Add compiled Python bytecode for report generator and spirometry table extractor services
- Generated bytecode for report_generator.py and spirometry_table_extractor.py - These changes include the compiled .pyc files in the __pycache__ directory - The report generator service handles the generation of medical reports from uploaded files - The spirometry table extractor service extracts data from PDF files and processes it for further analysis
This commit is contained in:
bolade
+288
-295
@@ -1,6 +1,12 @@
|
||||
"""
|
||||
Graph Generator Service
|
||||
|
||||
This service generates all the charts and visualizations required for the medical report.
|
||||
Based on the analysis notebooks in services_dfdf/.
|
||||
"""
|
||||
|
||||
import base64
|
||||
from pathlib import Path
|
||||
from typing import Dict
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import matplotlib.transforms as mtransforms
|
||||
@@ -11,13 +17,28 @@ from matplotlib.patches import FancyBboxPatch
|
||||
|
||||
|
||||
class GraphGenerator:
|
||||
"""Generate all charts for medical reports"""
|
||||
|
||||
def __init__(self, charts_dir: str = "graphs"):
|
||||
"""Initialize the GraphGenerator with output directory for charts"""
|
||||
"""
|
||||
Initialize the graph generator.
|
||||
|
||||
Args:
|
||||
charts_dir: Directory to save generated charts
|
||||
"""
|
||||
self.charts_dir = Path(charts_dir)
|
||||
self.charts_dir.mkdir(exist_ok=True)
|
||||
|
||||
def _image_to_base64(self, image_path: Path) -> str:
|
||||
"""Convert image to base64 string"""
|
||||
"""
|
||||
Convert image file to base64 string.
|
||||
|
||||
Args:
|
||||
image_path: Path to image file
|
||||
|
||||
Returns:
|
||||
Base64 encoded string
|
||||
"""
|
||||
try:
|
||||
with open(image_path, "rb") as image_file:
|
||||
return base64.b64encode(image_file.read()).decode("utf-8")
|
||||
@@ -25,27 +46,35 @@ class GraphGenerator:
|
||||
return ""
|
||||
|
||||
def generate_respiratory_chart(
|
||||
self, df: pd.DataFrame, save_as_base64: bool = False
|
||||
self, df: pd.DataFrame, save_as_base64: bool = True
|
||||
) -> str:
|
||||
"""Generate respiratory chart showing VT and Speed over time"""
|
||||
# Get phase times for background regions
|
||||
"""
|
||||
Generate respiratory chart (VT and Speed over time).
|
||||
|
||||
Args:
|
||||
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 VT with step-like appearance
|
||||
# Plot VT
|
||||
sns.lineplot(data=df, x="T(sec)", y="VT(l)_smoothed", label="VT (L)")
|
||||
ax1.set_xlabel("Time (sec)")
|
||||
ax1.set_ylabel("VT (L)")
|
||||
ax1.grid(True, alpha=0.1)
|
||||
ax1.set_ylim(0, min(8, df["VT(l)_smoothed"].max()))
|
||||
|
||||
# Plot speed as step function on secondary y-axis
|
||||
# Plot speed on secondary y-axis
|
||||
ax2 = ax1.twinx()
|
||||
ax1.set_xticks(np.arange(0, df["T(sec)"].max() + 200, 200))
|
||||
line2 = sns.lineplot(
|
||||
sns.lineplot(
|
||||
data=df,
|
||||
x="T(sec)",
|
||||
y="Speed",
|
||||
@@ -58,11 +87,9 @@ class GraphGenerator:
|
||||
ax2.set_ylabel("Speed")
|
||||
ax2.set_ylim(0, min(30, df["Speed"].max()) + 1)
|
||||
|
||||
# Remove default legends first
|
||||
# Combine legends
|
||||
ax1.get_legend().remove()
|
||||
ax2.get_legend().remove()
|
||||
|
||||
# Combine legends from both axes in the top left
|
||||
lines1, labels1 = ax1.get_legend_handles_labels()
|
||||
lines2, labels2 = ax2.get_legend_handles_labels()
|
||||
ax1.legend(lines1 + lines2, labels1 + labels2, loc="upper left")
|
||||
@@ -81,12 +108,23 @@ class GraphGenerator:
|
||||
return self._image_to_base64(chart_path) if save_as_base64 else str(chart_path)
|
||||
|
||||
def generate_fuel_utilization_chart(
|
||||
self, df: pd.DataFrame, save_as_base64: bool = False
|
||||
self, df: pd.DataFrame, save_as_base64: bool = True
|
||||
) -> str:
|
||||
"""Generate fuel utilization chart with stacked bars showing fat vs carbs"""
|
||||
# Group by speed and calculate mean for numeric columns only
|
||||
"""
|
||||
Generate fuel utilization chart (CHO vs FAT by stage).
