datetime=target_datetime,\n                                         logger=logger)\n        if not exchange:\n            return\n        exchanges.extend(exchange or [])\n\n    # add NO data, fetch once for every hour\n    # This introduces an error, because it doesn't use the average power flow\n    # during the hour, but rather only the value during the first minute of the\n    # hour!\n    zone_1, zone_2 = sorted(['NO', zone_key])\n    exchange_NO = [statnett.fetch_exchange(zone_key1=zone_1, zone_key2=zone_2,\n                                        session=r, target_datetime=dt.datetime,\n                                        logger=logger)\n                for dt in arrow.Arrow.range(\n            'hour',\n            arrow.get(min([e['datetime']\n                                for e in exchanges])).replace(minute=0),\n            arrow.get(max([e['datetime']\n                           for e in exchanges])).replace(minute=0))]\n    exchanges.extend(exchange_NO)\n\n    # add DK1 data (only for dates after operation)\n    if target_datetime > arrow.get('2019-08-24', 'YYYY-MM-DD') :\n        zone_1, zone_2 = sorted(['DK-DK1', zone_key])\n        df_dk = pd.DataFrame(DK.fetch_exchange(zone_key1=zone_1, zone_key2=zone_2,\n                                            session=r, target_datetime=target_datetime,\n                                            logger=logger))\n\n        # Because other exchanges and consumption data is only available per hour\n        # we floor the timpstamp to hour and group by hour with averaging of netFlow\n        df_dk['datetime'] = df_dk['datetime'].dt.floor('H')\n        exchange_DK = df_dk.groupby(['datetime']).aggregate({'netFlow' : 'mean', \n            'sortedZoneKeys': 'max', 'source' : 'max'}).reset_index()\n\n        # because averaging with high precision numbers leads to rounding errors\n        exchange_DK = exchange_DK.round({'netFlow': 3})\n\n        exchanges.extend(exchange_DK.to_dict(orient='records'))\n\n    # We want to know the net-imports into NL, so if NL is in zone_1 we need\n    # to flip the direction of the flow. E.g. 100MW for NL->DE means 100MW\n    # export to DE and needs to become -100MW for import to NL.\n    for e in exchanges:\n        if(e['sortedZoneKeys'].startswith('NL->')):\n            e['NL_import'] = -1 * e['netFlow']\n        else:\n            e['NL_import'] = e['netFlow']\n        del e['source']\n        del e['netFlow']\n\n    df_exchanges = pd.DataFrame.from_dict(exchanges).set_index('datetime')\n    # Sum all exchanges to NL imports\n    df_exchanges = df_exchanges.groupby('datetime').sum()\n\n    # Fill missing values by propagating the value forward\n    df_consumptions_with_exchanges = df_consumptions.join(df_exchanges).fillna(\n        method='ffill', limit=3)  # Limit to 3 x 15min\n\n    # Load = Generation + netImports\n    # => Generation = Load - netImports\n    df_total_generations = (df_consumptions_with_exchanges['consumption']\n                            - df_consumptions_with_exchanges['NL_import'])\n\n    # Fetch all production\n    # The energieopwek_nl parser is backwards compatible with ENTSOE parser.\n    # Because of data quality issues we switch to using energieopwek, but if\n    # data quality of ENTSOE improves we can switch back to using a single\n    # source.\n    productions_ENTSOE = ENTSOE.fetch_production(zone_key=zone_key, session=r,\n                            target_datetime=target_datetime, logger=logger)\n    if energieopwek_nl:\n        productions_eopwek = fetch_production_energieopwek_nl(session=r,\n                            target_datetime=target_datetime, logger=logger)\n        # For every production value we look up the corresponding ENTSOE\n        # values and copy the nuclear, gas, coal, biomass and unknown production. \n        productions = []\n        for p in productions_eopwek:\n            entsoe_value = next((pe for pe in productions_ENTSOE\n                if pe[\"datetime\"] == p[\"datetime\"]), None)\n            if entsoe_value:\n                p[\"production\"][\"nuclear\"] = entsoe_value[\"production\"][\"nuclear\"]\n                p[\"production\"][\"gas\"] = entsoe_value[\"production\"][\"gas\"]\n                p[\"production\"][\"coal\"] = entsoe_value[\"production\"][\"coal\"]\n                