diff --git a/src/dash_tools/style_configs.py b/src/dash_tools/style_configs.py
index 05b9d7edf1604ab249abd0f946db94b1c3d8773f..785e6090fb7be1dd8ea4119890ff1dc2d8f44f28 100644
--- a/src/dash_tools/style_configs.py
+++ b/src/dash_tools/style_configs.py
@@ -7,6 +7,100 @@ from datetime import timedelta, datetime
import plotly.express as px
import pandas as pd
+
+
+def create_model_forecasts_plot(df_forecasts, df_historical):
+ """Create a plot showing recent model forecasts alongside historical data"""
+ #TODO drop empty rows?
+ sensor_name = df_forecasts["sensor_name"][0]
+
+ colors = px.colors.qualitative.Set3
+ start_times = sorted(df_forecasts["tstamp"].unique())
+ start_time_colors = {t: colors[i % len(colors)] for i, t in enumerate(start_times)}
+
+
+ fig = go.Figure()
+ df_measured = df_historical[start_times[0]-timedelta(days=3):]
+ # Add historical data
+ fig.add_trace(go.Scatter(
+ x=df_measured.index,
+ y=df_measured[sensor_name],
+ name=f"Historical - {sensor_name}",
+ line=dict(color='black', width=2),
+ mode='lines'
+ ))
+
+ df_forecasts = df_forecasts.round(1)
+ members = df_forecasts['member'].unique()
+ for member in members:
+ member_data = df_forecasts[df_forecasts['member'] == member]
+
+
+ for _, row in member_data.iterrows():
+ start_time = row['tstamp']
+ legend_group = f'{start_time.strftime("%Y%m%d%H%M")}'
+ legendgrouptitle_text = f'{start_time.strftime("%Y-%m-%d %H:%M")}'
+
+ # Convert h1-h48 columns to forecast points
+ timestamps = [start_time + timedelta(hours=i) for i in range(1, 49)]
+ #values = row[5:]
+ values = [row[f'h{i}'] for i in range(1, 49)]
+
+ fig.add_trace(
+ go.Scatter(
+ x=timestamps,
+ y=values,
+ name=f'{start_time.strftime("%Y-%m-%d %H:%M")}',
+ #name=f'M{member} {start_time.strftime("%Y-%m-%d %H:%M")}',
+ legendgroup=legend_group,
+ showlegend=(member == members[0]).item(),# and sensor_idx == 1, # Show all traces in legend
+ line=dict(
+ color=start_time_colors[start_time],
+ width=1,
+ #dash='solid' if member == members[0] else 'dot'
+ ),
+ hovertemplate=(
+ 'Time: %{x}<br>'
+ 'Value: %{y:.2f}<br>'
+ f'Member: {member}<br>'
+ f'Start: {start_time}<extra></extra>'
+ )
+ ),
+ )
+
+ #fig.add_trace(go.Scatter(
+ # x=timestamps,
+ # y=row[5:],
+ # name=f"Forecast Member {member} ({row['tstamp']})",
+ # line=dict(color=colors[member % len(colors)], width=1, dash='dot'),
+ # opacity=1,
+ # showlegend=True
+ #))
+
+ ####
+ #sensor_data = df_forecast[df_forecast['sensor_name'] == sensor]
+ #members = sensor_data['member'].unique()
+ # Add trace to the subplot
+
+ ####
+ fig.update_layout(
+ height=600,
+ title='Model Forecasts',
+ xaxis_title='Time',
+ yaxis_title='Gauge [cm]',
+ #hovermode='x unified',
+ legend=dict(
+ yanchor="top",
+ y=0.99,
+ xanchor="left",
+ x=1.05,
+ #bgcolor='rgba(255, 255, 255, 0.8)'
+ )
+ )
+ return fig
+
+
+
def create_log_table(logs_data):
"""
Creates a configured DataTable for logging display
diff --git a/src/dashboard.py b/src/dashboard.py
index c899b0eca5b82fc231544ee89c06482ff92823b7..79befaad56817d29830be2d542df3954a27fc685 100644
--- a/src/dashboard.py
+++ b/src/dashboard.py
@@ -1,3 +1,4 @@
+from re import A
from dash import html, dcc, dash_table, Dash
from dash.dependencies import Input, Output, State, MATCH
import plotly.express as px
@@ -6,15 +7,20 @@ import pandas as pd
from datetime import datetime, timedelta
from sqlalchemy import create_engine, select, and_, desc
from sqlalchemy.orm import Session
-from utils.db_tools.orm_classes import Base, InputForecasts, Modell, PegelForecasts, Sensor, Log, ModellSensor, SensorData
