doc updated. no change in verison

codegreen_core/tools/carbon_emission.py

@@ -6,7 +6,6 @@
 
 from .carbon_intensity import compute_ci
 
-
 def compute_ce(
     server: dict,
     start_time: datetime,
@@ -46,7 +45,6 @@ def compute_ce(
     ce_total, ce_df = compute_ce_from_energy(server, ci_ts)
     return ce_total, ce_df
 
-
 def _compute_energy_used(
     runtime_minutes,
     number_core,

codegreen_core/tools/carbon_intensity.py

@@ -118,13 +118,24 @@ def _calculate_ci_from_energy_mix(energy_mix):
 
 def compute_ci(country: str, start_time: datetime, end_time: datetime) -> pd.DataFrame:
     """
-    Computes carbon intensity data for a given country and time period.
+    Computes the carbon intensity (CI) for a given country and time period.
 
-    If energy data is available, the carbon intensity is calculated from actual energy data for the specified  time range.
-    If energy data is not available for the country, a default carbon intensity value is used instead.
-    The default CI values for all countries are stored in utilities/ci_default_values.csv.
+    This function determines the energy data source for the country. 
+    - If energy data is available (e.g., from ENTSOE), it calculates CI using actual energy data.
+    - If energy data is unavailable, it uses default CI values from `ci_default_values.csv` for the country.
+
+    :param country: The 2 letter country code.
+    :type country: str
+    :param start_time: The start of the time range for which CI is computed.
+    :type start_time: datetime
+    :param end_time: The end of the time range for which CI is computed.
+    :type end_time: datetime
+
+    :returns: A pandas DataFrame containing timestamps (`startTimeUTC`) and corresponding carbon intensity values.
+    :rtype: pd.DataFrame
 
     """
+
     if not isinstance(country, str):
         raise ValueError("Invalid country")
 
@@ -133,6 +144,10 @@ def compute_ci(country: str, start_time: datetime, end_time: datetime) -> pd.Dat
 
     if not isinstance(end_time, datetime):
         raise ValueError("Invalid end_time")
+
+    if start_time >= end_time:
+        raise ValueError("start_time must be before end_time")
+
 
     e_source = get_country_energy_source(country)
     if e_source == "ENTSOE":
@@ -190,6 +205,7 @@ def compute_ci_from_energy(
         - `Nuclear` (float): Base carbon intensity value for nuclear energy.
         - `Solar` (float): Base carbon intensity value for solar energy.
         - `Wind` (float): Base carbon intensity value for wind energy.
+
     """
 
     if not isinstance(energy_data, pd.DataFrame):

codegreen_core/tools/loadshift_location.py

@@ -11,10 +11,7 @@ def predict_optimal_location_now(
     percent_renewable: int,
     hard_finish_date: datetime,
 ) -> tuple:
-    """
-    Given a list of countries, returns the best location where a computation can be run based on the input criteria
-    """
-    print()
+    # Given a list of countries, returns the best location where a computation can be run based on the input criteria
     # first get data
     start_time = datetime.now()
     forecast_data = (
@@ -45,9 +42,8 @@ def predict_optimal_location(
     hard_finish_date,
     request_date=None,
 ):
-    """
-    Determines the optimal location and time  to run a computation using  energy data of the selected locations
-    """
+
+    #Determines the optimal location and time  to run a computation using  energy data of the selected locations
     ## obtain the optimal start time for each country
     best_values = {}
     current_best = -1

codegreen_core/tools/loadshift_time.py

@@ -16,7 +16,6 @@
 
 # ========= the main methods  ============
 
-
 def _get_energy_data(country, start, end):
     """
     Get energy data and check if it must be cached based on the options set
@@ -129,7 +128,7 @@ def predict_optimal_time(
     request_time: datetime = None,
 ) -> tuple:
     """
-    Predicts the optimal time window to run a task based in energy data, run time estimates and renewable energy target.
+    Predicts the optimal time window to run a task within the given energy data time frame the run time estimate .
 
     :param energy_data: A DataFrame containing the energy data including startTimeUTC, totalRenewable,total,percent_renewable,posix_timestamp
     :param estimated_runtime_hours: The estimated runtime in hours