0.0.5

.github/workflows/workflow.yml

@@ -1,4 +1,4 @@
-name: Publish Python 🐍 distribution 📦 to PyPI
+name: Publish to PyPI
 
 on: 
   push:
@@ -7,7 +7,7 @@ on:
 
 jobs:
   build:
-    name: Build distribution 📦
+    name: Build distribution 
     runs-on: ubuntu-latest
 
     steps:
@@ -30,7 +30,7 @@ jobs:
         path: dist/
 
   publish-to-pypi:
-    name: Publish Python 🐍 distribution 📦 to PyPI
+    name: Publish to PyPI
     needs:
     - build
     runs-on: ubuntu-latest

codegreen_core/data/__init__.py

@@ -1,3 +1,5 @@
 from .main import *
+from .offline import * 
+# from . import main 
 
-__all__ = ["energy"]
+__all__ = ["info","energy","sync_offline_data",'get_offline_data']

codegreen_core/data/entsoe.py

@@ -47,6 +47,8 @@
     "Biomass": ["Biomass"],
 }
 
+
+
 # helper methods
 
 
@@ -287,6 +289,7 @@ def get_actual_production_percentage(country, start, end, interval60=False) -> d
       - `data_available`: A boolean indicating if data was successfully retrieved.
       - `data`: A pandas DataFrame containing the energy data if available, empty DataFrame if not.
       - `time_interval` : the time interval of the DataFrame
+      - `columns` :  a dict with column description
     :rtype: dict
     """
     try:
@@ -371,6 +374,7 @@ def get_actual_production_percentage(country, start, end, interval60=False) -> d
             "data": _format_energy_data(table),
             "data_available": True,
             "time_interval": duration,
+            "columns":gen_cols_from_data(table)
         }
     except Exception as e:
         # print(e)
@@ -380,8 +384,38 @@ def get_actual_production_percentage(country, start, end, interval60=False) -> d
             "data_available": False,
             "error": e,
             "time_interval": 0,
+            "columns":None
         }
 
+def gen_cols_from_data(df):
+    """generates list of columns for the given energy generation dataframe"""
+    allAddkeys = [
+            "Wind",
+            "Solar",
+            "Nuclear",
+            "Hydroelectricity",
+            "Geothermal",
+            "Natural Gas",
+            "Petroleum",
+            "Coal",
+            "Biomass",
+    ]
+
+    allCols = df.columns.tolist()
+    # find out which columns are present in the data out of all the possible columns in both the categories
+    renPresent = list(set(allCols).intersection(renewableSources))
+    nonRenPresent = list(set(allCols).intersection(nonRenewableSources))
+
+    cols = {
+            "renewable" : renPresent,
+            "nonRenewable": nonRenPresent,
+            "percentage":[]
+        }
+    for ky in allAddkeys:
+            fieldName = ky + "_per"
+            cols["percentage"].append(fieldName)
+    return cols
+
 
 def get_forecast_percent_renewable(
     country: str, start: datetime, end: datetime