p[\"production\"][\"biomass\"] = entsoe_value[\"production\"][\"biomass\"]\n                p[\"production\"][\"unknown\"] = entsoe_value[\"production\"][\"unknown\"]\n                productions.append(p)\n    else:\n        productions = productions_ENTSOE\n    if not productions:\n        return\n\n    # Flatten production dictionaries (we ignore storage)\n    for p in productions:\n        # if for some reason ther\u00e9's no unknown value\n        if not 'unknown' in p['production'] or p['production']['unknown'] == None:\n            p['production']['unknown'] = 0\n\n        Z = sum([x or 0 for x in p['production'].values()])\n        # Only calculate the difference if the datetime exists\n        # If total ENTSOE reported production (Z) is less than total generation\n        # (calculated from consumption and imports), then there must be some\n        # unknown production missing, so we add the difference.\n        # The difference can actually be negative, because consumption is based\n        # on TSO network load, but locally generated electricity may never leave\n        # the DSO network and be substantial (e.g. Solar).\n        if p['datetime'] in df_total_generations and Z < df_total_generations[p['datetime']]:\n            p['production']['unknown'] = round((\n                    df_total_generations[p['datetime']] - Z + p['production']['unknown']), 3)\n\n    # Filter invalid\n    # We should probably add logging to this\n    return [p for p in productions if p['production']['unknown'] > 0]\n\n\ndef fetch_production_energieopwek_nl(session=None, target_datetime=None,\n                                     logger=logging.getLogger(__name__)) -> list:\n    if target_datetime is None:\n        target_datetime = arrow.utcnow()\n\n    # Get production values for target and target-1 day\n    df_current = get_production_data_energieopwek(\n                            target_datetime, session=session)\n    df_previous = get_production_data_energieopwek(\n                            target_datetime.shift(days=-1), session=session)\n\n    # Concat them, oldest first to keep chronological order intact\n    df = pd.concat([df_previous, df_current])\n\n    output = []\n    base_time = arrow.get(target_datetime.date(), 'Europe/Paris').shift(days=-1).to('utc')\n\n    for i, prod in enumerate(df.to_dict(orient='records')):\n        output.append(\n            {\n                'zoneKey': 'NL',\n                'datetime': base_time.shift(minutes=i*15).datetime,\n                'production': prod,\n                'source': 'energieopwek.nl, entsoe.eu'\n            }\n        )\n    return output\n\ndef get_production_data_energieopwek(date, session=None):\n    r = session or requests.session()\n\n    # The API returns values per day from local time midnight until the last\n    # round 10 minutes if the requested date is today or for the entire day if\n    # it's in the past. 'sid' can be anything.\n    url = 'http://energieopwek.nl/jsonData.php?sid=2ecde3&Day=%s' % date.format('YYYY-MM-DD')\n    response = r.get(url)\n    obj = response.json()\n    production_input = obj['TenMin']['Country']\n\n    # extract the power values in kW from the different production types\n    # we only need column 0, 1 and 3 contain energy sum values\n    df_solar =    pd.DataFrame(production_input['Solar'])       .drop(['1','3'], axis=1).astype(int).rename(columns={\"0\" : \"solar\"})\n    df_offshore = pd.DataFrame(production_input['WindOffshore']).drop(['1','3'], axis=1).astype(int)\n    df_onshore =  pd.DataFrame(production_input['Wind'])        .drop(['1','3'], axis=1).astype(int)\n\n    # We don't differentiate between onshore and offshore wind so we sum them\n    # toghether and build a single data frame with named columns\n    df_wind = df_onshore.add(df_offshore).rename(columns={\"0\": \"wind\"})\n    df = pd.concat([df_solar, df_wind], axis=1)\n\n    # resample from 10min resolution to 15min resolution to align with ENTSOE data\n    # we duplicate every row and then group them per 3 and take the mean\n    df = pd.concat([df]*2).sort_index(axis=0).reset_index(drop=True).groupby(by=lambda x : math.floor(x/3)).mean()\n\n    # Convert kW to MW with kW resolution\n    df = df.apply(lambda x: round(x / 1000, 3))\n\n    return df\n\nif __name__ == '__main__':\n    print(fetch_production())\n", "path": "parsers/NL.py"}]}