From 845a7ca09934a81a3c559e8178775357fe841b7b Mon Sep 17 00:00:00 2001 From: bolade Date: Wed, 24 Sep 2025 09:57:15 +0100 Subject: [PATCH] Add body fat analysis graph for page 1 --- extracted_images/page_1_image_1.png | Bin 0 -> 71872 bytes extracted_images/page_1_image_2.png | Bin 0 -> 91 bytes extracted_images/page_1_image_3.png | Bin 0 -> 37720 bytes graphs/page_1_body_composition.png | Bin 0 -> 39710 bytes graphs/page_1_body_fat.png | Bin 0 -> 14374 bytes main.py | 341 +++++++++++ notebook.ipynb | 108 ++-- pdf_generation.ipynb | 304 +++++++++- report_gen/page_10.html | 65 +++ report_gen/page_11.html | 265 +++++++++ report_gen/page_13.html | 242 ++++++++ report_gen/page_14.html | 176 ++++++ report_gen/page_15.html | 97 ++++ report_gen/page_16.html | 84 +++ report_gen/page_17.html | 173 ++++++ report_gen/page_18.html | 371 ++++++++++++ report_gen/page_19.html | 850 ++++++++++++++++++++++++++++ report_gen/page_3.html | 83 +++ report_gen/page_4.html | 147 +++++ report_gen/page_5.html | 175 ++++++ report_gen/page_6.html | 246 ++++++++ report_gen/page_7.html | 180 ++++++ report_gen/page_8.html | 225 ++++++++ report_gen/page_9.html | 48 ++ 24 files changed, 4139 insertions(+), 41 deletions(-) create mode 100644 extracted_images/page_1_image_1.png create mode 100644 extracted_images/page_1_image_2.png create mode 100644 extracted_images/page_1_image_3.png create mode 100644 graphs/page_1_body_composition.png create mode 100644 graphs/page_1_body_fat.png diff --git a/extracted_images/page_1_image_1.png b/extracted_images/page_1_image_1.png new file mode 100644 index 0000000000000000000000000000000000000000..ec15f53442b2599d58bdb3dd7f1349c5f455cbae GIT binary patch literal 71872 zcmeFZXHZmG_czLnqo_!O2}D{D6DV0Ev?LW32?7!%gNT4+Y;s0XT17y~8Of4Cau!>1 z&PWuY$vKAx?mF!}@1LjM5BJNhy0>n34E6;*>|v9wj3qqm+1X zSDuXQpOa){hx`uz4Ntf{m$%{Lh{Xd{OENOl59I&90$5S>WPhE(O5DAzXcs;+VDEHt zJz`;T%%ttg!`_^MgxTy_vw7VfMrKL5RXJloy&P5lMEiElE2s;$iGjVgb{+W5WuA=|lEnZ&U!nI7-y}f$H>Ue z_di07Ijqh6WIuoT^5t~l{;vd!%_RbmI6W?C(!({<$JRzs{H)gZJVQ}(a-HSge95v5 znP6+*d?l@RM-&4+J-vz@oWe@N-yKuO#I>bLMR)y&bKTm<{f=E!ikg}O zwGqb{ganwGnctp-0qwgMY)eA>TZ{p+0N&Q`!Em)1eg&Wta175cO-zJL^WhhhgQbFu>Og6}KwX8#IH$z{7F=&{Z$pFh&R5>KvpcR|zkZ#X znv&)>Gc|2Ao+oZDS2fzq50)+;+S&7>S5#J(+HPI%kLD46@%#07^=l20jb8`ay~Gqz z#$Bn4yYkb*a`N&q<%hRYim(L9op=H3DMd@mY$F^U9bFsU>Qra_j>id*ZT7cVE`t{s zjUp?j$jfT9EBs2f_{4s979yvn&UpSNC^r`@6FH}hZ1*1;b# z4C5cP!&|yndeYiX+>nj9dHLMa{(=`(R96e%#GvEa{RioKimX%CyC0ip_w@7x2GSVj zZ>j$H@#6t~l)#IabHnQC&L4iQO!dx+oteEyK#DI zDvtjq>fFta==N}7$Gq|O)$X)}gsS?r+nl(ruE)*mS}`#(Lftg9wA;&iwj+I~I1q7i zSY7JJ0_!5sXF%GeLE2NV;s%Osw@bbRj1mZiQG;!5ZTaTf&AICNX4V;0DEyU6B=-atUw$w8T3lCW04jw!hX{=c;F`iJa(c0Q- zUb-4MKAlj0#h^J-X;xeEqVq^yU7bu5C>pgl3%O<6!8y1D?RCA`-rPvX1yvL+r`8RZ z2ZqFr#bTS;o3?WrhK7@CvweHJ>*Z4P$3T;o+eG>Jx-4h?uPXGXd>C4^{#j_5=oog8 zg2pa=YJ&aU*X->02-?%9ttZ~hKEnv0QQh;x#b8YC_de;#)S1iOissZP_@y>yb<@++ zlUX)0l~X(C`KKT;w4jajP4SWHDo`x5gZ}P?WG>A*!{6VKsA?0%O?RcKGkdl6_X`F(G=y3xrPSVT<1NOi*^dz!f6yw z)#5|X-(+<{m?3W3ap&)W2=X9K?LZM9ZggfC%@xncr!Vp(|OYwdmQ7WaZ5VYLog=x9BA&Rug4iJEiHNR4{?f< zzu#DiKJHxqP+EfQwZkT4tGDG*3-=Qv4smGb&Cdz4IP&drDw}EpMeZPj%UC(o)5$5Fp_W@$KVMPErd86j ztjbxkaOWEjqD)|0!>wKlT2FOcEb|Wva-6Gh#H4B#cUcCwSWoMnnOxo)=yk1E-D-K1 zp;fvfHOwUDQ4lnt+_R-0sh!6lWM^S)Y`nL-BOIjycDq%1vXOsirQcoDmW}c;=G3WE zc5}?a!kW2yW@cvPE;~g@UT3(!Z!4dR>tpp|5Wbli0^a!hS;7;J4qLr|xy#8Z$;tAw zbsNUUo12@F^bhvd${kjXnnjDs>>`4LCkicgwpJruopx5c1sz`9ob2nXQm3aon1Tl0 z3<%oqZjP4@N#`Xb=vTZts5R)+!o2>Kd~*2-F%Aw|!29PC+eAaAuV25eSbTl==+~9q zs_psm`NGFrW;wE}h9r=Y($$yg$tJJ1X;)1wI5SO&Hw6@%B%VuGNtv3T69*3HJ z7_5ueLR<{#capp9uFhNs0qo|JNo-%v4=imM6P--y16#Ac)bvhDt`2ccbHd;Sd$ws$ ztEs8QU3R!L;YW4$MvbK}C{TN%c8T4iB1a|o6B+3q1>qhKmNT7WWaKoZK7O3Z`RC_^ zg!@IVpE!7Vm2L*&hgKlD3)^=fVXaZL{@#=+KX%TI&VEtjucad3UV@|{C1=SwY> z!&<@m=kgDcjvrmzWMj;QEPj0(9ew7^+;(j;jF4QrIxab`qM|!#{8Sx7Am4U46Z7spon!=Whdo$!E_vez7}tgOGf zApky~O0xgtnv#<5SGXMlM4Y#RcY8vl6cqdxJ_=Pw~b$n8)!D z
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zrg*PeZkd+xUnu}zT}q6kLs^k-ED@AR`QQf+%7)s&OH~L+1&c?oP;yCY;LA7*wqdMf zk{Puhv^4kmkyb1n{Ys(V`e`eyqy+S5#F7BTEg`E?>B&JvxNIUp5+R>g@NOo7>M{Zq zp$n=6|DzRb%6nKC_`C%A6Mh|g&WFpouC4*w3m0q%m@b#^Wi#I&^2UG)Ne6{82HY>Po3?C^QVBmD z4N4<509Un~Itpe+x*z06D%_WVsAwya2dOYms+OPE!GWLAdbBM?Kt`q;{FH3SM=}KX z_&zRM7dUpLdd11Qf64~+1Npu#28JCez*dqV7mC(!i373Mg#Z{n(v7cnRr$iEc8+*{W02}T@#S?=iJ3u$ww6EG$sDFihk10NU-LmQ2k~`0G7cYB1&xCJw->tf zdp#Acx!_Rxi|3$F(*r!n5D^~#4?kgsoc@39f9ToR@%h?uhC#OeCFIFyC5@}(%Qx=+ EH#?g(C;$Ke literal 0 HcmV?d00001 diff --git a/main.py b/main.py index 956e02c..949673c 100644 --- a/main.py +++ b/main.py @@ -7,6 +7,347 @@ env = Environment(loader=FileSystemLoader("report_gen")) pages = [ ("page_1.html", {"name": "John Doe", "surname": "Moran", "date": "July 29, 2025"}), ("page_2.html", {"content": "This is page 2 content"}), + ( + "page_3.html", + { + "patient_name": "Keirstyn Moran", + "age": "34", + "height": "5'4\"", + "weight": "123lbs", + "focus": "Endurance", + "fat_mass": "27.6lbs", + "fat_percentage": "22.4%", + "lean_mass": "95.4lbs", + "lean_percentage": "77.6%", + "body_fat_percent": "22.4%", + "age_range": "20-39", + "gender": "F", + "contact_email": "info@ishplabs.com", + "website": "www.ishplabs.com", + "social": "@ishplabs", + "page_number": "4", + "body_composition_chart": "../graphs/page_1_body_composition.png", + "body_fat_chart": "../graphs/page_1_body_fat.png", + }, + ), + ( + "page_4.html", + { + "patient_name": "Keirstyn Moran", + "age": "34", + "height": "5'4\"", + "weight": "123lbs", + "focus": "Endurance", + "contact_email": "info@ishplabs.com", + "website": "www.ishplabs.com", + "social": "@ishplabs", + "page_number": "3", + }, + ), + ( + "page_5.html", + { + "patient_name": "Keirstyn Moran", + "age": "34", + "height": "5'4\"", + "weight": "123lbs", + "focus": "Endurance", + "resting_calories": "1386kCals", + "fat_percentage": "33%", + "carb_percentage": "67%", + "neat_calories": "762kCals", + "weight_loss_calories": "423kCals", + "weight_loss_rate": "1.1lbs", + "total_calories": "~1725kCals", + "contact_email": "info@ishplabs.com", + "website": "www.ishplabs.com", + "social": "@ishplabs", + "page_number": "5", + }, + ), + ( + "page_6.html", + { + "patient_name": "Keirstyn Moran", + "age": "34", + "height": "5'4\"", + "weight": "123lbs", + "focus": "Endurance", + "deficit_calories": "1725KCals", + "deficit_protein": "120g Protein", + "deficit_carbs": "155g Carbs", + "deficit_fat": "69g Fat", + "deficit_fiber": "25g Fibre", + "refeed_weekday_calories": "1615KCals", + "refeed_weekday_protein": "120g Protein", + "refeed_weekday_carbs": "142g Carbs", + "refeed_weekday_fat": "63g Fat", + "refeed_weekday_fiber": "24g Fibre", + "refeed_weekend_calories": "2000KCals", + "refeed_weekend_protein": "120g Protein", + "refeed_weekend_carbs": "190g Carbs", + "refeed_weekend_fat": "84g Fat", + "refeed_weekend_fiber": "30g Fibre", + "protein_percentage": "28%", + "carbs_percentage": "36%", + "fats_percentage": "36%", + "contact_email": "info@ishplabs.com", + "website": "www.ishplabs.com", + "social": "@ishplabs", + "page_number": "6", + }, + ), + ( + "page_7.html", + { + "patient_name": "Keirstyn