|
||||
|
||||
Args:
|
||||
df: Processed DataFrame with smoothed columns
|
||||
save_as_base64: If True, return base64 string, else return file path
|
||||
|
||||
Returns:
|
||||
Base64 string or file path
|
||||
"""
|
||||
# Group by speed and calculate mean
|
||||
speed_groups = df.groupby("Speed").mean(numeric_only=True).round(1)
|
||||
speed_groups = speed_groups.iloc[1:-1]
|
||||
|
||||
# Filter data
|
||||
filtered_data = speed_groups[
|
||||
(speed_groups.index >= 3.5) & (speed_groups.index <= 7.5)
|
||||
]
|
||||
@@ -94,19 +132,24 @@ class GraphGenerator:
|
||||
plt.figure(figsize=(15, 8))
|
||||
plt.style.use("default")
|
||||
|
||||
# Create stage labels and positions
|
||||
stage_labels = [f"Stage {i}" for i in range(1, len(filtered_data) + 1)]
|
||||
x_positions = np.arange(len(filtered_data))
|
||||
|
||||
# Calculate fat and carbs energy expenditure from percentages
|
||||
# Calculate fat and carbs energy expenditure
|
||||
fat_ee = filtered_data["EE(kcal/min)"] * filtered_data["FAT(%)"] / 100
|
||||
carbs_ee = filtered_data["EE(kcal/min)"] * filtered_data["CARBS(%)"] / 100
|
||||
|
||||
# Create the main axis for the stacked bars
|
||||
ax1 = plt.gca()
|
||||
|
||||
# Create stacked bar chart with colors
|
||||
ax1.bar(x_positions, fat_ee, color="#1f77b4", alpha=0.8, width=0.6, label="Fat")
|
||||
# Create stacked bar chart
|
||||
ax1.bar(
|
||||
x_positions,
|
||||
fat_ee,
|
||||
color="#1f77b4",
|
||||
alpha=0.8,
|
||||
width=0.6,
|
||||
label="Fat",
|
||||
)
|
||||
ax1.bar(
|
||||
x_positions,
|
||||
carbs_ee,
|
||||
@@ -117,16 +160,15 @@ class GraphGenerator:
|
||||
label="Carbs",
|
||||
)
|
||||
|
||||
# Set labels and formatting for primary axis
|
||||
ax1.set_xlabel("", fontsize=12)
|
||||
ax1.set_ylabel("Fuel (kcal/min)", fontsize=12)
|
||||
ax1.set_ylim(0, 20)
|
||||
|
||||
# Add individual values on each bar segment
|
||||
# Add values on bars
|
||||
for i, (fat_val, carb_val, total_val) in enumerate(
|
||||
zip(fat_ee, carbs_ee, filtered_data["EE(kcal/min)"])
|
||||
):
|
||||
if fat_val > 0.3: # Fat value
|
||||
if fat_val > 0.3:
|
||||
ax1.text(
|
||||
i,
|
||||
fat_val / 2,
|
||||
@@ -137,7 +179,7 @@ class GraphGenerator:
|
||||
fontweight="bold",
|
||||
color="white",
|
||||
)
|
||||
if carb_val > 0.3: # Carbs value
|
||||
if carb_val > 0.3:
|
||||
ax1.text(
|
||||
i,
|
||||
fat_val + carb_val / 2,
|
||||
@@ -148,7 +190,6 @@ class GraphGenerator:
|
||||
fontweight="bold",
|
||||
color="white",
|
||||
)
|
||||
# Total EE
|
||||
ax1.text(
|
||||
i,
|
||||
total_val + 0.5,
|
||||
@@ -160,7 +201,7 @@ class GraphGenerator:
|
||||
color="black",
|
||||
)
|
||||
|
||||
# Add speed labels below x-axis
|
||||
# Add speed labels
|
||||
for i, speed in enumerate(filtered_data.index):
|
||||
