Make log scales more robust

dashboard.py

@@ -139,9 +139,13 @@ class TimeSeries(Widget):
 
         def v2y(v):
             if self.yscale == "log":
-                return (1 -   math.log(v / min_value)
-                            / math.log(max_value / min_value)
-                       ) * 200
+                try:
+                    return (1 -   math.log(max(v, min_value) / min_value)
+                                / math.log(max_value / min_value)
+                           ) * 200
+                except ValueError:
+                    log.error(f"ValueError: v = {v}, min_value = {min_value}, max_value = {max_value}")
+                    return 0
             elif self.yscale == "linear":
                 return (1 - v/max_value) * 200
             else:

process_queue

@@ -4,6 +4,7 @@ import logging
 import logging.config
 import os
 import socket
+import statistics
 import time
 
 from ltsdb_json import LTS
@@ -44,12 +45,13 @@ class DiskFullPredictor:
         current_used_bytes = lts.data[-1][1]
         current_usable_bytes = usable_lts.data[-1][1]
         tuf = 1E9
-        for d in reversed(lts.data):
-            if d[1] < current_usable_bytes * 0.1:
+        for i in reversed(range(len(lts.data))):
+            m = statistics.mean(x[1] for x in lts.data[max(0, i - 2) : min(len(lts.data), i + 3)])
+            if m < current_usable_bytes * 0.1:
                 continue # for sanity
-            if current_used_bytes ** 2 / d[1] > current_usable_bytes:
-                log.info("d = %s, current_used_bytes = %s, current_usable_bytes = %s", d, current_used_bytes, current_usable_bytes)
-                tuf = now - d[0]
+            if current_used_bytes ** 2 / m > current_usable_bytes:
+                log.info("d = %s, current_used_bytes = %s, current_usable_bytes = %s", m, current_used_bytes, current_usable_bytes)
+                tuf = now - lts.data[i][0]
                 break
         desc = {**lts.description,
              "measure": "time_until_disk_full",