codegreen_core/data/main.py

@@ -10,54 +10,76 @@
 
 def energy(country, start_time, end_time, type="generation") -> dict:
     """
-    Returns hourly time series of energy production mix for a specified country and time range.
-
-    This method fetches the energy data for the specified country between the specified duration.
-    It checks if a valid energy data source is available. If not, None is returned. Otherwise, the
-    energy data is returned as a pandas DataFrame. The structure of data depends on the energy source.
-
-    For example, if the source is ENTSOE, the data contains:
-
-     ========================== ========== ================================================================
-      Column                     type       Description
-     ========================== ========== ================================================================
-      startTimeUTC               object    Start date in UTC (format YYYYMMDDhhmm)
-      startTime                  datetime  Start time in local timezone
-      Biomass                    float64 
-      Fossil Hard coal           float64
-      Geothermal                 float64
-      ....more energy sources    float64
-      **renewableTotal**         float64    The total based on all renewable sources
-      renewableTotalWS           float64    The total production using only Wind and Solar energy sources
-      nonRenewableTotal          float64
-      total                      float64    Total using all energy sources
-      percentRenewable           int64
-      percentRenewableWS         int64      Percentage of energy produced using only wind and solar energy
-      Wind_per                   int64      Percentages of individual energy sources
-      Solar_per                  int64
-      Nuclear_per                int64
-      Hydroelectricity_per       int64
-      Geothermal_per             int64
-      Natural Gas_per            int64
-      Petroleum_per              int64
-      Coal_per                   int64
-      Biomass_per                int64
-     ========================== ========== ================================================================
-
-    Note : fields marked bold are calculated based on the data fetched.
-
-    :param str country: The 2 alphabet country code.
-    :param datetime start_time: The start date for data retrieval. A Datetime object. Note that this date will be rounded to the nearest hour.
-    :param datetime end_time: The end date for data retrieval. A datetime object. This date is also rounded to the nearest hour.
-    :param str type: The type of data to retrieve; either 'generation' or 'forecast'. Defaults to 'generation'.
-    :param boolean interval60: To fix the time interval of data to 60 minutes. True by default. Only applicable for generation data
-
-    :return: A dictionary containing:
-      - `error`: A string with an error message, empty if no errors.
-      - `data_available`: A boolean indicating if data was successfully retrieved.
-      - `data`: A pandas DataFrame containing the energy data if available, empty DataFrame if not.
-      - `time_interval` : the time interval of the DataFrame 
+    Returns an hourly time series of the energy production mix for a specified country and time range, 
+    if a valid energy data source is available.
+
+    The data is returned as a pandas DataFrame along with additional metadata.  
+    The columns vary depending on the data source. For example, if the source is ENTSOE, 
+    the data includes fields such as "Biomass", "Geothermal", "Hydro Pumped Storage", 
+    "Hydro Run-of-river and Poundage", "Hydro Water Reservoir", etc.
+
+    However, some fields remain consistent across data sources:
+
+    ========================= ========== ================================================================
+    Column                     Type       Description
+    ========================= ========== ================================================================
+    startTimeUTC              object     Start time in UTC (format: YYYYMMDDhhmm)
+    startTime                 datetime   Start time in local timezone
+    renewableTotal            float64    The total production from all renewable sources
+    renewableTotalWS          float64    Total production using only Wind and Solar energy sources
+    nonRenewableTotal         float64    Total production from non-renewable sources
+    total                     float64    Total energy production from all sources
+    percentRenewable          int64      Percentage of total energy from renewable sources
+    percentRenewableWS        int64      Percentage of energy from Wind and Solar only
+    Wind_per                  int64      Percentage contribution from Wind energy
+    Solar_per                 int64      Percentage contribution from Solar energy
+    Nuclear_per               int64      Percentage contribution from Nuclear energy
+    Hydroelectricity_per      int64      Percentage contribution from Hydroelectricity
