From 100b47e9477104c45702a85c405d52ce4b65f370 Mon Sep 17 00:00:00 2001 From: bolade Date: Wed, 26 Nov 2025 11:23:04 +0100 Subject: [PATCH] Refactor code structure for improved readability and maintainability --- app/main.py | 2 + app/report_gen/page_13.html | 84 +- app/report_gen/page_14.html | 735 +++++++++++++----- app/report_gen/page_16.html | 171 ++-- app/report_gen/page_6.html | 560 ++++++++----- app/report_gen/page_9.html | 10 +- .../context_generator.cpython-312.pyc | Bin 47139 -> 47507 bytes .../graph_generator.cpython-312.pyc | Bin 67353 -> 67486 bytes app/services/context_generator.py | 12 +- app/services/graph_generator.py | 20 +- app/templates/upload.html | 255 ++++-- notebooks/graphs.ipynb | 127 ++- 12 files changed, 1335 insertions(+), 641 deletions(-) diff --git a/app/main.py b/app/main.py index 8dbedea..e80ee5c 100644 --- a/app/main.py +++ b/app/main.py @@ -110,6 +110,7 @@ async def upload_files( fat_percentage: float = Form(...), focus: str = Form(default="Endurance"), session_id: str = Form(default="default"), + next_testing_date: str = Form(...), spirometry_pdf: UploadFile = File(...), pnoe_csv: UploadFile = File(...), oxygenation_csv: UploadFile = File(None), @@ -187,6 +188,7 @@ async def upload_files( "fat_percentage": fat_percentage, "focus": focus, "session_id": session_id, + "next_testing_date": next_testing_date, } # Generate report diff --git a/app/report_gen/page_13.html b/app/report_gen/page_13.html index aa5ecdd..c4c1b51 100644 --- a/app/report_gen/page_13.html +++ b/app/report_gen/page_13.html @@ -27,55 +27,53 @@ -
+
-
-

+
+

Left Leg Analysis

-
-
-
+
+
+
Baseline SmO₂
-
+
{{ left_baseline_smo2 | default('75.4%') }}
-
-
+
+
Minimum SmO₂
-
+
{{ left_minimum_smo2 | default('69.3%') }}
-
+
{{ left_minimum_lap | default('Lap 6') }}
-
-
+
+
Oxygen Drop
-
+
{{ left_oxygen_drop | default('6.0%') }}
-
+
{{ left_drop_percentage | default('8% decrease') }}
-
-
+
+
Recovery
-
- "Optimal >100%" -
-
+
"Optimal >100%"
+
{{ left_recovery_percentage | default('109%') }}
@@ -83,53 +81,51 @@
-
-

+
+

Right Leg Analysis

-
-
-
+
+
+
Baseline SmO₂
-
+
{{ right_baseline_smo2 | default('82.9%') }}
-
-
+
+
Minimum SmO₂
-
+
{{ right_minimum_smo2 | default('73.7%') }}
-
+
{{ right_minimum_lap | default('Lap 6') }}
-
-
+
+
Oxygen Drop
-
+
{{ right_oxygen_drop | default('9.3%') }}
-
+
{{ right_drop_percentage | default('11% decrease') }}
-
-
+
+
Recovery
-
- "Optimal >100%" -
-
+
"Optimal >100%"
+
{{ right_recovery_percentage | default('97%') }}
@@ -138,9 +134,9 @@
-
-

Key Findings

-
+
+

Key Findings

+

• Left leg showed better oxygen maintenance during high-intensity work diff --git a/app/report_gen/page_14.html b/app/report_gen/page_14.html index 8c44c13..3fb3d73 100644 --- a/app/report_gen/page_14.html +++ b/app/report_gen/page_14.html @@ -1,216 +1,529 @@

- - -
- -

Training Recommendations

- - -
- -
- -
-

Zone 2 {{ zone2_frequency | default('3-4x/week') }}:

-
    -
  • {{ zone2_duration | default('40+ minutes') }} of Steady State Cardio (HR {{ zone2_hr_range | default('____') }} bpm)
    • -
  • {{ zone2_speed | default('____ mph') }} at {{ zone2_incline | default('2% Incline') }}
    • -
-
- - -
-

Zone 3 {{ zone3_frequency | default('1-2x/week') }}:

-
    -
  • {{ zone3_duration | default('10-20 minutes') }} in zone 3 (HR {{ zone3_hr_range | default('____ bpm') }})
    • -
  • {{ zone3_speed | default('____mph') }} + at {{ zone3_incline | default('2% Incline') }}
    • -
  • Slow down cadence until HR reaches {{ zone3_target_hr | default('___ bpm') }}
    • -
  • {{ zone3_recovery_speed | default('____mph') }} at {{ zone3_recovery_incline | default('2% Incline') }}
    • -
  • Maintain HR in zone 1 ({{ zone1_hr_range | default('____bpm') }}) for {{ zone1_duration | default('4-8 minutes') }}
    • -
  • Repeat {{ zone3_repeats | default('2-3 times') }}
    • -
-
-
+ + +
+ +

+ Training Recommendations +

- -
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
TypeSetsEffort DurationZoneRPERecovery Duration
Short{{ short_sets | default('8-10') }}{{ short_duration | default('10-30 seconds') }}{{ short_zone | default('5') }}{{ short_rpe | default('10') }}{{ short_recovery | default('20-60 seconds') }}
Medium{{ medium_sets | default('6-8') }}{{ medium_duration | default('30-90 seconds') }}{{ medium_zone | default('4') }}{{ medium_rpe | default('8-9') }}{{ medium_recovery | default('30-90 seconds') }}
Long{{ long_sets | default('4-6') }}{{ long_duration | default('5-10 minutes') }}{{ long_zone | default('3/4') }}{{ long_rpe | default('7-8') }}{{ long_recovery | default('2.5-5 minutes') }}
Tempo{{ tempo_sets | default('2-3') }}{{ tempo_duration | default('10-20 minutes') }}{{ tempo_zone | default('3') }}{{ tempo_rpe | default('6-7') }}{{ tempo_recovery | default('4-8 minutes') }}
Cardio{{ cardio_sets | default('1') }}{{ cardio_duration | default('>40 minutes') }}{{ cardio_zone | default('2') }}{{ cardio_rpe | default('4-5') }}{{ cardio_recovery | default('N/A') }}
-
+ +
+ +
+ +
+

+ Zone 2 {{ zone2_frequency | default('3-4x/week') }}: +

+
    +
  • + {{ zone2_duration | default('40+ minutes') }} of + Steady State Cardio (HR + {{ zone2_hr_range | default('____') }} + bpm) +
    • +
  • + {{ zone2_speed | default('___') }} + mph at {{ zone2_incline | default('2% Incline') }} +
    • +
+
+ + +
+

+ Zone 3: {{ zone3_frequency | default('1-2x/week') }}: +

+
    +
  • + {{ zone3_duration | default('10-20 minutes') }} in + zone 3 (HR + {{ zone3_hr_range | default('____') }} + bpm) +
    • +
  • + {{ zone3_speed | default('____') }}mph + at {{ zone3_incline | default('2% Incline') + }} +
    • +
  • + Slow down cadence until HR reaches + {{ zone3_target_hr | default('___') }}bpm +
    • +
  • + {{ zone3_recovery_speed | default('____') + }}mph at {{ zone3_recovery_incline | default('2% + Incline') }} +
    • +
  • + Maintain HR in zone 1 ({{ zone1_hr_range | default('____') }}bpm) for {{ zone1_duration | default('4-8 minutes') + }} +
    • +
  • + Repeat {{ zone3_repeats | default('2-3 times') }} +
    • +
+
+
+ + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ Type + + Sets + + Effort Duration + + Zone + + RPE + + Recovery Duration +
+ Short + + {{ short_sets | default('8-10') }} + + {{ short_duration | default('10-30 seconds') }} + + {{ short_zone | default('5') }} + + {{ short_rpe | default('10') }} + + {{ short_recovery | default('20-60 seconds') }} +
+ Medium + + {{ medium_sets | default('6-8') }} + + {{ medium_duration | default('30-90 seconds') }} + + {{ medium_zone | default('4') }} + + {{ medium_rpe | default('8-9') }} + + {{ medium_recovery | default('30-90 seconds') }} +
+ Long + + {{ long_sets | default('4-6') }} + + {{ long_duration | default('5-10 minutes') }} + + {{ long_zone | default('3/4') }} + + {{ long_rpe | default('7-8') }} + + {{ long_recovery | default('2.5-5 minutes') }} +
+ Tempo + + {{ tempo_sets | default('2-3') }} + + {{ tempo_duration | default('10-20 minutes') }} + + {{ tempo_zone | default('3') }} + + {{ tempo_rpe | default('6-7') }} + + {{ tempo_recovery | default('4-8 minutes') }} +
+ Cardio + + {{ cardio_sets | default('1') }} + + {{ cardio_duration | default('>40 minutes') }} + + {{ cardio_zone | default('2') }} + + {{ cardio_rpe | default('4-5') }} + + {{ cardio_recovery | default('N/A') }} +
+
+
+ + +
+

+ Training Week Example with Progression +

+ + +
+
+ +
+
+ Monday +
+
+ +
+
+ Tuesday +
+
+ +
+
+ Wednesday +
+
+ +
+
+ Thursday +
+
+ +
+
+ Friday +
+
+ +
+
+ Saturday +
+
+ +
+
+ Sunday +
+
+
+
+ +
+
+ {{ week1_mon_zone | default('Zone 2') }} +
+
+ {{ week1_mon_duration | default('45 mins') }} +
+
+ +
+
+ {{ week1_tue_zone | default('Zone 2') }} +
+
+ {{ week1_tue_duration | default('45 mins') }} +
+
+ +
+
+ {{ week1_wed_zone | default('Zone 3') }} +
+
+ {{ week1_wed_duration1 | default('10mins On') }} +
+
+ {{ week1_wed_duration2 | default('8mins Rest') }} +
+
+ {{ week1_wed_sets | default('x2') }} +
+
+ +
+
+ {{ week1_thu_content | default('') }} +
+
+ +
+
+ {{ week1_fri_zone | default('Zone 2') }} +
+
+ {{ week1_fri_duration | default('45 mins') }} +
+
+ +
+
+ {{ week1_sat_content | default('') }} +
+
+ +
+
+ {{ week1_sun_content | default('') }} +
+
+
+
+ + +
+
+ +
+
+ Monday +
+
+ +
+
+ Tuesday +
+
+ +
+
+ Wednesday +
+
+ +
+
+ Thursday +
+
+ +
+
+ Friday +
+
+ +
+
+ Saturday +
+
+ +
+
+ Sunday +
+
+
+
+ +
+
+ {{ week2_mon_zone | default('Zone 2') }} +
+
+ {{ week2_mon_duration | default('50 mins') }} +
+
+ +
+
+ {{ week2_tue_zone | default('Zone 2') }} +
+
+ {{ week2_tue_duration | default('50 mins') }} +
+
+ +
+
+ {{ week2_wed_zone | default('Zone 3') }} +
+
+ {{ week2_wed_duration1 | default('10mins On') }} +
+
+ {{ week2_wed_duration2 | default('6mins Rest') }} +
+
+ {{ week2_wed_sets | default('x2') }} +
+
+ +
+
+ {{ week2_thu_content | default('') }} +
+
+ +
+
+ {{ week2_fri_zone | default('Zone 2') }} +
+
+ {{ week2_fri_duration | default('50 mins') }} +
+
+ +
+
+ {{ week2_sat_content | default('') }} +
+
+ +
+
+ {{ week2_sun_content | default('') }} +
+
+
+
+
- -
-