ax1.text(i, -1.5, f"{speed:.1f} mph", ha="center", va="top", fontsize=9)
|
||||
ax1.text(
|
||||
@@ -175,8 +216,6 @@ class GraphGenerator:
|
||||
|
||||
# Create secondary y-axis for heart rate
|
||||
ax2 = ax1.twinx()
|
||||
|
||||
# Plot heart rate line
|
||||
ax2.plot(
|
||||
x_positions,
|
||||
filtered_data["HR(bpm)"],
|
||||
@@ -187,12 +226,11 @@ class GraphGenerator:
|
||||
label="Heart Rate",
|
||||
)
|
||||
|
||||
# Set heart rate axis formatting
|
||||
ax2.set_ylabel("Heart Rate (bpm)", fontsize=12, color="red")
|
||||
ax2.tick_params(axis="y", labelcolor="red")
|
||||
ax2.set_ylim(0, 220)
|
||||
|
||||
# Add HR values above the points
|
||||
# Add HR values
|
||||
for i, hr in enumerate(filtered_data["HR(bpm)"]):
|
||||
ax2.text(
|
||||
i,
|
||||
@@ -205,7 +243,6 @@ class GraphGenerator:
|
||||
color="red",
|
||||
)
|
||||
|
||||
# Set x-axis formatting
|
||||
ax1.set_xticks(x_positions)
|
||||
ax1.set_xticklabels(stage_labels, fontsize=11)
|
||||
|
||||
@@ -221,11 +258,9 @@ class GraphGenerator:
|
||||
shadow=True,
|
||||
)
|
||||
|
||||
# Add grid
|
||||
ax1.grid(True, alpha=0.3, linestyle="-", linewidth=0.5)
|
||||
ax1.set_axisbelow(True)
|
||||
|
||||
# Adjust layout
|
||||
plt.tight_layout()
|
||||
plt.subplots_adjust(bottom=0.1, top=0.9)
|
||||
|
||||
@@ -236,9 +271,18 @@ class GraphGenerator:
|
||||
return self._image_to_base64(chart_path) if save_as_base64 else str(chart_path)
|
||||
|
||||
def generate_vo2_pulse_chart(
|
||||
self, df: pd.DataFrame, save_as_base64: bool = False
|
||||
self, df: pd.DataFrame, save_as_base64: bool = True
|
||||
) -> str:
|
||||
"""Generate VO2 Pulse chart with heart rate and speed"""
|
||||
"""
|
||||
Generate VO2 Pulse chart with HR and Speed.
|
||||
|
||||
Args:
|
||||
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()
|
||||
|
||||
@@ -292,12 +336,14 @@ class GraphGenerator:
|
||||
|
||||
ax1.set_xticks(np.arange(0, df["T(sec)"].max() + 200, 200))
|
||||
|
||||
# Remove default legends first
|
||||
for ax in [ax1, ax2, ax3]:
|
||||
if ax.get_legend():
|
||||
ax.get_legend().remove()
|
||||
# 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()
|
||||
|
||||
# Combine legends from all axes
|
||||
lines1, labels1 = ax1.get_legend_handles_labels()
|
||||
lines2, labels2 = ax2.get_legend_handles_labels()
|
||||
lines3, labels3 = ax3.get_legend_handles_labels()
|
||||
@@ -319,16 +365,24 @@ class GraphGenerator:
|
||||
return self._image_to_base64(chart_path) if save_as_base64 else str(chart_path)
|
||||
|
||||
def generate_vo2_breath_chart(
|
||||
self, df: pd.DataFrame, save_as_base64: bool = False
|
||||
self, df: pd.DataFrame, save_as_base64: bool = True
|
||||
) -> str:
|
||||
"""Generate VO2 per Breath chart"""
|
||||
"""
|
||||
Generate VO2 per Breath chart.