Moran", + "age": "34", + "height": "5'4\"", + "weight": "123lbs", + "focus": "Endurance", + "fvc_value": "4.24L → 112.0%", + "fev1_value": "3.26L → 103.3%", + "fev1_fvc_ratio": "76.89% → 91.8%", + "indication": "No Respiratory Capacity Limitation", + "respiratory_graph": "../graphs/respiratory_chart.png", + "peak_vt_value": "2.38L/Breath which occurs at 172bpm (Zone 3)", + "peak_vt_percentage": "73% of FEV1", + "contact_email": "info@ishplabs.com", + "website": "www.ishplabs.com", + "social": "@ishplabs", + "page_number": "7", + }, + ), + ( + "page_8.html", + { + "patient_name": "Keirstyn Moran", + "age": "34", + "height": "5'4\"", + "weight": "123lbs", + "focus": "Endurance", + "vo2_max_value": "49.5", + "vo2_max_percentile": "100th percentile", + "age_range": "30-39", + "very_poor_range": "19.0-24.1", + "poor_range": "24.1-28.2", + "fair_range": "28.2-32.2", + "good_range": "32.2-35.7", + "excellent_range": "35.7-45.8", + "superior_range": "45.8+", + "zone1_percentage": "55-65% of Max Heart Rate", + "zone2_percentage": "65-75% of Max Heart Rate", + "zone3_percentage": "80-85% of Max Heart Rate", + "zone4_percentage": "85-88% of Max Heart Rate", + "zone5_percentage": "90% of Max Heart Rate", + "zone1_bpm": "81-96bpm", + "zone2_bpm": "96-100bpm", + "zone3_bpm": "100-178bpm", + "zone4_bpm": "178-188bpm", + "zone5_bpm": "188-198bpm", + "zone1_speed": "3.5mph", + "zone2_speed": "3.5-4.0mph", + "zone3_speed": "4.0-6.5mph", + "zone4_speed": "6.5-7.0mph", + "zone5_speed": "7.0-8.0mph", + "zone1_incline": "2% Incline", + "zone2_incline": "2% Incline", + "zone3_incline": "2% Incline", + "zone4_incline": "2% Incline", + "zone5_incline": "2% Incline", + "zone1_pace": "10:39min/km Pace", + "zone2_pace": "10:39-9:19min/km Pace", + "zone3_pace": "9:19-5:44min/km Pace", + "zone4_pace": "5:44-5:20min/km Pace", + "zone5_pace": "5:20-4:40min/km Pace", + "zone1_calories": "4.4kcals/minute", + "zone2_calories": "5.9kcals/minute", + "zone3_calories": "9.4kcals/minute", + "zone4_calories": "12.5kcals/minute", + "zone5_calories": "12.8kcals/minute", + "zone1_carb": "Avg: 0.4g/min Carb Utilization", + "zone2_carb": "Avg: 0.6g/min Carb Utilization", + "zone3_carb": "Avg: 1.9g/min Carb Utilization", + "zone4_carb": "Avg: 2.9g/min Carb Utilization", + "zone5_carb": "Avg: 3.1g/min Carb Utilization", + "zone1_breaths": "Avg: 27 breaths", + "zone2_breaths": "Avg: 28 breaths", + "zone3_breaths": "Avg: 31 breaths", + "zone4_breaths": "Avg: 42 breaths", + "zone5_breaths": "Avg: 51 breaths", + "zone1_breath_range": "Ideal Range: 15-20 breaths", + "zone2_breath_range": "Ideal Range: 20-25 breaths", + "zone3_breath_range": "Ideal Range: 25-30 breaths", + "zone4_breath_range": "Ideal Range: 30-35 breaths", + "zone5_breath_range": "Ideal Range: 40+ breaths", + "contact_email": "info@ishplabs.com", + "website": "www.ishplabs.com", + "social": "@ishplabs", + "page_number": "8", + }, + ), + ( + "page_9.html", + { + "patient_name": "Keirstyn Moran", + "age": "34", + "height": "5'4\"", + "weight": "123lbs", + "focus": "Endurance", + "fuel_utilization_chart": "../graphs/fuel_utilization_chart.png", + "client_name": "Keirstyn Moran", + "assessment_date": "July 29 2025", + "contact_email": "info@ishplabs.com", + "website": "www.ishplabs.com", + "social": "@ishplabs", + "page_number": "9", + }, + ), + ( + "page_10.html", + { + "patient_name": "Keirstyn Moran", + "age": "34", + "height": "5'4\"", + "weight": "123lbs", + "focus": "Endurance", + "vo2_pulse_drop_bpm": "180 bpm", + "vo2_pulse_drop_zone": "Zone 4", + "vo2_pulse_chart": "../graphs/vo2_pulse_chart.png", + "vo2_breath_drop_bpm": "173 bpm", + "vo2_breath_drop_zone": "Zone 3", + "vo2_breath_chart": "../graphs/vo2_breath_chart.png", + "contact_email": "info@ishplabs.com", + "website": "www.ishplabs.com", + "social": "@ishplabs", + "page_number": "9", + }, + ), + ( + "page_11.html", + { + "patient_name": "Keirstyn Moran", + "age": "34", + "height": "5'4\"", + "weight": "123lbs", + "focus": "Endurance", + "fat_max_optimal": "*Optimal 10-12Kcals/minute", + "fat_max_value": "3.8Kcals/min", + "fat_max_heart_rate": "49% of Max Heart Rate", + "fat_max_bpm": "97 bpm", + "crossover_bpm": "100bpm", + "crossover_heart_rate": "51% of Max Heart Rate", + "fat_metabolism_note": "100bpm at a speed of 4.0mph and incline of 2%", + "fat_metabolism_chart": "../graphs/fat_metabolism_chart.png", + "cardiac_recovery_time": "(1 minute)", + "cardiac_recovery_percentage": "33%", + "metabolic_recovery_time": "(2 minute)", + "metabolic_recovery_percentage": "65%", + "breath_recovery_time": "(2.5 minute)", + "breath_recovery_percentage": "76%", + "recovery_chart": "../graphs/recovery_chart.png", + "resting_heart_rate": "53bpm", + "hr_age_range": "26-35", + "hr_poor": "82bpm +", + "hr_below_avg": "75-81bpm", + "hr_average": "71-74bpm", + "hr_above_avg": "66-70bpm", + "hr_good": "62-65bpm", + "hr_excellent": "55-61bpm", + "hr_athlete": "44-54bpm", + "contact_email": "info@ishplabs.com", + "website": "www.ishplabs.com", + "social": "@ishplabs", + "page_number": "10", + }, + ), + ( + "page_13.html", + { + "patient_name": "Keirstyn Moran", + "age": "34", + "height": "5'4\"", + "weight": "123lbs", + "focus": "Endurance", + "zone2_frequency": "3-4x/week", + "zone2_duration": "40+ minutes", + "zone2_hr_range": "____", + "zone2_speed": "____ mph", + "zone2_incline": "2% Incline", + "zone3_frequency": "1-2x/week", + "zone3_duration": "10-20 minutes", + "zone3_hr_range": "____ bpm", + "zone3_speed": "____mph", + "zone3_incline": "2% Incline", + "zone3_target_hr": "___ bpm", + "zone3_recovery_speed": "____mph", + "zone3_recovery_incline": "2% Incline", + "zone1_hr_range": "____bpm", + "zone1_duration": "4-8 minutes", + "zone3_repeats": "2-3 times", + "short_sets": "8-10", + "short_duration": "10-30 seconds", + "short_zone": "5", + "short_rpe": "10", + "short_recovery": "20-60 seconds", + "medium_sets": "6-8", + "medium_duration": "30-90 seconds", + "medium_zone": "4", + "medium_rpe": "8-9", + "medium_recovery": "30-90 seconds", + "long_sets": "4-6", + "long_duration": "5-10 minutes", + "long_zone": "3/4", + "long_rpe": "7-8", + "long_recovery": "2.5-5 minutes", + "tempo_sets": "2-3", + "tempo_duration": "10-20 minutes", + "tempo_zone": "3", + "tempo_rpe": "6-7", + "tempo_recovery": "4-8 minutes", + "cardio_sets": "1", + "cardio_duration": ">40 minutes", + "cardio_zone": "2", + "cardio_rpe": "4-5", + "cardio_recovery": "N/A", + "week1_mon_zone": "Zone 2", + "week1_mon_duration": "45 mins", + "week1_tue_zone": "Zone 2", + "week1_tue_duration": "45 mins", + "week1_wed_zone": "Zone 3", + "week1_wed_duration1": "10mins On", + "week1_wed_duration2": "8mins Rest", + "week1_wed_sets": "x2", + "week1_thu_content": "", + "week1_fri_zone": "Zone 2", + "week1_fri_duration": "45 mins", + "week1_sat_content": "", + "week1_sun_content": "", + "week2_mon_zone": "Zone 2", + "week2_mon_duration": "50 mins", + "week2_tue_zone": "Zone 