add visualization into sdk + tests

Signed-off-by: simonselbig <simon.selbig@gmx.de>

Author: simonselbig

amos_team_resources/shell/pipeline_shell_data.py

@@ -72,6 +72,8 @@ dependencies:
     - great-expectations>=0.18.8,<1.0.0
     - statsmodels>=0.14.1,<0.15.0
     - pmdarima>=2.0.4
+    - plotly>=5.0.0 
+    - kaleido>=0.2.0
     - pip:
           # protobuf installed via pip to avoid libabseil conflicts with conda libarrow
           - protobuf>=5.29.0,<5.30.0
@@ -98,6 +100,4 @@ dependencies:
           - autogluon.timeseries>=1.1.1,<2.0.0
           - scikit-learn>=1.3.0,<2.0.0
           - xgboost>=2.0.0,<3.0.0
-          - plotly>=5.0.0 
-          - kaleido>=0.2.0
           #- tensorflow>=2.13.0,<3.0.0  

environment.yml

@@ -0,0 +1,47 @@
+# Copyright 2025 RTDIP
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+RTDIP Visualization Module.
+
+This module provides standardized visualization components for time series forecasting,
+anomaly detection, and model comparison. It supports both Matplotlib (static) and
+Plotly (interactive) backends.
+
+Submodules:
+    - matplotlib: Static visualization using Matplotlib/Seaborn
+    - plotly: Interactive visualization using Plotly
+
+Example:
+    ```python
+    from rtdip_sdk.pipelines.visualization.matplotlib.forecasting import ForecastPlot
+    from rtdip_sdk.pipelines.visualization.plotly.forecasting import ForecastPlotInteractive
+
+    # Static plot
+    plot = ForecastPlot(historical_df, forecast_df, forecast_start)
+    fig = plot.plot()
+    plot.save("forecast.png")
+
+    # Interactive plot
+    plot_interactive = ForecastPlotInteractive(historical_df, forecast_df, forecast_start)
+    fig = plot_interactive.plot()
+    plot_interactive.save("forecast.html")
+    ```
+"""
+
+from . import config
+from . import utils
+from . import validation
+from .interfaces import VisualizationBaseInterface
+from .validation import VisualizationDataError

src/sdk/python/rtdip_sdk/pipelines/visualization/__init__.py

@@ -0,0 +1,265 @@
+# Copyright 2025 RTDIP
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Standardized visualization configuration for RTDIP time series forecasting.
+
+This module defines standard colors, styles, and settings to ensure consistent
+visualizations across all forecasting, anomaly detection, and model comparison tasks.
+
+Supports both Matplotlib (static) and Plotly (interactive) backends.
+
+Example
+--------
+```python
+from rtdip_sdk.pipelines.visualization import config
+
+# Use predefined colors
+historical_color = config.COLORS['historical']
+
+# Get model-specific color
+model_color = config.get_model_color('autogluon')
+
+# Get figure size for grid
+figsize = config.get_figsize_for_grid(6)
+```
+"""
+
+from typing import Dict, Tuple
+
+# BACKEND CONFIGURATION
+VISUALIZATION_BACKEND: str = "matplotlib"  # Options: 'matplotlib' or 'plotly'
+
+# COLOR SCHEMES
+
+# Primary colors for different data types
+COLORS: Dict[str, str] = {
+    # Time series data
+    "historical": "#2C3E50",  # historical data
+    "forecast": "#27AE60",  # predictions
+    "actual": "#2980B9",  # ground truth
+    "anomaly": "#E74C3C",  # anomalies/errors
+    # Confidence intervals
+    "ci_60": "#27AE60",  # alpha=0.3
+    "ci_80": "#27AE60",  # alpha=0.15
+    "ci_90": "#27AE60",  # alpha=0.1
+    # Special markers
+    "forecast_start": "#E74C3C",  # forecast start line
+    "threshold": "#F39C12",  # thresholds
+}
+
+# Model-specific colors (for comparison plots)
+MODEL_COLORS: Dict[str, str] = {
+    "autogluon": "#2ECC71",
+    "lstm": "#E74C3C",
+    "xgboost": "#3498DB",
+    "arima": "#9B59B6",
+    "prophet": "#F39C12",
+    "ensemble": "#1ABC9C",
+}
+
+# Confidence interval alpha values
+CI_ALPHA: Dict[int, float] = {
+    60: 0.3,  # 60% - most opaque
+    80: 0.2,  # 80% - medium
+    90: 0.1,  # 90% - most transparent
+}
+
+# FIGURE SIZES
+
+FIGSIZE: Dict[str, Tuple[float, float]] = {
+    "single": (12, 6),  # Single time series plot
+    "single_tall": (12, 8),  # Single plot with more vertical space
+    "comparison": (14, 6),  # Side-by-side comparison
+    "grid_small": (14, 8),  # 2-3 subplot grid
+    "grid_medium": (16, 10),  # 4-6 subplot grid
+    "grid_large": (18, 12),  # 6-9 subplot grid
+    "dashboard": (20, 16),  # Full dashboard with 9+ subplots
+    "wide": (16, 5),  # Wide single plot
+}
+
+# EXPORT SETTINGS
+
+EXPORT: Dict[str, any] = {
+    "dpi": 300,  # High resolution
+    "format": "png",  # Default format
+    "bbox_inches": "tight",  # Tight bounding box
+    "facecolor": "white",  # White background
+    "edgecolor": "none",  # No edge color
+}
+
+# STYLE SETTINGS
+
+STYLE: str = "seaborn-v0_8-whitegrid"
+
+FONT_SIZES: Dict[str, int] = {
+    "title": 14,
+    "subtitle": 12,
+    "axis_label": 12,
+    "tick_label": 10,
+    "legend": 10,
+    "annotation": 9,
+}
+
+LINE_SETTINGS: Dict[str, float] = {
+    "linewidth": 2.0,  # Default line width
+    "linewidth_thin": 1.5,  # Thin lines (for CI, grids)
+    "marker_size": 4,  # Default marker size for line plots
+    "scatter_size": 80,  # Scatter plot marker size
+    "anomaly_size": 100,  # Anomaly marker size
+}
+
+GRID: Dict[str, any] = {
+    "alpha": 0.3,  # Grid transparency
+    "linestyle": "--",  # Dashed grid lines
+    "linewidth": 0.5,  # Thin grid lines
+}
+
+TIME_FORMATS: Dict[str, str] = {
+    "hourly": "%Y-%m-%d %H:%M",
+    "daily": "%Y-%m-%d",
+    "monthly": "%Y-%m",
+    "display": "%m/%d %H:%M",
+}
+
+METRICS: Dict[str, Dict[str, str]] = {
+    "mae": {"name": "MAE", "format": ".3f"},
+    "mse": {"name": "MSE", "format": ".3f"},
+    "rmse": {"name": "RMSE", "format": ".3f"},
+    "mape": {"name": "MAPE (%)", "format": ".2f"},
+    "smape": {"name": "SMAPE (%)", "format": ".2f"},
+    "r2": {"name": "R²", "format": ".4f"},
+    "mae_p50": {"name": "MAE (P50)", "format": ".3f"},
+    "mae_p90": {"name": "MAE (P90)", "format": ".3f"},
+}
+
+# Metric display order (left to right, top to bottom)
+METRIC_ORDER: list = ["mae", "rmse", "mse", "mape", "smape", "r2"]
+
+# OUTPUT DIRECTORY SETTINGS
+DEFAULT_OUTPUT_DIR: str = "output_images"
+
+# COLORBLIND-FRIENDLY PALETTE
+
+COLORBLIND_PALETTE: list = [
+    "#0173B2",
+    "#DE8F05",
+    "#029E73",
+    "#CC78BC",
+    "#CA9161",
+    "#949494",
+    "#ECE133",
+    "#56B4E9",
+]
+
+
+# HELPER FUNCTIONS
+
+
+def get_grid_layout(n_plots: int) -> Tuple[int, int]:
+    """
+    Calculate optimal subplot grid layout (rows, cols) for n_plots.
+
+    Prioritizes 3 columns for better horizontal space usage.
+
+    Args:
+        n_plots: Number of subplots needed
+
+    Returns:
+        Tuple of (n_rows, n_cols)
+
+    Example
+    --------
+    ```python
+    from rtdip_sdk.pipelines.visualization.config import get_grid_layout
+
+    rows, cols = get_grid_layout(5)  # Returns (2, 3)
+    ```
+    """
+    if n_plots <= 0:
+        return (0, 0)
+    elif n_plots == 1:
+        return (1, 1)
+    elif n_plots == 2:
+        return (1, 2)
+    elif n_plots <= 3:
+        return (1, 3)
+    elif n_plots <= 6:
+        return (2, 3)
+    elif n_plots <= 9:
+        return (3, 3)
+    elif n_plots <= 12:
+        return (4, 3)
+    else:
+        n_cols = 3
+        n_rows = (n_plots + n_cols - 1) // n_cols
+        return (n_rows, n_cols)
+
+
+def get_model_color(model_name: str) -> str:
+    """
+    Get color for a specific model, with fallback to colorblind palette.
+
+    Args:
+        model_name: Model name (case-insensitive)
+
+    Returns:
+        Hex color code string
+
+    Example
+    --------
+    ```python
+    from rtdip_sdk.pipelines.visualization.config import get_model_color
+
+    color = get_model_color('AutoGluon')  # Returns '#2ECC71'
+    color = get_model_color('custom_model')  # Returns color from palette
+    ```
+    """
+    model_name_lower = model_name.lower()
+
+    if model_name_lower in MODEL_COLORS:
+        return MODEL_COLORS[model_name_lower]
+
+    idx = hash(model_name) % len(COLORBLIND_PALETTE)
+    return COLORBLIND_PALETTE[idx]
+
+
+def get_figsize_for_grid(n_plots: int) -> Tuple[float, float]:
+    """
+    Get appropriate figure size for a grid of n plots.
+
+    Args:
+        n_plots: Number of subplots
+
+    Returns:
+        Tuple of (width, height) in inches
+
+    Example
+    --------
+    ```python
+    from rtdip_sdk.pipelines.visualization.config import get_figsize_for_grid
+
+    figsize = get_figsize_for_grid(4)  # Returns (16, 10) for grid_medium
+    ```
+    """
+    if n_plots <= 1:
+        return FIGSIZE["single"]
+    elif n_plots <= 3:
+        return FIGSIZE["grid_small"]
+    elif n_plots <= 6:
+        return FIGSIZE["grid_medium"]
+    elif n_plots <= 9:
+        return FIGSIZE["grid_large"]
+    else:
+        return FIGSIZE["dashboard"]