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Add beta 1.2.2.1

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MacRimi
2026-06-05 17:12:23 +02:00
parent e855fca0b3
commit 3629fe8848
12 changed files with 907 additions and 325 deletions
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49
AppImage/scripts/flask_server.py
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@@ -4858,7 +4858,8 @@ def get_proxmox_vms():
'netin': resource.get('netin', 0),
'netout': resource.get('netout', 0),
'diskread': resource.get('diskread', 0),
'diskwrite': resource.get('diskwrite', 0)
'diskwrite': resource.get('diskwrite', 0),
'maxcpu': resource.get('maxcpu', 0)
}
# Decorate LXC rows with the apt update status if the
# managed_installs registry has it. Absent key means
@@ -7640,14 +7641,26 @@ def api_system():
try:
from health_monitor import health_monitor
_hist = health_monitor.state_history.get('cpu_usage') or []
cpu_usage = _hist[-1]['value'] if _hist else psutil.cpu_percent(interval=0.1)
if _hist:
_last = _hist[-1]
cpu_usage = _last['value']
cpu_user_pct = _last.get('user', 0)
cpu_system_pct = _last.get('system', 0)
else:
cpu_usage = psutil.cpu_percent(interval=0.1)
cpu_user_pct = 0
cpu_system_pct = 0
except Exception:
cpu_usage = psutil.cpu_percent(interval=0.1)
cpu_user_pct = 0
cpu_system_pct = 0
memory = psutil.virtual_memory()
memory_used_gb = memory.used / (1024 ** 3)
memory_total_gb = memory.total / (1024 ** 3)
memory_usage_percent = memory.percent
# Preview restyle: cached + buffers in GB
memory_cached_gb = round((getattr(memory, 'cached', 0) + getattr(memory, 'buffers', 0)) / (1024 ** 3), 1)
# Get temperature
temp = get_cpu_temperature()
@@ -7677,9 +7690,12 @@ def api_system():
return jsonify({
'cpu_usage': round(cpu_usage, 1),
'cpu_user': cpu_user_pct,
'cpu_system': cpu_system_pct,
'memory_usage': round(memory_usage_percent, 1),
'memory_total': round(memory_total_gb, 1),
'memory_used': round(memory_used_gb, 1),
'memory_cached': memory_cached_gb,
'temperature': temp,
'temperature_sparkline': temp_sparkline,
'uptime': uptime,
@@ -9616,6 +9632,35 @@ def api_node_metrics():
if 'zfsarc' not in item or item.get('zfsarc', 0) == 0:
item['zfsarc'] = zfs_arc_size
# 24h downsampling: RRD returns ~1440 minute-level points which
# plots as a dense thicket of vertical spikes. Group into 5-min
# buckets and average each numeric field — same shape that
# `get_temperature_history` uses for its 24h view so the look
# is consistent across the dashboard's 24h charts.
if timeframe == 'day' and rrd_data:
bucket_seconds = 300 # 5-min
buckets = {}
for item in rrd_data:
t = item.get('time')
if t is None:
continue
bk = (int(t) // bucket_seconds) * bucket_seconds
if bk not in buckets:
buckets[bk] = {'_count': 0, '_sums': {}}
b = buckets[bk]
b['_count'] += 1
for k, v in item.items():
if k == 'time' or not isinstance(v, (int, float)) or isinstance(v, bool):
continue
b['_sums'][k] = b['_sums'].get(k, 0) + v
rrd_data = []
for bk in sorted(buckets.keys()):
b = buckets[bk]
point = {'time': bk}
for k, total in b['_sums'].items():
point[k] = total / b['_count']
rrd_data.append(point)
payload = {
'node': local_node,
'timeframe': timeframe,