2", + "week2_tue_duration": "50 mins", + "week2_wed_zone": "Zone 3", + "week2_wed_duration1": "10mins On", + "week2_wed_duration2": "6mins Rest", + "week2_wed_sets": "x2", + "week2_thu_content": "", + "week2_fri_zone": "Zone 2", + "week2_fri_duration": "50 mins", + "week2_sat_content": "", + "week2_sun_content": "", + "contact_email": "info@ishplabs.com", + "website": "www.ishplabs.com", + "social": "@ishplabs", + "page_number": "12", + }, + ), + ("page_14.html", {}), + ("page_15.html", {}), + ("page_16.html", {}), + ("page_17.html", {}), + ("page_18.html", {}), + ("page_19.html", {}), ] # Render each template with its own context diff --git a/notebook.ipynb b/notebook.ipynb index afa9f57..7bdfe87 100644 --- a/notebook.ipynb +++ b/notebook.ipynb @@ -512,7 +512,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 31, "id": "52642f49", "metadata": {}, "outputs": [ @@ -899,7 +899,7 @@ "[63 rows x 147 columns]" ] }, - "execution_count": 10, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -911,13 +911,13 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 36, "id": "2056096d", "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -930,19 +930,19 @@ "body_fat_chart = {\n", " \"male\": {\n", " \"20-39\": {\n", - " \"bad\": [(0, 5), (20, 50)],\n", - " \"okay\": [(5, 10), (15, 20)],\n", - " \"good\": [(10, 15)]\n", + " \"bad\": [(0, 5), (25, 50)],\n", + " \"okay\": [(5, 10), (20, 25)],\n", + " \"good\": [(10, 20)]\n", " },\n", " \"40-59\": {\n", - " \"bad\": [(0, 10), (25, 50)],\n", - " \"okay\": [(10, 15), (20, 25)],\n", - " \"good\": [(15, 20)]\n", + " \"bad\": [(0, 5), (30, 50)],\n", + " \"okay\": [(5, 10), (20, 30)],\n", + " \"good\": [(10, 20)]\n", " },\n", " \"60-79\": {\n", - " \"bad\": [(0, 15), (25, 50)],\n", - " \"okay\": [(15, 20), (20, 25)],\n", - " \"good\": []\n", + " \"bad\": [(0, 5), (30, 50)],\n", + " \"okay\": [(5, 10), (20, 25)],\n", + " \"good\": [(10, 25)]\n", " }\n", " },\n", " \"female\": {\n", @@ -963,6 +963,7 @@ " }\n", " }\n", "}\n", + "\n", "def create_body_fat_visualization(gender, age, body_fat_percentage):\n", " # Determine age group\n", " if 20 <= age <= 39:\n", @@ -981,7 +982,7 @@ " fig, ax = plt.subplots(figsize=(12, 3))\n", " \n", " # Define colors for different categories\n", - " colors = {'bad': '#ff6b6b', 'okay': '#ffeb3b', 'good': '#4caf50'}\n", + " colors = {'bad': '#ff6b6b', 'okay': '#ffeb3b', 'good': '#90ee90'}\n", " \n", " # Create the horizontal segments\n", " bar_height = 0.4\n", @@ -993,50 +994,55 @@ " start, end = range_tuple\n", " width = end - start\n", " ax.barh(y_position, width, left=start, height=bar_height, \n", - " color=colors[category], alpha=0.8, edgecolor='white', linewidth=1)\n", + " color=colors[category], alpha=0.9, edgecolor='black', linewidth=0.5)\n", " \n", " # Add the user's body fat percentage marker (triangle pointing down)\n", - " ax.plot(body_fat_percentage, y_position, 'v', markersize=15, color='black', \n", - " markeredgecolor='white', markeredgewidth=2)\n", + " ax.plot(body_fat_percentage, y_position + bar_height / 2 + 0.05, 'v', markersize=15, color='black')\n", " \n", " # Customize the chart\n", " ax.set_xlim(0, 50)\n", - " ax.set_ylim(-0.4, 0.4)\n", - " ax.set_xlabel('', fontsize=12)\n", + " ax.set_ylim(-1, 1)\n", + " ax.set_xlabel('')\n", " ax.set_title(f'Body Fat Percent - {body_fat_percentage:.1f}%', fontsize=16, fontweight='bold', pad=20)\n", " \n", - " # Add age group and gender label below the bar\n", - " ax.text(25, -0.25, f'{age_group}\\n({gender[0]})', ha='center', va='center', fontsize=12)\n", + " # Add age group and gender label on the left side\n", + " ax.text(-5, y_position, f'{age_group}\\n({gender[0].upper()})', ha='center', va='center', fontsize=12)\n", + " \n", + " # Adjust x-axis ticks to match the image\n", + " ax.set_xticks(range(0, 51, 5))\n", + " ax.set_xticklabels([f'{i}%' for i in range(0, 51, 5)])\n", + " \n", + " # Draw vertical lines for the tick marks\n", + " for tick in range(0, 51, 5):\n", + " ax.plot([tick, tick], [y_position - bar_height / 2, y_position - bar_height / 2 - 0.1], color='black', linewidth=1.5)\n", " \n", " # Remove y-axis and top/right spines\n", " ax.set_yticks([])\n", " ax.spines['left'].set_visible(False)\n", " ax.spines['right'].set_visible(False)\n", " ax.spines['top'].set_visible(False)\n", - " \n", - " # Set x-axis ticks every 5%\n", - " ax.set_xticks([0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50])\n", - " ax.set_xticklabels(['0%', '5%', '10%', '15%', '20%', '25%', '30%', '35%', '40%', '45%', '50%'])\n", + " ax.spines['bottom'].set_visible(False)\n", " \n", " plt.tight_layout()\n", + " plt.savefig('graphs/page_1_body_fat.png')\n", " plt.show()\n", "\n", "# Create the chart using Keirstyn's data\n", "gender = 'female'\n", - "age = 60\n", - "fat_percentage = 28.7\n", + "age = 25\n", + "fat_percentage = 22.4\n", "create_body_fat_visualization(gender, age, fat_percentage)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "id": "bf55717b", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -1050,24 +1056,48 @@ "keirstyn_data = df_2[df_2['LastName'].str.contains('Moran', case=False, na=False)]\n", "# Get the fat mass percentage for Keirstyn\n", "fat_percentage = keirstyn_data['Adult_FMP'].iloc[0]\n", - "weight_kg = keirstyn_data['Weight_kg'].iloc[0]\n", + "weight_kg = keirstyn_data['Weight'].iloc[0]\n", "age = keirstyn_data['Age'].iloc[0]\n", "gender = keirstyn_data['Gender'].iloc[0]\n", "lean_percentage = 100 - fat_percentage\n", "\n", "# Create donut chart\n", - "plt.figure(figsize=(8, 8))\n", + "fat_mass_lbs = 27.6\n", + "lean_mass_lbs = 95.4\n", + "\n", + "# Calculate percentages from the provided weights\n", + "total_weight = fat_mass_lbs + lean_mass_lbs\n", + "fat_percentage = (fat_mass_lbs / total_weight) * 100\n", + "lean_percentage = (lean_mass_lbs / total_weight) * 100\n", + "\n", + "# Data for the chart\n", "sizes = [fat_percentage, lean_percentage]\n", - "labels = ['Fat Mass', 'Lean Body Mass']\n", - "colors = ['#ff9999', '#66b3ff']\n", + "labels = ['Fat Mass (27.6lbs)', 'Lean Mass (95.4lbs)']\n", + "colors = ['#fde3ac', '#ff9966'] # Light yellow/tan and orange from the image\n", "\n", - "# Create the donut chart with a wedge\n", - "wedges, texts, autotexts = plt.pie(sizes, labels=labels, colors=colors, autopct='%1.1f%%',\n", - " startangle=90, wedgeprops=dict(width=0.5))\n", + "plt.figure(figsize=(8, 8))\n", + "# Create the donut chart\n", + "wedges, texts, autotexts = plt.pie(sizes,\n", + " autopct='%1.1f%%',\n", + " startangle=90,\n", + " wedgeprops=dict(width=0.5, edgecolor='w'),\n", + " colors=colors)\n", "\n", - "plt.title(f'Body Composition - {keirstyn_data[\"FirstName\"].iloc[0]} {keirstyn_data[\"LastName\"].iloc[0]}', \n", - " fontsize=14, fontweight='bold')\n", - "plt.