bolade
1683 lines
54 KiB
Python
1683 lines
54 KiB
Python
"""
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Graph Generator Service
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This service generates all the charts and visualizations required for the medical report.
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Based on the analysis notebooks in services_dfdf/.
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"""
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import base64
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from pathlib import Path
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import matplotlib
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matplotlib.use("Agg") # Use non-interactive backend
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import matplotlib.pyplot as plt
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import matplotlib.transforms as mtransforms
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import numpy as np
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import pandas as pd
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import seaborn as sns
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from matplotlib.patches import FancyBboxPatch
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class GraphGenerator:
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"""Generate all charts for medical reports"""
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def __init__(self, charts_dir: str = "graphs"):
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"""
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Initialize the graph generator.
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Args:
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charts_dir: Directory to save generated charts
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"""
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self.charts_dir = Path(charts_dir)
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self.charts_dir.mkdir(exist_ok=True)
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def _image_to_base64(self, image_path: Path) -> str:
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"""
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Convert image file to base64 string.
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Args:
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image_path: Path to image file
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Returns:
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Base64 encoded string
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"""
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try:
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with open(image_path, "rb") as image_file:
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return base64.b64encode(image_file.read()).decode("utf-8")
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except FileNotFoundError:
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return ""
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def generate_respiratory_chart(
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self, df: pd.DataFrame, save_as_base64: bool = True
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) -> str:
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"""
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Generate respiratory chart (VT and Speed over time).
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Args:
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df: Processed DataFrame with smoothed columns
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save_as_base64: If True, return base64 string, else return file path
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Returns:
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Base64 string or file path
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"""
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first_unique_phase = df.drop_duplicates(subset="PHASE")
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phase_times = first_unique_phase["T(sec)"].tolist()
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plt.figure(figsize=(18, 5))
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ax1 = plt.subplot()
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# Plot VT
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sns.lineplot(data=df, x="T(sec)", y="VT(l)_smoothed", label="VT (L)")
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ax1.set_xlabel("Time (sec)")
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ax1.set_ylabel("VT (L)")
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ax1.grid(True, alpha=0.1)
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ax1.set_ylim(0, min(8, df["VT(l)_smoothed"].max()))
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# Plot speed on secondary y-axis
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ax2 = ax1.twinx()
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ax1.set_xticks(np.arange(0, df["T(sec)"].max() + 200, 200))
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sns.lineplot(
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data=df,
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x="T(sec)",
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y="Speed",
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color="green",
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ax=ax2,
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drawstyle="steps-post",
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linewidth=2,
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label="Speed",
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)
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ax2.set_ylabel("Speed")
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ax2.set_ylim(0, min(30, df["Speed"].max()) + 1)
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# Combine legends
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ax1.get_legend().remove()
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ax2.get_legend().remove()
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lines1, labels1 = ax1.get_legend_handles_labels()
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lines2, labels2 = ax2.get_legend_handles_labels()
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ax1.legend(lines1 + lines2, labels1 + labels2, loc="upper left")
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# Add colored background regions
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if len(phase_times) >= 4:
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ax1.axvspan(0, phase_times[1], alpha=0.2, color="lightblue")
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ax1.axvspan(phase_times[1], phase_times[2], alpha=0.2, color="purple")
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ax1.axvspan(phase_times[2], phase_times[3], alpha=0.2, color="lightgreen")
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ax1.axvspan(phase_times[3], df["T(sec)"].max(), alpha=0.2, color="blue")
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chart_path = self.charts_dir / "respiratory.png"
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plt.savefig(chart_path, dpi=300, bbox_inches="tight")
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plt.close()
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return self._image_to_base64(chart_path) if save_as_base64 else str(chart_path)
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def generate_fuel_utilization_chart(
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self, df: pd.DataFrame, save_as_base64: bool = True
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) -> str:
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"""
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Generate fuel utilization chart (CHO vs FAT by stage).
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Args:
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df: Processed DataFrame with smoothed columns
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save_as_base64: If True, return base64 string, else return file path
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Returns:
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Base64 string or file path
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"""
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# Group by speed and calculate mean
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speed_groups = df.groupby("Speed").mean(numeric_only=True).round(1)
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speed_groups = speed_groups.iloc[1:-1]
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# Filter data
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filtered_data = speed_groups[
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(speed_groups.index >= 3.5) & (speed_groups.index <= 7.5)
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]
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plt.figure(figsize=(15, 8))
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plt.style.use("default")
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stage_labels = [f"Stage {i}" for i in range(1, len(filtered_data) + 1)]
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x_positions = np.arange(len(filtered_data))
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# Calculate fat and carbs energy expenditure
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fat_ee = filtered_data["EE(kcal/min)"] * filtered_data["FAT(%)"] / 100
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carbs_ee = filtered_data["EE(kcal/min)"] * filtered_data["CARBS(%)"] / 100
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ax1 = plt.gca()
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# Create stacked bar chart
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ax1.bar(
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x_positions,
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fat_ee,
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color="#1f77b4",
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alpha=0.8,
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width=0.6,
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label="Fat",
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)
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ax1.bar(
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x_positions,
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carbs_ee,
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bottom=fat_ee,
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color="#ff7f0e",
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alpha=0.8,
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width=0.6,
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label="Carbs",
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)
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ax1.set_xlabel("", fontsize=12)
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ax1.set_ylabel("Fuel (kcal/min)", fontsize=12)
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ax1.set_ylim(0, 20)
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# Add values on bars
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for i, (fat_val, carb_val, total_val) in enumerate(
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zip(fat_ee, carbs_ee, filtered_data["EE(kcal/min)"])
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):
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if fat_val > 0.3:
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ax1.text(
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i,
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fat_val / 2,
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f"{fat_val:.1f}",
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ha="center",
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va="center",
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fontsize=9,
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fontweight="bold",
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color="white",
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)
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if carb_val > 0.3:
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ax1.text(
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i,
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fat_val + carb_val / 2,
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f"{carb_val:.1f}",
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ha="center",
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va="center",
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fontsize=9,
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fontweight="bold",
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color="white",
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)
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ax1.text(
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i,
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total_val + 0.5,
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f"{total_val:.1f} kcal",
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ha="center",
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va="bottom",
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fontsize=10,
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fontweight="bold",
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color="black",
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)
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# Add speed labels
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for i, speed in enumerate(filtered_data.index):
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ax1.text(i, -1.5, f"{speed:.1f} mph", ha="center", va="top", fontsize=9)
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ax1.text(
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i,
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-2.8,
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f"{speed * 1.609:.1f} min/km",
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ha="center",
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va="top",
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fontsize=8,
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color="gray",
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)
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# Create secondary y-axis for heart rate
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ax2 = ax1.twinx()
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ax2.plot(
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x_positions,
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filtered_data["HR(bpm)"],
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marker="o",
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linewidth=3,
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markersize=8,
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color="red",
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label="Heart Rate",
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)
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ax2.set_ylabel("Heart Rate (bpm)", fontsize=12, color="red")
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ax2.tick_params(axis="y", labelcolor="red")
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ax2.set_ylim(0, 220)
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# Add HR values
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for i, hr in enumerate(filtered_data["HR(bpm)"]):
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ax2.text(
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i,
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hr + 10,
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f"{int(hr)}bpm",
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ha="center",
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va="bottom",
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fontsize=10,
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fontweight="bold",
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color="red",
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)
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ax1.set_xticks(x_positions)
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ax1.set_xticklabels(stage_labels, fontsize=11)
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# Create legend
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lines1, labels1 = ax1.get_legend_handles_labels()
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lines2, labels2 = ax2.get_legend_handles_labels()
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ax1.legend(
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lines1 + lines2,
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labels1 + labels2,
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loc="upper left",
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frameon=True,
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fancybox=True,
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shadow=True,
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)
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ax1.grid(True, alpha=0.3, linestyle="-", linewidth=0.5)
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ax1.set_axisbelow(True)
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plt.tight_layout()
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plt.subplots_adjust(bottom=0.1, top=0.9)
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chart_path = self.charts_dir / "fuel_utilization_chart.png"
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plt.savefig(chart_path, dpi=300)
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plt.close()
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return self._image_to_base64(chart_path) if save_as_base64 else str(chart_path)
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def generate_vo2_pulse_chart(
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self, df: pd.DataFrame, save_as_base64: bool = True
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) -> str:
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"""
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Generate VO2 Pulse chart with HR and Speed.