Training Week Example with Progression

- - -
-
- -
-
Monday
-
{{ week1_mon_zone | default('Zone 2') }}
-
{{ week1_mon_duration | default('45 mins') }}
-
- - -
-
Tuesday
-
{{ week1_tue_zone | default('Zone 2') }}
-
{{ week1_tue_duration | default('45 mins') }}
-
- - -
-
Wednesday
-
{{ week1_wed_zone | default('Zone 3') }}
-
{{ week1_wed_duration1 | default('10mins On') }}
-
{{ week1_wed_duration2 | default('8mins Rest') }}
-
{{ week1_wed_sets | default('x2') }}
-
- - -
-
Thursday
-
{{ week1_thu_content | default('') }}
-
- - -
-
Friday
-
{{ week1_fri_zone | default('Zone 2') }}
-
{{ week1_fri_duration | default('45 mins') }}
-
- - -
-
Saturday
-
{{ week1_sat_content | default('') }}
-
- - -
-
Sunday
-
{{ week1_sun_content | default('') }}
-
-
-
- - -
-
- -
-
Monday
-
{{ week2_mon_zone | default('Zone 2') }}
-
{{ week2_mon_duration | default('50 mins') }}
-
- - -
-
Tuesday
-
{{ week2_tue_zone | default('Zone 2') }}
-
{{ week2_tue_duration | default('50 mins') }}
-
- - -
-
Wednesday
-
{{ week2_wed_zone | default('Zone 3') }}
-
{{ week2_wed_duration1 | default('10mins On') }}
-
{{ week2_wed_duration2 | default('6mins Rest') }}
-
{{ week2_wed_sets | default('x2') }}
-
- - -
-
Thursday
-
{{ week2_thu_content | default('') }}
-
- - -
-
Friday
-
{{ week2_fri_zone | default('Zone 2') }}
-
{{ week2_fri_duration | default('50 mins') }}
-
- - -
-
Saturday
-
{{ week2_sat_content | default('') }}
-
- - -
-
Sunday
-
{{ week2_sun_content | default('') }}
-
-
-
-
-
- - +
diff --git a/app/report_gen/page_16.html b/app/report_gen/page_16.html index 2184aee..4fb634e 100644 --- a/app/report_gen/page_16.html +++ b/app/report_gen/page_16.html @@ -1,70 +1,109 @@
+ +
+ +

Next Steps:

- -
- -

Next Steps:

- - -
-

Calorie Tracking

-
    -
  • Download and create an account with My Fitness Pal (or preferred nutrition tracker)
    • -
  • Fill out the "My Profile" section with your goals (ie: height, weight, target weight) -
      -
    • Input your Macros
      • -
    • Click the three dots on the bottom right corner
      • -
    • Click "Goals"
      • -
    • Click "Calorie, Carbs, Protein and Fat Goals" under the Nutrition Goals
      • -
    • Set the Calories, Carbs, Protein, and Fat to the recommended macro outlined above.
      • -
    -
    • -
  • Once completed fill out your food intake from each meal on the main page
    • -
- - -
-

It's highly recommended to purchase a weight and food scale for more accurate results.

-
+ +
+

Calorie Tracking

+
    +
  • + Download and create an account with My Fitness Pal (or + preferred nutrition tracker) +
    • +
  • + Fill out the "My Profile" section with your goals (ie: + height, weight, target weight) +
      +
    • Input your Macros
      • +
    • Click the three dots on the bottom right corner
      • +
    • Click "Goals"
      • +
    • + Click "Calorie, Carbs, Protein and Fat Goals" under + the Nutrition Goals +
      • +
    • + Set the Calories, Carbs, Protein, and Fat to the + recommended macro outlined above. +
      • +
    +
    • +
  • + Once completed fill out your food intake from each meal on + the main page +
    • +
+ + +
+

+ It's highly recommended to purchase a weight and food scale + for more accurate results. +

+
+
+ + +
+

Daily Tasks

+
    +
  • + Weigh yourself in the morning, after your first bowel + movement, and naked +
    • +
  • Log your weight into your my fitness pal app
    • +
  • + Track calories in grams - FOLLOW YOUR PERSONAL + RECOMMENDATIONS. +
    • +
  • + Log in a diary: +
      +
    • + Log any additional prescribed recommendation (i.e + breath work) +
      • +
    • + Complete the prescribed training recommendations + (i.e Zone 2 Training) +
      • +
    • + Log additional physical activity (i.e Monday - + Strength Training 1 hour) +
      • +
    +
    • +
+
+ + +
+

+ Two weeks after Appointment +

+
    +
  • + Should you find the macronutrient breakdown difficult to + follow, reach out to us to discuss a change within your + caloric parameters +
    • +
+
+ + +
+

+ Should you have any questions or concerns please contact us! +

+
+ + +
+

+ Recommended Next Testing Date: + {{ next_testing_date }} +

+
- - -
-

Daily Tasks

-
    -
  • Weigh yourself in the morning, after your first bowel movement, and naked
    • -
  • Log your weight into your my fitness pal app
    • -
  • Track calories in grams - FOLLOW YOUR PERSONAL RECOMMENDATIONS.
    • -
  • Log in a diary: -
      -
    • Log any additional prescribed recommendation (i.e breath work)
      • -
    • Complete the prescribed training recommendations (i.e Zone 2 Training)
      • -
    • Log additional physical activity (i.e Monday - Strength Training 1 hour)
      • -
    -
    • -
-
- - -
-

Two weeks after Appointment

-
    -
  • Should you find the macronutrient breakdown difficult to follow, reach out to us to discuss a change within your caloric parameters
    • -
-
- - -
-

Should you have any questions or concerns please contact us!

-
- - -
-

- Recommended Next Testing Date: - October 2025 -

-
-
-
diff --git a/app/report_gen/page_6.html b/app/report_gen/page_6.html index c15046d..ef54354 100644 --- a/app/report_gen/page_6.html +++ b/app/report_gen/page_6.html @@ -1,219 +1,351 @@
- - -
- -

Weekly Meal Plan Breakdown

- - -
-

Caloric Deficit Example

- - -
- -
-
Monday
-
{{ deficit_calories | default('1725KCals') }}
-
-
{{ deficit_protein | default('120g Protein') }}
-
{{ deficit_carbs | default('155g Carbs') }}
-
{{ deficit_fat | default('69g Fat') }}
-
{{ deficit_fiber | default('25g Fibre') }}
-
-
- - -
-
Tuesday
-
{{ deficit_calories | default('1725KCals') }}
-
-
{{ deficit_protein | default('120g Protein') }}
-
{{ deficit_carbs | default('155g Carbs') }}
-
{{ deficit_fat | default('69g Fat') }}
-
{{ deficit_fiber | default('25g Fibre') }}
-
-
- - -
-
Wednesday
-
{{ deficit_calories | default('1725KCals') }}
-
-
{{ deficit_protein | default('120g Protein') }}
-
{{ deficit_carbs | default('155g Carbs') }}
-
{{ deficit_fat | default('69g Fat') }}
-
{{ deficit_fiber | default('25g Fibre') }}
-
-
- - -
-
Thursday
-
{{ deficit_calories | default('1725KCals') }}
-
-
{{ deficit_protein | default('120g Protein') }}
-
{{ deficit_carbs | default('155g Carbs') }}
-
{{ deficit_fat | default('69g Fat') }}
-
{{ deficit_fiber | default('25g Fibre') }}
-
-
- - -
-
Friday
-
{{ deficit_calories | default('1725KCals') }}
-
-
{{ deficit_protein | default('120g Protein') }}
-
{{ deficit_carbs | default('155g Carbs') }}
-
{{ deficit_fat | default('69g Fat') }}
-
{{ deficit_fiber | default('25g Fibre') }}
-
-
- - -
-
Saturday
-
{{ deficit_calories | default('1725KCals') }}
-
-
{{ deficit_protein | default('120g Protein') }}
-
{{ deficit_carbs | default('155g Carbs') }}
-
{{ deficit_fat | default('69g Fat') }}
-
{{ deficit_fiber | default('25g Fibre') }}
-
-
- - -
-
Sunday
-
{{ deficit_calories | default('1725KCals') }}
-
-
{{ deficit_protein | default('120g Protein') }}
-
{{ deficit_carbs | default('155g Carbs') }}
-
{{ deficit_fat | default('69g Fat') }}
-
{{ deficit_fiber | default('25g Fibre') }}
-
-
-
-
+ + +
+ +

+ Weekly Meal Plan Breakdown +

- -
-

Caloric Deficit with Maintenance/Refeed Example

- - -
- -
-
Monday
-
{{ refeed_weekday_calories | default('1615KCals') }}
-
-
{{ refeed_weekday_protein | default('120g Protein') }}
-
{{ refeed_weekday_carbs | default('142g Carbs') }}
-
{{ refeed_weekday_fat | default('63g Fat') }}
-
{{ refeed_weekday_fiber | default('24g Fibre') }}
-
-
- - -
-
Tuesday
-
{{ refeed_weekday_calories | default('1615KCals') }}
-
-
{{ refeed_weekday_protein | default('120g Protein') }}
-
{{ refeed_weekday_carbs | default('142g Carbs') }}
-
{{ refeed_weekday_fat | default('63g Fat') }}
-
{{ refeed_weekday_fiber | default('24g Fibre') }}
-
-
- - -
-
Wednesday
-
{{ refeed_weekday_calories | default('1615KCals') }}
-
-
{{ refeed_weekday_protein | default('120g Protein') }}
-
{{ refeed_weekday_carbs | default('142g Carbs') }}
-
{{ refeed_weekday_fat | default('63g Fat') }}
-
{{ refeed_weekday_fiber | default('24g Fibre') }}
-
-
- - -
-
Thursday
-
{{ refeed_weekday_calories | default('1615KCals') }}
-
-
{{ refeed_weekday_protein | default('120g Protein') }}
-
{{ refeed_weekday_carbs | default('142g Carbs') }}
-
{{ refeed_weekday_fat | default('63g Fat') }}
-
{{ refeed_weekday_fiber | default('24g Fibre') }}
-
-
- - -
-
Friday
-
{{ refeed_weekday_calories | default('1615KCals') }}
-
-
{{ refeed_weekday_protein | default('120g Protein') }}
-
{{ refeed_weekday_carbs | default('142g Carbs') }}
-
{{ refeed_weekday_fat | default('63g Fat') }}
-
{{ refeed_weekday_fiber | default('24g Fibre') }}
-
-
- - -
-
Saturday
-
{{ refeed_weekend_calories | default('2000KCals') }}
-
-
{{ refeed_weekend_protein | default('120g Protein') }}
-
{{ refeed_weekend_carbs | default('190g Carbs') }}
-
{{ refeed_weekend_fat | default('84g Fat') }}
-
{{ refeed_weekend_fiber | default('30g Fibre') }}
-
-
- - -
-
Sunday
-
{{ refeed_weekend_calories | default('2000KCals') }}
-
-
{{ refeed_weekend_protein | default('120g Protein') }}
-
{{ refeed_weekend_carbs | default('190g Carbs') }}
-
{{ refeed_weekend_fat | default('84g Fat') }}
-
{{ refeed_weekend_fiber | default('30g Fibre') }}
-
-
-
-
+ +
+