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle\n", + "# Customize the text for labels and percentages\n", + "for i, (text, autotext) in enumerate(zip(texts, autotexts)):\n", + " # Set the label text to be the full label, not just the percentage\n", + " text.set_text(labels[i])\n", + " text.set_fontsize(14)\n", + " text.set_color('black')\n", + "\n", + " # Position the percentage text inside the donut\n", + " autotext.set_fontsize(14)\n", + " autotext.set_color('black')\n", + "\n", + "# Set the title\n", + "plt.title('Body Composition', fontsize=18, fontweight='bold')\n", + "plt.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle\n", + "plt.savefig('graphs/page_1_body_composition.png')\n", "plt.show()" ] }, diff --git a/pdf_generation.ipynb b/pdf_generation.ipynb index 2749ec1..f4ac490 100644 --- a/pdf_generation.ipynb +++ b/pdf_generation.ipynb @@ -2,12 +2,312 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "6eee3ddd", "metadata": {}, "outputs": [], "source": [ - "import pandas as pd" + "import pandas as pd\n", + "import fitz" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7b50e3ea", + "metadata": {}, + "outputs": [], + "source": [ + "file = fitz.open(\"data/~Moran~K~19910201~Spirometry Exam~20250729~20250729032843.pdf\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b7e1c3ee", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 3 image(s) on page 1\n", + "Saved: page_1_image_1.png\n", + "Saved: page_1_image_2.png\n", + "Saved: page_1_image_3.png\n", + "\n", + "Total images extracted: 3\n", + "Images saved in: extracted_images/\n" + ] + } + ], + "source": [ + "import os\n", + "\n", + "# Create directory to save images if it doesn't exist\n", + "output_dir = \"extracted_images\"\n", + "os.makedirs(output_dir, exist_ok=True)\n", + "\n", + "# Extract all images from the PDF\n", + "image_count = 0\n", + "for page_num in range(len(file)):\n", + " page = file[page_num]\n", + " \n", + " # Get list of images on this page\n", + " image_list = page.get_images()\n", + " \n", + " if image_list:\n", + " print(f\"Found {len(image_list)} image(s) on page {page_num + 1}\")\n", + " \n", + " for img_index, img in enumerate(image_list):\n", + " # Get image reference number\n", + " xref = img[0]\n", + " \n", + " # Extract image data\n", + " base_image = file.extract_image(xref)\n", + " image_bytes = base_image[\"image\"]\n", + " image_ext = base_image[\"ext\"]\n", + " \n", + " # Create filename\n", + " image_filename = f\"page_{page_num + 1}_image_{img_index + 1}.{image_ext}\"\n", + " image_path = os.path.join(output_dir, image_filename)\n", + " \n", + " # Save image\n", + " with open(image_path, \"wb\") as image_file:\n", + " image_file.write(image_bytes)\n", + " \n", + " print(f\"Saved: {image_filename}\")\n", + " image_count += 1\n", + " else:\n", + " print(f\"No images found on page {page_num + 1}\")\n", + "\n", + "print(f\"\\nTotal images extracted: {image_count}\")\n", + "print(f\"Images saved in: {output_dir}/\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e2af9631", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Error extracting tables from page 1: object of type 'TableFinder' has no len()\n", + "\n", + "Extracted text from 1 pages\n", + "Found 0 tables total\n", + "\n", + "First 1000 characters of extracted text:\n", + "\n", + "--- Page 1 ---\n", + "PRE#1\n", + "PRE#2\n", + "PRE#3\n", + "Spirometry Results\n", + "VISIT DATE 2025-07-29\n", + "ID\n", + "Last Name\n", + "Moran\n", + "First Name\n", + "K\n", + "Date of birth\n", + "1991-02-01\n", + "Origin\n", + "Caucasian\n", + "Age\n", + "34\n", + "Gender\n", + "F\n", + "Height\n", + "163 cm\n", + "Weight\n", + "54 kg\n", + "BMI\n", + "20.3\n", + "ACCEPTABILITY CRITERIA\n", + "Quality Grade PRE F Variability FEV1=0.05(1.56%), FVC=0.07(1.68%)\n", + "Acceptable trials 0\n", + "LLN\n", + "Predicted\n", + "FVC\n", + "FEV1\n", + "FEV1/FVC\n", + "-5\n", + "-4\n", + "-3\n", + "-2\n", + "-1\n", + "0\n", + "1\n", + "2\n", + "3\n", + "Spirometry\n", + "Parameters\n", + "FVC\n", + "FEV1\n", + "FEV1/FVC\n", + "PEF\n", + "FEF2575\n", + "FEF25\n", + "FEF50\n", + "FEF75\n", + "PEFTime\n", + "EVol\n", + "FEV6\n", + "L\n", + "L\n", + "%\n", + "L/m\n", + "L/s\n", + "L/s\n", + "L/s\n", + "ms\n", + "mL\n", + "L\n", + "L/s\n", + "Best\n", + "3.26\n", + "76.89\n", + "684\n", + "2.74\n", + "6.08\n", + "3.06\n", + "1.06\n", + "79\n", + "78.0\n", + "4.24\n", + "4.22\n", + "LLN\n", + "3.03\n", + "2.53\n", + "72.47\n", + "222\n", + "2.15\n", + "0.0\n", + "0.0\n", + "0.71\n", + "-\n", + "-\n", + "3.03\n", + "Pred.\n", + "3.79\n", + "3.16\n", + "384\n", + "3.42\n", + "0.0\n", + "0.0\n", + "1.41\n", + "-\n", + "-\n", + "3.79\n", + "83.78\n", + "%Pred.\n", + "112.0\n", + "103.3\n", + "91.8\n", + "178.7\n", + "80.2\n", + "-\n", + "-\n", + "75.1\n", + "-\n", + "-\n", + "111.4\n", + "ZScore\n", + "0.95\n", + "0.28\n", + "-1.05\n", + "-\n", + "-0.84\n", + "0.0\n", + "0.0\n", + "-0.72\n", + "-\n", + "-\n", + "-\n", + "PRE#1\n", + "4.24\n", + "3.26\n", + "76.9\n", + "444\n", + "2.74\n", + "6.08\n", + "3.06\n", + "1.06\n", + "79\n", + "78.0\n", + "4.22\n", + "PRE#2\n", + "4.17\n", + "3.21\n", + "77.0\n", + "438\n", + "2.68\n", + "6.0\n", + "1.12\n", + "49\n", + "77.0\n", + "4.17\n", + "3.1\n", + "PRE#3\n", + "0.94\n", + "684\n", + "4.15\n", + "2.77\n", + "197.0\n", + "4.13\n", + "75.7\n", + "39\n", + "2.48\n", + "3.14\n", + "5.53\n", + "NOTE\n", + "Spirobank Smart Z114689 Sent on 2025-07-29 15:28\n", + "BTPS 1.111 21.0 °C \n" + ] + } + ], + "source": [ + "# Extract text and tables from the PDF\n", + "text_content = \"\"\n", + "tables_data = []\n", + "\n", + "for page_num in range(len(file)):\n", + " page = file[page_num]\n", + " \n", + " # Extract text from the page\n", + " page_text = page.get_text()\n", + " text_content += f\"\\n--- Page {page_num + 1} ---\\n\"\n", + " text_content += page_text\n", + " \n", + " # Try to find tables using PyMuPDF's table detection\n", + " try:\n", + " tables = page.find_tables()\n", + " if tables:\n", + " print(f\"Found {len(tables)} table(s) on page {page_num + 1}\")\n", + " for i, table in enumerate(tables):\n", + " table_data = table.extract()\n", + " tables_data.append({\n", + " 'page': page_num + 1,\n", + " 'table_index': i,\n", + " 'data': table_data\n", + " })\n", + " print(f\"Table {i+1} on page {page_num + 1}:\")\n", + " for row in table_data:\n", + " print(row)\n", + " print(\"-\" * 50)\n", + " except Exception as e:\n", + " print(f\"Error extracting tables from page {page_num + 1}: {e}\")\n", + "\n", + "print(f\"\\nExtracted text from {len(file)} pages\")\n", + "print(f\"Found {len(tables_data)} tables total\")\n", + "\n", + "# Display first 1000 characters of text content to see what we have\n", + "print(\"\\nFirst 1000 characters of extracted text:\")\n", + "print(text_content[:1000])" ] }, { diff --git a/report_gen/page_10.html b/report_gen/page_10.html index e69de29..f31d4de 100644 --- a/report_gen/page_10.html +++ b/report_gen/page_10.html @@ -0,0 +1,65 @@ +
+ +
+
+
ISHP
+
+
+
+ Name: {{ patient_name | default('Keirstyn Moran') }} + Age: {{ age | default('34') }} + Height: {{ height | default('5\'4"') }} + Weight: {{ weight | default('123lbs') }} + Focus: {{ focus | default('Endurance') }} +
+
+ + +
+ +
+ +
+