|
||||
|
||||
Args:
|
||||
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 VO2 per Breath
|
||||
sns.lineplot(
|
||||
data=df,
|
||||
x="T(sec)",
|
||||
@@ -340,7 +394,7 @@ class GraphGenerator:
|
||||
ax1.set_ylim(0, df["VO2 Breath_smoothed"].max() + 1)
|
||||
ax1.grid(True, alpha=0.1)
|
||||
|
||||
# Plot speed as step function on secondary y-axis
|
||||
# Plot speed on secondary y-axis
|
||||
ax2 = ax1.twinx()
|
||||
ax1.set_xticks(np.arange(0, df["T(sec)"].max() + 200, 200))
|
||||
sns.lineplot(
|
||||
@@ -356,11 +410,9 @@ class GraphGenerator:
|
||||
ax2.set_ylim(0, df["Speed"].max() + 1)
|
||||
ax2.set_ylabel("Speed")
|
||||
|
||||
# Remove default legends first
|
||||
# Combine legends
|
||||
ax1.get_legend().remove()
|
||||
ax2.get_legend().remove()
|
||||
|
||||
# Combine legends from both axes in the top left
|
||||
lines1, labels1 = ax1.get_legend_handles_labels()
|
||||
lines2, labels2 = ax2.get_legend_handles_labels()
|
||||
ax1.legend(lines1 + lines2, labels1 + labels2, loc="upper left")
|
||||
@@ -379,9 +431,18 @@ class GraphGenerator:
|
||||
return self._image_to_base64(chart_path) if save_as_base64 else str(chart_path)
|
||||
|
||||
def generate_fat_metabolism_chart(
|
||||
self, df: pd.DataFrame, save_as_base64: bool = False
|
||||
self, df: pd.DataFrame, save_as_base64: bool = True
|
||||
) -> str:
|
||||
"""Generate CHO and FAT metabolism chart"""
|
||||
"""
|
||||
Generate fat metabolism chart (CHO vs FAT over time).
|
||||
|
||||
Args:
|
||||
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()
|
||||
|
||||
@@ -391,7 +452,7 @@ class GraphGenerator:
|
||||
# Plot CHO
|
||||
sns.lineplot(data=df, x="T(sec)", y="CHO_smoothed", label="CHO (kcal/min)")
|
||||
ax1.set_xlabel("Time (sec)")
|
||||
ax1.set_ylabel("CHO (kcal/min)")
|
||||
ax1.set_ylabel("CHO (g/min)")
|
||||
ax1.grid(True, alpha=0.1)
|
||||
|
||||
# Plot FAT on secondary y-axis
|
||||
@@ -408,11 +469,9 @@ class GraphGenerator:
|
||||
ax2.set_ylabel("FAT (kcal/min)")
|
||||
ax2.set_ylim(0, 15)
|
||||
|
||||
# Remove default legends first
|
||||
# Combine legends
|
||||
ax1.get_legend().remove()
|
||||
ax2.get_legend().remove()
|
||||
|
||||
# Combine legends from both axes in the top left
|
||||
lines1, labels1 = ax1.get_legend_handles_labels()
|
||||
lines2, labels2 = ax2.get_legend_handles_labels()
|
||||
ax1.legend(lines1 + lines2, labels1 + labels2, loc="upper left")
|
||||
@@ -431,9 +490,18 @@ class GraphGenerator:
|
||||
return self._image_to_base64(chart_path) if save_as_base64 else str(chart_path)
|
||||
|
||||
def generate_recovery_chart(
|
||||
self, df: pd.DataFrame, save_as_base64: bool = False
|
||||
self, df: pd.DataFrame, save_as_base64: bool = True
|
||||
) -> str:
|
||||
"""Generate recovery chart with VCO2, HR, and BF"""
|
||||
"""
|
||||
Generate recovery chart (VCO2, HR, and BF).
|
||||
|
||||
Args:
|
||||
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()
|
||||
|
||||
@@ -449,7 +517,7 @@ class GraphGenerator:
|
||||
color="blue",
|
||||
)
|
||||
ax1.set_xlabel("Time (sec)")
|
||||
ax1.set_ylabel("VCO2 (ml/min)")
|
||||
ax1.set_ylabel("VO2 Pulse (mL/beat)")
|
||||
ax1.set_ylim(0, df["VCO2(ml/min)"].max())
|
||||
ax1.grid(True, alpha=0.1)
|
||||
|
||||
@@ -468,7 +536,7 @@ class GraphGenerator:
|
||||
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 breathing frequency
|
||||
# Create third y-axis for BF
|
||||
ax3 = ax1.twinx()
|
||||
ax3.spines["right"].set_position(("outward", 60))
|
||||
sns.lineplot(
|
||||
@@ -485,12 +553,14 @@ class GraphGenerator:
|
||||
ax3.set_ylim(0, df["BF(bpm)_smoothed"].max() + 1)
|
||||
ax1.set_xticks(np.arange(0, df["T(sec)"].max() + 200, 200))
|
||||
|
||||
# Remove default legends first
|
||||
for ax in [ax1, ax2, ax3]:
|
||||
if ax.get_legend():
|
||||
ax.get_legend().remove()
|
||||
# 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()
|
||||
|
||||
# Combine legends from all axes in the top left
|
||||
lines1, labels1 = ax1.get_legend_handles_labels()
|
||||
lines2, labels2 = ax2.get_legend_handles_labels()
|
||||
lines3, labels3 = ax3.get_legend_handles_labels()
|
||||
@@ -511,129 +581,41 @@ class GraphGenerator:
|
||||
|
||||
return self._image_to_base64(chart_path) if save_as_base64 else str(chart_path)
|
||||
|
||||
def generate_body_fat_percentage_chart(
|
||||
self,
|
||||
gender: str,
|
||||
age: int,
|
||||
body_fat_percentage: float,
|
||||
save_as_base64: bool = False,
|
||||
) -> str:
|
||||
"""Generate body fat percentage chart with ranges"""
|
||||
# Define the segments with muted colors
|
||||
segments = [
|
||||
("#F8A8A8", 0, 15), # Muted Red/Salmon: 0% to 15%
|
||||
("#FFEECC", 15, 5), # Pale Yellow/Cream: 15% to 20%
|
||||
("#D0F0C0", 20, 15), # Pale Green/Mint: 20% to 35%
|
||||
("#FFEECC", 35, 5), # Pale Yellow/Cream: 35% to 40%
|
||||
("#F8A8A8", 40, 10), # Muted Red/Salmon: 40% to 50%
|
||||
]
|
||||
|
||||
# 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 = "N/A"
|
||||
|
||||
demographic = f"{age_group}\n({gender[0].upper()})"
|
||||
|
||||
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(
|
||||
body_fat_percentage,
|
||||
1.05,
|
||||
marker="v",
|
||||
color="black",
|
||||
markersize=10,
|
||||
clip_on=False,
|
||||
transform=ax.get_xaxis_transform(),
|
||||
)
|
||||
|
||||
# Set Axis Properties and Labels
|
||||
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,
|
||||
)
|
||||
|
||||
ax.set_xlim(0, 50)
|
||||
ticks = range(0, 51, 5)
|
||||
ax.set_xticks(ticks)
|
||||
labels = [f"{t}%" for t in ticks]
|
||||
ax.set_xticklabels(labels)
|
||||
|
||||
# Clean up spines and add small ticks
|
||||
ax.spines["right"].set_visible(False)
|
||||
ax.spines["top"].set_visible(False)
|
||||
ax.spines["left"].set_visible(False)
|
||||
ax.spines["bottom"].set_visible(True)
|
||||
|
||||
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_percentage_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_body_composition_chart(
