feat: Enhance context generation with new table images for VO2 Max and Heart Rate Zones
- Added functionality to generate VO2 Max and Heart Rate Zones tables in the context_generator.py. - Integrated graph_generator to create table images with specified data and styles. - Updated report_generator.py to pass graph_generator to context generation. - Introduced a new method in graph_generator.py to generate table images with customizable options. - Created test scripts for Page 5 (RMR and NEAT calculations) and Page 6 (Meal Plan calculations) using actual patient data. - Updated Jupyter notebook metadata for better environment identification.
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bolade
@@ -6,7 +6,7 @@ of the medical report. It performs analysis on Pnoe, Spirometry, and SECA data.
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"""
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from datetime import datetime
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from typing import Dict, Optional, Tuple
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from typing import Any, Dict, Optional, Tuple
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import pandas as pd
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@@ -626,6 +626,7 @@ class ContextGenerator:
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patient_name: str,
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graphs: Dict[str, str],
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metric_overrides: Optional[Dict] = None,
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graph_generator: Optional[Any] = None,
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) -> Dict[str, Dict]:
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"""Main method to generate all page contexts
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@@ -720,6 +721,127 @@ class ContextGenerator:
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"vo2_pulse_chart": graphs.get("vo2_pulse", ""),
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}
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if graph_generator:
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# VO2 Max Table
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vo2_max_columns = [
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"Age (F)",
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"Very Poor",
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"Poor",
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"Fair",
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"Good",
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"Excellent",
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"Superior",
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]
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vo2_max_data = [
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[
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contexts["page_8"]["age_range"],
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"19.0-24.1",
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"24.1-28.2",
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"28.2-32.2",
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"32.2-35.7",
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"35.7-45.8",
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"45.8+",
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]
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]
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vo2_max_colors = [
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[
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"#b2ebf2",
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"#f5f5f5",
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"#f5f5f5",
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"#f5f5f5",
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"#f5f5f5",
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"#f5f5f5",
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"#f5f5f5",
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]
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]
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contexts["page_8"]["vo2_max_table"] = graph_generator.generate_table_image(
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data=vo2_max_data,
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columns=vo2_max_columns,
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cell_colors=vo2_max_colors,
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header_color="#4dd0e1",
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save_as_base64=True,
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)
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# Heart Rate Zones Table
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hr_zones_columns = ["Zone 1", "Zone 2", "Zone 3", "Zone 4", "Zone 5"]
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hr_zones_data = [
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[
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"Improves health and recovery capacity",
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"Improves endurance and fat burning",
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"Improves Aerobic fitness",
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"Improves maximum performance capacity",
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"Develops maximum performance and speed",
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],
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[
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"55-65% of Max Heart Rate",
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"65-75% of Max Heart Rate",
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"80-85% of Max Heart Rate",
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"85-88% of Max Heart Rate",
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"90% of Max Heart Rate",
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],
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[
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pnoe_metrics.get("zone1_bpm", "81-96bpm"),
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pnoe_metrics.get("zone2_bpm", "96-100bpm"),
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pnoe_metrics.get("zone3_bpm", "100-178bpm"),
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pnoe_metrics.get("zone4_bpm", "178-188bpm"),
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pnoe_metrics.get("zone5_bpm", "188-198bpm"),
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],
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[
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"3.5mph\n2% Incline",
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"3.5-4.0mph\n2% Incline",
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"4.0-6.5mph\n2% Incline",
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"6.5-7.0mph\n2% Incline",
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"7.0-8.0mph\n2% Incline",
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],
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[
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"10:39min/km Pace",
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"10:39-9:19min/km Pace",
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"9:19-5:44min/km Pace",
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"5:44-5:20min/km Pace",
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"5:20-4:40min/km Pace",
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],
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[
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"Avg:\n4.4kcals/minute",
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"Avg:\n5.9kcals/minute",
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"Avg:\n9.4kcals/minute",
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"Avg:\n12.5kcals/minute",
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"Avg:\n12.8kcals/minute",
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],
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[
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"Avg: 0.4g/min\nCarb Utilization",
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"Avg: 0.6g/min\nCarb Utilization",
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"Avg: 1.9g/min\nCarb Utilization",
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"Avg: 2.9g/min\nCarb Utilization",
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"Avg: 3.1g/min\nCarb Utilization",
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],
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[
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"Avg: 27 breaths\nIdeal: 15-20",
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"Avg: 28 breaths\nIdeal: 20-25",
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"Avg: 31 breaths\nIdeal: 25-30",
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"Avg: 42 breaths\nIdeal: 30-35",
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"Avg: 51 breaths\nIdeal: 40+",
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],
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]
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hr_zones_colors = [
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["#ffffff"] * 5,
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["#ffffff"] * 5,
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["#ffcdd2", "#ffcdd2", "#fff9c4", "#c8e6c9", "#c8e6c9"],
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["#ffffff"] * 5,
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["#ffffff"] * 5,
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["#ffffff"] * 5,
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["#ffffff"] * 5,
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["#ffcdd2", "#ffcdd2", "#fff9c4", "#c8e6c9", "#c8e6c9"],
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]
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contexts["page_8"]["hr_zones_table"] = graph_generator.generate_table_image(
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data=hr_zones_data,
