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- Rename dbt project from toronto_housing to portfolio - Restructure dbt models into domain subdirectories: - shared/ for cross-domain dimensions (dim_time) - staging/toronto/, intermediate/toronto/, marts/toronto/ - Update SQLAlchemy models for raw_toronto schema - Add explicit cross-schema FK relationships for FactRentals - Namespace figure factories under figures/toronto/ - Namespace notebooks under notebooks/toronto/ - Update Makefile with domain-specific targets and env loading - Update all documentation for multi-dashboard structure This enables adding new dashboard projects (e.g., /football, /energy) without structural conflicts or naming collisions. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
239 lines
6.6 KiB
Python
239 lines
6.6 KiB
Python
"""Bar chart figure factories for dashboard visualizations."""
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from typing import Any
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import pandas as pd
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import plotly.express as px
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import plotly.graph_objects as go
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def create_ranking_bar(
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data: list[dict[str, Any]],
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name_column: str,
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value_column: str,
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title: str | None = None,
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top_n: int = 10,
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bottom_n: int = 10,
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color_top: str = "#4CAF50",
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color_bottom: str = "#F44336",
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value_format: str = ",.0f",
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) -> go.Figure:
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"""Create horizontal bar chart showing top and bottom rankings.
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Args:
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data: List of data records.
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name_column: Column name for labels.
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value_column: Column name for values.
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title: Optional chart title.
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top_n: Number of top items to show.
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bottom_n: Number of bottom items to show.
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color_top: Color for top performers.
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color_bottom: Color for bottom performers.
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value_format: Number format string for values.
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Returns:
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Plotly Figure object.
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"""
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if not data:
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return _create_empty_figure(title or "Rankings")
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df = pd.DataFrame(data).sort_values(value_column, ascending=False)
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# Get top and bottom
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top_df = df.head(top_n).copy()
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bottom_df = df.tail(bottom_n).copy()
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top_df["group"] = "Top"
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bottom_df["group"] = "Bottom"
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# Combine with gap in the middle
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combined = pd.concat([top_df, bottom_df])
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combined["color"] = combined["group"].map(
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{"Top": color_top, "Bottom": color_bottom}
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)
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fig = go.Figure()
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# Add top bars
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fig.add_trace(
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go.Bar(
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y=top_df[name_column],
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x=top_df[value_column],
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orientation="h",
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marker_color=color_top,
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name="Top",
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text=top_df[value_column].apply(lambda x: f"{x:{value_format}}"),
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textposition="auto",
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hovertemplate=f"%{{y}}<br>{value_column}: %{{x:{value_format}}}<extra></extra>",
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)
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)
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# Add bottom bars
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fig.add_trace(
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go.Bar(
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y=bottom_df[name_column],
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x=bottom_df[value_column],
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orientation="h",
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marker_color=color_bottom,
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name="Bottom",
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text=bottom_df[value_column].apply(lambda x: f"{x:{value_format}}"),
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textposition="auto",
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hovertemplate=f"%{{y}}<br>{value_column}: %{{x:{value_format}}}<extra></extra>",
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)
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)
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fig.update_layout(
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title=title,
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barmode="group",
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showlegend=True,
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legend={"orientation": "h", "yanchor": "bottom", "y": 1.02},
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paper_bgcolor="rgba(0,0,0,0)",
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plot_bgcolor="rgba(0,0,0,0)",
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font_color="#c9c9c9",
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xaxis={"gridcolor": "rgba(128,128,128,0.2)", "title": None},
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yaxis={"autorange": "reversed", "title": None},
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margin={"l": 10, "r": 10, "t": 40, "b": 10},
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)
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return fig
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def create_stacked_bar(
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data: list[dict[str, Any]],
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x_column: str,
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value_column: str,
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category_column: str,
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title: str | None = None,
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color_map: dict[str, str] | None = None,
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show_percentages: bool = False,
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) -> go.Figure:
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"""Create stacked bar chart for breakdown visualizations.
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Args:
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data: List of data records.
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x_column: Column name for x-axis categories.
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value_column: Column name for values.
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category_column: Column name for stacking categories.
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title: Optional chart title.
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color_map: Mapping of category to color.
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show_percentages: Whether to normalize to 100%.
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Returns:
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Plotly Figure object.
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"""
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if not data:
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return _create_empty_figure(title or "Breakdown")
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df = pd.DataFrame(data)
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# Default color scheme
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if color_map is None:
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categories = df[category_column].unique()
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colors = px.colors.qualitative.Set2[: len(categories)]
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color_map = dict(zip(categories, colors, strict=False))
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fig = px.bar(
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df,
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x=x_column,
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y=value_column,
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color=category_column,
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color_discrete_map=color_map,
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barmode="stack",
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text=value_column if not show_percentages else None,
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)
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if show_percentages:
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fig.update_traces(texttemplate="%{y:.1f}%", textposition="inside")
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fig.update_layout(
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title=title,
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paper_bgcolor="rgba(0,0,0,0)",
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plot_bgcolor="rgba(0,0,0,0)",
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font_color="#c9c9c9",
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xaxis={"gridcolor": "rgba(128,128,128,0.2)", "title": None},
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yaxis={"gridcolor": "rgba(128,128,128,0.2)", "title": None},
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legend={"orientation": "h", "yanchor": "bottom", "y": 1.02},
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margin={"l": 10, "r": 10, "t": 60, "b": 10},
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)
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return fig
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def create_horizontal_bar(
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data: list[dict[str, Any]],
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name_column: str,
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value_column: str,
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title: str | None = None,
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color: str = "#2196F3",
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value_format: str = ",.0f",
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sort: bool = True,
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) -> go.Figure:
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"""Create simple horizontal bar chart.
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Args:
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data: List of data records.
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name_column: Column name for labels.
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value_column: Column name for values.
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title: Optional chart title.
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color: Bar color.
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value_format: Number format string.
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sort: Whether to sort by value descending.
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Returns:
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Plotly Figure object.
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"""
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if not data:
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return _create_empty_figure(title or "Bar Chart")
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df = pd.DataFrame(data)
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if sort:
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df = df.sort_values(value_column, ascending=True)
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fig = go.Figure(
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go.Bar(
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y=df[name_column],
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x=df[value_column],
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orientation="h",
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marker_color=color,
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text=df[value_column].apply(lambda x: f"{x:{value_format}}"),
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textposition="outside",
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hovertemplate=f"%{{y}}<br>Value: %{{x:{value_format}}}<extra></extra>",
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)
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)
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fig.update_layout(
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title=title,
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paper_bgcolor="rgba(0,0,0,0)",
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plot_bgcolor="rgba(0,0,0,0)",
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font_color="#c9c9c9",
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xaxis={"gridcolor": "rgba(128,128,128,0.2)", "title": None},
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yaxis={"title": None},
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margin={"l": 10, "r": 10, "t": 40, "b": 10},
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)
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return fig
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def _create_empty_figure(title: str) -> go.Figure:
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"""Create an empty figure with a message."""
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fig = go.Figure()
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fig.add_annotation(
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text="No data available",
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xref="paper",
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yref="paper",
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x=0.5,
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y=0.5,
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showarrow=False,
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font={"size": 14, "color": "#888888"},
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)
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fig.update_layout(
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title=title,
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paper_bgcolor="rgba(0,0,0,0)",
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plot_bgcolor="rgba(0,0,0,0)",
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font_color="#c9c9c9",
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xaxis={"visible": False},
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yaxis={"visible": False},
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)
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return fig
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