VO2 Pulse

+

Begins to drop at {{ vo2_pulse_drop_bpm | default('180 bpm') }} ({{ vo2_pulse_drop_zone | default('Zone 4') }})

+
+ + +
+ VO2 Pulse Chart +
+
+ + +
+ +
+

VO2 Breath

+

Begins to drop at {{ vo2_breath_drop_bpm | default('173 bpm') }} ({{ vo2_breath_drop_zone | default('Zone 3') }})

+
+ + +
+ VO2 Breath Chart +
+
+
+ + +
+
+
+ CONTACT: {{ contact_email | default('info@ishplabs.com') }} + WEBSITE: {{ website | default('www.ishplabs.com') }} + SOCIAL: {{ social | default('@ishplabs') }} +
+
+ {{ page_number | default('9') }} +
+
+
+ diff --git a/report_gen/page_11.html b/report_gen/page_11.html index e69de29..5dfe8a9 100644 --- a/report_gen/page_11.html +++ b/report_gen/page_11.html @@ -0,0 +1,265 @@ +
+ +
+
+
ISHP
+
+
+
+ Name: {{ patient_name | default('Keirstyn Moran') }} + Age: {{ age | default('34') }} + Height: {{ height | default('5\'4"') }} + Weight: {{ weight | default('123lbs') }} + Focus: {{ focus | default('Endurance') }} +
+
+ + +
+ +
+

+ Fat Metabolism +

+ + +
+ +
+

Fat Max

+

+ {{ fat_max_optimal | default('*Optimal + 10-12Kcals/minute') }} +

+

+ {{ fat_max_value | default('3.8Kcals/min') }} +

+

+ {{ fat_max_heart_rate | default('49% of Max Heart Rate') + }} +

+

+ {{ fat_max_bpm | default('97 bpm') }} +

+
+ + +
+

+ Carbs and Fat Crossover +

+

+ {{ crossover_bpm | default('100bpm') }} +

+

+ {{ crossover_heart_rate | default('51% of Max Heart + Rate') }} +

+
+
+ + +
+
+

+ {{ fat_metabolism_note | default('100bpm at a speed of + 4.0mph and incline of 2%') }} +

+
+ +
+ Fat Metabolism Chart +
+
+
+ + +
+

+ Recovery +

+ + +
+ +
+

+ Cardiac Recovery +

+

+ {{ cardiac_recovery_time | default('(1 minute)') }} +

+

+ {{ cardiac_recovery_percentage | default('33%') }} +

+
+ + +
+

+ Metabolic (CO2) Recovery +

+

+ {{ metabolic_recovery_time | default('(2 minute)') }} +

+

+ {{ metabolic_recovery_percentage | default('65%') }} +

+
+ + +
+

+ Breath Frequency Recovery +

+

+ {{ breath_recovery_time | default('(2.5 minute)') }} +

+

+ {{ breath_recovery_percentage | default('76%') }} +

+
+
+ + +
+ Recovery Chart +
+
+ + +
+

+ Resting Heart Rate - {{ resting_heart_rate | default('53bpm') }} +

+ + + + + + + + + + + + + + + + + + + + + + + + + + +
+ Age (F) + + Poor + + Below Average + + Average + + Above Average + + Good + + Excellent + + Athlete + +
+ +
+
+ {{ hr_age_range | default('26-35') }} + + {{ hr_poor | default('82bpm +') }} + + {{ hr_below_avg | default('75-81bpm') }} + + {{ hr_average | default('71-74bpm') }} + + {{ hr_above_avg | default('66-70bpm') }} + + {{ hr_good | default('62-65bpm') }} + + {{ hr_excellent | default('55-61bpm') }} + + {{ hr_athlete | default('44-54bpm') }} +
+
+
+ + +
+
+
+ CONTACT: {{ contact_email | default('info@ishplabs.com') + }} + WEBSITE: {{ website | default('www.ishplabs.com') }} + SOCIAL: {{ social | default('@ishplabs') }} +
+
+ {{ page_number | default('10') }} +
+
+
+
diff --git a/report_gen/page_13.html b/report_gen/page_13.html index e69de29..3b012e5 100644 --- a/report_gen/page_13.html +++ b/report_gen/page_13.html @@ -0,0 +1,242 @@ +
+ +
+
+
ISHP
+
+
+
+ Name: {{ patient_name | default('Keirstyn Moran') }} + Age: {{ age | default('34') }} + Height: {{ height | default('5\'4"') }} + Weight: {{ weight | default('123lbs') }} + Focus: {{ focus | default('Endurance') }} +
+
+ + +
+ +