|
||||
self, fat_mass_lbs: float, lean_mass_lbs: float, save_as_base64: bool = False
|
||||
self, fat_mass_lbs: float, lean_mass_lbs: float, save_as_base64: bool = True
|
||||
) -> str:
|
||||
"""Generate donut chart for body composition"""
|
||||
"""
|
||||
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
|
||||
|
||||
# Data for the chart
|
||||
sizes = [fat_percentage, lean_percentage]
|
||||
colors = ["#fde3ac", "#ff9966"] # Light yellow/tan and orange
|
||||
colors = ["#fde3ac", "#ff9966"]
|
||||
|
||||
plt.figure(figsize=(8, 8))
|
||||
|
||||
# Create the donut chart without labels first
|
||||
wedges, texts, autotexts = plt.pie(
|
||||
# Create donut chart
|
||||
plt.pie(
|
||||
sizes,
|
||||
autopct="", # Remove auto percentages
|
||||
autopct="",
|
||||
startangle=90,
|
||||
wedgeprops=dict(width=0.5, edgecolor="w"),
|
||||
colors=colors,
|
||||
labels=["", ""],
|
||||
) # Remove default labels
|
||||
)
|
||||
|
||||
# Add custom text annotations positioned manually
|
||||
# Add custom text annotations
|
||||
plt.text(
|
||||
-1,
|
||||
1,
|
||||
@@ -656,8 +638,7 @@ class GraphGenerator:
|
||||
bbox=dict(boxstyle="round,pad=0.3", facecolor="white", alpha=0.8),
|
||||
)
|
||||
|
||||
# Set the title
|
||||
plt.axis("equal") # Equal aspect ratio ensures that pie is drawn as a circle
|
||||
plt.axis("equal")
|
||||
|
||||
chart_path = self.charts_dir / "body_composition_chart.png"
|
||||
plt.savefig(chart_path, bbox_inches="tight", dpi=600)
|
||||
@@ -665,16 +646,142 @@ class GraphGenerator:
|
||||
|
||||
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 = False
|
||||
def generate_body_fat_percent_chart(
|
||||
self,
|
||||
fat_percentage: float,
|
||||
age: int,
|
||||
gender: str,
|
||||
save_as_base64: bool = True,
|
||||
) -> str:
|
||||
"""Generate spirometry chart with Z-scores and ranges"""
|
||||
"""
|
||||
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
|
||||
|
||||
demographic = f"{age_group}\n({gender[0].upper()})"
|
||||
|
||||
# Define segments based on gender (female example)
|
||||
if gender.lower() == "female":
|
||||
segments = [
|
||||
("#F8A8A8", 0, 15), # Muted Red: 0% to 15%
|
||||
("#FFEECC", 15, 5), # Pale Yellow: 15% to 20%
|
||||
("#D0F0C0", 20, 15), # Pale Green: 20% to 35%
|
||||
("#FFEECC", 35, 5), # Pale Yellow: 35% to 40%
|
||||
("#F8A8A8", 40, 10), # Muted Red: 40% to 50%
|
||||
]
|
||||
else: # male
|
||||
segments = [
|
||||
("#F8A8A8", 0, 5), # Muted Red: 0% to 5%
|
||||
("#FFEECC", 5, 5), # Pale Yellow: 5% to 10%
|
||||
("#D0F0C0", 10, 10), # Pale Green: 10% to 20%
|
||||
("#FFEECC", 20, 5), # Pale Yellow: 20% to 25%
|
||||
("#F8A8A8", 25, 25), # Muted Red: 25% to 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
|
||||
for col in ["Best", "LLN", "Pred.", "%Pred.", "ZScore"]:
|
||||
if col in spirometry_df.columns:
|
||||
spirometry_df[col] = pd.to_numeric(spirometry_df[col], errors="coerce")
|
||||
|
||||
# Select rows of interest and prepare display values
|