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Args:
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df: Processed DataFrame with smoothed columns
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save_as_base64: If True, return base64 string, else return file path
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Returns:
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Base64 string or file path
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"""
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first_unique_phase = df.drop_duplicates(subset="PHASE")
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phase_times = first_unique_phase["T(sec)"].tolist()
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plt.figure(figsize=(18, 5))
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ax1 = plt.subplot()
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# Plot VO2 Pulse
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sns.lineplot(
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data=df,
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x="T(sec)",
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y="VO2 Pulse_smoothed",
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label="VO2 Pulse (mL/beat)",
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color="blue",
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)
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ax1.set_xlabel("Time (sec)")
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ax1.set_ylabel("VO2 Pulse (mL/beat)")
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ax1.set_ylim(0, df["VO2 Pulse_smoothed"].max())
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ax1.grid(True, alpha=0.1)
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# Create second y-axis for heart rate
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ax2 = ax1.twinx()
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sns.lineplot(
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data=df,
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x="T(sec)",
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y="HR(bpm)_smoothed",
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color="red",
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ax=ax2,
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linewidth=2,
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label="Heart Rate (bpm)",
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)
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ax2.set_ylabel("Heart Rate (bpm)", color="red")
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ax2.tick_params(axis="y", labelcolor="red")
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ax2.set_ylim(0, df["HR(bpm)_smoothed"].max() + 1)
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# Create third y-axis for speed
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ax3 = ax1.twinx()
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ax3.spines["right"].set_position(("outward", 60))
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sns.lineplot(
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data=df,
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x="T(sec)",
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y="Speed",
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color="green",
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ax=ax3,
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drawstyle="steps-post",
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linewidth=2,
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label="Speed",
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)
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ax3.set_ylabel("Speed", color="green")
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ax3.tick_params(axis="y", labelcolor="green")
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ax3.set_ylim(0, df["Speed"].max() + 1)
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ax1.set_xticks(np.arange(0, df["T(sec)"].max() + 200, 200))
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# Combine legends
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if ax1.get_legend():
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ax1.get_legend().remove()
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if ax2.get_legend():
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ax2.get_legend().remove()
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if ax3.get_legend():
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ax3.get_legend().remove()
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lines1, labels1 = ax1.get_legend_handles_labels()
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lines2, labels2 = ax2.get_legend_handles_labels()
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lines3, labels3 = ax3.get_legend_handles_labels()
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ax1.legend(
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lines1 + lines2 + lines3, labels1 + labels2 + labels3, loc="upper left"
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)
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# Add colored background regions
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if len(phase_times) >= 4:
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ax1.axvspan(0, phase_times[1], alpha=0.2, color="lightblue")
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ax1.axvspan(phase_times[1], phase_times[2], alpha=0.2, color="purple")
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ax1.axvspan(phase_times[2], phase_times[3], alpha=0.2, color="lightgreen")
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ax1.axvspan(phase_times[3], df["T(sec)"].max(), alpha=0.2, color="blue")
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chart_path = self.charts_dir / "vo2_pulse_chart.png"
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plt.savefig(chart_path, bbox_inches="tight", dpi=300)
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plt.close()
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return self._image_to_base64(chart_path) if save_as_base64 else str(chart_path)
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def generate_vo2_breath_chart(
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self, df: pd.DataFrame, save_as_base64: bool = True
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) -> str:
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"""
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Generate VO2 per Breath chart.
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Args:
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df: Processed DataFrame with smoothed columns
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save_as_base64: If True, return base64 string, else return file path
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Returns:
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Base64 string or file path
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"""
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first_unique_phase = df.drop_duplicates(subset="PHASE")
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phase_times = first_unique_phase["T(sec)"].tolist()
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plt.figure(figsize=(18, 5))
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ax1 = plt.subplot()
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sns.lineplot(
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data=df,
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x="T(sec)",
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y="VO2 Breath_smoothed",
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label="VO2 per Breath (mL/breath)",
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)
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ax1.set_xlabel("Time (sec)")
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ax1.set_ylabel("VO2 per Breath (mL/breath)")
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ax1.set_ylim(0, df["VO2 Breath_smoothed"].max() + 1)
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ax1.grid(True, alpha=0.1)
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# Plot speed on secondary y-axis
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ax2 = ax1.twinx()
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ax1.set_xticks(np.arange(0, df["T(sec)"].max() + 200, 200))
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sns.lineplot(
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data=df,
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x="T(sec)",
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y="Speed",
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color="green",
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ax=ax2,
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drawstyle="steps-post",
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linewidth=2,
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label="Speed",
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)
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ax2.set_ylim(0, df["Speed"].max() + 1)
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ax2.set_ylabel("Speed")
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# Combine legends
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ax1.get_legend().remove()
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ax2.get_legend().remove()
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lines1, labels1 = ax1.get_legend_handles_labels()
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lines2, labels2 = ax2.get_legend_handles_labels()
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ax1.legend(lines1 + lines2, labels1 + labels2, loc="upper left")
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# Add colored background regions
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if len(phase_times) >= 4:
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ax1.axvspan(0, phase_times[1], alpha=0.2, color="lightblue")
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ax1.axvspan(phase_times[1], phase_times[2], alpha=0.2, color="purple")
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ax1.axvspan(phase_times[2], phase_times[3], alpha=0.2, color="lightgreen")
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ax1.axvspan(phase_times[3], df["T(sec)"].max(), alpha=0.2, color="blue")
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chart_path = self.charts_dir / "vo2_breath_chart.png"
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plt.savefig(chart_path, bbox_inches="tight", dpi=300)
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plt.close()
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return self._image_to_base64(chart_path) if save_as_base64 else str(chart_path)
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def generate_fat_metabolism_chart(
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self, df: pd.DataFrame, save_as_base64: bool = True
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) -> str:
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"""
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Generate fat metabolism chart (CHO vs FAT over time).
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Args:
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df: Processed DataFrame with smoothed columns
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save_as_base64: If True, return base64 string, else return file path
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|
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Returns:
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Base64 string or file path
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"""
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first_unique_phase = df.drop_duplicates(subset="PHASE")
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phase_times = first_unique_phase["T(sec)"].tolist()
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plt.figure(figsize=(18, 5))
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ax1 = plt.subplot()
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# Plot CHO
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sns.lineplot(data=df, x="T(sec)", y="CHO_smoothed", label="CHO (kcal/min)")
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ax1.set_xlabel("Time (sec)")
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ax1.set_ylabel("CHO (g/min)")
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ax1.grid(True, alpha=0.1)
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# Plot FAT on secondary y-axis
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ax2 = ax1.twinx()
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ax1.set_xticks(np.arange(0, df["T(sec)"].max() + 200, 200))
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sns.lineplot(
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data=df,
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x="T(sec)",
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y="FAT_smoothed",
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color="green",
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ax=ax2,
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label="FAT (kcal/min)",
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)
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ax2.set_ylabel("FAT (kcal/min)")
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ax2.set_ylim(0, 15)
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# Combine legends
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ax1.get_legend().remove()
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ax2.get_legend().remove()
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lines1, labels1 = ax1.get_legend_handles_labels()
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lines2, labels2 = ax2.get_legend_handles_labels()
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ax1.legend(lines1 + lines2, labels1 + labels2, loc="upper left")
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|
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# Add colored background regions
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if len(phase_times) >= 4:
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ax1.axvspan(0, phase_times[1], alpha=0.2, color="lightblue")
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ax1.axvspan(phase_times[1], phase_times[2], alpha=0.2, color="purple")
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ax1.axvspan(phase_times[2], phase_times[3], alpha=0.2, color="lightgreen")
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ax1.axvspan(phase_times[3], df["T(sec)"].max(), alpha=0.2, color="blue")
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chart_path = self.charts_dir / "fat_metabolism_chart.png"
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plt.savefig(chart_path, bbox_inches="tight", dpi=300)
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plt.close()
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return self._image_to_base64(chart_path) if save_as_base64 else str(chart_path)
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|
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def generate_recovery_chart(
|
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self, df: pd.DataFrame, save_as_base64: bool = True
|
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) -> str:
|
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"""
|
|
Generate recovery chart (VCO2, HR, and BF).
