adding no match when uuid/baseline is set

orion.py

@@ -21,12 +21,13 @@ def cli():
 
 # pylint: disable=too-many-locals
 @click.command()
+@click.option("--cmr", is_flag=True, help="Compare regression")
 @click.option("--uuid", default="", help="UUID to use as base for comparisons")
 @click.option("--baseline", default="", help="UUID to use as base for comparisons")
 @click.option("--config", default="config.yaml", help="Path to the configuration file")
 @click.option("--output", default="output.csv", help="Path to save the output csv file")
 @click.option("--debug", is_flag=True, help="log level ")
-def orion(uuid, baseline, config, debug, output):
+def orion(cmr,uuid, baseline, config, debug, output):
     """Orion is the cli tool to detect regressions over the runs
 
     Args:
@@ -77,16 +78,24 @@ def orion(uuid, baseline, config, debug, output):
             ids = uuids
         else:
             index = "ripsaw-kube-burner"
-            runs = match.match_kube_burner(uuids)
-            ids = match.filter_runs(runs, runs)
+            if baseline == "":
+                runs = match.match_kube_burner(uuids)
+                ids = match.filter_runs(runs, runs)
+            else: 
+                ids = uuids
 
         metrics = test["metrics"]
         dataframe_list = get_metric_data(ids, index, metrics, match, logger)
-
-        merged_df = reduce(
-            lambda left, right: pd.merge(left, right, on="uuid", how="inner"),
-            dataframe_list,
-        )
+        print('data frame list'+ str(dataframe_list))
+        if not cmr: 
+            merged_df = reduce(
+                lambda left, right: pd.merge(left, right, on="uuid", how="inner"),
+                dataframe_list,
+            )
+        else: 
+            # average all data points but last one
+            # combine data into 1 line per metric 
+            merged_df = pd_merge(dataframe_list)
         match.save_results(merged_df, csv_file_path=output)
 
 
@@ -195,6 +204,88 @@ def get_uuid_metadata(uuid, match):
     return metadata
 
 
+def pd_merge(dataframe_list):
+    """ 
+    Merge the previous data frame with current data frame by last column 
+
+    Args:
+        previous_data DataFrame: Previous data, dataframe 
+        current_data DataFrame: Current data, dataframe 
+        columns list[str]: Column titles 
+
+    Returns:
+        DataFrame: Merged previous and current data frame
+    """
+    #pd_merged = current_data.merge(previous_data, how="outer", on=columns[:-1])
+    for df in dataframe_list: 
+        print('df ' + str((df)))
+
+        print('gropu ' + str(df.groupby("uuid")))
+        pd.merge(df, on=df.columns[-1], how="outer")
+    pd_merged = reduce(
+                lambda left, right: pd.merge(left, right, how="outer"),
+                dataframe_list,
+            )
+    print("pd merged" + str(pd_merged))
+    return pd_merged
+
+
+def average_all_results(df): 
+
+    df.mean()
+
+def find_result(row, tolerancy):
+    """
+    Find if row difference is higher or lower than given tolerancy
+    Fail if difference is more negative than tolerany and
+        if difference is higher than tolerancy
+
+    Args:
+        row: Row of data frame to give pass/fail value of difference
+        tolerancy: amount difference we want to pass/fail on 
+
+    Returns:
+        Pass or Fail: if difference is greater than tolerancy
+    """
+
+    print('row difference' + str(row['difference']))
+    print('tolerancy' + str(tolerancy))
+    # Fail if value is a more negative than tolerancy
+    if row["difference"] < 0 and tolerancy < abs(row["difference"]):
+        return "Fail"
+    elif row["difference"] > 0 and tolerancy < row["difference"]:
+        return "Fail"
+    else:
+        return "Pass"
+
+
+def tolerancy(all_data_points, tolerancy_percent):
+    """
+    Find the percent difference in last 2 columns of each row
+    Give this number as a percent change and generate pass/fail on value
+
+    Args:
+        all_data_points DataFrame: Data frame of all previous and current data points
+        tolerancy_percent int: number to determine pass/fail rate on 
+
+    Returns:
+        DataFrame: All data points with percent difference and pass/fail column
+    """
+    print("tolerancy " + str(all_data_points))
+    all_data_points.fillna(method="ffill")
+
+    result = all_data_points.iloc[:, [-2, -1]].pct_change(axis=1).iloc[:, 1] * 100
+
+    all_with_result = all_data_points.assign(difference=result)
+
+    all_with_result["result"] = all_with_result.apply(
+        lambda row: find_result(row, tolerancy_percent), axis=1
+    )
+
+    return all_with_result
+
+
+
 def set_logging(level, logger):
     """sets log level and format