+    Geothermal_per            int64      Percentage contribution from Geothermal energy
+    Natural Gas_per           int64      Percentage contribution from Natural Gas
+    Petroleum_per             int64      Percentage contribution from Petroleum
+    Coal_per                  int64      Percentage contribution from Coal
+    Biomass_per               int64      Percentage contribution from Biomass
+    ========================= ========== ================================================================
+
+    :param str country: 
+        The 2-letter country code (e.g., "DE" for Germany, "FR" for France, etc.).  
+    :param datetime start_time: 
+        The start date for data retrieval (rounded to the nearest hour).  
+    :param datetime end_time: 
+        The end date for data retrieval (rounded to the nearest hour).  
+    :param str type: 
+        The type of data to retrieve; either 'generation' or 'forecast'. Defaults to 'generation'.  
+
+    :return: A dictionary containing the following keys:
+
+        - **error** (*str*): An error message, empty if no errors occurred.
+        - **data_available** (*bool*): Indicates whether data was successfully retrieved.
+        - **data** (*pandas.DataFrame*): The retrieved energy data if available; an empty DataFrame otherwise.
+        - **time_interval** (*int*): The time interval of the DataFrame (constant value: ``60``).
+        - **source** (*str*): Specifies the origin of the retrieved data. Defaults to ``'public_data'``, indicating it was fetched from an external source. If the offline storage feature is enabled, this value may change if the data is available locally.
+        - **columns** : a dict of columns for renewable and non renewable energy sources in the data
+
     :rtype: dict
+
+    **Example Usage:**
+
+    Get generation data for Germany 
+
+    .. code-block:: python
+
+        from datetime import datetime
+        from codegreen_core.data import energy
+        result = energy(country="DE", start_time=datetime(2025, 1, 1), end_time=datetime(2025, 1, 2), type="generation")
+
+    Get forecast data for Norway 
+
+    .. code-block:: python
+
+        from datetime import datetime
+        from codegreen_core.data import energy
+        result = energy(country="NO", start_time=datetime(2025, 1, 1), end_time=datetime(2025, 1, 2), type="forecast")
+
     """
     if not isinstance(country, str):
         raise ValueError("Invalid country")
@@ -75,27 +97,41 @@ def energy(country, start_time, end_time, type="generation") -> dict:
     e_source = meta.get_country_energy_source(country)
     if e_source == "ENTSOE":
         if type == "generation":
-            """
-            let local_found= false
-            see if caching is enabled, if yes, first check in the cache
-            if not, 
-                check if offline data is enabled
-                if yes, check is data is available locally 
-                if no, go online 
-            """
             offline_data = off.get_offline_data(country,start_time,end_time)
             if offline_data["available"] is True and offline_data["partial"] is False and offline_data["data"] is not None:
                 # todo fix this if partial get remaining data and merge instead of fetching the complete data
-                return {"data":offline_data["data"],"data_available":True,"error":"None","time_interval":60,"source":offline_data["source"]}
+                return {"data":offline_data["data"],"data_available":True,"error":"None","time_interval":60,"source":offline_data["source"],"columns":et.gen_cols_from_data(offline_data["data"])}
             else:
                 energy_data = et.get_actual_production_percentage(country, start_time, end_time, interval60=True)
-                energy_data["data"] = energy_data["data"]
+                #energy_data["data"] = energy_data["data"]
                 energy_data["source"] = "public_data"
+                #energy_data["columns"] = 
                 return energy_data            
         elif type == "forecast":
             energy_data = et.get_forecast_percent_renewable(country, start_time, end_time)
-            energy_data["data"] = energy_data["data"]
+            # energy_data["data"] = energy_data["data"]
             return energy_data
     else:
         raise CodegreenDataError(Message.NO_ENERGY_SOURCE)
     return None
+
+def info()-> list:
+    """
+    Returns a list of countries (in two-letter codes) and energy sources for which data can be fetched using the package.
+
+    :return: A list of dictionary containing:
+
+      - name of the country
+      - `energy_source` : the publicly available energy data source 
+      - `carbon_intensity_method` : the methodology used to calculate carbon intensity 
+      - `code` : the 2 letter country code 
+
+    :rtype: list
+    """
+    data = meta.get_country_metadata()
+    data_list = []
+    for key , value in data.items():
+        c = value
+        c["code"]  = key
+        data_list.append(c)
+    return  data_list