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') }}
    • +
+
+
+ + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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') }}
+
+
+ + +
+

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('') }}
+
+
+
+
+
+ + +
+
+
+ CONTACT: {{ contact_email | default('info@ishplabs.com') }} + WEBSITE: {{ website | default('www.ishplabs.com') }} + SOCIAL: {{ social | default('@ishplabs') }} +
+
+ {{ page_number | default('12') }} +
+
+
+ diff --git a/report_gen/page_14.html b/report_gen/page_14.html index e69de29..a3371fe 100644 --- a/report_gen/page_14.html +++ b/report_gen/page_14.html @@ -0,0 +1,176 @@ +
+ +
+
+
ISHP
+
+
+
+ Name: Keirstyn Moran + Age: 34 + Height: 5'4" + Weight: 123lbs + Focus: Endurance +
+
+ + +
+ +
+

Training Week

+

(To be filled out by your trainer)

+
+ + +
+
+ +
+
Monday
+
+
+
Tuesday
+
+
+
Wednesday
+
+
+
Thursday
+
+
+
Friday
+
+
+
Saturday
+
+
+
Sunday
+
+
+ + +
+
+
+
+
+
+
+
+
+
+ + +
+
+ +
+
Monday
+
+
+
Tuesday
+
+
+
Wednesday
+
+
+
Thursday
+
+
+
Friday
+
+
+
Saturday
+
+
+
Sunday
+
+
+ + +
+
+
+
+
+
+
+
+
+
+ + +
+

Training Week Guidelines

+ + +
+ +
+

Zone 1

+
    +
  • Zone 1 training is low intensity, for active recovery.
    • +
  • It can be done daily or even consecutively, depending on fitness, volume, and health.
    • +
+
+ + +
+

Zone 2

+
    +
  • Zone 2 training can be done on consecutive or daily basis with moderate sessions.
    • +
  • Can be steady state or interval sessions.
    • +
+
+ + +
+

Zone 3

+
    +
  • Zone 3 training can be done 1-5 times per week.
    • +
  • Wait 24 to 48 hours between sessions for adequate recovery.
    • +
+
+ + +
+

Zone 4

+
    +
  • Zone 4 training: 1-4 times per week.
    • +
  • Wait 24 to 48 hours between intense sessions for recovery.
    • +
+
+ + +
+

Zone 5

+
    +
  • Zone 5 training: 1-2 times per week.
    • +
  • Wait 48 hours between sessions for recovery.
    • +
  • Zone 5 increases VO2 max and endurance at VO2 max.
    • +
+
+
+ + +
+

Zone 3, 4, 5 can be combined with Zone 1 or 2 - the higher zone should be done first!

+
+
+
+ + +
+
+
+ CONTACT: info@sportandhighperformance.com + WEBSITE: www.sportandhighperformance.com + SOCIAL: @sportandhighperformance +
+
+ 13 +
+
+
+ diff --git a/report_gen/page_15.html b/report_gen/page_15.html index e69de29..c5ec13c 100644 --- a/report_gen/page_15.html +++ b/report_gen/page_15.html @@ -0,0 +1,97 @@ +
+ +
+
+
ISHP
+
+
+
+ Name: Keirstyn Moran + Age: 34 + Height: 5'4" + Weight: 123lbs + Focus: Endurance +
+
+ + +
+ +

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.

+
+
+ + +
+

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 +

+
+
+ + +
+
+
+ CONTACT: info@ishplabs.com + WEBSITE: www.ishplabs.com + SOCIAL: @ishplabs +
+
+ 14 +
+
+
+ diff --git a/report_gen/page_16.html b/report_gen/page_16.html index e69de29..3d2030a 100644 --- a/report_gen/page_16.html +++ b/report_gen/page_16.html @@ -0,0 +1,84 @@ +
+ +
+
+
ISHP
+
+
+
+ Name: Keirstyn Moran + Age: 34 + Height: 5'4" + Weight: 123lbs + Focus: Endurance +
+
+ + +
+ +

Glossary

+ + +
+

Body Fat Percentage:

+

The percentage of your overall body weight that is composed of fat cells. Body fat percentage can be reduced by either losing weight from fat mass, while maintaining lean mass, or maintaining fat mass while increasing lean mass.

+
+ + +
+

Metabolic Rate:

+

Metabolic Rate measures the number of calories your body burns for basic functions and movement, based on factors like weight, age, gender, and height. A higher metabolic rate helps prevent weight gain and supports weight loss by ensuring you burn enough calories. Tracking metabolic rate is key for managing weight and preventing conditions linked to metabolic dysfunction. Positive influences include resistance exercise, proper sleep, and adequate protein, while negative factors include extreme dieting, yo-yo dieting, and excessive cardio. Improving it involves resistance training and optimal nutrition.

+
+ + +
+

Fuel Source:

+

Fat-burning efficiency measures your cells' ability to use fat as fuel, reflecting mitochondrial and cellular health. It indicates how well your body balances fat and carbohydrate usage to support energy needs, assessed by analyzing oxygen and carbon dioxide in your breath. High fat-burning efficiency suggests strong metabolic and mitochondrial function, linked to better weight management and longevity.

+

To improve fat-burning efficiency, focus on Zone 2 endurance training and potentially intermittent fasting to enhance oxygen absorption and cellular function. Zone 5 interval training will also help improve fat burning mitochondrial density and capillarization. Factors that reduce fat burning ability include diets high in processed foods, alcohol, and large meals before bed. Conditions related to metabolic stress also hinder fat burning abilities.

+
+ + +
+

NEAT (Non-Exercise Activity Thermogenesis)

+

refers to the energy expended for all activities that are not deliberate exercise or structured physical activity. This includes daily movements such as walking, fidgeting, standing, cleaning, typing, and even simple tasks like cooking or shopping. NEAT contributes significantly to the total caloric expenditure and plays a key role in maintaining body weight and overall energy balance. It varies widely among individuals, depending on lifestyle, occupation, and habits.

+
+ + +
+

Spirometry:

+

Spirometry is a diagnostic device used to provide objective measurements of lung volumes and capacities. Lung function is crucial for oxygen delivery during physical activity, and comparing spirometry results to expected values can highlight any potential limitations to performance.

+

"From a Performance standpoint, it is essential in making informed training recommendations related to respiratory health to optimize endurance performance and metabolic health."

+ + +
    +
  • FEV1: Forced Expiratory Volume - the total amount of air expelled in the first second.
    • +
  • FVC: Forced Vital Capacity - the maximum amount of air exhaled in one breath after a maximum inhalation
    • +
  • FEV1/FVC: Calculated ratio used in the diagnosis of obstructive & restrictive lung disease.
    • +
+ +

By comparing these measurements to expected values based on age, gender, height and ethnicity, healthcare professionals can diagnose a range of lung conditions such as asthma, COPD, restrictive lung diseases, and more.

+
+ + +
+

VO2 max:

+

VO2 Max, or maximal oxygen consumption serves as a valuable indicator of overall fitness, cardiovascular health, and endurance capacity. VO2 max reflects the efficiency of your heart lung system in pumping oxygen-rich blood to working muscles. A higher VO2 max indicates a stronger cardiovascular system, which is associated with a reduced risk of heart disease and other cardiovascular issues.

+

Understanding and training to increase your VO2 max can contribute to enhanced physical performance, longevity and well-being.