||||
# Select rows of interest
|
||||
rows_map = {
|
||||
"Lung Volume": "FVC",
|
||||
"Lung Power": "FEV1",
|
||||
@@ -707,7 +814,7 @@ class GraphGenerator:
|
||||
)
|
||||
|
||||
x_min, x_max = -5, 3
|
||||
# Segment colors: red -> orange -> yellow -> green
|
||||
# Segment colors
|
||||
segments = [
|
||||
(-5, -4, "#f4a7a7"), # red-ish
|
||||
(-4, -3, "#f7c49a"), # orange-ish
|
||||
@@ -726,10 +833,10 @@ class GraphGenerator:
|
||||
0, width=b - a, left=a, height=0.6, color=color, edgecolor="none"
|
||||
)
|
||||
|
||||
# LLN (-1) and Predicted (0) markers
|
||||
# LLN and Predicted markers
|
||||
ax.axvline(0, color="black", lw=1)
|
||||
|
||||
# Z-score pointer (downward triangle) at top of each panel
|
||||
# Z-score pointer
|
||||
if pd.notna(rec["z"]):
|
||||
trans = mtransforms.blended_transform_factory(
|
||||
ax.transData, ax.transAxes
|
||||
@@ -744,7 +851,7 @@ class GraphGenerator:
|
||||
clip_on=False,
|
||||
)
|
||||
|
||||
# Labels, ticks, and styling
|
||||
# Labels and styling
|
||||
ax.set_title(
|
||||
rec["label"], loc="left", fontsize=11, fontweight="bold", pad=2
|
||||
)
|
||||
@@ -760,15 +867,11 @@ class GraphGenerator:
|
||||
|
||||
# Right-side summary boxes
|
||||
fig.subplots_adjust(right=0.78)
|
||||
box_ax = fig.add_axes(
|
||||
[0.805, 0.06, 0.18, 0.90]
|
||||
) # [left, bottom, width, height]
|
||||
box_ax = fig.add_axes([0.805, 0.06, 0.18, 0.90])
|
||||
box_ax.axis("off")
|
||||
|
||||
# Helper to draw a pill-shaped text box
|
||||
def pill(ax, xy, text):
|
||||
x, y = xy
|
||||
# Draw rounded rectangle background
|
||||
bbox = FancyBboxPatch(
|
||||
(x - 0.48, y - 0.09),
|
||||
0.96,
|
||||
@@ -801,7 +904,7 @@ class GraphGenerator:
|
||||
box_ax.set_xlim(0, 1)
|
||||
box_ax.set_ylim(0, 1)
|
||||
|
||||
# Prepare display strings and positions (top to bottom)
|
||||
# Prepare display strings
|
||||
right_items = []
|
||||
for rec in records:
|
||||
name = (
|
||||
@@ -814,7 +917,7 @@ class GraphGenerator:
|
||||
pct_fmt = f"{rec['pct']:.1f}%"
|
||||
right_items.append((name, value_fmt, pct_fmt))
|
||||
|
||||
# Sort to match image order on the right (FVC, FEV1, FEV1/FVC)
|
||||
# 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
|
||||
@@ -830,113 +933,3 @@ class GraphGenerator:
|
||||
plt.close()
|
||||
|
||||
return self._image_to_base64(chart_path) if save_as_base64 else str(chart_path)
|
||||
|
||||
def generate_all_charts(
|
||||
self,
|
||||
pnoe_df: pd.DataFrame,
|
||||
spirometry_df: pd.DataFrame,
|
||||
patient_data: Dict,
|
||||
save_as_base64: bool = False,
|
||||
) -> Dict[str, str]:
|
||||
"""Generate all charts at once and return dictionary of paths/base64 strings"""
|
||||
charts = {}
|
||||
|
||||
# Generate physiological charts
|
||||
charts["respiratory"] = self.generate_respiratory_chart(pnoe_df, save_as_base64)
|
||||
charts["fuel_utilization_chart"] = self.generate_fuel_utilization_chart(
|
||||
pnoe_df, save_as_base64
|
||||
)
|
||||
charts["vo2_pulse_chart"] = self.generate_vo2_pulse_chart(