codegreen_core/data/offline.py

@@ -243,16 +243,27 @@ def _get_offline_cache_data(country,start,end):
         return False,None
 
 
-def get_offline_data(country,start,end,sync_first=False):
+def get_offline_data(country,start,end,sync_first=False)->dict:
     """
-    This method returns locally stored energy data. 
-    Data is stored in 2 sources : one. Redis cache and second : csv files. 
-    Redis cache contains data only for the last 72 hours from when it was last synced
-    Offline data files can contain data for longer durations.
-    Both these options can be configured in the config file
-    returns {available:True/False, data:dataframe}
-    Note that this method assumes that syncing of the sources is being handled separately  
+    This method returns locally stored energy data.
+
+    Data is stored in two sources:
+
+    1. **Redis cache**: Contains data for a limited number of hours from the last sync.
+    2. **CSV files**: Contain data for longer durations.
+
+    Both storage options can be configured in the configuration file.
+
+    **Note**: Unless you specify the ``sync_first`` flag, the method assumes that syncing of the data sources is handled separately. If ``sync_first`` is set to ``True`` and data files are not initialized in advance, the method may take longer to complete
+
+    :return: A dictionary with the following keys:
+        - **available** (*bool*): Indicates if the data is available.
+        - **data** (*pandas.DataFrame*): The energy data, if available. Otherwise, an empty DataFrame.
+
+    :rtype: dict
+
     """
+
     output = {"available":False,"data":None, "partial":False,"source":""}
     offline = Config.get("enable_offline_energy_generation")
     cache =  Config.get("enable_energy_caching")
@@ -264,7 +275,7 @@ def get_offline_data(country,start,end,sync_first=False):
     if cache :
         # first look in the cache
         if(sync_first):
-            print("will first sync the cache to get the latest data")
+            #print("will first sync the cache to get the latest data")
             _sync_offline_cache(country)
         partial,data = _get_offline_cache_data(country,start,end)   
         if data is not None and partial is False:
@@ -278,37 +289,39 @@ def get_offline_data(country,start,end,sync_first=False):
     if offline:
         # first look if data files are available, if yes, return data 
         if(sync_first):
-            print("will first sync the offline files to get the latest data")
+            #print("will first sync the offline files to get the latest data")
             _sync_offline_file(country)
         partial,data = _get_offline_file_data(country,start,end)
         output["partial"] = partial
         output["data"] = data
         output["available"] = True
         output["source"] = "offline_file"
-        print("just got the data from offline file")
+        #print("just got the data from offline file")
 
     return output
 
 
 def sync_offline_data(file=False,cache=False):
     """
-    This method syncs offline data for offline sources enabled in the cache. 
-    Data is synced for all available countries 
-    You need to run this before getting offline data. you can even setup a CRON job to call this method on regular intervals
+    This method syncs offline data for offline sources enabled in the configuration file. The data is synced for all available countries. 
+
+    You need to run this method before retrieving offline data. It is also possible to set up a CRON job to call this method at regular intervals to keep data synchronized.
+
+    The sync operation can take some time, depending on the data size and the selected sync options (file, cache, or both).
+
+    :param bool file: If ``True``, sync data in offline files. Defaults to ``False``.
+    :param bool cache: If ``True``, sync data in the cache. Defaults to ``False``.
     """
     c_keys = meta.get_country_metadata()
     if  Config.get("enable_offline_energy_generation") == True  and file == True:
         for key in c_keys:
             try:
                 _sync_offline_file(key)
             except Exception as e:
-                # print(e)
                 log_stuff("Error in syncing offline file for "+key+". Message"+ str(e))
     if  Config.get("enable_energy_caching") == True and cache == True :
         for key in c_keys:
             try:
                 _sync_offline_cache(key)
             except Exception as e:
-                # print(e)
                 log_stuff("Error in syncing offline file for "+key+". Message: "+ str(e))
-

codegreen_core/tools/loadshift_time.py

@@ -70,6 +70,31 @@ def predict_now(
     :type criteria: str
     :return: Tuple[timestamp, message, average_percent_renewable]
     :rtype: tuple
+
+    **Example usage**:
+
+    .. code-block:: python
+
+        from datetime import datetime,timedelta 
+        from codegreen_core.tools.loadshift_time import predict_now
+
+        country_code = "DK"
+        est_runtime_hour = 10
+        est_runtime_min = 0
+        now = datetime.now()
+        hard_finish_date = now + timedelta(days=1)
+        criteria = "percent_renewable"
+        per_renewable = 50 
+
+        time = predict_now(country_code,
+                            est_runtime_hour,
+                            est_runtime_min,
+                            hard_finish_date,
+                            criteria,
+                            per_renewable)
+        # (1728640800.0, <Message.OPTIMAL_TIME: 'OPTIMAL_TIME'>, 76.9090909090909)
+
+
     """
     if criteria == "percent_renewable":
         try: