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"""
Base chart class for creating visualizations.
"""
import plotly.graph_objects as go
import plotly.express as px
import pandas as pd
import logging
from datetime import datetime
from typing import List, Dict, Any, Optional, Tuple
from abc import ABC, abstractmethod
from ..config.constants import CHART_CONFIG, CHART_COLORS, Y_AXIS_RANGES, FILE_PATHS
from ..data.data_processor import DataProcessor
logger = logging.getLogger(__name__)
class BaseChart(ABC):
"""Base class for all chart visualizations."""
def __init__(self, data_processor: DataProcessor = None):
self.data_processor = data_processor or DataProcessor()
self.config = CHART_CONFIG
self.colors = CHART_COLORS
self.y_ranges = Y_AXIS_RANGES
self.file_paths = FILE_PATHS
@abstractmethod
def create_chart(self, df: pd.DataFrame, **kwargs) -> go.Figure:
"""Create the chart visualization."""
pass
def _create_base_figure(self) -> go.Figure:
"""Create a base figure with common settings."""
return go.Figure()
def _add_background_shapes(self, fig: go.Figure, min_time: datetime, max_time: datetime,
y_min: float, y_max: float) -> None:
"""Add background shapes for positive and negative regions."""
# Add shape for positive region (above zero)
fig.add_shape(
type="rect",
fillcolor=self.colors['positive_region'],
line=dict(width=0),
y0=0, y1=y_max,
x0=min_time, x1=max_time,
layer="below"
)
# Add shape for negative region (below zero)
fig.add_shape(
type="rect",
fillcolor=self.colors['negative_region'],
line=dict(width=0),
y0=y_min, y1=0,
x0=min_time, x1=max_time,
layer="below"
)
def _add_zero_line(self, fig: go.Figure, min_time: datetime, max_time: datetime) -> None:
"""Add a zero line to the chart."""
fig.add_shape(
type="line",
line=dict(dash="solid", width=1.5, color=self.colors['zero_line']),
y0=0, y1=0,
x0=min_time, x1=max_time
)
def _update_layout(self, fig: go.Figure, title: str, y_axis_title: str = None,
height: int = None, y_range: List[float] = None) -> None:
"""Update the figure layout with common settings."""
fig.update_layout(
title=dict(
text=title,
font=dict(
family=self.config['font_family'],
size=self.config['title_size'],
color="black",
weight="bold"
)
),
xaxis_title=None,
yaxis_title=None,
template=self.config['template'],
height=height or self.config['height'],
autosize=True,
legend=dict(
orientation="h",
yanchor="bottom",
y=1.05,
xanchor="center",
x=0.5,
groupclick="toggleitem",
font=dict(
family=self.config['font_family'],
size=self.config['legend_font_size'],
color="black",
weight="bold"
)
),
margin=dict(r=30, l=120, t=80, b=60),
hovermode="closest"
)
# Add y-axis annotation if provided
if y_axis_title:
fig.add_annotation(
x=-0.08,
y=0 if y_range is None else (y_range[0] + y_range[1]) / 2,
xref="paper",
yref="y",
text=y_axis_title,
showarrow=False,
font=dict(
size=16,
family=self.config['font_family'],
color="black",
weight="bold"
),
textangle=-90,
align="center"
)
def _update_axes(self, fig: go.Figure, x_range: List[datetime] = None,
y_range: List[float] = None, y_auto: bool = False) -> None:
"""Update the axes with common settings."""
# Update y-axis
y_axis_config = {
'showgrid': True,
'gridwidth': 1,
'gridcolor': 'rgba(0,0,0,0.1)',
'tickformat': ".2f",
'tickfont': dict(
size=self.config['axis_font_size'],
family=self.config['font_family'],
color="black",
weight="bold"
),
'title': None
}
if y_auto:
y_axis_config['autorange'] = True
elif y_range:
y_axis_config['autorange'] = False
y_axis_config['range'] = y_range
fig.update_yaxes(**y_axis_config)
# Update x-axis
x_axis_config = {
'showgrid': True,
'gridwidth': 1,
'gridcolor': 'rgba(0,0,0,0.1)',
'tickformat': "%b %d",
'tickangle': -30,
'tickfont': dict(
size=self.config['axis_font_size'],
family=self.config['font_family'],
color="black",
weight="bold"
),
'title': None
}
if x_range:
x_axis_config['autorange'] = False
x_axis_config['range'] = x_range
fig.update_xaxes(**x_axis_config)
def _add_agent_data_points(self, fig: go.Figure, df: pd.DataFrame, value_column: str,
color_map: Dict[str, str], max_visible: int = None) -> None:
"""Add individual agent data points to the chart."""
if df.empty:
return
unique_agents = df['agent_name'].unique()
max_visible = max_visible or self.config['max_visible_agents']
# Calculate agent activity to determine which to show by default
agent_counts = df['agent_name'].value_counts()
top_agents = agent_counts.nlargest(min(max_visible, len(agent_counts))).index.tolist()
logger.info(f"Showing {len(top_agents)} agents by default out of {len(unique_agents)} total agents")
for agent_name in unique_agents:
agent_data = df[df['agent_name'] == agent_name]
x_values = agent_data['timestamp'].tolist()
y_values = agent_data[value_column].tolist()
# Determine visibility
is_visible = False # Hide all agent data points by default
fig.add_trace(
go.Scatter(
x=x_values,
y=y_values,
mode='markers',
marker=dict(
color=color_map.get(agent_name, 'gray'),
symbol='circle',
size=10,
line=dict(width=1, color='black')
),
name=f'Agent: {agent_name} ({value_column.upper()})',
hovertemplate=f'Time: %{{x}}<br>{value_column.upper()}: %{{y:.2f}}<br>Agent: {agent_name}<extra></extra>',
visible=is_visible
)
)
logger.info(f"Added {value_column} data points for agent {agent_name} with {len(x_values)} points (visible: {is_visible})")
def _add_moving_average_line(self, fig: go.Figure, avg_data: pd.DataFrame,
value_column: str, line_name: str, color: str,
width: int = 2, hover_data: List[str] = None) -> None:
"""Add a moving average line to the chart."""