+ Caloric Deficit Example +

- -
-

Macronutrients Recommendations

- - -
- -
-
{{ protein_percentage | default('28%') }}
-
Protein
-
- - -
-
{{ carbs_percentage | default('36%') }}
-
Carbs
-
- - -
-
{{ fats_percentage | default('36%') }}
-
Fats
-
-
-
-
+ +
+ +
+
+ Monday +
+
+ {{ deficit_calories | default('1725KCals') }} +
+
+
+ {{ deficit_protein | default('120g Protein') }} +
+
{{ deficit_carbs | default('155g Carbs') }}
+
{{ deficit_fat | default('69g Fat') }}
+
{{ deficit_fiber | default('25g Fibre') }}
+
+
+ +
+
+ Tuesday +
+
+ {{ deficit_calories | default('1725KCals') }} +
+
+
+ {{ deficit_protein | default('120g Protein') }} +
+
{{ deficit_carbs | default('155g Carbs') }}
+
{{ deficit_fat | default('69g Fat') }}
+
{{ deficit_fiber | default('25g Fibre') }}
+
+
+ + +
+
+ Wednesday +
+
+ {{ deficit_calories | default('1725KCals') }} +
+
+
+ {{ deficit_protein | default('120g Protein') }} +
+
{{ deficit_carbs | default('155g Carbs') }}
+
{{ deficit_fat | default('69g Fat') }}
+
{{ deficit_fiber | default('25g Fibre') }}
+
+
+ + +
+
+ Thursday +
+
+ {{ deficit_calories | default('1725KCals') }} +
+
+
+ {{ deficit_protein | default('120g Protein') }} +
+
{{ deficit_carbs | default('155g Carbs') }}
+
{{ deficit_fat | default('69g Fat') }}
+
{{ deficit_fiber | default('25g Fibre') }}
+
+
+ + +
+
+ Friday +
+
+ {{ deficit_calories | default('1725KCals') }} +
+
+
+ {{ deficit_protein | default('120g Protein') }} +
+
{{ deficit_carbs | default('155g Carbs') }}
+
{{ deficit_fat | default('69g Fat') }}
+
{{ deficit_fiber | default('25g Fibre') }}
+
+
+ + +
+
+ Saturday +
+
+ {{ deficit_calories | default('1725KCals') }} +
+
+
+ {{ deficit_protein | default('120g Protein') }} +
+
{{ deficit_carbs | default('155g Carbs') }}
+
{{ deficit_fat | default('69g Fat') }}
+
{{ deficit_fiber | default('25g Fibre') }}
+
+
+ + +
+
+ Sunday +
+
+ {{ deficit_calories | default('1725KCals') }} +
+
+
+ {{ deficit_protein | default('120g Protein') }} +
+
{{ deficit_carbs | default('155g Carbs') }}
+
{{ deficit_fat | default('69g Fat') }}
+
{{ deficit_fiber | default('25g Fibre') }}
+
+
+
+
+ + +
+

+ Caloric Deficit with Maintenance/Refeed Example +

+ + +
+ +
+
+ Monday +
+
+ {{ refeed_weekday_calories | default('1615KCals') }} +
+
+
+ {{ refeed_weekday_protein | default('120g Protein') + }} +
+
+ {{ refeed_weekday_carbs | default('142g Carbs') }} +
+
{{ refeed_weekday_fat | default('63g Fat') }}
+
+ {{ refeed_weekday_fiber | default('24g Fibre') }} +
+
+
+ + +
+
+ Tuesday +
+
+ {{ refeed_weekday_calories | default('1615KCals') }} +
+
+
+ {{ refeed_weekday_protein | default('120g Protein') + }} +
+
+ {{ refeed_weekday_carbs | default('142g Carbs') }} +
+
{{ refeed_weekday_fat | default('63g Fat') }}
+
+ {{ refeed_weekday_fiber | default('24g Fibre') }} +
+
+
+ + +
+
+ Wednesday +
+
+ {{ refeed_weekday_calories | default('1615KCals') }} +
+
+
+ {{ refeed_weekday_protein | default('120g Protein') + }} +
+
+ {{ refeed_weekday_carbs | default('142g Carbs') }} +
+
{{ refeed_weekday_fat | default('63g Fat') }}
+
+ {{ refeed_weekday_fiber | default('24g Fibre') }} +
+
+
+ + +
+
+ Thursday +
+
+ {{ refeed_weekday_calories | default('1615KCals') }} +
+
+
+ {{ refeed_weekday_protein | default('120g Protein') + }} +
+
+ {{ refeed_weekday_carbs | default('142g Carbs') }} +
+
{{ refeed_weekday_fat | default('63g Fat') }}
+
+ {{ refeed_weekday_fiber | default('24g Fibre') }} +
+
+
+ + +
+
+ Friday +
+
+ {{ refeed_weekday_calories | default('1615KCals') }} +
+
+
+ {{ refeed_weekday_protein | default('120g Protein') + }} +
+
+ {{ refeed_weekday_carbs | default('142g Carbs') }} +
+
{{ refeed_weekday_fat | default('63g Fat') }}
+
+ {{ refeed_weekday_fiber | default('24g Fibre') }} +
+
+
+ + +
+
+ Saturday +
+
+ {{ refeed_weekend_calories | default('2000KCals') }} +
+
+
+ {{ refeed_weekend_protein | default('120g Protein') + }} +
+
+ {{ refeed_weekend_carbs | default('190g Carbs') }} +
+
{{ refeed_weekend_fat | default('84g Fat') }}
+
+ {{ refeed_weekend_fiber | default('30g Fibre') }} +
+
+
+ + +
+
+ Sunday +
+
+ {{ refeed_weekend_calories | default('2000KCals') }} +
+
+
+ {{ refeed_weekend_protein | default('120g Protein') + }} +
+
+ {{ refeed_weekend_carbs | default('190g Carbs') }} +
+
{{ refeed_weekend_fat | default('84g Fat') }}
+
+ {{ refeed_weekend_fiber | default('30g Fibre') }} +
+
+
+
+
+ + +
+

+ Macronutrients Recommendations +

+ + +
+ +
+
+ {{ protein_percentage | default('28%') }} +
+
Protein
+
+ + +
+
+ {{ carbs_percentage | default('36%') }} +
+
Carbs
+
+ + +
+
+ {{ fats_percentage | default('36%') }} +
+
Fats
+
+
+
+
diff --git a/app/report_gen/page_9.html b/app/report_gen/page_9.html index 01ba125..9b1ecaf 100644 --- a/app/report_gen/page_9.html +++ b/app/report_gen/page_9.html @@ -1,6 +1,12 @@
+ +

+ Fuel Utilization Report - Institute of Science, Health and + Performance +

+

- Client: {{ client_name | default('Keirstyn Moran') }} | - Assessment Date: {{ assessment_date | default('July 29 2025') }} + Client: {{ client_name }} | Assessment Date: {{ assessment_date + }}

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Tjuj?W6X|oY{0RwEqJ-%$VZ!g7 diff --git a/app/services/context_generator.py b/app/services/context_generator.py index 040196c..8690fe9 100644 --- a/app/services/context_generator.py +++ b/app/services/context_generator.py @@ -1297,6 +1297,8 @@ class ContextGenerator: # Page 9 contexts["page_9"] = { + "client_name": self.patient_info["name"], + "assessment_date": datetime.now().strftime("%B %d %Y"), "fat_max_value": f"{pnoe_metrics['fat_max_value']:.2f}", "fat_max_hr": f"{int(pnoe_metrics['fat_max_hr'])}", "fuel_utilization_chart": graphs.get("fuel_utilization", ""), @@ -1410,10 +1412,16 @@ class ContextGenerator: # Pages 14-18 (previously 13-17) for i in range(1, 6): - contexts[f"page_{i + 13}"] = { + page_num = i + 13 + contexts[f"page_{page_num}"] = { "patient_name": self.patient_info["name"], - "page_number": i + 13, + "page_number": page_num, } + # Add next_testing_date to page 16 + if page_num == 16: + contexts["page_16"]["next_testing_date"] = self.patient_info.get( + "next_testing_date", "Contact us for scheduling" + ) # Page 19 - Glossary with Body Fat Percentage Master Chart (previously page 18) contexts["page_19"] = { diff --git a/app/services/graph_generator.py b/app/services/graph_generator.py index 0bcb6b5..edde5c2 100644 --- a/app/services/graph_generator.py +++ b/app/services/graph_generator.py @@ -1141,15 +1141,20 @@ class GraphGenerator: fig, ax = plt.subplots(figsize=(10, 2.5)) # Chart data and positions + # Use normalized positions (0-100 scale) for uniform bar length categories = ["Very Slow", "Slow", "Average", "Fast", "Very Fast"] - positions = [1500, 3000, 4500, 6000, 7500] - indicator_pos = rmr_kcal - highlight_end = rmr_kcal + positions = [10, 30, 50, 70, 90] # Normalized positions on 0-100 scale - # Main Bar (Background) + # Normalize the kcal value to 0-100 scale (assuming range 0-9000 kcal) + max_kcal = 9000 + normalized_value = (rmr_kcal / max_kcal) * 100 + indicator_pos = normalized_value + highlight_end = normalized_value + + # Main Bar (Background) - using 0-100 scale main_bar = FancyBboxPatch( (0, 0.4), - 9000, + 100, 0.2, boxstyle="round,pad=0,rounding_size=0.1", ec="none", @@ -1168,7 +1173,7 @@ class GraphGenerator: ) ax.add_patch(highlight_bar) - # Text and Labels + # Text and Labels (show actual kcal value) ax.text( highlight_end / 2, 0.5, @@ -1192,7 +1197,7 @@ class GraphGenerator: # Chart Styling ax.set_title("Slow vs Fast Metabolism", fontsize=18, weight="bold", loc="left") - ax.set_xlim(0, 9000) + ax.set_xlim(0, 100) # Normalized scale for uniformity ax.set_ylim(0, 1) ax.axis("off") @@ -1296,6 +1301,7 @@ class GraphGenerator: # Chart Styling ax.set_title("Fuel Source", fontsize=18, weight="bold", loc="left") + ax.set_xlim(0, 100) # Normalized scale for uniformity ax.set_ylim(0, 1) ax.axis("off") diff --git a/app/templates/upload.html b/app/templates/upload.html index 6f18a5a..f0e11cd 100644 --- a/app/templates/upload.html +++ b/app/templates/upload.html @@ -1,55 +1,116 @@ -{% extends "base.html" %} - -{% block title %}Upload Patient Data - Report Generator{% endblock %} - -{% block content %} +{% extends "base.html" %} {% block title %}Upload Patient Data - Report +Generator{% endblock %} {% block content %}
-

Upload Patient Data and Files

- +

+ Upload Patient Data and Files +

+ {% if error %}

{{ error }}

{% endif %} - -
+ +
-

Patient Information

+

+ Patient Information +

- - -
-
- - -
-
- - -
-
- - First Name + + />
- - Last Name + + />
- - +
+
+ + +
+
+ + +
+
+ +
- - + +
- - + + +
+
+ +
-