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|
|
Args:
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|
df: Processed DataFrame with smoothed columns
|
|
save_as_base64: If True, return base64 string, else return file path
|
|
|
|
Returns:
|
|
Base64 string or file path
|
|
"""
|
|
first_unique_phase = df.drop_duplicates(subset="PHASE")
|
|
phase_times = first_unique_phase["T(sec)"].tolist()
|
|
|
|
plt.figure(figsize=(18, 5))
|
|
ax1 = plt.subplot()
|
|
|
|
# Plot VCO2
|
|
sns.lineplot(
|
|
data=df,
|
|
x="T(sec)",
|
|
y="VCO2(ml/min)_smoothed",
|
|
label="VCO2 (ml/min)",
|
|
color="blue",
|
|
)
|
|
ax1.set_xlabel("Time (sec)")
|
|
ax1.set_ylabel("VO2 Pulse (mL/beat)")
|
|
ax1.set_ylim(0, df["VCO2(ml/min)"].max())
|
|
ax1.grid(True, alpha=0.1)
|
|
|
|
# Create second y-axis for heart rate
|
|
ax2 = ax1.twinx()
|
|
sns.lineplot(
|
|
data=df,
|
|
x="T(sec)",
|
|
y="HR(bpm)_smoothed",
|
|
color="red",
|
|
ax=ax2,
|
|
linewidth=2,
|
|
label="Heart Rate (bpm)",
|
|
)
|
|
ax2.set_ylabel("Heart Rate (bpm)", color="red")
|
|
ax2.set_ylim(df["HR(bpm)_smoothed"].min(), df["HR(bpm)_smoothed"].max() + 1)
|
|
ax2.tick_params(axis="y", labelcolor="red")
|
|
|
|
# Create third y-axis for BF
|
|
ax3 = ax1.twinx()
|
|
ax3.spines["right"].set_position(("outward", 60))
|
|
sns.lineplot(
|
|
data=df,
|
|
x="T(sec)",
|
|
y="BF(bpm)_smoothed",
|
|
color="green",
|
|
ax=ax3,
|
|
linewidth=2,
|
|
label="BF (bpm)",
|
|
)
|
|
ax3.set_ylabel("BF (bpm)", color="green")
|
|
ax3.tick_params(axis="y", labelcolor="green")
|
|
ax3.set_ylim(0, df["BF(bpm)_smoothed"].max() + 1)
|
|
ax1.set_xticks(np.arange(0, df["T(sec)"].max() + 200, 200))
|
|
|
|
# Combine legends
|
|
if ax1.get_legend():
|
|
ax1.get_legend().remove()
|
|
if ax2.get_legend():
|
|
ax2.get_legend().remove()
|
|
if ax3.get_legend():
|
|
ax3.get_legend().remove()
|
|
|
|
lines1, labels1 = ax1.get_legend_handles_labels()
|
|
lines2, labels2 = ax2.get_legend_handles_labels()
|
|
lines3, labels3 = ax3.get_legend_handles_labels()
|
|
ax1.legend(
|
|
lines1 + lines2 + lines3, labels1 + labels2 + labels3, loc="upper left"
|
|
)
|
|
|
|
# Add colored background regions
|
|
if len(phase_times) >= 4:
|
|
ax1.axvspan(0, phase_times[1], alpha=0.2, color="lightblue")
|
|
ax1.axvspan(phase_times[1], phase_times[2], alpha=0.2, color="purple")
|
|
ax1.axvspan(phase_times[2], phase_times[3], alpha=0.2, color="lightgreen")
|
|
ax1.axvspan(phase_times[3], df["T(sec)"].max(), alpha=0.2, color="blue")
|
|
|
|
chart_path = self.charts_dir / "recovery_chart.png"
|
|
plt.savefig(chart_path, bbox_inches="tight", dpi=300)
|
|
plt.close()
|
|
|
|
return self._image_to_base64(chart_path) if save_as_base64 else str(chart_path)
|
|
|
|
def generate_tsi_chart(
|
|
self, oxygenation_df: pd.DataFrame, save_as_base64: bool = True
|
|
) -> str:
|
|
"""
|
|
Generate TSI (Tissue Saturation Index) chart with trend lines per stage.
|
|
|
|
Args:
|
|
oxygenation_df: DataFrame with Time, TSI, and TSI-second columns
|
|
save_as_base64: If True, return base64 string, else return file path
|
|
|
|
Returns:
|
|
Base64 string or file path
|
|
"""
|
|
from numpy.polynomial.polynomial import Polynomial
|
|
|
|
plt.figure(figsize=(12, 5.5))
|
|
|
|
# Plot TSI (Left Leg)
|
|
plt.plot(
|
|
oxygenation_df["Time"],
|
|
oxygenation_df["TSI"],
|
|
label="TSI (Left Leg)",
|
|
color="steelblue",
|
|
linewidth=2,
|
|
)
|
|
|
|
# Plot TSI2 (Right Leg)
|
|
plt.plot(
|
|
oxygenation_df["Time"],
|
|
oxygenation_df["TSI-second"],
|
|
label="TSI2 (Right Leg)",
|
|
color="orange",
|
|
linewidth=2,
|
|
)
|
|
|
|
# Define time intervals for stages (adjust these based on your test protocol)
|
|
max_time = oxygenation_df["Time"].max()
|
|
intervals = [
|
|
(0, 250),
|
|
(250, 500),
|
|
(500, 750),
|
|
(750, 1000),
|
|
(1000, 1250),
|
|
(1250, 1500),
|
|
(1500, max_time),
|
|
]
|
|
|
|
# Calculate and plot trend lines for each interval
|
|
for start_time, end_time in intervals:
|
|
# Filter data for this interval
|
|
mask_interval = (oxygenation_df["Time"] >= start_time) & (
|
|
oxygenation_df["Time"] <= end_time
|
|
)
|
|
|
|
# TSI (Left Leg) trend for this interval
|
|
mask_left = mask_interval & ~oxygenation_df["TSI"].isna()
|
|
if mask_left.sum() > 1: # Need at least 2 points for a line
|
|
x_left = oxygenation_df.loc[mask_left, "Time"]
|
|
y_left = oxygenation_df.loc[mask_left, "TSI"]
|
|
coefs_left = Polynomial.fit(x_left, y_left, 1).convert().coef
|
|
trend_left = coefs_left[0] + coefs_left[1] * x_left
|
|
plt.plot(
|
|
x_left,
|
|
trend_left,
|
|
color="black",
|
|
linestyle="--",
|
|
linewidth=2,
|
|
alpha=0.8,
|
|
)
|
|
|
|
# TSI-second (Right Leg) trend for this interval
|
|
mask_right = mask_interval & ~oxygenation_df["TSI-second"].isna()
|
|
if mask_right.sum() > 1: # Need at least 2 points for a line
|
|
x_right = oxygenation_df.loc[mask_right, "Time"]
|
|
y_right = oxygenation_df.loc[mask_right, "TSI-second"]
|
|
coefs_right = Polynomial.fit(x_right, y_right, 1).convert().coef
|
|
trend_right = coefs_right[0] + coefs_right[1] * x_right
|
|
plt.plot(
|
|
x_right,
|
|
trend_right,
|
|
color="black",
|
|
linestyle="--",
|
|
linewidth=2,
|
|
alpha=0.8,
|
|
)
|
|
|
|
plt.xlabel("Time (s)")
|
|
plt.ylabel("TSI (%)")
|
|
plt.title("TSI (Left) and TSI2 (Right) with Black Slope Lines per Stage")
|
|
plt.legend(fontsize=10, loc="upper right")
|
|
plt.grid(alpha=0.25)
|
|
plt.tight_layout()
|
|
|
|
chart_path = self.charts_dir / "tsi_chart.png"
|
|
plt.savefig(chart_path, bbox_inches="tight", dpi=160)
|
|
plt.close()
|
|
|
|
return self._image_to_base64(chart_path) if save_as_base64 else str(chart_path)
|
|
|
|
def generate_body_composition_chart(
|
|
self, fat_mass_lbs: float, lean_mass_lbs: float, save_as_base64: bool = True
|
|
) -> str:
|
|
"""
|
|
Generate body composition donut chart.
