133 problem with extracting data for championnat national 25 26

impectPy/helpers.py

@@ -10,9 +10,43 @@
 
 # create logger for this module
 logger = logging.getLogger("impectPy")
-# logger = logging.LoggerAdapter(logger, {"id": "-", "url": "-"})
 logger.addHandler(logging.NullHandler())
 
+
+######
+#
+# This class inherits from Response and adds a function converts the response from an API call to a pandas dataframe, flattens it and fixes the column names
+#
+######
+
+class ImpectResponse(requests.Response):
+    def process_response(self, endpoint: str, raise_exception: bool = True) -> pd.DataFrame:
+        # validate and get data from response
+        result = validate_response(response=self, endpoint=endpoint, raise_exception=raise_exception)
+
+        # convert to df
+        result = pd.json_normalize(result)
+
+        # fix column names using regex
+        result = result.rename(columns=lambda x: re.sub(r"\.(.)", lambda y: y.group(1).upper(), x))
+
+        # return result
+        return result
+
+
+######
+#
+# This class inherits from Session and ensure the response is of type ImpectResponse
+#
+######
+
+class ImpectSession(requests.Session):
+    def request(self, *args, **kwargs) -> ImpectResponse:
+        response = super().request(*args, **kwargs)
+        response.__class__ = ImpectResponse
+        return response
+
+
 ######
 #
 # This class creates an object to handle rate-limited API requests
@@ -31,25 +65,25 @@ class ForbiddenError(HTTPError):
 
 
 class RateLimitedAPI:
-    def __init__(self, session: Optional[requests.Session] = None):
+    def __init__(self, session: Optional[ImpectSession] = None):
         """
         Initializes a RateLimitedAPI object.
 
         Args:
-            session (requests.Session): The session object to use for the API calls.
+            session (ImpectSession): The session object to use for the API calls.
         """
-        self.session = session or requests.Session()  # use the provided session or create a new session
+        self.session = session or ImpectSession()  # use the provided session or create a new session
         self.bucket = None  # TokenBucket object to manage rate limit tokens
 
     # make a rate-limited API request
     def make_api_request_limited(
             self, url: str, method: str, data: Optional[Dict[str, str]] = None
-    ) -> requests.Response:
+    ) -> ImpectResponse:
         """
         Executes an API call while applying the rate limit.
 
         Returns:
-            requests.Response: The response returned by the API.
+            ImpectResponse: The response returned by the API.
         """
 
         # check if bucket is not initialized
@@ -73,7 +107,6 @@ def make_api_request_limited(
                 remaining=int(response.headers["RateLimit-Remaining"])
             )
 
-            # return response
             return response
 
         # check if a token is available
@@ -102,12 +135,12 @@ def make_api_request_limited(
     def make_api_request(
             self, url: str, method: str, data: Optional[Dict[str, Any]] = None,
             max_retries: int = 3, retry_delay: int = 1
-    ) -> requests.Response:
+    ) -> ImpectResponse:
         """
         Executes an API call.
 
         Returns:
-            requests.Response: The response returned by the API.
+            ImpectResponse: The response returned by the API.
         """
         # try API call
         for i in range(max_retries):
@@ -205,31 +238,6 @@ def consumeToken(self):
         return True  # return True to indicate successful token consumption
 
 
-######
-#
-# This function converts the response from an API call to a pandas dataframe, flattens it and fixes the column names
-#
-######
-
-
-def process_response(self: requests.Response, endpoint: str, raise_exception: bool = True) -> pd.DataFrame:
-    # validate and get data from response
-    result = validate_response(response=self, endpoint=endpoint, raise_exception=raise_exception)
-
-    # convert to df
-    result = pd.json_normalize(result)
-
-    # fix column names using regex
-    result = result.rename(columns=lambda x: re.sub(r"\.(.)", lambda y: y.group(1).upper(), x))
-
-    # return result
-    return result
-
-
-# attach method to requests module
-requests.Response.process_response = process_response
-
-
 ######
 #
 # This function unnests the idMappings key from an API response
@@ -301,7 +309,7 @@ def unnest_mappings_df(df: pd.DataFrame, mapping_col: str) -> pd.DataFrame:
 
 
 # define function to validate JSON response and return data
-def validate_response(response: requests.Response, endpoint: str, raise_exception: bool = True) -> dict:
+def validate_response(response: ImpectResponse, endpoint: str, raise_exception: bool = True) -> dict:
     # get data from response
     data = response.json()["data"]
 

impectPy/player_iteration_averages.py

@@ -90,12 +90,17 @@ def getPlayerIterationAveragesFromHost(
                     f"squads/{squad_id}/player-kpis",
                 method="GET"
             ).process_response(
-                endpoint="PlayerAverages"
+                endpoint="PlayerAverages",
+                raise_exception=False
             ).assign(
                 iterationId=iteration,
                 squadId=squad_id
             )
 
+        # skip if empty repsonse
+        if len(averages_raw) == 0:
+            continue
+
         # unnest scorings
         averages_raw = averages_raw.explode("kpis").reset_index(drop=True)
 

impectPy/player_iteration_scores.py

@@ -81,7 +81,7 @@ def fetch_player_iteration_scores(connection, url):
         return connection.make_api_request_limited(
             url=url,
             method="GET"
-        ).process_response(endpoint="Player Match Scores")
+        ).process_response(endpoint="Player Match Scores", raise_exception=False)
 
     # get player scores
     if positions is None:
@@ -99,7 +99,10 @@ def fetch_player_iteration_scores(connection, url):
                 iterationId=iteration,
                 squadId=squad_id
             )
-            scores_list.append(scores)
+
+            # check if response is empty
+            if len(scores) > 0:
+                scores_list.append(scores)
         scores_raw = pd.concat(scores_list).reset_index(drop=True).reset_index(drop=True)
 
     else:
@@ -122,17 +125,21 @@ def fetch_player_iteration_scores(connection, url):
                 squadId=squad_id,
                 positions=position_string
             )
-            scores_list.append(scores)
+
+            # check if resonse is empty
+            if len(scores) > 0:
+                scores_list.append(scores)
         scores_raw = pd.concat(scores_list).reset_index(drop=True).reset_index(drop=True)
 
     # raise exception if no player played at given positions in entire iteration
     if len(scores_raw) == 0:
         raise Exception(f"No players played at given position in iteration {iteration}.")
 
     # print squads without players at given position
-    error_list = [str(squadId) for squadId in squad_ids if squadId not in scores_raw.squadId.to_list()]
-    if len(error_list) > 0:
-        print(f"No players played at positions {positions} for iteration {iteration} for following squads:\n\t{', '.join(error_list)}")
+    if positions is not None:
+        error_list = [str(squadId) for squadId in squad_ids if squadId not in scores_raw.squadId.to_list()]
+        if len(error_list) > 0:
+            print(f"No players played at positions {positions} for iteration {iteration} for following squads:\n\t{', '.join(error_list)}")
 
     # get players
     players = connection.make_api_request_limited(