Upload Files

+

+ Upload Files +

- - + +
- - + +
- - Body Fat Percentage (%) + + placeholder="22.5" + />
- - -

Upload NIRS muscle oxygen CSV file to generate TSI graph

+ + +

+ Upload NIRS muscle oxygen CSV file to generate + TSI graph +

-
@@ -110,4 +252,3 @@
{% endblock %} - diff --git a/notebooks/graphs.ipynb b/notebooks/graphs.ipynb index 7156f26..6508bf9 100644 --- a/notebooks/graphs.ipynb +++ b/notebooks/graphs.ipynb @@ -42,7 +42,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_680949/3076306744.py:3: FutureWarning: errors='ignore' is deprecated and will raise in a future version. Use to_numeric without passing `errors` and catch exceptions explicitly instead\n", + "/tmp/ipykernel_1419466/3076306744.py:3: FutureWarning: errors='ignore' is deprecated and will raise in a future version. Use to_numeric without passing `errors` and catch exceptions explicitly instead\n", " df = df.apply(pd.to_numeric, errors='ignore')\n" ] } @@ -238,7 +238,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 34, "id": "470e871e", "metadata": {}, "outputs": [ @@ -252,7 +252,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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E1bl16xZWrVqFsLAwZGVlwdTUFP7+/pgxYwZatmz5VG3+8ssv8PDwQN++fVXcWyIijmATERERUSN08OBBjBw5EufOnUNQUBA++eQTjB49GmFhYRg5ciQOHTr0VO2uWrUKhw8fViofPnw4IiMj4eDg8F+7TkTNGEewiYiIiKhRSUhIwDvvvAMnJyds2rQJ5ubmwraJEyciODgY77zzDnbt2gUnJyeVvKdYLIZYLFZJW0TUfHEEm4iIiIgald9++w1FRUX44osvFMI1AJibm+Pzzz9HYWEhVq9eDQBYvnw5PD09cefOHcyZMwcdOnSAv78/Fi5ciOLiYuG1np6eKCwsREhICDw9PeHp6Yn33nsPQPXXQffu3RvTp09HWFgYgoKC4OPjg2HDhiEsLAyAfJR92LBhaNeuHYKCghAdHa3Q1xs3buC9995Dnz590K5dOwQGBmLBggXIysp6JvuNiBoeR7CJiIiIqFE5duwYHBwc0KlTp2q3d+7cGQ4ODjhx4oRC+dy5c+Hg4IC33noLERER2LBhA3Jzc/HNN98AAL755ht8+OGH8PHxwUsvvQQAcHZ2fmxf7t69i7feegtjx47FCy+8gLVr12LGjBn47LPP8MMPP2DcuHEAgF9//RVz587F/v37oaEhH8M6e/Ys7t27h6CgIFhZWeHWrVvYunUrbt++ja1bt0IkEv2n/UREjQ8DNhERERE1Gnl5eUhNTUWfPn0eW8/T0xNHjx5Ffn6+UObo6Iiff/4ZABAcHAxDQ0Ns3rwZr776Kry8vDB8+HB8+umncHJywvDhw2vVn7i4OGzZsgXt27cHAHh4eGDKlCn46KOPsG/fPtjb2wMATExM8PHHH+PChQvw9/cHAIwfPx6vvvqqQnt+fn743//+h4sXL9Z4AoGImi5OESciIiKiRqOgoAAAYGBg8Nh6Fdsr6gPyUF3VhAkTAAAnT5586v54eHgI4RoAfH19AQBdu3YVwnXV8nv37gllurq6wuPi4mJkZmYK9a5du/bUfSKixosj2ERERETUaFQXnKtTXRBv0aKFQh1nZ2doaGj8p/tL29nZKTw3MjICANja2iqUGxoaAgByc3OFsuzsbKxYsQJ79+5FRkaGQv28vLyn7hMRNV4M2ERERETUaBgZGcHKygoxMTGPrRcTEwMbGxsh2FZHFdc417SyeE3lMplMeDx37lxcvnwZU6ZMQevWraGvr4/y8nK89tprCvWISH1wijgRERERNSq9evXC/fv3ER4eXu328PBwJCYmomfPngrld+/eVXpeXl4OR0fHZ9XVGuXk5CA0NBRTp07F7Nmz0a9fPwQGBqrstmJE1DgxYBMRERFRozJlyhTo6urik08+UbqlVXZ2Nj755BPo6enhtddeU9i2adMmhecbN24EADz//PNCmb6+vsI07melphHu9evXP/P3JqKGwyniRERERNSouLi4YPHixZg/fz6GDRuG0aNHw9HREYmJidi+fTuysrLw/fffK91i6/79+5gxYwa6d++OiIgI7Nq1C0OHDoWXl5dQx9vbG6GhoVi3bh2sra3h6OgoLDymSoaGhujcuTN+++03lJaWwsbGBmfOnPlP14MTUePHgE1EREREjc6gQYPg5uaGX3/9Fdu3b0d2djZMTU3h7++P6dOno1WrVkqvWbp0KX788Ud899130NTUxIQJE/DOO+8o1Hnvvffw8ccfY+nSpZBIJBg5cuQzCdgA8N133+GLL77A5s2bIZPJEBgYiNWrV6N79+7P5P2IqOGJZFxhgYiIiIiasOXLl2PFihUIDQ2Fubl5Q3eHiJoxXoNNREREREREpAIM2EREREREREQqwGuwiYhqKSMjA3l5eSprz9jYmFMZiYgakW3btmHJkiV45513MHr06IbuDhE1QbwGm4ioFkpLS/H222+r9NYuJiYmWLJkCbS0tFTWJhERPb3WrVvjxo0b8PLywvXr1xu6O0TUBHGKOBFRLWhqasLCwgIikUgl7YlEIpibm0NTkxOJiIgai4pZSqqcrUREzQsDNhFRLYhEIowcORKqmvQjk8kwcuRIlQV2IiIiImp4DNhERLXUtm1buLq6/udQLBKJ4OrqirZt26qoZ0RERETUGDBgExHVkqpGsTl6TURERKSeGLCJiOrgv45ic/SaiIiISH0xYBMR1cF/HcXm6DURERGR+mLAJiKqo6cdxeboNREREZF6Y8AmIqqjpx3F5ug1ERERkXpjwCYiegp1HcXm6DURERGR+mPAJiJ6CnUdxeboNREREZH6Y8AmInpKtR3F5ug1ERERUfPAgE1E9JRqO4rN0WsiIiKi5oEBm4joP3jSKDZHr4mIiIiaDwZsIqL/4Emj2By9JiIiImo+GLCJiP6jmkaxOXpNRERE1LwwYBMR/Uc1jWJz9JqIiIioeWHAJiJSgUdHsTl6TURERNT8MGATEanAo6PYHL0moqagU6dO6NSpE4YNG9bQXSEiUguaDd2BZ0VWeAMoiGroblA9+3XjKazedKbG7YYGOji2fV6d2tx9KBIPUnIAAONGdIaRoe5/6iOpL28bGVxa2CP+bhKMnV0R5dYW1wobuldE1FSUFBUhcvdO3D55HOnxsSiVFMHA3BKWrm7w6tMfnr36Qayl9Uzeu0AGbOHfK8gMjYT/5/4genZ8tYDWz+bPWYNT24CN0oyG7gGpiT2HruLS1XsAgKH92jFgU41EIhFGDe2M33eeh+vwURy9JqJaS4+PRch785CTlKhQnpuchNzkJMSGnoalqzusW3o2UA+bh5c++gK7ly7BsLnzG7orRGotrRxo3dCdeEbUN2DLShu6B9TAnuvkhsljAhTKxGJeFUHPlncrW0z+7Ctc5Z8gIqqlotwc7Jg/G3kpyQAAQ0srdB77MizdPVBSWIj7ERcRtW+3St+zpKgI2np6Km1THfiPHA3/kaMbuhtEaq+0+rubqgU1DtjShu4BNTAzU334tXVSKi+SlODH1ccQfesBUtJykZsvgY62FtycLTB8oC+GD/AFAFyMvIsZ7/6p8Nrhk34RHv/z+wzY25ji6Okb2BwSjtvxaSguKYWxkR6c7Mzg28YBs17tyVHM5kYmhVSNvzSISPXCt2wUwrWOoSGCV62HkZW1sL1l957oMmEyNMRi5KWl4syan5EScwP56akozs+Htr4BrDxaocPosWjZvafwuoTL4dg6ZwYAwHvgULgHPo/Q9b8h824cugRPQuCr0xX6kfMgCUeXfYuES+HQ1NaCZ6/+eH7mbIUgfvP4EVzcthlpsbchLS6GrrEJzBwcYd/WF8/PeJPfeURUK+qc1BiwqdkpKCzBjr2XFcqk0mJcvZGEqzeSkJqeh6nB3WrV1sXIBCxY9A/KyysTVWZWATKzCnAl+j5mTuoBTTF/bDQrMqlaf2kQkerdOHpQeNzxxfEK4bqCgZk5ACDp2lVE7VUczZbk5eLe5XDcuxyOQe9/Cu+BQ5Vef//KZVw78C8gq/4MYElhIbbMmoq8tBQAQGkREPH3NmQ/SMToJcsAAPciLmL3pwsgKy8XXleYmYHCzAwkXr2C7lNfh0hTfX9aEpHqqPNghPr+FWTAbvb+PRyFfw8rLnQ3pG9bvD2jH6a/3B0uThYwMtCBpqYGMrIKsWrDSSQkZmHjjvOY9FIAPN1tsHpJMJb8fAg3Y1MBAIvfHwELMwMAgKWZIbbuuiSE69cn9UBbTztk5RThzt00HDtzE4zWzZCsTK2/NIhItUoKCxWuu3b0af/Y+gbmFug+fRbMHJ2hY2gIkYYG8lKScfynH1GUnYVzf6ytNmDnPEiErVcbdB4/EWKxJrT09RW2S3JzYOJpjz5z5yM3NQUnVy2HVCJBfNhZ3DlzEu6Bz+POmVNCuO4+7Q3YtW6LwuwspMfdwa2TxwCOXhNRLalzUmPApmbH0EAHnu42+OufcMTcSUFevgRlVUagC4tKEH8/Ay1dreHX1gmGBjrCttatbGFvYyo819SsvKbb2d4MLd1sYGqsB6A1Zk58vj4+DjU2HMEmojooLshXeG5gafXY+iZ29jAwt8SlbX8iLfa2/PVVRqWz7ieguCAfOgaGCq/T0tPHqG+XQ8/YpMa2h37yFcwc5ZdWFWZm4NyGtQCAW6eOwz3weWhUGZ02dXSClUdL6JmYAgC6vTbziZ+ViKiCOg9GqHHALmvoHlADq26RM3MzAxw9E4N3F4Y89rV5+ZJavcfAXt74M+QCSkrL8N5Xf8vfw1QfPm0cMXpoB/i3d3marlNTxmuwiagOHg3CBelpsGjhUmP98K2bcHzFD49tszhfOWA7tPN9bLjWNTYRwjUA2Lb2Fh7nPJCPsLfuNxAXt21GWUkJdn/8HgBA38wcDm194TdyNFp08n9sv4iIKqjzYIRaBmyZrAwAf+E2dzUtcrZo+X7h8dB+7TCwZxvoaGvit81nEHY5HgAgq+EatUd5uFjhj+WTELIvAtduPED8/QxkZhfi+NmbOHnuFn5dEgzfNo4q+TzURMik4ALiRFRb2vr6MLF3EKaJJ0ZdgXPHzjXWv7xjq/C487iJcO0SAA0tLRz+fjHSY28DgMI10hX0H17DXVvVLVZm5eaBl1dvQOSuEDy4HoXMhHgUZmXi1qljuH3mBMYuXw2Hdr51eh8iap5kAMpkgDouVaSWAZvTw+lxUtMrp+PNn9kP+nraKC+XITUjv9r6GhqVR/6jv1lkMhncW1jh7Rn9hLKjp2/g3S//Rnm5DCdCbzFgNzccwSaiOvLq3R9hG9cBAML/2oR2Q4bD8JGp4gVZmdAQi5GfLl8TRM/EBD1mzgYgv+VWfnraY9/jSat7S3JzkHX/njCK/SC6cg0TEzsHAPLvPEtXd/Se87aw7ebxI9j18buQlZfj9unjDNhEVGtSAOKG7sQzwIBNzY6dtTESEjMBAKs2nELXjq7Ye+Qa4hLSq61vZKgrPP57fwQCO7tDR1sTbVrZ4Y9tYbh4NQHdOrvDxtoYejpaCL0YJ9QvKeV/i82PDGWQAVzijohqqdPYCYg+tA95Kckozs/DpumT0GnsBFi5eaCksAD3Ht4He8yPq2BsY4es+wkoyslB2MbfYeXugUvbt0CSm/Of+/Hv5x+g68QpyEtLxcXtlbep9OjWAwBwfvN63Iu4BLeugTC2sYWWnh7iL5wT6pWVcP4OEdWeVAboqOHPJfUM2JweTo8xcpCfMBV8c8gFbA65AB1tTbRuaYvrt5KV6nfyaYFjZ24CANZvPYf1W8/BztoYu9a/DmlZGULDYxEaHqv0Og0NEfp1b/1MPws1TsqTM4mIaqZnbIJRS5Yh5L15yElKRF5aCo4t/67auj7DRuLEzz8CAE79ukL+ehNTmDu3QGbC3afug46hIXKSH+Dv999SKG/RyR/ugfJFO8ulUsSHnUV82Fml14s0NODZu+9Tvz8RNT/qmtjUM2DX8vpZap76dPfCgjcHYtPOMKSk5cHdxQqzp/TC7kOR1QbsoMF+SEnPw8ET0UhNz1O453VgZ3ekpufhSnQi0tLzUFBYDEMDHbRuaYcJo/3h683p4URE9GSWLm54Zd0WRO7eiVsnjyEjPg6lRYXQNzOHhYsbWvcdAAsXN1i6eaCstASRe/5GYXYW7Fp7o/fs+Tjy4zf/MWAbYfS3y3F0+fdIjLwMsZY2PHv1RY+Zc4Tp5W5duyE/LRWJV68gPz0NxYUF0DEwhK1na3Qe9zIc2vmpaG8QUXOgrolNJKvtak5NiEyaB2QdaOhuEFEztdtgFAo5RZyIiIioRkN0ASONJ9dratTwIwHqez6EiIiIiIio6VPXxKamAZuIqOGo6xcGERERkaqo6+8lNQ3Y6vrPRURNAf8CERERETVPahqwiYiIiIiIqLFS1wEJNQ3Y6vrPRURERERERI2VmgZsIiIiIiIiovqlpgGbt8chIiIiIiJqrNQ1sWk2dAeas9txqVi/LQw3bicjPTMfRZJSGBrooKWrNYYP8MHAXt5C3b1HonD+cjyibyUjPTMPEokUNlZGCOzijtfGBcLURL9W71lWVo7tey5h37FriE/IgKS4FIaGumjlZo3RQ9qjdzcvhfrpmflY8+dZnDl/G2mZ+TAy0EVHX2dMn9AdLk4WCnWPnYnB7kNXcTsuFVk5RZCWlcHcxADtWttjwih/tPWyV6hfWlqGTSHnsf9YNO4nZUFTUwwvDxu8PNofgZ3dlfp+LykLv20+jfOX7yI7txBmJvp4rpMbpk3oDmtLI6X6R07dwF+7wnHzTiqk5eVwtjfDCwN88eLQDhCLFc8tFRQWY+2WUBw/G4MHKbnQ09WCTxsHTBkXqNRvALhxOxlrt5xFRNR95BcUw8rCED2fa4Up4wJhbKSrUFcmkyFkXwT+3n8FcQkZEIkA9xZWePGFDhjcu61S23XZ5wBwISIef2wPw7WYJBQXS2FnY4IBPdtg4otdoaOteIg3ln3+ICUHP6w+gktX7wEAOrZzxrzpfWBrZSzUkZaVY/zra5GdU4ida6bB0EBX6f0aK3X9wiCqi5zkBzi/cR3izp9DQUYatPT0YWrvgJbP94L/hMkAgKh9u7F/0WePbcfRrwPGLvv1ie93+9RxxBw/gqSoSOQ8SBTKp/61CyZ2yn/HpSUlCP9rI6IP7kPOg0Ro6erB0ccPAa9MhY2nl1J9mUyG6AP/4uqef5AWewtlJaXQMzODXZu2CHjlNVi5eTx128kx1xH6+2okXr2CUkkRTOwc4D1gMDqNmQCxltZ/6jfbfvq2i3JzcOHPDUiKikTyjWuQFhcDALwHDsWg9z9VaLNUIsHp337GzeOHIcnLg5W7B56fMRuOPn4K9Q4u+RKRu0MwbuUaOLTzVeobETV9IplMpnYXLMukuUDWwYbuxhPtPRqFT5bsqXH765N6YPKYAABA4AtLUFJaVm09e1sT/LFsEkyM9J74nguX7sU/ByJr3P7uG/0xemgHAEBKWi5enbcBqRl5SvUM9LTx89fj0bqlba3aFos18NOisejQzhmAPDy9+cFfCL9yt9r6C94ciKDBfsLzm7EpmDZ/MwoKi5XqWlkY4rfvJsDexlQoW7XhFH7bfKbatgf0bIOF774gPM8vKMZrb2/Enfg0pbqamhpY8lEQunWp/OEUejEW//t0O6TScqX6rs6WWPPdBBgZVobBT7/dg3+PRFXbl0ljAvDGpB7C87ru838OXMGXP+5DdUdxh3ZOWPnVWGhqigE0nn1eXi7Dy2+uw624VHz29jAAwCff7oanuw3W/zgJGhryePrn3xfw/aoj+GDOIIwY2LR+hOwyGIUixmxqxhKvRmDHO3NQUlCgtM3UwRGv/fk3gNoF7BaduuDF73964nv+/f5buH36hFJ5dQG7XCrF9vmzkXDxvFJ9sbY2gr5eihYdu1TWLyvDv59/iJhjh6p978Effo42/Qc/Vdvx588hZME8lJWWKtV36dwVQd/8CA2xmG03QNupt2Lwx5RgpXrVBewjPy7B5R1/oeNL4+HRrQf2fPo+SiUSTFr/F4xt5N/bKTdvYOO0ifDqMwBDPvpCqV2i5mawLmCshvOp1fAjAU1l/MjEUA8jBvris/lDsfKrsVj0/gi0a+0gbP/rn3DhsUgkgp+3Ixa8OQArvhqLGRO7Q+thcEpKzsGWv8OV2n9UXr4Euw9dFZ7PnPg8Vnw1Fv17tBbKtv97WXj80/oTQtDr1sUdyxa+hCnjngMAFBSV4LPv/0XV8zNO9uaYNCYAXy0Yjp8WjcUHcwbB2kI+yllWVo4dVdree/iqEPScHczx7cdB+GjeYBjoaQMAvv/1MJLTcoX63/58SAh6L/T3wbKFLwlhMC0jH0t+qvzRk5CYibVbzgIA9HS18P7sgfj2k1FwdbYEABw4Ho0jp24I9TfuCBPCdTsveyz97EX8b3ofiDVEkErL8cXSfSiSlACQj2AsWrZfCNevvNQVyxa+hJ7PtQIAxCWk4+c/TgptX4iIF8K1uZkBFr77Ar5aMBxWFoYAgPVbQ3Et5sFT7fP8Agm+X3UEMhmgoSHCnNd6YennL8LP2xEAcOnqPfxZ5b+LxrLPE5IycTM2FR4uVhjU2xuDenvDvYUVbtxOwf0HWQCA7JxCrN50Gl4eNnihvw+amqbxF4jo2ZDk5WHXx++hpKAAIrEYvsNHYfiX32LUt8vRZ967cOvaTajr2jUQY1f8pvQ/e+/K496jW89ava+RjS1a9xuEPvPehY6h8gybqiL+3i4EMktXd7zwxTfoOnEKAKCspAT7v/oM0pISof6FLRuEcG3u3AL93lqAF79fiWGfLUansS/DwMLyqdouLZZg/+LPhLDXdeIUvPDFN7B0lc8oir9wDlf+2cG2G6htDS0tOPp2QJfgSWg7uPLEfHVuHjsMAAh45TU4+XVEq159UVJYgPjzoUKdo8u+haaODp6f8eZj2yKipo1TxBtQYBd3BHZRnJbrZG+GCbPWAQAKiyq/3L/9eBS6dnQVnvu3d0FObpEQoKJvPsCTFBSVoLxcHs5MjfXw6sPgZmlugIMnrgOQB+EKZy/ECo/feb0/7GxMENDRDUdOxyD+XgbuxKchMjoRvg8D3SsvdVV4v84PP8MPvx5R+jxnwyvbnjwmAD0C5AE1OuYBduy9jOJiKfYejsKr455DYVEJIq7dBwBoaYrx3qwB0NISo7OfC/Yfi0ZhUQnOXLiD5LRc2FoZ49ylOOFz9unuhZGD/AAAxcVSfLD4HwDAzn0R6NPdS6kvb0zuiY4+zgiEO0IvxiE0PBaZWQU4fvYWBvX2RmxCOh6kykOovY0JZk3uCQBo5WaD42dvAgD+PRyFOa/1ho62pkLbo4e0x4CebQDIT4qsWHccMhkQsj8C3p52dd7nEdfuC/u0fVsnTBjlDwAwMdLD5Hl/AABC9kXg5dH+jWqflz6ciVExsg4AWlryxyUlUgDAT+tPIi+/GG/P6CeMaDclIhF4MwNqtiL3hKAgIx0A8NykqQh45bUa6xqYmcPAzFyhrCAjHckx0QAAbX0DeA8cUqv37TNnvvA4dP1vj60bUSVE9X/nQ9h7t0OrHr2RfCMa8edDkZeWgtizp9CqZx9Ii4tx4c8NAAB9cwuMW7kGeiamwus9e/V96rbvnDmF/HT5CV6XLgHo9tpMAIChpRU2z5RPo7/yzw60D3qJbTdA25Yubhi7/NeH77MdUXt3oSYVoV1DUz7FXKwlP3ktLZGfqL5+eD8SIyPQberrMLKyrrEdouak6f3Cqx31HMEWNb1/rvJyGdIy8hCyN0Io6+jjLDyuGq4rODlU/ijR09VS2v4oG0sjONmbAQCyc4uw9s+zCLscjzWbzwp1BlW57ju/ytTgqu1XfXwl+n617yWVliEuIR1HTleOFFf9PPkF1betW03bBYXFwhRoTU0NIYxpiisfy2TA1evya+4Kami76uOr1xOFQFhQ0+fUUe5L1X7r1tB2YVEJbselKtfXqb5+5LXKfViXfV6bfXgvKQuZ2QW1rl8f+9zF0QJWFoa4FZeKmDspuBmbgltxqbC2NEILRwvE3EnBPweuYEDPNsLJGyJqOmLPnBIey2Qy/P7KGCztG4hVLw7FyVUrhOtYaxK552+US+Un29r0HwxtfQOV9q8oNweZd+MAABqamrD1aiNss29bOXJ+PzICAJAUFQlJbg4AwMrNA4d/+Bo/De+Ppf274a85M5B4NeKp26762qrbbT1bQ0NTPgaSHncHkrxctl3PbdeVc8fOAIDoA/+iKCcbsWdPQiQWw8mvE0olEpz8eTlM7B3QacyEOrdNRE2LegbsJnY+ZPLcP+A/5GsMnrASO/Zehkgknx780bzBj33dsTMxwuPnOrk98X1EIhG++SgIHi5WAICf/ziJWe9vwaGT12FirId33+gvjGoDQAvHygW1/vw7HEWSEpy7GIebd1KE8pQ0xS+h4hIpOg9ajIBhS/DS9N8QGZ0IHR1NjB/ZGWNHdK7SduXJgb/3X0F2bhESEjMVPlNKunyqtLmpAQwNdAAARZJS7Nx7GRJJKXYfikRObpFSX6q2feLsLcTfy0BungQ791ZOUS+SlCI3XwJAPl26wtbdF5FfUIzrt5IRdjleqW1ne3Ph/E38vQycPn8bRZISbA65oLAfquvLvmPXkJKWi7SMPOw5XDlVv+JzyuvXfp9XrXsxMgFRN5JQUFiMrbsuPtKXPKW+NOQ+19ISY9H7I2BvY4IJs9Yh+I11sLcxweL3R0BLS4zvfjkEHW1NzJ7SCwAgkZQiJ6/yPZuCpvUXiEi1Mu5WzpY5u3YV0uPuQFpSjLyUZJzf9Dv+fv8t1LT8S3lZGSJ3hQjP/UaOVnn/ch9UzvjSMzYRrrcFAH0zM+FxxUJp6fGVn+dueBhijh5CYVYmpBIJ7l0Ox19zZiDhUvhTtZ37IEkoqzqSr6GpCV1jkyr1k9h2PbddV71nz4dzxy44/P1irBzWFwWZGej/9vuwcvdA2MZ1yEtLQc835kFTWxvlUikKMjPq/B5E6kZdfy+p6RTxpn3eQEMkgqZYQxhhrc7P60/iQoT8etp2XvYY3Lddrdo2NtSFq7Mlbj+yoFdObhGOnL6BXoGesDCTjxYEB3XG59/vBQCs3XJWuMa2quIaFl6rSlNDQ34EVflBNXpoB/xzIBLFJVKcuxSHfmN+VHpdxXRhsVgD40Z0wupN8gW0Fi0/gEXLD1RTX96X7v4t4exgjoTETKRm5OHFaaur7VdF++NHdsbZ8FiUl8vw7+Eo/HtYeUGyigXmzEz1MbhPW/x7OArl5TLM+2R7tW0XP+zLkD7tsH7rOWTnFuFWbCqGTlReqKf4YT+Auu3z1i1t0aGdEy5dvYciSakwLbymz9mY9rlvG0fs+G060jPzAQCW5vJr0g8ej8blqPuYOfF5aGuJ8dZn23H6/B2Ul8vg7mKFz94eCk93m2rbbkzET65CpLYk+fnCY10jY/Sa/RYA4Niy7yDJy0X8hXO4c/oEPLr3VHrt7dMnkJcmP6Ho1L6jcG2sKpVKKk/YaTyyGrVYU6tKPflJ2OJ8xUUnvfoOQJv+gxF9cC9uHD6AcqkUx1d+j4lrNte57Yr/r76+pmL9Kt+hbLse2q4jQ0tLvPTDTyjOz4ckLxdG1jbQEIuR8yAJ4Vs2okWnLvAIfB4nf1mOi9v/RFlJCfRMTNFn7nx49RlQ5/cjUgdiNU3YTTuJ1kTUtM4bvD9nIH75ejw+mz8UPm0cUFYuw/HQW/jfp9WHt6WrjwrBy8XJAt99Mhqa4if/U0qlZZj53mYcOnkd2lpi/LRoLE6G/A9zp/YGAIRfScAnS3YL9Yf188FbM/rCyFBHKDMx1lOY6m1kULkNALS1xFi9JBgrvxqL+TP7wcbKGAVFJdi88wJWrKtc3dXV2RI/fDYajnamQpmmpgZ6BLQUnhtWafu18d0weUwAdHQq/21trY2Fa5cBwPBhP7W0xFjx5Rh08q3sJyCfFVD1tlUVn6uznwu+eOcFIeQB8qnNVWcFVP2c780agJGD/BRuO+XWwhKuVW6hVdG2mak+Vi4aqxAKRSKgd6BntW3XdZ9/81EQegd6KlwV4dPGQeGzVOyXxrTPK1iaGwp9lUhKsWztcdjbmiB4VBd8+8thnDx3G/2eb40ZE7sj9m4a3lm4UwjpjZcYmk3wMhUiVal6myPfEaPgPWAIvAcMge/wUUL53WpWfAbkC1ZV8Bv54jPpn5Zu5R03Hl1JukxaWqWe/G4QYm1toUyspYX+8z+EW9dA9H/7A2FaceqtmyjKya5z2xX/L69f8kh9qUJ9tl2/bT8tHUNDmNjZC6Ppx1f+gPIyKXq9+RYi9/yN85vXw9LVHX3feg+ADHsXfqwwS4KoOWlaia321PNzNbGA3dK1crGL3s95ou+YH1FcIsX1W8m4ez9TmH5bXi7D4hUHELIv4uHrrLD8y7EwM63dPbAvRiYgIVG+SnMn3xbo7OcCAAgO6oJf/jgFSXEpwi7HQyIpFa7LHTu8E0YPaY/YhHSIxRpwtjfH4hUHcDEyAYA8WFYlEong19YJANClvQvcWlhi5nt/ApCvJF0R5gF5sN25ZjoSErNQJCmBk70ZToTewonQW0pta2iI8PqkHnh17HOIv58BXV0tONmZYdYHfwl13KvUt7Mxwc+LxyMtIw+p6fmwszFGSWkZhj0cQbazNoaebuWPpv49WqNPN0/E389AWVk5nB3MsXHHeWFhsKp90dWRr5I9e0ovJCRmwthIDw62Jhj56iqhTtX6rdxssHHFZCSlZCM7pwiOdma4czcNRx9OzX50H9Zln5sY6eHrD0ciO6cQick5sDQ3gLmpAXqN/gGAfIEyJ7vKaXKNaZ8/6vetoUhJy8U3H46EplgDR07fgJamGB/MGQg9XW2cDY9FZHQirt18gPYP/xtrlESaavqHlah2jK1tkZkQL39sU3lCruJWRQBQXM3tuzLv3UXCJfnlNoaWVmhZy9XD69w/u8o+SXKyUS6VCkG5IKNy2q6JnfyuHsbWlf3WNTaBtp481Gnr60PP2ESY6ltcUFD3tqvcPqwwM1N4XC6VQpKTXaW+vcK0erb97NtWhYSLF3Dr5DF0GDUWlq7uOLJ0CQDg+emz0KKTP/JSUxC2YR1unTgKS5cnX+pHpG7U9feSWo5gi0QiNIVJmpJi5XswAlC4ICG/QD5NSVpWjo+X7BbCdTsve/zydbAwnbs2sqtcO1tx2ylAPkVZWlY51btQonhWV1NTjFZuNnBvYYXM7AIcOnn9YbkGAjq6Cf2TVlmBXPgoVT5LXoHylCuRSIQWjubw8rCFro4W/vy78lrm7lXuPV1BV1cLXh62cHG0wM3YVFy6Kg+dJsZ6aOvloFTfysII3p52MDc1wMYdYUJ5N3/ltsViDbi3sEIrNxuUl8sUFpzrVk1fDA100KaVHRztTHH87E0kJmcDkIfUqveHrmBvY4o2rexgbKSr2Jdq2q7NPq/K1EQf3p52sLEyxrY9F4Vp5/4dXKCtrfjnqzHt8wpJKdnYuOM8Ovu1QK9AT+TkFUEqLYepiZ4Qyu2s5dfHZWQp/zBvVESa0OQANjVjDu0q71ufl5IsPM5NrXxsbK18qUdEyHZhyq/PC0FCUFI1PWMTmLeQLxxaXlaG5BvRwrYH1yrXx3D08QPwcDGsh19mktwcYfpwSVGRsBiWhqYmDCws6ty2Qzs/oSwpKlJ4nHwjGuUPv5ctXd2ha2TMtuu57f+qvKwMR5d9Cz0TUzz36nQAEE7GVJx4MrGVB/mqJwOImgtNNMl1qWtFXU8cyEexZU++PrghTZy9Hu287OHr7QhbK2Nk5RRi+55LKC6WhyMdHU24PJx2/O7CnTh57jYAwMbKGFMndEPs3crrqA0NdOBRZSS886DFAOSjhrvWvw5AcXTyctR9rPnzDLxb2WPP4avCfZ2tLAxhZiIfEb8Vl4qlq4+iTzcv2NuYIDE5G+v+CkWRRH5iYPgAX2F6b2p6Lqb8byMG92kLTzdrmJroIyExE39sOye8p5d75SgAAEx5awN6BrSCWwtL5OVLsOPfy7hxW37tnYeLFXo8vLc0AJw+fxu7Dkaiu78HrMyNcDs+Feu2hArXqU8c7a8wFfmrZfthZKCDtl720NAQ4XjoLex5eA9wPV0tBAd1EermF0jw5gdbMbBXGzg7miMtIx+bdp4X7kf9XCc3tGlVOSqxc+9lRF5PhH97V5gY6+FaTBLWV/mcr40PVPicb3++A57uNvDysEVJiRR7j0YJ/5aW5oYYMbDyx2hd9jkArNpwCjl5RWjf1gl6ulq4EHFXuH+6hoYIk8c+h6oayz5/1I+rj0FaVo63Zshvd2Nmog9dHS3k5RdDWlYOTbEGsnMKAcj/m27UOIJNzVy7oSNwde8uQCZDxD87YN7CBQBw5Z+dQp2WPXorvKZUIsG1/fJLlDQ0NeE7bGS1bec8SMLqMfL7ETv6dcDYZb8K25JvRCMnWb44VVmVe1jHhZ2FnqkptHT14NZV/vfZb/goHF32LQDgwJKFCHx1BlJv3kD8BfnfciMrG7g91x2AfOTdpUsA4sPOoqy0FAeXfIk2/Qch+sBeYVqyq/9z0NLRrXPb7oHdYWhphfz0NMRfOIdTq1fCplVrnFn7i9D/qlPr2Xb9tl0qkSD23GkAQOrNygVBc1MeIOa4/L7Xtl7eMLGt/I1QIeLv7UiPu4N+by2ArpH8vuwmtnbIvBuHwuwsmDk5ozBbPqvQ2NZW6fVE6k6dfyup72cTaQKyx98KpKFJJCXYdTASuw5GVrt9zmu9YaAvv2a1IpAB8pWbZ3+4VaFuh3ZOWPVN8GPfr6WrNfo+74XDJ+W3zvrlj1NKdd6Y1OPhDAD5lPTzl+Nxvspq2hXat3XCnNd6KZSlZ+YrBOqqDPS0MW+a4g+q2LvpiIxOVKprZWGIRR+MULiuXCotx7EzN3HszE2l+n27eymFt8zsAmG0vypNTQ18+tYQONiaCmUyGRAVk4SoGOVVQ12dLfHx/xTvwSoplta4GNr4oM7o93xrhbKklBxhCnZVBvo6WPz+CBgZVl7rVdd9nl9QjG27L2Hb7ksK5SIRMG9aH/i0Vhxhbiz7vKrwK3dx9EwMxrzQEe4trB72X4QRA32x5Z9wrNtyFq3crHHp6j14edigdUvlHzKNCkewqZmz926HzmMn4MKfGyDJzcHehR8rbO8y/hXYtPJSKLt+aB+KHy6O1vL5XjCwULx0pjYu79yKa/v3KJUf/l5+wtnY1g7TtspDvN+I0bh95iQSLp5HRlwsdn30jlBfrK2Nge9/As0q1173nfcO/nx9CgoyM3D90D5cP7RP2KZvboHes98WntelbS0dXQx87xOELJiHstJShG1Yp9B3l85dFcMk267XtguzMrH74/fwqHuXL+LeZfkdOwYu+AQmg4YpbC/KycbZtatg3bIVfKqcLPJ5IQhxYWdxYcsGADJE7d0NLT09tOn/+LvGEKkjdf6tpN4Bu5GbMMofp8JuIzYhHdk5hZDJ5EGnXWsHjBrS/plcZ/rFOy/Ap7UDDhyPRnxCBiTFpTA01EWbVnYYO7yTwsJeVhaGGNTbG5HRicjIKoBIJF9UbVBvb7w4tAM0NSun4Zsa62PiaH9cjrqHxORs5ORJoKUphr2tCTr7tsD4oM5K06ZHDPTFuYtxeJCai9JSKWysjPF815aY9FJXmJooXlfu4myB3oGeiL71AJlZBdDS0kRLVysMH+CLIX3bCicFKvQIaImsnEIk3M9EfmExTI310dHHGZPGBAi3Kaugo62J4QN9ERF1D6npeSiXyeBoZ4Y+3TwRHNQF+nqK1w239bJHYGd33IxNQXZOEfR0tdC6pS1eeqEjnu/aEo8a1MsbmmINJD7IRpGkFBbmBgjo6IbJYwJgZ2OiULcu+xyQX+d+Oz4NsXfTkJsvgZGBLnzbOCA4qItwLXxj3OcVysrK8d0vh2FirIdpE7orbJv1ak9oaIjw974rkBSXIrCzG96e2Q8aGo38LzJHsInQY+YcWLq64/LOrcICTlZuHmgfNAZt+g9Sql91cbP2I1965v3T0NRE0NdLEf7XRkQf2Iuc5CRo6erBoZ0vnps0DTaeiicATO0dEbxqPc6u+xVxYWdRlJ0FfTNzuHYNxHOTp8HIyvqp23bp0hXjflqL0HW/IvHqFZQWS2Bi5wDvAYPRacwEhVtPse36bftpnV7zCyR5uRj+5bcQaVSeuG7ZvSf6z/8A4X9txPa334SlqzsGvf8JDC2r/44kUmfq/FtJJKvpZpRNnCzrGCDlNS1EVM+0HRClF4BrjX2xcyIiIqIGYqkB9H36BfsbNbVc5AxAkxjBJiI1JNKEViMfZCciIiJqSOqc1NTus8XFxSE0NBRJiQmws9RCgJ8FXB0Nn/xCIqqTuPv5CI3IwIM0CeysdHmsVRAbwogBm1So4G4cssJDIUlOgq6tPcw6BcDg4crJRKQ6PNaI6kfB3ThcvxiK8OQk2NvbIyAgAK6u6nOsqdUU8bi4OGzatAllVW45JRaLMf7F/nB1Uc09DYkIiItPwuZtB3msVUfLEuUiXSQ27psYUBORFB+Hw1uUv9f6jg2GvYv6/Bghamg81ojqR03HWnBwsNqEbLUawQ4NDVX4xwKAsrIynLt4G26eNd8iiIjq5tzFkzzWHkMMwFmt/rpSQzl9vvrvtdjzoejqoR4/RIgaAx5rRPWjpmMtNDRUbQK2Wl2DnZCQUKdyIno6PNaI6gePNaL6wWONqH40h2NNrQK2VFr9sr01lRPR0+GxRlQ/eKwR1Q8ea0T1ozkca2oVsImIiIiIiIgaCgM2ERERERERkQowYBMRERERERGpAAM2ERERERERkQowYBMRERERERGpAAM2ERERERERkQqoVcDW1NSsUzkRPR0ea0T1g8caUf3gsUZUP5rDsaZWAdvZ2blO5UT0dHisEdUPHmtE9YPHGlH9aA7HmloF7ICAAIjFYoUysViMgICABuoRkXrisUZUP3isEdUPHmtE9aM5HGsimUwma+hOqFJcXBxCQ0ORlJQEe3t7BAQEwNXVtaG7RaR2eKwR1Q8ea0T1g8caUf1Q92NN7QI2ERERERERUUNQqyniRERERERERA2FAZuIiIiIiIhIBRiwiYiIiIiIiFSAAZuIiIiIiIhIBRiwiYiIiIiIiFSAAZuIiIiIiIhIBRiwiYiIiIiIiFSAAZuIiIiIiIhIBRiwiYiIiIiIiFSAAZuIiIiIiIhIBRiwiYiIiIiIiFSAAZuIiIiIiIhIBRiwiYiIiIiIiFSAAZuIiIiIiIhIBRiwiYiIiIiIiFSAAZuIiIiIiIhIBRiwiYiIiIiIiFSAAZuIiIiIiIhIBRiwiYiIiIiIiFSAAZuIiIiIiIhIBRiwiYiIiIiIiFSAAZuIiIiIiIhIBRiwiYiIiIiIiFSAAZuIiIiIiIhIBTQbugOqVFJSgl9++QV79+5FXl4ePDw8MHPmTHTt2rWhu0bUJF27dg179uzBxYsXkZSUBBMTE7Rr1w4zZ85EixYthHqffvop9uzZo/T6Fi1aYMeOHfXZZaImKTw8HDNmzKh227p169CuXTvh+ZUrV7Bs2TLcuHEDhoaG6Nu3L9544w3o6+vXV3eJmqyavq8q7N27F9bW1pg2bRouXbqktD0gIADLly9/ll0kanIKCwuxYcMGREVF4dq1a8jNzcUnn3yCYcOGKdWNi4vD999/j4iICGhpaSEwMBD/+9//YGZmplCvvLwcGzZswI4dO5Ceng5nZ2dMmjQJAwcOrK+P9dTUKmB/+umnOHLkCMaPHw8nJyfs2bMHc+bMwapVq+Dn59fQ3SNqctavX48rV66gb9++aNmyJdLT07Ft2zZMmDAB69atg4eHh1BXW1sbH374ocLrDQ0N67vLRE3a2LFj0aZNG4UyJycn4XFMTAxef/11uLi4YN68eUhNTcXGjRtx7949LFu2rL67S9TkBAUFoUuXLgplMpkMixYtgr29PaytrYVyGxsbvPHGGwp1rays6qWfRE1JdnY2Vq9eDVtbW7Rs2RIXL16stl5KSgqmTp0KQ0NDvPHGGygsLMTGjRtx584drF+/HlpaWkLdn376Cb///jtGjhyJNm3a4MSJE/jwww8hEokwYMCA+vpoT0VtAnZUVBQOHjyIOXPm4OWXXwYADBkyBGPGjMGyZcuwdu3aBu4hUdMTHByML7/8UuEPXv/+/TF27FisX78eX3zxhVAuFosxePDghugmkdrw8/ND3759a9y+cuVKGBkZYdWqVcIJLHt7eyxcuBDnzp3jjC2iJ/Dx8YGPj49CWUREBCQSidLImIGBAb/XiGrB0tIS+/fvh6WlJaKjozFx4sRq661btw5FRUXYuHEjbG1tAQDe3t544403sHv3bgQFBQGAcPL4xRdfxLvvvgsAGDFiBKZNm4Zly5ahb9++EIvF9fPhnoLaXIN95MgRiMVijBw5UijT0dHB8OHDERkZieTk5AbsHVHT5OvrqxCuAcDZ2Rlubm6Ii4tTql9WVob8/Pz66h6RWiooKIBUKlUqz8/PR1hYGAYNGqQwO2TIkCHQ19fHoUOH6rObRGpj//79EIlE1U49lUqlKCwsbIBeETUd2trasLS0fGK9o0ePonv37kK4BgB/f384Ozvj8OHDQtmJEycglUrx4osvCmUikQijRo1CSkoKrl69qtoPoGJqM4IdExMDZ2dnpSmp3t7eAICbN28q/GMS0dORyWTIzMyEm5ubQrlEIkGPHj0gkUhgbGyMAQMG4M033+R1oUR18Pnnn6OwsBBisRh+fn6YM2eOMGX89u3bKCsrU5pCrqWlhVatWiEmJqYhukzUpEmlUhw6dAg+Pj6wt7dX2JaQkIDu3bujtLQUFhYWGDFiBKZOnQpNTbX5+UxUb1JTU5GZmYnWrVsrbfP29sbZs2eF5zExMdDT04Orq6tCvbZt2wIAbty40agv/1WbvxDp6enVnjmpKEtLS6vvLhGppX379iE1NRXTp08XyiwtLTFx4kR4eXmhvLwcoaGh2LZtG27evIlVq1bxxwjRE2hpaaF3794IDAyEqakp4uLisGHDBkydOhVr1qyBl5cX0tPTAaDG77rLly/Xd7eJmrzQ0FDk5ORg0KBBCuWOjo7o1KkTPDw8UFRUhCNHjmDNmjVISEjAokWLGqi3RE3Xk77DcnJyUFJSAm1tbaSnp8Pc3BwikUipXtW2Giu1+dVbXFysNJUVkE9ZqNhORP9NfHw8vv76a/j4+GDo0KFC+axZsxTqDRgwAM7Ozvjpp59w5MiRRr8YBVFD8/X1ha+vr/C8R48e6NOnD8aOHYuVK1di+fLlwvdYTd91/J4jqrv9+/dDU1NTae2Djz/+WOH5kCFD8OWXXyIkJATjx49XWNmfiJ6s4juqIptVpaOjI9Sp+D6rrl5TyXVqcw22jo4OSktLlcpLSkqE7UT09NLT0zFnzhwYGhri66+/fuLiEuPHj4eGhgbOnz9fTz0kUi9OTk7o0aMHwsPDUVZWJnyP1fRdx+85oropLCzEiRMnEBAQAFNT0yfWDw4OBgCEhYU9454RqZ+K76iKbFZVRWCuqKOjo1NtvaaS69QmYFtaWlY7XaCijLdVIHp6+fn5mD17NvLz87F8+fJaHU+6urowMTFBbm5uPfSQSD3Z2NigtLQURUVFj50al56ezu85ojo6fvx4tauH16RiLR9+rxHV3ZO+w0xMTIQRaktLS2RkZEAmkynVq9pWY6U2AdvT0xMJCQlKKxhHRUUBAFq1atUQ3SJq8oqLizFv3jwkJCTghx9+UFrcrCYFBQXIzs6u1agAEVUvMTEROjo60NfXh4eHB8RiMaKjoxXqlJaW4ubNm/yeI6qj/fv3Q19fHz169KhV/fv37wMAzMzMnmW3iNSStbU1zMzMcP36daVt165dU/gOa9WqFSQSidIdaypynaen57Pt7H+kNgG7T58+KCsrQ0hIiFBWUlKC3bt3o23btlxBnOgplJWVYcGCBYiMjMTixYuV7h0KyAN4QUGBUvlvv/0GmUyG5557rj66StSkZWVlKZXdvHkTJ0+ehL+/PzQ0NGBoaAh/f3/s27dP4Zj7999/UVhY+Nj7ZxORoqysLISFhaFnz57Q1dVV2Jafn680PVUmk2HNmjUAgICAgHrrJ5E66d27N06dOqVw++Tz588jISEBffr0Ecp69OgBTU1NbNu2TSiTyWTYsWMHrK2tq/092piozSJnbdu2Rd++fbFixQpkZmbCyckJe/bsQVJSEj766KOG7h5Rk7R06VKcPHkS3bt3R25uLvbu3auwffDgwcjIyEBwcDAGDBgAFxcXAPJVWc+cOYPnnnuu1iMDRM3ZggULoKOjAx8fH5ibmyM2NhYhISHQ1dXFm2++KdR7/fXX8eqrr2LatGkYOXIkUlNTsWnTJnTt2pUns4jq4ODBgygrK1NaPRyQ3wLogw8+wIABA+Dk5ITi4mIcO3YMV65cwciRI+Hl5dUAPSZq3P766y/k5eUJ07hPnjyJlJQUAMDYsWNhaGiIyZMn4/Dhw5gxYwbGjh2LoqIibNiwAR4eHnjhhReEtmxsbDBu3Dhs2LABUqkU3t7eOH78OC5fvoyFCxc+cR2ghiaSPTq5vQkrLi7GL7/8gr179yIvLw8eHh6YOXMmzzQSPaVp06bh0qVLNW4PDw9HXl4evvnmG0RFRSEtLQ3l5eVwdHTEoEGD8PLLL/MWXUS1sGXLFuzbtw/3799Hfn4+zMzM0LlzZ0ybNg1OTk4KdSMiIrBs2TLExMRAX18fffv2xaxZs2BgYNBAvSdqeiZPnozExETs27dP6cd6YmIili9fjujoaGRkZEAkEsHV1RUjRoxAUFCQ0q2DiAgYNmwYHjx4UO22Xbt2CfeZv3PnDn744QdERERAS0sL3bp1w9y5c2FhYaHwmvLycqxfvx47d+5Eeno6nJycMHny5GpPijU2ahWwiYiIiIiIiBqK2lyDTURERERERNSQGLCJiIiIiIiIVIABm4iIiIiIiEgFGLCJiIiIiIiIVIABm4iIiIiIiEgFGLCJiIiIiIiIVIABm4iIiIiIiEgFGLCJiIiIiIiIVIABm4iIiIiIiEgFGLCJiIiIiIiIVIABm4iIiIiIiEgFGLCJiIiIiIiIVIABm4iIiIiIiEgFGLCJiIiIiIiIVIABm4iIiIiIiEgFGLCJiIiIiIiIVIABm4iIiIiIiEgFGLCJiIiIiIiIVIABm4iIiIiIiEgFGLCJiIiIiIiIVIABm4iIiIiIiEgFGLCJiIiIiIiIVIABm4iIiIiIiEgFGLCJiIiIiIiIVIABm4iIiIiIiEgFGLCJiIiIiIiIVIABm4iIiIiIiEgFGLCJiIiIiIiIVIABm4iIiIiIiEgFGLCJiIiIiIiIVIABm4iIiIiIiEgFGLCJiIiIiIiIVOD/ta/dS6E5p0oAAAAASUVORK5CYII=", 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", 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", 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", 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", 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", 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" ] @@ -284,6 +324,9 @@ "import matplotlib.pyplot as plt\n", "import matplotlib.patches as patches\n", "\n", + "# Enable inline display for matplotlib\n", + "%matplotlib inline\n", + "\n", "def plot_metabolism_chart():\n", " \"\"\"\n", " Generates and displays the 'Slow vs Fast Metabolism' chart.\n", @@ -292,7 +335,10 @@ "\n", " # --- Chart data and positions ---\n", " categories = ['Very Slow', 'Slow', 'Average', 'Fast', 'Very Fast']\n", - " positions = [1500, 3000, 4500, 6000, 7500]\n", + " # Use normalized positions (0-100 scale) for uniform bar length\n", + " positions = [10, 30, 50, 70, 90]\n", + " kcal_ranges = [1500, 3000, 4500, 6000, 7500] # Actual kcal values for reference\n", + " \n", " # Step 1: Filter resting phase (usually lowest VO2 or MET values)\n", " rest_phase = df[df['MET'] <= 1.1] # assuming <1.1 MET means rest\n", "\n", @@ -301,15 +347,17 @@ "\n", " print(f\"Estimated RMR from data: {rmr:.0f} kcal/day\")\n", " kcal_value = rmr\n", - " # Position the indicator and highlight based on the kcal value\n", - " # For this example, we'll place it in the 'Very Slow' section.