|
|
|
|
Args:
|
|
fat_mass_lbs: Fat mass in pounds
|
|
lean_mass_lbs: Lean mass in pounds
|
|
save_as_base64: If True, return base64 string, else return file path
|
|
|
|
Returns:
|
|
Base64 string or file path
|
|
"""
|
|
# Calculate percentages
|
|
total_weight = fat_mass_lbs + lean_mass_lbs
|
|
fat_percentage = (fat_mass_lbs / total_weight) * 100
|
|
lean_percentage = (lean_mass_lbs / total_weight) * 100
|
|
|
|
sizes = [fat_percentage, lean_percentage]
|
|
colors = ["#fde3ac", "#ff9966"]
|
|
|
|
plt.figure(figsize=(8, 8))
|
|
|
|
# Create donut chart
|
|
plt.pie(
|
|
sizes,
|
|
autopct="",
|
|
startangle=90,
|
|
wedgeprops=dict(width=0.5, edgecolor="w"),
|
|
colors=colors,
|
|
labels=["", ""],
|
|
)
|
|
|
|
# Add custom text annotations
|
|
plt.text(
|
|
-1,
|
|
1,
|
|
f"Fat Mass ({fat_mass_lbs:.1f}lbs)\n{fat_percentage:.1f}%",
|
|
fontsize=14,
|
|
fontweight="bold",
|
|
ha="center",
|
|
va="center",
|
|
bbox=dict(boxstyle="round,pad=0.3", facecolor="white", alpha=0.8),
|
|
)
|
|
|
|
plt.text(
|
|
1,
|
|
-1,
|
|
f"Lean Mass ({lean_mass_lbs:.1f}lbs)\n{lean_percentage:.1f}%",
|
|
fontsize=14,
|
|
fontweight="bold",
|
|
ha="center",
|
|
va="center",
|
|
bbox=dict(boxstyle="round,pad=0.3", facecolor="white", alpha=0.8),
|
|
)
|
|
|
|
plt.axis("equal")
|
|
|
|
chart_path = self.charts_dir / "body_composition_chart.png"
|
|
plt.savefig(chart_path, bbox_inches="tight", dpi=600)
|
|
plt.close()
|
|
|
|
return self._image_to_base64(chart_path) if save_as_base64 else str(chart_path)
|
|
|
|
def generate_body_fat_percent_chart(
|
|
self,
|
|
fat_percentage: float,
|
|
age: int,
|
|
gender: str,
|
|
save_as_base64: bool = True,
|
|
) -> str:
|
|
"""
|
|
Generate body fat percentage chart.
|
|
|
|
Args:
|
|
fat_percentage: Body fat percentage
|
|
age: Patient age
|
|
gender: Patient gender ('male' or 'female')
|
|
save_as_base64: If True, return base64 string, else return file path
|
|
|
|
Returns:
|
|
Base64 string or file path
|
|
"""
|
|
# Determine age group
|
|
if 20 <= age <= 39:
|
|
age_group = "20-39"
|
|
elif 40 <= age <= 59:
|
|
age_group = "40-59"
|
|
elif 60 <= age <= 79:
|
|
age_group = "60-79"
|
|
else:
|
|
age_group = "20-39" # Default
|
|
|
|
gender_abbrev = "M" if gender.lower() == "male" else "F"
|
|
demographic = f"{age_group}\n({gender_abbrev})"
|
|
|
|
# Define segments based on gender and age group
|
|
if gender.lower() == "female":
|
|
if age_group == "20-39":
|
|
segments = [
|
|
("#F8A8A8", 0, 15), # Bad: 0-15%
|
|
("#FFEECC", 15, 5), # Okay: 15-20%
|
|
("#D0F0C0", 20, 15), # Good: 20-35%
|
|
("#FFEECC", 35, 5), # Okay: 35-40%
|
|
("#F8A8A8", 40, 10), # Bad: 40-50%
|
|
]
|
|
else: # 40-59 and 60-79 have same ranges for females
|
|
segments = [
|
|
("#F8A8A8", 0, 20), # Bad: 0-20%
|
|
("#FFEECC", 20, 5), # Okay: 20-25%
|
|
("#D0F0C0", 25, 10), # Good: 25-35%
|
|
("#FFEECC", 35, 5), # Okay: 35-40%
|
|
("#F8A8A8", 40, 10), # Bad: 40-50%
|
|
]
|
|
else: # male
|
|
if age_group == "20-39":
|
|
segments = [
|
|
("#F8A8A8", 0, 5), # Bad: 0-5%
|
|
("#FFEECC", 5, 5), # Okay: 5-10%
|
|
("#D0F0C0", 10, 10), # Good: 10-20%
|
|
("#FFEECC", 20, 5), # Okay: 20-25%
|
|
("#F8A8A8", 25, 25), # Bad: 25-50%
|
|
]
|
|
elif age_group == "40-59":
|
|
segments = [
|
|
("#F8A8A8", 0, 5), # Bad: 0-5%
|
|
("#FFEECC", 5, 5), # Okay: 5-10%
|
|
("#D0F0C0", 10, 10), # Good: 10-20%
|
|
("#FFEECC", 20, 10), # Okay: 20-30%
|
|
("#F8A8A8", 30, 20), # Bad: 30-50%
|
|
]
|
|
else: # 60-79
|
|
segments = [
|
|
("#F8A8A8", 0, 5), # Bad: 0-5%
|
|
("#FFEECC", 5, 5), # Okay: 5-10%
|
|
("#D0F0C0", 10, 15), # Good: 10-25%
|
|
("#FFEECC", 25, 5), # Okay: 25-30%
|
|
("#F8A8A8", 30, 20), # Bad: 30-50%
|
|
]
|
|
|
|
fig, ax = plt.subplots(figsize=(10, 2))
|
|
|
|
# Create the segmented bar
|
|
for color, start, length in segments:
|
|
ax.barh(
|
|
y=0,
|
|
width=length,
|
|
left=start,
|
|
height=1,
|
|
color=color,
|
|
edgecolor="black",
|
|
linewidth=0.5,
|
|
)
|
|
|
|
# Add the indicator (triangle)
|
|
ax.plot(
|
|
fat_percentage,
|
|
1.05,
|
|
marker="v",
|
|
color="black",
|
|
markersize=10,
|
|
clip_on=False,
|
|
transform=ax.get_xaxis_transform(),
|
|
)
|
|
|
|
# Set axis properties
|
|
ax.set_xlim(0, 50)
|
|
ax.set_xticks(range(0, 51, 5))
|
|
ax.set_yticks([])
|
|
ax.text(
|
|
-0.05,
|
|
0,
|
|
demographic,
|
|
transform=ax.get_yaxis_transform(),
|
|
va="center",
|
|
ha="right",
|
|
fontsize=12,
|
|
)
|
|
|
|
ticks = range(0, 51, 5)
|
|
ax.set_xticks(ticks)
|
|
labels = [f"{t}%" for t in ticks]
|
|
ax.set_xticklabels(labels)
|
|
|
|
# Clean up spines
|
|
ax.spines["right"].set_visible(False)
|
|
ax.spines["top"].set_visible(False)
|
|
ax.spines["left"].set_visible(False)
|
|
ax.spines["bottom"].set_visible(True)
|
|
|
|
# Add tick marks
|
|
for x in range(0, 51, 5):
|
|
ax.plot(
|
|
[x, x],
|
|
[-0.05, -0.01],
|
|
color="black",
|
|
transform=ax.get_xaxis_transform(),
|
|
clip_on=False,
|
|
)
|
|
|
|
plt.tight_layout()
|
|
|
|
chart_path = self.charts_dir / "body_fat_percent_chart.png"
|
|
plt.savefig(chart_path, bbox_inches="tight", dpi=300)
|
|
plt.close()
|
|
|
|
return self._image_to_base64(chart_path) if save_as_base64 else str(chart_path)
|
|
|
|
def generate_spirometry_chart(
|
|
self, spirometry_df: pd.DataFrame, save_as_base64: bool = True
|
|
) -> str:
|
|
"""
|
|
Generate spirometry chart with Z-scores.