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columns=hr_zones_columns,
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cell_colors=hr_zones_colors,
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header_color="#4dd0e1",
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save_as_base64=True,
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)
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# Page 9
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contexts["page_9"] = {
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"fat_max_value": f"{pnoe_metrics['fat_max_value']:.2f}",
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@@ -752,6 +874,40 @@ class ContextGenerator:
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**resting_hr_metrics,
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}
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if graph_generator:
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# Page 11 Resting Heart Rate Table
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rhr_columns = [
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"Age (F)",
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"Poor",
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"Below Average",
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"Average",
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"Above Average",
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"Good",
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"Excellent",
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"Athlete",
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]
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rhr_data = [
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[
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contexts["page_11"]["hr_age_range"],
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contexts["page_11"]["hr_poor"],
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contexts["page_11"]["hr_below_avg"],
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contexts["page_11"]["hr_average"],
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contexts["page_11"]["hr_above_avg"],
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contexts["page_11"]["hr_good"],
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contexts["page_11"]["hr_excellent"],
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contexts["page_11"]["hr_athlete"],
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]
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]
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rhr_colors = [["#b2ebf2"] + ["#f5f5f5"] * 7]
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contexts["page_11"]["rhr_table"] = graph_generator.generate_table_image(
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data=rhr_data,
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columns=rhr_columns,
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cell_colors=rhr_colors,
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header_color="#4dd0e1",
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save_as_base64=True,
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)
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# Pages 12-17
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for i in range(6):
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contexts[f"page_{i + 12}"] = {
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@@ -1305,3 +1305,86 @@ class GraphGenerator:
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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_table_image(
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self,
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data: list[list],
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columns: list[str],
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title: str = None,
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col_widths: list[float] = None,
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cell_colors: list[list[str]] = None,
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header_color: str = "#4dd0e1",
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save_as_base64: bool = True,
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) -> str:
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"""
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Generate a table as an image.
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Args:
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data: List of rows (each row is a list of values)
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columns: List of column headers
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title: Optional title for the table
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col_widths: Optional list of column widths
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cell_colors: Optional matrix of cell colors (same shape as data)
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header_color: Color for the header row
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save_as_base64: If True, return base64 string
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Returns:
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Base64 string or file path
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"""
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# Calculate figure size based on rows and columns
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# Approximate height: header + rows
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height = (len(data) + 1) * 0.5 + (0.5 if title else 0)
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width = len(columns) * 2.5 if not col_widths else sum(col_widths) * 10
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fig, ax = plt.subplots(figsize=(width, height))
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ax.axis("off")
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if title:
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plt.title(title, pad=20, fontsize=14, fontweight="bold")
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# Create table
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table = ax.table(
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cellText=data,
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colLabels=columns,
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cellLoc="center",
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loc="center",
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colColours=[header_color] * len(columns),
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)
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# Style the table
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table.auto_set_font_size(False)
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table.set_fontsize(10)
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table.scale(1, 1.5) # Increase row height
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# Apply cell colors if provided
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if cell_colors:
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for i, row_colors in enumerate(cell_colors):
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for j, color in enumerate(row_colors):
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if color:
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# (row_idx, col_idx) - row_idx starts at 1 for data (0 is header)
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cell = table[(i + 1, j)]
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cell.set_facecolor(color)
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# Bold headers
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for (row, col), cell in table.get_celld().items():
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if row == 0:
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cell.set_text_props(weight="bold")
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cell.set_height(0.1)
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plt.tight_layout()
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if save_as_base64:
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import io
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buf = io.BytesIO()
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plt.savefig(buf, format="png", bbox_inches="tight", dpi=300)
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plt.close(fig)
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buf.seek(0)
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return base64.b64encode(buf.read()).decode("utf-8")
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else:
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output_path = (
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self.charts_dir / f"table_{pd.Timestamp.now().timestamp()}.png"
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)
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plt.savefig(output_path, bbox_inches="tight", dpi=300)
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plt.close(fig)
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return str(output_path)
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@@ -507,7 +507,10 @@ class ReportGeneratorService:
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"gender": gender,
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}
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contexts = self.context_generator.generate_all_contexts(
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patient_name, graphs_dict, metric_overrides=metric_overrides
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patient_name,
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graphs_dict,
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metric_overrides=metric_overrides,
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graph_generator=self.graph_generator,
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)
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# Step 5: Calculate analysis metrics
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