Merge branch 'main' into add-link-flapping-to-log-analyzer

plugins/ufm_log_analyzer_plugin/README.md

@@ -1,6 +1,6 @@
 # UFM LOGANALYZER
 
-**Warning:** This feature is still under development and right now should only be used internally
+**Warning:** This feature is still under development and should only be used internally
 
 ## What
 This tool should help developers find issues in a UFM sysdump or logs.
@@ -59,7 +59,6 @@ options:
                         Depth of logs tar extraction, default is 1
   --hours HOURS         How many hours to process from last logs. Default is 6 hours
   -i, --interactive     Should an interactive Ipython session start. Default is False
-  -s, --show-output     Should the output charts be presented. Default is False
   --skip-tar-extract    If the location is to an existing extracted tar or just UFM logs directory, skip the tar extraction and only copy the needed logs. Default is False
   --interval [{1min,10min,1h,24h}]
                         Time interval for the graphs. Choices are: '1min'- Every minute, '10min'- Every ten minutes, '1h'- Every one hour, '24h'- Every 24 hours. Default is '1H'.

plugins/ufm_log_analyzer_plugin/src/loganalyze/log_analyzer.py

@@ -27,9 +27,6 @@
 from pathlib import Path
 import traceback
 from typing import Callable, List, Set, Tuple
-import subprocess
-import platform
-import matplotlib.pyplot as plt
 
 
 from loganalyze.log_analyzers.base_analyzer import BaseImageCreator
@@ -197,12 +194,6 @@ def parse_args():
         action="store_true",
         help="Should an interactive Ipython session start. Default is False",
     )
-    parser.add_argument(
-        "-s",
-        "--show-output",
-        action="store_true",
-        help="Should the output charts be presented. Default is False",
-    )
     parser.add_argument(
         "--skip-tar-extract",
         action="store_true",
@@ -375,16 +366,6 @@ def create_analyzer(parsed_args, full_extracted_logs_list,
         files_types_to_delete.add("csv") #tmp csv + telemetery samples
         files_types_to_delete.add("gz") #gz files of logs and samples
         delete_files_by_types(args.destination, files_types_to_delete)
-        if args.show_output:
-            if platform.system() == "Windows":
-                os.startfile(pdf_path)  # pylint: disable=no-member
-            elif platform.system() == "Darwin":  # macOS
-                subprocess.call(("open", pdf_path))  # pylint: disable=no-member
-            else:  # Linux
-                subprocess.call(("xdg-open", pdf_path))  # pylint: disable=no-member
-
-            # This will show all the graphs we created
-            plt.show()
         if args.interactive:
             import IPython
 

plugins/ufm_log_analyzer_plugin/src/loganalyze/log_analyzers/console_log_analyzer.py

@@ -54,7 +54,7 @@ def print_exceptions(self):
     def print_exceptions_per_time_count(self):
         error_data = self._log_data_sorted[self._log_data_sorted["type"] == "Error"]
         errors_per_hour = error_data.groupby(DataConstants.AGGREGATIONTIME).size()
-        self._save_data_based_on_timestamp(
+        images_created = self._save_data_based_on_timestamp(
             errors_per_hour,
             "Time",
             "Amount of exceptions",

plugins/ufm_log_analyzer_plugin/src/loganalyze/log_analyzers/events_log_analyzer.py

@@ -105,7 +105,7 @@ def print_link_up_down_count_per_hour(self):
         pivot_links_data = counted_links_events_by_time.pivot(
             index=DataConstants.AGGREGATIONTIME, columns="event", values="count"
         ).fillna(0)
-        self._save_pivot_data_in_bars(
+        graph_images = self._save_pivot_data_in_bars(
             pivot_links_data,
             "Time",
             "Number of Events",

plugins/ufm_log_analyzer_plugin/src/loganalyze/log_analyzers/rest_api_log_analyzer.py

@@ -60,7 +60,7 @@ def analyze_endpoints_freq(self, endpoints_count_to_show=10):
                                              values='amount_per_uri').fillna(0)
         data_to_show = data_to_show[top_x_uris]
 
-        self._save_pivot_data_in_bars(data_to_show,
+        return self._save_pivot_data_in_bars(data_to_show,
                                                       "time",
                                                       "requests count",
                                                       f"Top {endpoints_count_to_show} "\

plugins/ufm_log_analyzer_plugin/src/loganalyze/log_analyzers/ufm_health_analyzer.py

@@ -38,7 +38,7 @@ def print_failed_tests_per_hour(self):
         pivot_failed_by_time = grouped_failed_only_relevant_by_time.pivot(
             index=DataConstants.AGGREGATIONTIME, columns="test_name", values="count"
         ).fillna(0)
-        self._save_pivot_data_in_bars(
+        graph_images = self._save_pivot_data_in_bars(
             pivot_failed_by_time,
             "Time",
             "Number of failures",

plugins/ufm_log_analyzer_plugin/src/loganalyze/log_analyzers/ufm_log_analyzer.py

@@ -99,7 +99,7 @@ def full_analyze_ufm_loading_time(self):
         if not matched_logs_df.empty:
             matched_logs_df.drop(columns=["timestamp_end"], inplace=True)
             matched_logs_df.set_index("timestamp_start", inplace=True)
-            self._save_data_based_on_timestamp(
+            create_images = self._save_data_based_on_timestamp(
                 matched_logs_df,
                 DataConstants.TIMESTAMP,
                 "Loading time Time (seconds)",
@@ -214,7 +214,7 @@ def full_analyze_fabric_analysis_time(self):
             )
             merged_logs.set_index("timestamp_start", inplace=True)
             title = "Fabric analysis run time"
-            self._save_data_based_on_timestamp(
+            create_images = self._save_data_based_on_timestamp(
                 merged_logs, "Time", "Processing Time (s)", title
             )
 
@@ -269,8 +269,7 @@ def full_telemetry_processing_time_report(self):
 
         # Plot the data within the filtered time range
         title = "Telemetry processing time"
-
-        self._save_data_based_on_timestamp(
+        create_images = self._save_data_based_on_timestamp(
             minutely_mean_processing_time,
             "Time",
             "Processing Time (s)",