+from utils.db_tools.orm_classes import InputForecasts, Modell, PegelForecasts,Log, ModellSensor, SensorData
import oracledb
import os
from sqlalchemy import select, func
-from dash_tools.style_configs import create_log_table, create_historical_plot, create_historical_table,create_input_forecasts_plot,create_inp_forecast_status_table
+from dash_tools.style_configs import (create_log_table,
+ create_historical_plot,
+ create_historical_table,
+ create_input_forecasts_plot,
+ create_inp_forecast_status_table,
+ create_model_forecasts_plot)
from dash_tools.layout_helper import create_collapsible_section
NUM_RECENT_INPUT_FORECASTS = 12
-
+NUM_RECENT_PEGEL_FORECASTS = 5
class ForecastMonitor:
@@ -27,7 +33,7 @@ class ForecastMonitor:
self.con = oracledb.connect(**self.db_params)
self.engine = create_engine("oracle+oracledb://", creator=lambda: self.con)
- self.app = Dash(__name__)
+ self.app = Dash(__name__, suppress_callback_exceptions=True)
self.setup_layout()
self.setup_callbacks()
@@ -86,6 +92,38 @@ class ForecastMonitor:
print(f"Error getting model status: {str(e)}")
return None
+ def get_recent_forecasts(self, actual_model_name, limit=NUM_RECENT_PEGEL_FORECASTS):
+ """Get recent forecasts for a specific model and sensor"""
+ try:
+ subq = (
+ select(
+ PegelForecasts,
+ func.row_number()
+ .over(
+ partition_by=[PegelForecasts.member],
+ order_by=PegelForecasts.tstamp.desc()
+ ).label('rn'))
+ .where(PegelForecasts.model == actual_model_name)
+ .subquery()
+ )
+
+ stmt = (
+ select(subq)
+ .where(subq.c.rn <= NUM_RECENT_PEGEL_FORECASTS)
+ .order_by(
+ subq.c.member,
+ subq.c.tstamp.desc()
+ )
+ )
+ df = pd.read_sql(sql=stmt, con=self.engine)
+ df.drop(columns=['rn'], inplace=True)
+ return df
+
+
+ except Exception as e:
+ print(f"Error getting recent forecasts: {str(e)}")
+ return pd.DataFrame()
+
def get_input_forecasts(self, sensor_names):
"""Get 3 most recent input forecasts for the given sensor names"""
try:
@@ -120,7 +158,7 @@ class ForecastMonitor:
return df
except Exception as e:
- raise RuntimeError(f"Error getting input forecasts: {str(e)}")
+ raise RuntimeError(f"Error getting input forecasts: {str(e)}") from e
def get_recent_logs(self, sensor_name):
@@ -164,7 +202,7 @@ class ForecastMonitor:
# Get last 144 hours of sensor data
time_threshold = datetime.now() - timedelta(hours=144)
- #time_threshold= pd.to_datetime("2024-09-13 14:00:00.000") - timedelta(hours=144) #TODO rausnehmen
+ time_threshold= pd.to_datetime("2024-09-13 14:00:00.000") - timedelta(hours=144) #TODO rausnehmen
stmt = select(SensorData).where(
SensorData.tstamp >= time_threshold,
@@ -177,7 +215,7 @@ class ForecastMonitor:
except Exception as e:
print(f"Error getting historical data: {str(e)}")
- return [], []
+ return pd.DataFrame()
def setup_layout(self):
self.app.layout = html.Div([
@@ -223,7 +261,11 @@ class ForecastMonitor:
# Right column
html.Div([
dcc.Store(id='current-sensor-names'), # Store current sensor names
-
+ create_collapsible_section(
+ "Model Forecasts",
+ html.Div(id='fcst-view'),
+ is_open=True
+ ),
create_collapsible_section(
"Recent Logs",
html.Div(id='log-view'),
@@ -329,7 +371,10 @@ class ForecastMonitor:
{'name': 'Last Valid', 'id': 'last_forecast_time'},
{'name': 'Created', 'id': 'forecast_created'},
{'name': 'Target Sensor', 'id': 'sensor_name'},
- {'name': 'model_id', 'id': 'model_id', 'hideable': True}
+ {'name': 'model_id', 'id': 'model_id', 'hideable': True},
+ #{'name': 'actual_model_name', 'id': 'actual_model_name', 'hidden': True}
+ #{'name': 'actual_model_name', 'id': 'actual_model_name', 'visible': False}
+ {'name': 'actual_model_name', 'id': 'actual_model_name', 'hideable': True}
],
data=[{