|
|
|
|
Args:
|
|
spirometry_df: Spirometry DataFrame with parameters
|
|
save_as_base64: If True, return base64 string, else return file path
|
|
|
|
Returns:
|
|
Base64 string or file path
|
|
"""
|
|
# Coerce numeric columns - handle various column name formats
|
|
# Map standard column names to possible variations
|
|
column_aliases = {
|
|
"Best": ["Best", "best", "BEST"],
|
|
"LLN": ["LLN", "lln"],
|
|
"Pred.": ["Pred.", "Pred", "pred", "Predicted", "predicted"],
|
|
"%Pred.": [
|
|
"%Pred.",
|
|
"%Pred",
|
|
"%pred",
|
|
"% Pred.",
|
|
"% Pred",
|
|
"Pred %",
|
|
"Pred%",
|
|
],
|
|
"ZScore": ["ZScore", "Z-Score", "z-score", "Zscore", "zscore", "Z Score"],
|
|
}
|
|
|
|
# Find and normalize column names
|
|
column_mapping = {}
|
|
for target_col, possible_names in column_aliases.items():
|
|
for col_name in possible_names:
|
|
if col_name in spirometry_df.columns:
|
|
column_mapping[target_col] = col_name
|
|
# Convert to numeric
|
|
spirometry_df[col_name] = pd.to_numeric(
|
|
spirometry_df[col_name], errors="coerce"
|
|
)
|
|
break
|
|
|
|
# If standard columns don't exist, create aliases
|
|
for target_col, source_col in column_mapping.items():
|
|
if target_col not in spirometry_df.columns and source_col != target_col:
|
|
spirometry_df[target_col] = spirometry_df[source_col]
|
|
|
|
# Select rows of interest
|
|
rows_map = {
|
|
"Lung Volume": "FVC",
|
|
"Lung Power": "FEV1",
|
|
"Power/Volume": "FEV1/FVC%",
|
|
}
|
|
|
|
records = []
|
|
for label, param in rows_map.items():
|
|
# Try exact match first
|
|
row = spirometry_df.loc[spirometry_df["Parameters"].str.strip() == param]
|
|
if row.empty:
|
|
# Try case-insensitive match
|
|
row = spirometry_df.loc[
|
|
spirometry_df["Parameters"].str.strip().str.upper() == param.upper()
|
|
]
|
|
if row.empty:
|
|
# Try matching without % sign
|
|
if "%" in param:
|
|
param_no_pct = param.replace("%", "")
|
|
row = spirometry_df.loc[
|
|
spirometry_df["Parameters"].str.strip() == param_no_pct
|
|
]
|
|
if row.empty:
|
|
print(f"Warning: Could not find parameter '{param}' in spirometry data")
|
|
print(f"Available parameters: {spirometry_df['Parameters'].tolist()}")
|
|
continue
|
|
row = row.iloc[0]
|
|
# Get values with fallbacks for column name variations
|
|
best_val = row.get("Best", row.get("best", pd.NA))
|
|
pct_val = row.get(
|
|
"%Pred.", row.get("%Pred", row.get("Pred %", row.get("Pred%", pd.NA)))
|
|
)
|
|
z_val = row.get("ZScore", row.get("Z-Score", row.get("Zscore", pd.NA)))
|
|
|
|
records.append(
|
|
{
|
|
"label": label,
|
|
"param": param,
|
|
"best": best_val,
|
|
"pct": pct_val,
|
|
"z": z_val,
|
|
}
|
|
)
|
|
|
|
# Validate we have exactly 3 records
|
|
if len(records) != 3:
|
|
raise ValueError(
|
|
f"Expected 3 spirometry parameters (FVC, FEV1, FEV1/FVC%), "
|
|
f"but found {len(records)}. Found: {[r['param'] for r in records]}"
|
|
)
|
|
|
|
# Figure setup
|
|
fig, axes = plt.subplots(
|
|
nrows=3,
|
|
ncols=1,
|
|
figsize=(11.5, 3.6),
|
|
sharex=True,
|
|
gridspec_kw={"hspace": 0.65},
|
|
)
|
|
|
|
x_min, x_max = -5, 3
|
|
# Segment colors
|
|
segments = [
|
|
(-5, -4, "#f4a7a7"), # red-ish
|
|
(-4, -3, "#f7c49a"), # orange-ish
|
|
(-3, -1.7, "#f6e3a3"), # yellow-ish
|
|
(-1.7, 3, "#c9f0cc"), # green-ish
|
|
]
|
|
|
|
ticks = np.arange(x_min, x_max + 1, 1)
|
|
labels = [str(i) for i in ticks]
|
|
|
|
# Plot each row
|
|
for ax, rec in zip(axes, records):
|
|
# Background segments
|
|
for a, b, color in segments:
|
|
ax.barh(
|
|
0, width=b - a, left=a, height=0.6, color=color, edgecolor="none"
|
|
)
|
|
|
|
# LLN and Predicted markers
|
|
ax.axvline(0, color="black", lw=1)
|
|
|
|
# Z-score pointer
|
|
if pd.notna(rec["z"]):
|
|
trans = mtransforms.blended_transform_factory(
|
|
ax.transData, ax.transAxes
|
|
)
|
|
ax.plot(
|
|
float(rec["z"]),
|
|
1.2,
|
|
marker="v",
|
|
markersize=12,
|
|
color="dimgray",
|
|
transform=trans,
|
|
clip_on=False,
|
|
)
|
|
|
|
# Labels and styling
|
|
ax.set_title(
|
|
rec["label"], loc="left", fontsize=11, fontweight="bold", pad=2
|
|
)
|
|
ax.set_xlim(x_min, x_max)
|
|
ax.set_yticks([])
|
|
ax.set_xticks(ticks)
|
|
ax.set_xticklabels(labels, fontsize=8)
