refactor: reorganize repository to src/ layout
Move 6 packages (core, config, data_processing, analysis, visualization, cli) into src/ to reduce root clutter. Merge tools/data.py into data_processing/transforms.py. Move docs to docs/. Path resolution via .pth file (setup_dev.py), pytest pythonpath config, and sys.path bootstrap in rxconfig.py and CLI entry points. Clean up pyproject.toml deps (remove stale pins, add snowflake-connector-python). Fix tomllib import for Python 3.10 compatibility. All 113 tests pass.
This commit is contained in:
Andrew Charlwood
@@ -1,231 +0,0 @@
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"""
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Plotly chart generation for patient pathway analysis.
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This module contains functions for creating interactive icicle charts
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that visualize patient treatment pathways. The charts display hierarchical
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data: Trust → Directory → Drug → Pathway.
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"""
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import webbrowser
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from typing import Optional
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import numpy as np
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import pandas as pd
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import plotly.graph_objects as go
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from core.logging_config import get_logger
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logger = get_logger(__name__)
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def create_icicle_figure(ice_df: pd.DataFrame, title: str) -> go.Figure:
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"""
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Create Plotly icicle figure from prepared DataFrame.
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This function generates an interactive icicle chart showing patient pathway
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hierarchies with custom data including costs, dates, and treatment durations.
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Args:
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ice_df: DataFrame with columns:
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- parents: Parent node in hierarchy
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- ids: Unique identifier for each node
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- labels: Display label for each node
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- value: Number of patients
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- colour: Color value for visualization
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- cost: Total cost
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- costpp: Cost per patient
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- cost_pp_pa: Cost per patient per annum
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- First seen: First intervention date
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- Last seen: Last intervention date
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- First seen (Parent): Earliest date in parent group
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- Last seen (Parent): Latest date in parent group
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- average_spacing: Formatted string with dosing information
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- avg_days: Average treatment duration
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title: Chart title
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Returns:
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Plotly Figure object ready for display or export
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"""
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ice_df = ice_df.copy()
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ice_df.sort_values(by=["labels"], ascending=True, inplace=True, ignore_index=True)
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first_seen = ice_df["First seen"].astype(str).replace("NaT", "N/A").to_list()
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last_seen = ice_df["Last seen"].astype(str).replace("NaT", "N/A").to_list()
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first_seen_parent = ice_df["First seen (Parent)"].astype(str).to_list()
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last_seen_parent = ice_df["Last seen (Parent)"].astype(str).to_list()
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average_spacing = ice_df.average_spacing.astype(str).to_list()
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fig = go.Figure(
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go.Icicle(
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labels=ice_df.labels,
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ids=ice_df.ids,
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parents=ice_df.parents,
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customdata=np.stack(
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(
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ice_df.value,
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ice_df.colour,
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ice_df.cost,
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ice_df.costpp,
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first_seen,
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last_seen,
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first_seen_parent,
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last_seen_parent,
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average_spacing,
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ice_df.cost_pp_pa,
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),
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axis=1,
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),
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values=ice_df.value,
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branchvalues="total",
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marker=dict(colors=ice_df.colour, colorscale="Viridis"),
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maxdepth=3,
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texttemplate="<b>%{label}</b> "
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"<br><b>Total patients:</b> %{customdata[0]} (including children/further treatments)"
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"<br><b>First seen:</b> %{customdata[4]}"
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"<br><b>Last seen (including further treatments):</b> %{customdata[7]}"
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"<br><b>Average treatment duration:</b> %{customdata[8]}"
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"<br><b>Total cost:</b> £%{customdata[2]:.3~s}"
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"<br><b>Average cost per patient:</b> £%{customdata[3]:.3~s}"
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"<br><b>Average cost per patient per annum:</b> £%{customdata[9]:.3~s}",
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hovertemplate="<b>%{label}</b>"
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"<br><b>Total patients:</b> %{customdata[0]} - %{customdata[1]:.3p} of patients in level"
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"<br><b>Total cost:</b> £%{customdata[2]:.3~s}"
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"<br><b>Average cost per patient:</b> £%{customdata[3]:.3~s}"
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"<br><b>Average cost per patient per annum:</b> £%{customdata[9]:.3~s}"
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"<br><b>First seen:</b> %{customdata[4]}"
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"<br><b>Last seen (including further treatments):</b> %{customdata[7]}"
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"<br><b>Average treatment duration:</b>"
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"%{customdata[8]}"
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"<extra></extra>",
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)
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)
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fig.update_traces(sort=False)
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fig.update_layout(
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margin=dict(t=60, l=1, r=1, b=60),
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title=f"Norfolk & Waveney ICS high-cost drug patient pathways - {title}",
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title_x=0.5,
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hoverlabel=dict(font_size=16),
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)
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return fig
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def save_figure_html(
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fig: go.Figure, save_dir: str, title: str, open_browser: bool = False
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) -> str:
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"""
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Save Plotly figure to HTML file.