+
+
+ + +
+
+
+ CONTACT: info@ishplabs.com + WEBSITE: www.ishplabs.com + SOCIAL: @ishplabs +
+
+ 15 +
+
+
+ diff --git a/report_gen/page_17.html b/report_gen/page_17.html index e69de29..541cb45 100644 --- a/report_gen/page_17.html +++ b/report_gen/page_17.html @@ -0,0 +1,173 @@ + + +
+
+
ISHP
+
+
+
+ Name: Keirstyn Moran + Age: 34 + Height: 5'4" + Weight: 123lbs + Focus: Endurance +
+
+ + +
+

Glossary

+ +
+ +
+

Peak VT:

+

+ Peak Volume of air moved throughout the test. +

+

+ Respiratory Capability Limitations that can be found + include: +

+
    +
  • + Endurance: Normal capacity, but + cannot maintain their VT over time. +
    • +
  • + Strength/Power: Normal capacity, + but peak VT is not 75-85% of their FEV1 despite FEV1 + being normal +
    • +
  • + Coordination (Hyper/Hypo-Ventilation): + Normal capacity, but uses low volumes +/- high BFs + at lower intensities. A breathing coordination + limitation can also be identified by the loss of + volume at higher intensities, which are then + recovered upon recovery/stop of activity. +
    • +
+
+ + +
+

VO2 Pulse:

+

+ VO2 Pulse refers to the relationship between oxygen + consumption (VO2) and heart rate (HR) during exercise. + This measure gives insight into how efficiently the body + is using oxygen in relation to the heart's output. A + higher VO2 Pulse suggests that an individual is able to + deliver oxygen more efficiently to the muscles with each + heartbeat. +

+
+ + +
+

VO2 Breath:

+

+ VO2 Breath refers to the amount of oxygen consumed per + breath during exercise, which indicates how effectively + the body delivers oxygen to the bloodstream through the + lungs with each breath. A more efficient VO2 Breath + means the body requires less effort to obtain the same + amount of oxygen, indicating better respiratory + efficiency and oxygen utilization. +

+
+ + +
+

Carb & Fat Crossover:

+

+ The point during exercise at which the body shifts its + predominant fuel source from fats to carbohydrates. This + transition typically occurs as exercise intensity + increases, and marks the transition from Zone 2 into + Zone 3. As exercise intensity increases, the body starts + to rely more on carbohydrates because they provide + faster energy. +

+

+ Endurance training (e.g., long, steady-state cardio + within Zones 1 & 2) increases the body's ability to burn + fat efficiently at higher intensities, shifting the + crossover point to a faster speed, or higher heart + rate/intensity. Because fat stores are much larger and + can provide a steady stream of energy for prolonged + periods, a higher CHO/FAT crossover can help delay + fatigue, which is especially beneficial in + longer-duration events, where carbohydrate depletion can + lead to a significant drop in performance. +

+
+ + +
+

Cardiovascular Recovery:

+

+ The percentage your heart rate drops within the first + minute of the inactive recovery phase in relation to the + lowest heart rate recorded prior to the start of the + test. +

+
+ + +
+

Metabolic (CO2) Recovery:

+

+ The percentage that your VCO2 levels (amount of CO2 you + are exhaling) drop within the first 1.5 minutes of the + inactive recovery phase in relation to the lowest VCO2 + recorded prior to the start of the test. +

+

+ refers to the rate at which the body clears carbon + dioxide (CO2) after exercise, reflecting the efficiency + of the cardiovascular and respiratory systems in + returning CO2 levels to baseline. A faster VCO2 recovery + indicates effective management of metabolic byproducts, + signaling a healthier metabolic system and lower risk of + metabolic disorders. +

+
+ + +
+

Breath Frequency Recovery:

+

+ Refers to the speed at which the body returns to a + normal breathing rate after physical exertion. Faster + breath frequency recovery indicates a well-conditioned + cardiovascular and respiratory system, allowing the body + to efficiently regulate oxygen and CO2 levels. It + supports better endurance, faster recovery between + intervals, and the ability to sustain higher performance + during repeated efforts or prolonged activity. + Additionally, a quick return to baseline signals that + the autonomic nervous system is functioning well, + reducing stress on the body and promoting more efficient + recovery. This also reflects a healthier metabolic + system, better management of metabolic byproducts like + CO2, and a lower risk of chronic conditions. +

+
+
+
+ + +
+
CONTACT: info@ishplabs.com
+
WEBSITE: www.ishplabs.com
+
SOCIAL: @ishplabs
+
17
+
+ + diff --git a/report_gen/page_18.html b/report_gen/page_18.html index e69de29..9a47c0d 100644 --- a/report_gen/page_18.html +++ b/report_gen/page_18.html @@ -0,0 +1,371 @@ + + + + + + Glossary - Page 18 + + + + +
+
+
ISHP
+
+
+
+ Name: Keirstyn Moran + Age: 34 + Height: 5'4" + Weight: 123lbs + Focus: Endurance +
+
+ + +
+

Glossary

+ + +
+

Local Muscle Activity/SMO2:

+

+ SmO2 testing is a valuable tool for understanding how + muscles respond to various physiological stressors and how + to fine-tune training, nutrition and hydration accordingly. + Monitoring changes in tissue oxygen saturation and + utilization helps identify an individual's optimal intensity + to work at, as well as how well they recover between bouts + of intensity. This can help optimize training to improve + performance, prevent overtraining, and tailor strategies for + better results. +

+

+ During competitions, athletes can also use SmO2 data to pace + themselves effectively. Adjusting intensity based on muscle + oxygenation can help prevent premature fatigue and optimize + race outcomes +

+
+ + +
+

+ Body Fat Percent Master Chart +

+ + +
+
+
+ Age (M) +
+
+ +
+ 20-39 +
+
+
+
+
+
+
+
+
+
+
+
+
+ +
+
+
+ +
+ 40-59 +
+
+
+
+
+
+
+
+
+
+
+
+
+ +
+
+
+ +
+ 60-79 +
+
+
+
+
+
+
+
+
+
+
+
+
+ + +
+
+
+
0%
+
5%
+
10%
+
15%
+
20%
+
25%
+
30%
+
35%
+
40%
+
45%
+
50%
+
+
+
+ + +
+
+
+ Age (F) +
+
+ +
+ 20-39 +
+
+
+
+
+
+
+
+
+
+
+
+
+ +
+
+
+ +
+ 40-59 +
+
+
+
+
+
+
+
+
+
+
+
+
+ +
+
+
+ +
+ 60-79 +
+
+
+
+
+
+
+
+
+
+
+
+
+ + +
+
+
+
0%
+
5%
+
10%
+
15%
+
20%
+
25%
+
30%
+
35%
+
40%
+
45%
+
50%
+
+
+
+
+
+ + +
+
CONTACT: info@ishplabs.com
+
WEBSITE: www.ishplabs.com
+
SOCIAL: @ishplabs
+
18
+
+ + diff --git a/report_gen/page_19.html b/report_gen/page_19.html index e69de29..9090b05 100644 --- a/report_gen/page_19.html +++ b/report_gen/page_19.html @@ -0,0 +1,850 @@ + + + + + + Glossary - Page 19 + + + +
+ +
+
+
ISHP
+
+
+
+ Name: Keirstyn Moran + Age: 34 + Height: 5'4" + Weight: 123lbs + Focus: Endurance +
+
+ + +
+