\n", - " indicator_pos = kcal_value\n", - " highlight_end = kcal_value\n", + " \n", + " # Normalize the kcal value to 0-100 scale (assuming range 0-9000 kcal)\n", + " max_kcal = 9000\n", + " normalized_value = (kcal_value / max_kcal) * 100\n", + " indicator_pos = normalized_value\n", + " highlight_end = normalized_value\n", "\n", " # --- Main Bar (Background) ---\n", - " # Create a rounded rectangle for the main bar\n", + " # Create a rounded rectangle for the main bar (0-100 scale)\n", " main_bar = patches.FancyBboxPatch(\n", - " (0, 0.4), 9000, 0.2,\n", + " (0, 0.4), 100, 0.2,\n", " boxstyle=\"round,pad=0,rounding_size=0.1\",\n", " ec=\"none\", fc=\"#E0E0E0\",\n", " )\n", @@ -325,8 +373,8 @@ " ax.add_patch(highlight_bar)\n", "\n", " # --- Text and Labels ---\n", - " # Add the kcal text inside the highlighted bar\n", - " ax.text(highlight_end / 2, 0.5, f'{kcal_value}kCals', \n", + " # Add the kcal text inside the highlighted bar (show actual kcal value)\n", + " ax.text(highlight_end / 2, 0.5, f'{int(kcal_value)}kCals', \n", " ha='center', va='center', color='#006400', fontsize=14, weight='bold')\n", "\n", " # --- Indicator Triangle ---\n", @@ -341,7 +389,7 @@ "\n", " # --- Chart Styling ---\n", " ax.set_title('Slow vs Fast Metabolism', fontsize=18, weight='bold', loc='left')\n", - " ax.set_xlim(0, 9000) # Add padding before and after\n", + " ax.set_xlim(0, 100) # Normalized scale\n", " ax.set_ylim(0, 1)\n", " \n", " # Hide axes and spines for a cleaner look\n", @@ -349,7 +397,8 @@ " \n", " plt.tight_layout()\n", " plt.savefig(f'{base_dir}/graphs/metabolism_chart.png', bbox_inches='tight', dpi=300)\n", - " plt.show()\n", + " plt.close(fig) # Close figure to prevent display warning\n", + " return fig\n", "\n", "\n", "def plot_fuel_source_chart():\n", @@ -370,7 +419,7 @@ " optimal_point = 75\n", "\n", " # --- Main Bars (Fats and Carbs) ---\n", - " # Fats bar (yellow)\n", + " # Fats bar (yellow) - using 0-100 scale for uniformity\n", " fats_bar = patches.FancyBboxPatch(\n", " (0, 0.4), fat_percentage, 0.2,\n", " boxstyle=\"round,pad=0,rounding_size=0.1\",\n", @@ -388,9 +437,9 @@ "\n", " # --- Text and Labels ---\n", " # Add percentage labels inside the bars\n", - " ax.text(fat_percentage / 2, 0.5, f'Fats\\n{fat_percentage}%', \n", + " ax.text(fat_percentage / 2, 0.5, f'Fats\\n{fat_percentage:.0f}%', \n", " ha='center', va='center', color='#333333', fontsize=12, weight='bold')\n", - " ax.text(fat_percentage + carb_percentage / 2, 0.5, f'Carbs\\n{100-fat_percentage}%', \n", + " ax.text(fat_percentage + carb_percentage / 2, 0.5, f'Carbs\\n{100-fat_percentage:.0f}%', \n", " ha='center', va='center', color='#333333', fontsize=12, weight='bold')\n", " \n", " # Add 'Optimal' label\n", @@ -410,7 +459,7 @@ "\n", " # --- Chart Styling ---\n", " ax.set_title('Fuel Source', fontsize=18, weight='bold', loc='left')\n", - " # ax.set_xlim(-5, 105) # Add padding\n", + " ax.set_xlim(0, 100) # Normalized scale for uniformity\n", " ax.set_ylim(0, 1)\n", "\n", " # Hide axes and spines\n", @@ -418,16 +467,19 @@ "\n", " plt.tight_layout()\n", " plt.savefig(f'{base_dir}/graphs/fuel_source_chart.png', bbox_inches='tight', dpi=300)\n", - " plt.show()\n", + " plt.close(fig) # Close figure to prevent display warning\n", + " return fig\n", "\n", "\n", "if __name__ == '__main__':\n", " # Call the functions to display each plot\n", " print(\"Displaying Metabolism Chart...\")\n", - " plot_metabolism_chart()\n", + " fig1 = plot_metabolism_chart()\n", + " display(fig1)\n", " \n", " print(\"\\nDisplaying Fuel Source Chart...\")\n", - " plot_fuel_source_chart()\n" + " fig2 = plot_fuel_source_chart()\n", + " display(fig2)" ] }, { @@ -2058,7 +2110,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 21, "id": "9c00c366", "metadata": {}, "outputs": [ @@ -2271,7 +2323,7 @@ "[5 rows x 24 columns]" ] }, - "execution_count": 26, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -2282,7 +2334,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 22, "id": "0a624fa0", "metadata": {}, "outputs": [ @@ -2299,7 +2351,7 @@ " dtype='object')" ] }, - "execution_count": 25, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -2325,7 +2377,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 23, "id": "d18e5c4a", "metadata": {}, "outputs": [ @@ -2467,7 +2519,7 @@ "9 1.27 0 72.49 87.07 72" ] }, - "execution_count": 27, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -2503,7 +2555,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 24, "id": "a6f97a9b", "metadata": {}, "outputs": [ @@ -2607,7 +2659,7 @@ "4 87.099833 " ] }, - "execution_count": 28, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -2637,7 +2689,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 25, "id": "e6943523", "metadata": {}, "outputs": [ @@ -2690,7 +2742,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 26, "id": "f4d26f1b", "metadata": {}, "outputs": [ @@ -2748,7 +2800,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 27, "id": "bf54eda5", "metadata": {}, "outputs": [ @@ -2843,7 +2895,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 28, "id": "128c9f3e", "metadata": {}, "outputs": [ @@ -2972,7 +3024,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 29, "id": "e80da1c3", "metadata": {}, "outputs": [ @@ -3128,7 +3180,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 30, "id": "fbeb6168", "metadata": {}, "outputs": [ @@ -3266,7 +3318,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 31, "id": "a9064ffe", "metadata": {}, "outputs": [ @@ -3274,7 +3326,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "Loading oxygenation data...\n", "✅ Graph generation successful!\n", "\n", "Chart size: 871248 characters (base64)\n",