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Args:
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fig: Plotly Figure object
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save_dir: Directory to save the HTML file
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title: Title used for filename
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open_browser: If True, open the file in the default browser
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Returns:
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Path to the saved HTML file
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"""
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filepath = f"{save_dir}/{title}.html"
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fig.write_html(filepath)
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logger.info(f"Success! File saved to {filepath}")
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if open_browser:
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open_figure_in_browser(filepath)
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return filepath
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def open_figure_in_browser(filepath: str) -> None:
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"""
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Open an HTML file in the default browser.
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Args:
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filepath: Path to the HTML file
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"""
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webbrowser.open_new_tab("file:///" + filepath)
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def figure_legacy(ice_df: pd.DataFrame, dir_string: str, save_dir: str) -> None:
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"""
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Create and display icicle figure (legacy interface).
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This function maintains backward compatibility with the original figure()
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function signature. It creates the figure, saves it to HTML, and opens
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it in the browser.
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Args:
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ice_df: DataFrame with chart data
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dir_string: Title string (used for filename and chart title)
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save_dir: Directory to save the HTML file
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Note:
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This function is provided for backward compatibility.
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New code should use create_icicle_figure() + save_figure_html() instead.
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"""
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# Handle avg_days column for display
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ice_df = ice_df.copy()
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ice_df.sort_values(by=["labels"], ascending=True, inplace=True, ignore_index=True)
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first_seen = ice_df["First seen"].astype(str).replace("NaT", "N/A").to_list()
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last_seen = ice_df["Last seen"].astype(str).replace("NaT", "N/A").to_list()
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first_seen_parent = ice_df["First seen (Parent)"].astype(str).to_list()
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last_seen_parent = ice_df["Last seen (Parent)"].astype(str).to_list()
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average_spacing = ice_df.average_spacing.astype(str).to_list()
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avg_seen = ice_df["avg_days"].dt.round("D").astype(str).replace("0 days", "N/A").to_list()
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fig = go.Figure(
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go.Icicle(
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labels=ice_df.labels,
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ids=ice_df.ids,
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parents=ice_df.parents,
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customdata=np.stack(
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(
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ice_df.value,
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ice_df.colour,
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ice_df.cost,
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ice_df.costpp,
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first_seen,
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last_seen,
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first_seen_parent,
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last_seen_parent,
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average_spacing,
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ice_df.cost_pp_pa,
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),
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axis=1,
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),
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values=ice_df.value,
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branchvalues="total",
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marker=dict(colors=ice_df.colour, colorscale="Viridis"),
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maxdepth=3,
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texttemplate="<b>%{label}</b> "
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"<br><b>Total patients:</b> %{customdata[0]} (including children/further treatments)"
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"<br><b>First seen:</b> %{customdata[4]}"
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"<br><b>Last seen (including further treatments):</b> %{customdata[7]}"
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"<br><b>Average treatment duration:</b> %{customdata[8]}"
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"<br><b>Total cost:</b> £%{customdata[2]:.3~s}"
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"<br><b>Average cost per patient:</b> £%{customdata[3]:.3~s}"
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"<br><b>Average cost per patient per annum:</b> £%{customdata[9]:.3~s}",
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hovertemplate="<b>%{label}</b>"
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"<br><b>Total patients:</b> %{customdata[0]} - %{customdata[1]:.3p} of patients in level"
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"<br><b>Total cost:</b> £%{customdata[2]:.3~s}"
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"<br><b>Average cost per patient:</b> £%{customdata[3]:.3~s}"
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"<br><b>Average cost per patient per annum:</b> £%{customdata[9]:.3~s}"
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"<br><b>First seen:</b> %{customdata[4]}"
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"<br><b>Last seen (including further treatments):</b> %{customdata[7]}"
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"<br><b>Average treatment duration:</b>"
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"%{customdata[8]}"
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"<extra></extra>",
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)
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)
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fig.update_traces(sort=False)
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fig.update_layout(
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margin=dict(t=60, l=1, r=1, b=60),
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title=f"Norfolk & Waveney ICS high-cost drug patient pathways - {dir_string}",
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title_x=0.5,
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hoverlabel=dict(font_size=16),
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)
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filepath = f"{save_dir}/{dir_string}.html"
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fig.write_html(filepath)
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logger.info(f"Success! File saved to {filepath}")
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webbrowser.open_new_tab("file:///" + filepath)
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