|
||||
pnoe_df, save_as_base64
|
||||
)
|
||||
charts["vo2_breath_chart"] = self.generate_vo2_breath_chart(
|
||||
pnoe_df, save_as_base64
|
||||
)
|
||||
charts["fat_metabolism_chart"] = self.generate_fat_metabolism_chart(
|
||||
pnoe_df, save_as_base64
|
||||
)
|
||||
charts["recovery_chart"] = self.generate_recovery_chart(pnoe_df, save_as_base64)
|
||||
|
||||
# Generate body composition charts
|
||||
if (
|
||||
"gender" in patient_data
|
||||
and "age" in patient_data
|
||||
and "fat_percentage" in patient_data
|
||||
):
|
||||
charts["body_fat_percentage_chart"] = (
|
||||
self.generate_body_fat_percentage_chart(
|
||||
patient_data["gender"],
|
||||
patient_data["age"],
|
||||
patient_data["fat_percentage"],
|
||||
save_as_base64,
|
||||
)
|
||||
)
|
||||
|
||||
if "fat_mass_lbs" in patient_data and "lean_mass_lbs" in patient_data:
|
||||
charts["body_composition_chart"] = self.generate_body_composition_chart(
|
||||
patient_data["fat_mass_lbs"],
|
||||
patient_data["lean_mass_lbs"],
|
||||
save_as_base64,
|
||||
)
|
||||
|
||||
# Generate spirometry chart
|
||||
charts["spirometry_chart"] = self.generate_spirometry_chart(
|
||||
spirometry_df, save_as_base64
|
||||
)
|
||||
|
||||
return charts
|
||||
|
||||
|
||||
# Example usage
|
||||
if __name__ == "__main__":
|
||||
# Initialize graph generator
|
||||
generator = GraphGenerator()
|
||||
|
||||
# Load sample data (you would pass your actual dataframes)
|
||||
pnoe_df = pd.read_csv("data/Pnoe_20250729_1550-Moran_Keirstyn.csv", delimiter=";")
|
||||
spirometry_df = pd.read_csv("data/spirometry_data.csv")
|
||||
|
||||
# Preprocess pnoe data (same as in your notebook)
|
||||
pnoe_df = pnoe_df.apply(pd.to_numeric, errors="ignore")
|
||||
pnoe_df["VO2 Pulse"] = pnoe_df["VO2(ml/min)"] / pnoe_df["HR(bpm)"]
|
||||
pnoe_df["VO2 Breath"] = pnoe_df["VO2(ml/min)"] / pnoe_df["BF(bpm)"]
|
||||
pnoe_df["CHO"] = pnoe_df["EE(kcal/min)"] * pnoe_df["CARBS(%)"] / 100
|
||||
pnoe_df["FAT"] = pnoe_df["EE(kcal/min)"] * pnoe_df["FAT(%)"] / 100
|
||||
|
||||
# Apply smoothing
|
||||
window_size = 10
|
||||
columns_to_smooth = [
|
||||
"VO2(ml/min)",
|
||||
"VCO2(ml/min)",
|
||||
"HR(bpm)",
|
||||
"VT(l)",
|
||||
"BF(bpm)",
|
||||
"VE(l/min)",
|
||||
"VO2 Pulse",
|
||||
"VO2 Breath",
|
||||
"CHO",
|
||||
"FAT",
|
||||
]
|
||||
for col in columns_to_smooth:
|
||||
if col in pnoe_df.columns:
|
||||
pnoe_df[f"{col}_smoothed"] = (
|
||||
pnoe_df[col].rolling(window=window_size, min_periods=1).mean()
|
||||
)
|
||||
|
||||
# Patient data
|
||||
patient_data = {
|
||||
"gender": "female",
|
||||
"age": 25,
|
||||
"fat_percentage": 22.4,
|
||||
"fat_mass_lbs": 27.6,
|
||||
"lean_mass_lbs": 95.4,
|
||||
}
|
||||
|
||||
# Generate all charts
|
||||
charts = generator.generate_all_charts(
|
||||
pnoe_df, spirometry_df, patient_data, save_as_base64=True
|
||||
)
|
||||
|
||||
print(f"Generated {len(charts)} charts:")
|
||||
for chart_name in charts.keys():
|
||||
print(f"- {chart_name}")
|
||||
|
||||
Reference in New Issue
Block a user