|
|
ax.set_xlabel("")
|
|
|
|
# Top annotations
|
|
axes[0].text(-1.7, 0.45, "LLN", ha="center", va="bottom", fontsize=9)
|
|
axes[0].text(0, 0.45, "Predicted", ha="center", va="bottom", fontsize=9)
|
|
|
|
# Right-side summary boxes
|
|
fig.subplots_adjust(right=0.78)
|
|
box_ax = fig.add_axes([0.805, 0.06, 0.18, 0.90])
|
|
box_ax.axis("off")
|
|
|
|
def pill(ax, xy, text):
|
|
x, y = xy
|
|
bbox = FancyBboxPatch(
|
|
(x - 0.48, y - 0.09),
|
|
0.96,
|
|
0.18,
|
|
boxstyle="round,pad=0.02,rounding_size=0.08",
|
|
ec="#dddddd",
|
|
fc="#f3f3f3",
|
|
linewidth=1.0,
|
|
)
|
|
ax.add_patch(bbox)
|
|
ax.text(
|
|
x,
|
|
y + 0.025,
|
|
text,
|
|
ha="center",
|
|
va="center",
|
|
fontsize=11,
|
|
fontweight="bold",
|
|
)
|
|
ax.text(
|
|
x,
|
|
y - 0.055,
|
|
"of predicted",
|
|
ha="center",
|
|
va="center",
|
|
fontsize=9,
|
|
color="#555555",
|
|
)
|
|
|
|
box_ax.set_xlim(0, 1)
|
|
box_ax.set_ylim(0, 1)
|
|
|
|
# Prepare display strings
|
|
right_items = []
|
|
for rec in records:
|
|
name = (
|
|
"FVC"
|
|
if rec["param"] == "FVC"
|
|
else ("FEV1" if rec["param"] == "FEV1" else "FEV1/FVC")
|
|
)
|
|
unit = "L" if rec["param"] in ("FVC", "FEV1") else "%"
|
|
value_fmt = f"{rec['best']:.2f}{unit}"
|
|
pct_fmt = f"{rec['pct']:.1f}%"
|
|
right_items.append((name, value_fmt, pct_fmt))
|
|
|
|
# Sort to match order
|
|
order = ["FVC", "FEV1", "FEV1/FVC"]
|
|
right_items_sorted = [
|
|
next(item for item in right_items if item[0] == k) for k in order
|
|
]
|
|
|
|
ys = [0.82, 0.48, 0.15]
|
|
for (name, value_fmt, pct_fmt), y in zip(right_items_sorted, ys):
|
|
main_line = f"{name}\n{value_fmt} → {pct_fmt}"
|
|
pill(box_ax, (0.5, y), main_line)
|
|
|
|
chart_path = self.charts_dir / "spirometry_chart.png"
|
|
plt.savefig(chart_path, dpi=300, bbox_inches="tight")
|
|
plt.close()
|
|
|
|
return self._image_to_base64(chart_path) if save_as_base64 else str(chart_path)
|
|
|
|
def generate_metabolism_chart(
|
|
self, rmr_kcal: float, save_as_base64: bool = True
|
|
) -> str:
|
|
"""
|
|
Generate metabolism chart (Slow vs Fast Metabolism).
|
|
|
|
Args:
|
|
rmr_kcal: Resting metabolic rate in kcal/day
|
|
save_as_base64: If True, return base64 string, else return file path
|
|
|
|
Returns:
|
|
Base64 string or file path
|
|
"""
|
|
from matplotlib.patches import FancyBboxPatch
|
|
|
|
fig, ax = plt.subplots(figsize=(10, 2.5))
|
|
|
|
# Chart data and positions
|
|
categories = ["Very Slow", "Slow", "Average", "Fast", "Very Fast"]
|
|
positions = [1500, 3000, 4500, 6000, 7500]
|
|
indicator_pos = rmr_kcal
|
|
highlight_end = rmr_kcal
|
|
|
|
# Main Bar (Background)
|
|
main_bar = FancyBboxPatch(
|
|
(0, 0.4),
|
|
9000,
|
|
0.2,
|
|
boxstyle="round,pad=0,rounding_size=0.1",
|
|
ec="none",
|
|
fc="#E0E0E0",
|
|
)
|
|
ax.add_patch(main_bar)
|
|
|
|
# Highlighted Bar
|
|
highlight_bar = FancyBboxPatch(
|
|
(0, 0.4),
|
|
highlight_end,
|
|
0.2,
|
|
boxstyle="round,pad=0,rounding_size=0.1",
|
|
ec="none",
|
|
fc="#B2FFC8",
|
|
)
|
|
ax.add_patch(highlight_bar)
|
|
|
|
# Text and Labels
|
|
ax.text(
|
|
highlight_end / 2,
|
|
0.5,
|
|
f"{rmr_kcal:.0f}kCals",
|
|
ha="center",
|
|
va="center",
|
|
color="#006400",
|
|
fontsize=14,
|
|
weight="bold",
|
|
)
|
|
|
|
# Indicator Triangle
|
|
ax.plot(indicator_pos, 0.65, "v", markersize=15, color="#606060", clip_on=False)
|
|
|
|
# Ticks and Labels
|
|
for pos, label in zip(positions, categories):
|
|
ax.text(
|
|
pos, 0.15, label, ha="center", va="center", fontsize=12, color="#333333"
|
|
)
|
|
ax.plot([pos, pos], [0.35, 0.39], color="grey", lw=5)
|
|
|
|
# Chart Styling
|
|
ax.set_title("Slow vs Fast Metabolism", fontsize=18, weight="bold", loc="left")
|
|
ax.set_xlim(0, 9000)
|
|
ax.set_ylim(0, 1)
|
|
ax.axis("off")
|
|
|
|
plt.tight_layout()
|
|
|
|
chart_path = self.charts_dir / "metabolism_chart.png"
|
|
plt.savefig(chart_path, bbox_inches="tight", dpi=300)
|
|
plt.close()
|
|
|
|
return self._image_to_base64(chart_path) if save_as_base64 else str(chart_path)
|
|
|
|
def generate_fuel_source_chart(
|
|
self, fat_percentage: float, save_as_base64: bool = True
|
|
) -> str:
|
|
"""
|
|
Generate fuel source chart (Fats vs Carbs).