if avg_data.empty or 'moving_avg' not in avg_data.columns:
return
# Filter out NaT values before processing - be more aggressive
clean_data = avg_data.copy()
# Remove rows with NaT timestamps more comprehensively
clean_data = clean_data.dropna(subset=['timestamp'])
clean_data = clean_data[clean_data['timestamp'].notna()]
clean_data = clean_data[~clean_data['timestamp'].isnull()]
# Additional check for pandas NaT specifically
if hasattr(pd, 'NaT'):
clean_data = clean_data[clean_data['timestamp'] != pd.NaT]
# Also filter out NaN moving averages
clean_data = clean_data.dropna(subset=['moving_avg'])
clean_data = clean_data[clean_data['moving_avg'].notna()]
if clean_data.empty:
logger.warning("No valid timestamps found for " + str(line_name))
return
x_values = clean_data['timestamp'].tolist()
y_values = clean_data['moving_avg'].tolist()
# Create hover text without any f-strings to avoid strftime issues
if hover_data:
hover_text = hover_data
else:
hover_text = []
for _, row in clean_data.iterrows():
try:
# Convert timestamp to string safely
ts = row['timestamp']
# More comprehensive NaT checking
if pd.isna(ts) or pd.isnull(ts) or (hasattr(pd, 'NaT') and ts is pd.NaT):
time_str = "Invalid Date"
elif hasattr(ts, 'strftime'):
try:
time_str = ts.strftime('%Y-%m-%d %H:%M:%S')
except (ValueError, TypeError):
time_str = str(ts)
else:
time_str = str(ts)
# Build hover text using string concatenation only
hover_line = "Time: " + time_str + "<br>"
# Safely format moving average value
try:
avg_val = row['moving_avg']
if pd.isna(avg_val) or pd.isnull(avg_val):
avg_str = "N/A"
else:
avg_str = "{:.2f}".format(float(avg_val))
except (ValueError, TypeError):
avg_str = "N/A"
hover_line += "Avg " + value_column.upper() + " (7d window): " + avg_str
hover_text.append(hover_line)
except Exception as e:
logger.warning("Error formatting timestamp for hover text: " + str(e))
# Fallback hover text
hover_line = "Time: Invalid Date<br>"
hover_line += "Avg " + value_column.upper() + " (3d window): N/A"
hover_text.append(hover_line)
fig.add_trace(
go.Scatter(
x=x_values,
y=y_values,
mode='lines',
line=dict(color=color, width=width, shape='spline', smoothing=1.3),
name=line_name,
hovertext=hover_text,
hoverinfo='text',
visible=True
)
)
logger.info("Added moving average line '" + str(line_name) + "' with " + str(len(x_values)) + " points")
def _filter_outliers(self, df: pd.DataFrame, column: str) -> pd.DataFrame:
"""Filter outliers from the data - DISABLED: Return data unchanged."""
# Outlier filtering disabled - return original data
logger.info(f"Outlier filtering disabled for {column} column - returning all data")
return df
def _calculate_moving_average(self, df: pd.DataFrame, value_column: str) -> pd.DataFrame:
"""Calculate moving average for the data."""
return self.data_processor.calculate_moving_average(df, value_column)
def _save_chart(self, fig: go.Figure, html_filename: str, png_filename: str = None) -> None:
"""Save the chart to HTML and optionally PNG."""
try:
fig.write_html(html_filename, include_plotlyjs='cdn', full_html=False)
logger.info(f"Chart saved to {html_filename}")
if png_filename:
try:
fig.write_image(png_filename)
logger.info(f"Chart also saved to {png_filename}")
except Exception as e:
logger.error(f"Error saving PNG image: {e}")
logger.info(f"Chart saved to {html_filename} only")
except Exception as e:
logger.error(f"Error saving chart: {e}")
def generate_visualization(self, df: pd.DataFrame, **kwargs) -> Tuple[go.Figure, Optional[str]]:
"""Generate the complete visualization including chart and CSV export."""
if df.empty:
logger.info("No data available for visualization.")
fig = self._create_empty_chart("No data available")
return fig, None
# Create the chart
fig = self.create_chart(df, **kwargs)
# Save to CSV
csv_filename = kwargs.get('csv_filename')
if csv_filename:
csv_path = self.data_processor.save_to_csv(df, csv_filename)
else:
csv_path = None
return fig, csv_path
def _create_empty_chart(self, message: str) -> go.Figure:
"""Create an empty chart with a message."""
fig = go.Figure()
fig.add_annotation(
x=0.5, y=0.5,
text=message,
font=dict(size=20),
showarrow=False
)
fig.update_layout(
xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
yaxis=dict(showgrid=False, zeroline=False, showticklabels=False)
)
return fig
def _get_color_map(self, agents: List[str]) -> Dict[str, str]:
"""Generate a color map for agents."""
colors = px.colors.qualitative.Plotly[:len(agents)]
return {agent: colors[i % len(colors)] for i, agent in enumerate(agents)}
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