'model_name': row['model_name'],
@@ -337,8 +382,10 @@ class ForecastMonitor:
'last_forecast_time': row['last_forecast_time'].strftime('%Y-%m-%d %H:%M:%S') if row['last_forecast_time'] else 'No valid forecast',
'forecast_created': row['forecast_created'].strftime('%Y-%m-%d %H:%M:%S') if row['forecast_created'] else 'N/A',
'sensor_name': row['sensor_name'],
- 'model_id': row['model_id']
+ 'model_id': row['model_id'],
+ 'actual_model_name': row['actual_model_name']
} for row in status['model_status']],
+ hidden_columns=['model_id', 'actual_model_name'], # Specify hidden columns here
style_data_conditional=[
{
'if': {'filter_query': '{has_current_forecast} = "✓"', "column_id": "has_current_forecast"},
@@ -380,7 +427,8 @@ class ForecastMonitor:
@self.app.callback(
- [Output('log-view', 'children'),
+ [Output('fcst-view', 'children'),
+ Output('log-view', 'children'),
Output('historical-view', 'children'),
Output('inp-fcst-view', 'children'),
Output('current-sensor-names', 'data')], # Removed input-forecasts-view
@@ -389,7 +437,8 @@ class ForecastMonitor:
)
def update_right_column(selected_rows, table_data):
if not selected_rows:
- return (html.Div("Select a model to view logs"),
+ return (html.Div("Select a model to view Forecasts"),
+ html.Div("Select a model to view logs"),
html.Div("Select a model to view Input Forecasts"),
html.Div("Select a model to view historical data"),
None) # Removed input forecasts return
@@ -398,6 +447,7 @@ class ForecastMonitor:
sensor_name = selected_row['sensor_name']
model_id = selected_row['model_id']
model_name = selected_row['model_name']
+ actual_model_name = selected_row['actual_model_name']
# Get logs
logs = self.get_recent_logs(sensor_name)
@@ -407,6 +457,8 @@ class ForecastMonitor:
log_table
])
+
+
# Get historical data
df_historical = self.get_historical_data(model_id)
sensor_names = list(df_historical.columns)
@@ -423,21 +475,35 @@ class ForecastMonitor:
else:
historical_view = html.Div("No historical data available")
+ # Get forecast data
+ df_forecasts = self.get_recent_forecasts(actual_model_name)
+ if not df_forecasts.empty:
+ fig_fcst = create_model_forecasts_plot(df_forecasts,df_historical)
+ fcst_view = html.Div([
+ html.H4(f"Gauge Forecasts for {model_name}"),
+ dcc.Graph(figure=fig_fcst),
+ #html.H4("Input Forecast Status", style={'marginTop': '20px', 'marginBottom': '10px'}),
+ #html.Div(inp_fcst_table, style={'width': '100%', 'padding': '10px'})
+ ])
+ else:
+ fcst_view = html.Div("No forecasts available")
+
# Get Input Forecasts
df_inp_fcst = self.get_input_forecasts(sensor_names)
if not df_inp_fcst.empty:
- fig_fcst = create_input_forecasts_plot(df_inp_fcst,df_historical)
+ fig_inp_fcst = create_input_forecasts_plot(df_inp_fcst,df_historical)
inp_fcst_table = create_inp_forecast_status_table(df_inp_fcst)
inp_fcst_view = html.Div([
html.H4(f"Input Forecasts for {model_name}"),
- dcc.Graph(figure=fig_fcst),
+ dcc.Graph(figure=fig_inp_fcst),
html.H4("Input Forecast Status", style={'marginTop': '20px', 'marginBottom': '10px'}),
html.Div(inp_fcst_table, style={'width': '100%', 'padding': '10px'})
])
else:
inp_fcst_view = html.Div("No input forecasts available")
- return (log_view,
+ return (fcst_view,
+ log_view,
historical_view,
inp_fcst_view,
sensor_names) # Removed input forecasts return
@@ -470,5 +536,5 @@ if __name__ == '__main__':
dsn="localhost/XE"
)
#monitor.run(host="172.17.0.1", port=8050, debug=True)
- monitor.run(host="134.245.232.166", port=8050, debug=True)
-
\ No newline at end of file
+ #monitor.run(host="134.245.232.166", port=8050, debug=True)
+ monitor.run()
\ No newline at end of file