|
|
|
|
Args:
|
|
fat_percentage: Fat percentage at rest
|
|
save_as_base64: If True, return base64 string, else return file path
|
|
|
|
Returns:
|
|
Base64 string or file path
|
|
"""
|
|
from matplotlib.patches import FancyBboxPatch
|
|
|
|
fig, ax = plt.subplots(figsize=(10, 2.5))
|
|
|
|
carb_percentage = 100 - fat_percentage
|
|
optimal_point = 75
|
|
|
|
# Main Bars (Fats and Carbs)
|
|
# Fats bar (yellow)
|
|
fats_bar = FancyBboxPatch(
|
|
(0, 0.4),
|
|
fat_percentage,
|
|
0.2,
|
|
boxstyle="round,pad=0,rounding_size=0.1",
|
|
ec="none",
|
|
fc="#FEEAAB",
|
|
)
|
|
ax.add_patch(fats_bar)
|
|
|
|
# Carbs bar (blue) - starts where the fats bar ends
|
|
carbs_bar = FancyBboxPatch(
|
|
(fat_percentage, 0.4),
|
|
carb_percentage,
|
|
0.2,
|
|
boxstyle="round,pad=0,rounding_size=0.1",
|
|
ec="none",
|
|
fc="#A7F5FF",
|
|
)
|
|
ax.add_patch(carbs_bar)
|
|
|
|
# Text and Labels
|
|
ax.text(
|
|
fat_percentage / 2,
|
|
0.5,
|
|
f"Fats\n{fat_percentage:.1f}%",
|
|
ha="center",
|
|
va="center",
|
|
color="#333333",
|
|
fontsize=12,
|
|
weight="bold",
|
|
)
|
|
ax.text(
|
|
fat_percentage + carb_percentage / 2,
|
|
0.5,
|
|
f"Carbs\n{carb_percentage:.1f}%",
|
|
ha="center",
|
|
va="center",
|
|
color="#333333",
|
|
fontsize=12,
|
|
weight="bold",
|
|
)
|
|
|
|
# Add 'Optimal' label
|
|
ax.text(optimal_point, 0.75, "Optimal", ha="center", va="center", fontsize=12)
|
|
|
|
# Indicator Triangle
|
|
ax.plot(
|
|
fat_percentage, 0.65, "v", markersize=15, color="#606060", clip_on=False
|
|
)
|
|
|
|
# Ticks and Labels
|
|
positions = [0, 25, 50, 75, 100]
|
|
for pos in positions:
|
|
ax.text(
|
|
pos,
|
|
0.15,
|
|
str(pos),
|
|
ha="center",
|
|
va="center",
|
|
fontsize=12,
|
|
color="#333333",
|
|
)
|
|
ax.plot([pos, pos], [0.35, 0.39], color="grey", lw=5)
|
|
|
|
# Add a special tick for the 'Optimal' point
|
|
ax.plot([optimal_point, optimal_point], [0.6, 0.7], color="black", lw=2)
|
|
|
|
# Chart Styling
|
|
ax.set_title("Fuel Source", fontsize=18, weight="bold", loc="left")
|
|
ax.set_ylim(0, 1)
|
|
ax.axis("off")
|
|
|
|
plt.tight_layout()
|
|
|
|
chart_path = self.charts_dir / "fuel_source_chart.png"
|
|
plt.savefig(chart_path, bbox_inches="tight", dpi=300)
|
|
plt.close()
|
|
|
|
return self._image_to_base64(chart_path) if save_as_base64 else str(chart_path)
|
|
|
|
def generate_vo2_max_table(
|
|
self,
|
|
data: list[list],
|
|
columns: list[str],
|
|
vo2_max_value: float = None,
|
|
category: str = None,
|
|
cell_colors: list[list[str]] = None,
|
|
save_as_base64: bool = True,
|
|
) -> str:
|
|
"""
|
|
Generate VO2 Max table as an image with optimized sizing, highlighting the patient's category.
|
|
|
|
Args:
|
|
data: List of rows (each row is a list of values)
|
|
columns: List of column headers
|
|
vo2_max_value: Patient's VO2 max value (for title and arrow)
|
|
category: Category that the patient falls into (e.g., 'Good', 'Excellent')
|
|
cell_colors: Optional matrix of cell colors
|
|
save_as_base64: If True, return base64 string
|
|
|
|
Returns:
|
|
Base64 string or file path
|
|
"""
|
|
import io
|
|
|
|
from matplotlib.patches import FancyArrowPatch, RegularPolygon
|
|
|
|
# Fixed optimal sizing for VO2 Max table (7 columns, 1 data row)
|
|
fig, ax = plt.subplots(figsize=(14, 2.2))
|
|
ax.axis("off")
|
|
|
|
# Create table
|
|
table = ax.table(
|
|
cellText=data,
|
|
colLabels=columns,
|
|
cellLoc="center",
|
|
loc="center",
|
|
bbox=[0, 0, 1, 1],
|
|
)
|
|
|
|
# Style the table
|
|
table.auto_set_font_size(False)
|
|
table.set_fontsize(11)
|
|
table.scale(1, 1.8)
|
|
|
|
# Header row styling (cyan background)
|
|
for i in range(len(columns)):
|
|
cell = table[(0, i)]
|
|
cell.set_facecolor("#7dd3fc") # cyan-300 equivalent
|
|
cell.set_text_props(weight="bold", color="black", fontsize=12)
|
|
cell.set_edgecolor("#9ca3af") # gray-400
|
|
cell.set_linewidth(1)
|
|
|
|
# Find the column index for the category (if provided)
|
|
category_index = None
|
|
if category and category in columns:
|
|
category_index = columns.index(category)
|
|
|
|
# Data row styling
|
|
for i in range(len(data[0])):
|
|
cell = table[(1, i)]
|
|
if i == 0: # Age column
|
|
cell.set_facecolor("#a5f3fc") # cyan-200
|
|
cell.set_text_props(weight="semibold", color="black", fontsize=11)
|
|
else:
|
|
cell.set_facecolor("#f3f4f6") # gray-100
|
|
cell.set_text_props(color="black", fontsize=10)
|
|
# Bold the cell that corresponds to the patient's category
|
|
if category_index is not None and i == category_index:
|
|
cell.set_text_props(weight="bold", color="black", fontsize=11)
|
|
cell.set_edgecolor("#9ca3af") # gray-400
|
|
cell.set_linewidth(1)
|
|
|
|
# Add arrow indicator below the category column
|
|
if category_index is not None:
|
|
# Calculate position
|
|
cell_width = 1.0 / len(columns)
|
|
arrow_x = (category_index + 0.5) * cell_width
|
|
|
|
# Draw arrow pointing up
|
|
arrow = FancyArrowPatch(
|
|
(arrow_x, -0.15),
|
|
(arrow_x, -0.05),
|
|
arrowstyle="->",
|
|
mutation_scale=20,
|
|
linewidth=2,
|
|
color="black",
|
|
transform=ax.transAxes,
|
|
)
|
|
ax.add_patch(arrow)
|
|
|
|
# Add triangle at the top
|
|
triangle = RegularPolygon(
|
|
(arrow_x, -0.05),
|
|
3,
|
|
radius=0.02,
|
|
orientation=np.pi / 2,
|
|
color="black",
|
|
transform=ax.transAxes,
|
|
)
|
|
ax.add_patch(triangle)
|
|
|
|
# Set title - calculate approximate percentile
|
|
if vo2_max_value is not None:
|
|
if category == "Superior":
|
|
percentile = "100th percentile"
|
|
else:
|
|
percentile_map = {
|
|
"Very Poor": "1st-10th percentile",
|
|
"Poor": "10th-20th percentile",
|
|
"Fair": "20th-40th percentile",
|
|
"Good": "40th-60th percentile",
|
|
"Excellent": "60th-80th percentile",
|
|
}
|
|
percentile = percentile_map.get(category, "N/A")
|
|
|
|
title = f"VO2 Max - {vo2_max_value:.1f} ({percentile})"
|
|
ax.set_title(title, fontsize=14, fontweight="bold", pad=10)
|
|
|
|
if save_as_base64:
|
|
buf = io.BytesIO()
|
|
plt.savefig(
|
|
buf,
|
|
format="png",
|
|
bbox_inches="tight",
|
|
dpi=300,
|
|
facecolor="white",
|
|
pad_inches=0.05,
|
|
)
|
|
plt.close(fig)
|
|
buf.seek(0)
|
|
return base64.b64encode(buf.read()).decode("utf-8")
|
|
else:
|
|
output_path = (
|
|
self.charts_dir / f"vo2_max_table_{pd.Timestamp.now().timestamp()}.png"
|
|
)
|
|
plt.savefig(
|
|
output_path,
|
|
bbox_inches="tight",
|
|
dpi=300,
|
|
facecolor="white",
|
|
pad_inches=0.05,
|
|
)
|
|
plt.close(fig)
|
|
return str(output_path)
|
|
|
|
def generate_heart_rate_zones_table(
|
|
self,
|
|
data: list[list],
|
|
columns: list[str],
|
|
cell_colors: list[list[str]] = None,
|
|
save_as_base64: bool = True,
|
|
) -> str:
|
|
"""
|
|
Generate Heart Rate Zones table as an image with optimized sizing.
|
|
|
|
Args:
|
|
data: List of rows (each row is a list of values)
|
|
columns: List of column headers (Zone 1-5)
|
|
cell_colors: Optional matrix of cell colors (IGNORED - using notebook colors)
|
|
save_as_base64: If True, return base64 string
|
|
|
|
Returns:
|
|
Base64 string or file path
|
|
"""
|
|
import io
|
|
|
|
# Optimal sizing for HR Zones table (5 columns, 8 rows) - match notebook exactly
|
|
fig, ax = plt.subplots(figsize=(12, 8))
|
|
ax.axis("off")
|
|
|
|
# Data comes pre-formatted with newlines from context_generator - use as-is
|
|
# No text wrapping needed
|
|
|
|
# Create table without rowLabels - match notebook exactly
|
|
table = ax.table(
|
|
cellText=data,
|
|
colLabels=columns,
|
|
loc="center",
|
|
cellLoc="center",
|
|
)
|
|
|
|
# Style the table - match notebook exactly
|
|
table.auto_set_font_size(False)
|
|
table.set_fontsize(10)
|
|
table.scale(1, 3.5) # Increased vertical scale for multi-line text
|
|
|
|
# Header row styling
|
|
for j, label in enumerate(columns):
|
|
cell = table[(0, j)]
|
|
cell.set_facecolor("#7dd3fc") # cyan-300
|
|
cell.set_text_props(weight="bold")
|
|
|
|
# Row specific styling - match notebook colors exactly
|
|
colors = ["#fecaca", "#fecaca", "#fef08a", "#bbf7d0", "#bbf7d0"]
|
|
|
|
# HR BPM row is at index 2 (0-based in data) -> row 3 in table (0 is header)
|
|
for j in range(len(columns)):
|
|
cell = table[(3, j)]
|
|
cell.set_facecolor(colors[j])
|
|
cell.set_text_props(weight="bold")
|
|
|
|
# Breathing row is at index 7 -> row 8 in table
|
|
for j in range(len(columns)):
|
|
cell = table[(8, j)]
|
|
cell.set_facecolor(colors[j])
|
|
cell.set_text_props(weight="bold")
|
|
|
|
# Add title matching notebook
|
|
plt.title(
|
|
"Personalized Heart Rate Zones", fontsize=16, fontweight="bold", pad=5
|
|
)
|
|
plt.tight_layout()
|
|
|
|
if save_as_base64:
|
|
buf = io.BytesIO()
|
|
plt.savefig(
|
|
buf,
|
|
format="png",
|
|
bbox_inches="tight",
|
|
dpi=300,
|
|
facecolor="white",
|
|
)
|
|
plt.close(fig)
|
|
buf.seek(0)
|
|
return base64.b64encode(buf.read()).decode("utf-8")
|
|
else:
|
|
output_path = (
|
|
self.charts_dir / f"hr_zones_table_{pd.Timestamp.now().timestamp()}.png"
|
|
)
|
|
plt.savefig(
|
|
output_path,
|
|
bbox_inches="tight",
|
|
dpi=300,
|
|
facecolor="white",
|
|
)
|
|
plt.close(fig)
|
|
return str(output_path)
|
|
|
|
def generate_resting_heart_rate_table(
|
|
self,
|
|
data: list[list],
|
|
columns: list[str],
|
|
rhr_value: float = None,
|
|
category: str = None,
|
|
cell_colors: list[list[str]] = None,
|
|
save_as_base64: bool = True,
|
|
) -> str:
|
|
"""
|
|
Generate Resting Heart Rate table as an image with optimized sizing, highlighting the patient's category.
|
|
|
|
Args:
|
|
data: List of rows (each row is a list of values)
|
|
columns: List of column headers
|
|
rhr_value: Patient's resting heart rate value in bpm (for title and arrow)
|
|
category: Category that the patient falls into (e.g., 'Good', 'Excellent')
|
|
cell_colors: Optional matrix of cell colors
|
|
save_as_base64: If True, return base64 string
|
|
|
|
Returns:
|
|
Base64 string or file path
|
|
"""
|
|
import io
|
|
|
|
from matplotlib.patches import FancyArrowPatch, RegularPolygon
|
|
|
|
# Optimal sizing for RHR table (8 columns, 1 data row)
|
|
fig, ax = plt.subplots(figsize=(16, 2.2))
|
|
ax.axis("off")
|
|
|
|
# Create table
|
|
table = ax.table(
|
|
cellText=data,
|
|
colLabels=columns,
|
|
cellLoc="center",
|
|
loc="center",
|
|
bbox=[0, 0, 1, 1],
|
|
)
|
|
|
|
# Style the table
|
|
table.auto_set_font_size(False)
|
|
table.set_fontsize(11)
|
|
table.scale(1, 1.8)
|
|
|
|
# Header row styling (cyan background)
|
|
for i in range(len(columns)):
|
|
cell = table[(0, i)]
|
|
cell.set_facecolor("#7dd3fc") # cyan-300 equivalent
|
|
cell.set_text_props(weight="bold", color="black", fontsize=12)
|
|
cell.set_edgecolor("#9ca3af") # gray-400
|
|
cell.set_linewidth(1)
|
|
|
|
# Find the column index for the category (if provided)
|
|
category_index = None
|
|
if category and category in columns:
|
|
category_index = columns.index(category)
|
|
|
|
# Data row styling
|
|
for i in range(len(data[0])):
|
|
cell = table[(1, i)]
|
|
if i == 0: # Age column
|
|
cell.set_facecolor("#a5f3fc") # cyan-200
|
|
cell.set_text_props(weight="semibold", color="black", fontsize=11)
|
|
else:
|
|
# Highlight the category cell with light green background
|
|
if category_index is not None and i == category_index:
|
|
cell.set_facecolor("#d1fae5") # green-200 equivalent
|
|
cell.set_text_props(weight="bold", color="black", fontsize=11)
|
|
else:
|
|
cell.set_facecolor("#f3f4f6") # gray-100
|
|
cell.set_text_props(color="black", fontsize=10)
|
|
cell.set_edgecolor("#9ca3af") # gray-400
|
|
cell.set_linewidth(1)
|
|
|
|
# Add arrow indicator below the category column
|
|
if category_index is not None:
|
|
# Calculate position
|
|
cell_width = 1.0 / len(columns)
|
|
arrow_x = (category_index + 0.5) * cell_width
|
|
|
|
# Draw arrow pointing up
|
|
arrow = FancyArrowPatch(
|
|
(arrow_x, -0.15),
|
|
(arrow_x, -0.05),
|
|
arrowstyle="->",
|
|
mutation_scale=20,
|
|
linewidth=2,
|
|
color="black",
|
|
transform=ax.transAxes,
|
|
)
|
|
ax.add_patch(arrow)
|
|
|
|
# Add triangle at the top
|
|
triangle = RegularPolygon(
|
|
(arrow_x, -0.05),
|
|
3,
|
|
radius=0.02,
|
|
orientation=np.pi / 2,
|
|
color="black",
|
|
transform=ax.transAxes,
|
|
)
|
|
ax.add_patch(triangle)
|
|
|
|
# Set title
|
|
if rhr_value is not None:
|
|
title = f"Resting Heart Rate - {rhr_value:.0f}bpm"
|
|
ax.set_title(title, fontsize=14, fontweight="bold", pad=10)
|
|
|
|
if save_as_base64:
|
|
buf = io.BytesIO()
|
|
plt.savefig(
|
|
buf,
|
|
format="png",
|
|
bbox_inches="tight",
|
|
dpi=300,
|
|
facecolor="white",
|
|
pad_inches=0.05,
|
|
)
|
|
plt.close(fig)
|
|
buf.seek(0)
|
|
return base64.b64encode(buf.read()).decode("utf-8")
|
|
else:
|
|
output_path = (
|
|
self.charts_dir / f"rhr_table_{pd.Timestamp.now().timestamp()}.png"
|
|
)
|
|
plt.savefig(
|
|
output_path,
|
|
bbox_inches="tight",
|
|
dpi=300,
|
|
facecolor="white",
|
|
pad_inches=0.05,
|
|
)
|
|
plt.close(fig)
|
|
return str(output_path)
|