Glossary

+ + +
+

+ Resting Heart Rate +

+ + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ Age (M) + + Poor + + Below Average + + Average + + Above Average + + Good + + Excellent + + Athlete +
+ 18-25 + + 85bpm + + + 76-84bpm + + 74-78bpm + + 70-73bpm + + 66-69bpm + + 61-65bpm + + 60-60bpm +
+ 26-35 + + 83bpm + + + 77-82bpm + + 73-76bpm + + 69-72bpm + + 65-68bpm + + 60-64bpm + + 55-59bpm +
+ 36-45 + + 85bpm + + + 79-84bpm + + 74-78bpm + + 70-73bpm + + 65-69bpm + + 60-64bpm + + 55-59bpm +
+ 46-55 + + 84bpm + + + 76-83bpm + + 73-77bpm + + 70-72bpm + + 66-69bpm + + 61-65bpm + + 56-60bpm +
+ 56-65 + + 85bpm + + + 78-84bpm + + 74-77bpm + + 70-73bpm + + 65-69bpm + + 60-64bpm + + 50-59bpm +
+ 65+ + + 84bpm + + + 77-83bpm + + 73-76bpm + + 70-73bpm + + 65-69bpm + + 60-64bpm + + 55-59bpm +
+
+ + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ Age (F) + + Poor + + Below Average + + Average + + Above Average + + Good + + Excellent + + Athlete +
+ 18-25 + + 81bpm + + + 74-81bpm + + 73-78bpm + + 66-69bpm + + 62-65bpm + + 56-61bpm + + 50-55bpm +
+ 26-35 + + 82bpm + + + 75-81bpm + + 71-74bpm + + 66-70bpm + + 62-65bpm + + 55-61bpm + + 54-54bpm +
+ 36-45 + + 83bpm + + + 76-82bpm + + 71-75bpm + + 67-70bpm + + 63-66bpm + + 57-62bpm + + 47-56bpm +
+ 46-55 + + 84bpm + + + 77-83bpm + + 72-76bpm + + 68-71bpm + + 64-67bpm + + 58-63bpm + + 49-57bpm +
+ 56-65 + + 82bpm + + + 76-81bpm + + 72-75bpm + + 68-71bpm + + 62-67bpm + + 57-61bpm + + 51-56bpm +
+ 65+ + + 80bpm + + + 74-79bpm + + 70-73bpm + + 66-69bpm + + 62-65bpm + + 56-61bpm + + 52-55bpm +
+
+
+ + +
+

+ VO2 Master Chart +

+ + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ Age (M) + + Very Poor + + Poor + + Fair + + Good + + Excellent + + Superior +
+ 20-29 + + 29.0-38.1 + + 38.1-44.9 + + 44.9-50.2 + + 50.2-61.8 + + 57.1-66.3 + + 66.3+ +
+ 30-39 + + 27.2-34.1 + + 34.1-39.6 + + 39.6-45.2 + + 45.2-51.6 + + 51.6-59.8 + + 59.8+ +
+ 40-49 + + 24.2-30.5 + + 30.5-35.7 + + 35.7-40.3 + + 40.3-46.7 + + 46.7-55.6 + + 55.6+ +
+ 50-59 + + 20.9-26.1 + + 26.1-30.7 + + 30.7-35.1 + + 35.1-41.2 + + 41.2-50.7 + + 50.7+ +
+ 60-69 + + 17.4-22.4 + + 22.4-26.6 + + 26.6-30.5 + + 30.5-36.1 + + 36.1-43.0 + + 43.0+ +
+
+ + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ Age (F) + + Very Poor + + Poor + + Fair + + Good + + Excellent + + Superior +
+ 20-29 + + 21.7-28.6 + + 28.6-34.6 + + 34.6-40.6 + + 40.6-46.5 + + 46.5-56.0 + + 56.0+ +
+ 30-39 + + 19.0-24.1 + + 24.1-28.2 + + 28.2-32.2 + + 32.2-35.7 + + 35.7-45.8 + + 45.8+ +
+ 40-49 + + 17.0-21.3 + + 21.3-24.9 + + 24.9-28.7 + + 28.7-34.0 + + 34.0-41.7 + + 41.7+ +
+ 50-59 + + 16.0-19.1 + + 19.1-24.4 + + 21.8-27.6 + + 25.2-28.6 + + 28.6-35.9 + + 35.9+ +
+ 60-69 + + 13.4-16.5 + + 16.5-18.9 + + 18.9-21.2 + + 21.2-24.6 + + 24.6-29.4 + + 29.4+ +
+
+
+
+ + +
+
CONTACT: info@ishplabs.com
+
WEBSITE: www.ishplabs.com
+
SOCIAL: @ishplabs
+
19
+
+
+ + diff --git a/report_gen/page_3.html b/report_gen/page_3.html index e69de29..b3b9f28 100644 --- a/report_gen/page_3.html +++ b/report_gen/page_3.html @@ -0,0 +1,83 @@ +
+ +
+
+
ISHP
+
+
+
+ Name: {{ patient_name | default('Keirstyn Moran') }} + Age: {{ age | default('34') }} + Height: {{ height | default('5\'4"') }} + Weight: {{ weight | default('123lbs') }} + Focus: {{ focus | default('Endurance') }} +
+
+ + +
+ +

Nutrition Guidelines

+ + +

Ultrasound & Body Composition Assessment

+

Designed to track and optimize exercise and diet. Its proven technology can accurately measure tissue structure and body composition.

+ + +
+

Body Composition

+ + +
+
+ Body Composition Chart + + +
+
Fat Mass ({{ fat_mass | default('27.6lbs') }})
+
{{ fat_percentage | default('22.4%') }}
+
+ +
+
Lean Mass ({{ lean_mass | default('95.4lbs') }})
+
{{ lean_percentage | default('77.6%') }}
+
+
+
+ + +
+

Body Fat Percent - {{ body_fat_percent | default('22.4%') }}

+ + +
+ Body Fat Percentage Chart +
+ + +
+ {{ age_range | default('20-39') }} + ({{ gender | default('F') }}) +
+
+
+
+ + +
+
+
+ CONTACT: {{ contact_email | default('info@ishplabs.com') }} + WEBSITE: {{ website | default('www.ishplabs.com') }} + SOCIAL: {{ social | default('@ishplabs') }} +
+
+ {{ page_number | default('4') }} +
+
+
+ diff --git a/report_gen/page_4.html b/report_gen/page_4.html index e69de29..62a0d01 100644 --- a/report_gen/page_4.html +++ b/report_gen/page_4.html @@ -0,0 +1,147 @@ +
+ +
+
+
ISHP
+
+
+
+ Name: {{ patient_name | default('Keirstyn Moran') }} + Age: {{ age | default('34') }} + Height: {{ height | default('5\'4"') }} + Weight: {{ weight | default('123lbs') }} + Focus: {{ focus | default('Endurance') }} +
+
+ + +
+ +

Overview

+ + +
+ +
+
+
+

Metabolic

+
+ +
+ +
Resting Metabolic Rate
+
Active Metabolic Rate
+ + +
Fat/Carbohydrate Ratio
+
Metabolic Efficiency Low Intensity
+ + +
Metabolism
+
Metabolic Efficiency High Intensity
+ + +
Breathing Frequency
+
+ + +
Breath Volume
+
+ + +
Heart Rate
+
+
+
+ + +
+
+
+

Respiratory

+
+ +
+
Lung Function
+
Lung Capacity
+
Lung Capability
+
Breathing Frequency Zones
+
+
+
+ + +
+ +
+
+
+

Cardiovascular

+
+ +
+
Active Metabolic Rate
+
Aerobic Health (VO2 Max)
+
Training Zones
+
Metabolic Efficiency (VO2 Pulse)
+
+
+ + +
+
+
+

Strength

+
+ +
+
Strength - High Intensity
+
CO2/O2 (RER)
+
Heart Rate
+
Breath Frequency
+
Muscle Efficiency
+
+
+
+ + +
+ +
+
+
+

Recovery

+
+ +
+
Active Metabolic Rate
+
+ Heart Rate + 44 +
+
Metabolic (CO2)
+
Muscle Oxygen
+
Breath Frequency
+
+
+ + +
+
+
+ + +
+
+
+ CONTACT: {{ contact_email | default('info@ishplabs.com') }} + WEBSITE: {{ website | default('www.ishplabs.com') }} + SOCIAL: {{ social | default('@ishplabs') }} +
+
+ {{ page_number | default('3') }} +
+
+
+ diff --git a/report_gen/page_5.html b/report_gen/page_5.html index e69de29..edfee10 100644 --- a/report_gen/page_5.html +++ b/report_gen/page_5.html @@ -0,0 +1,175 @@ +
+ +
+
+
ISHP
+
+
+
+ Name: {{ patient_name | default('Keirstyn Moran') }} + Age: {{ age | default('34') }} + Height: {{ height | default('5\'4"') }} + Weight: {{ weight | default('123lbs') }} + Focus: {{ focus | default('Endurance') }} +
+
+ + +
+ +

Nutrition Guidelines

+ + +

Resting Metabolic Rate Assessment

+

The resting metabolic rate assessment determines the number of calories that you burn at rest, and metabolic health. It is also an indicator of overall health and well-being.

+ + +
+
+

Slow vs Fast Metabolism

+ + +
+ +
+ +
+ {{ resting_calories | default('1386kCals') }} +
+ +
+
+ + +
+ Very Slow + Slow + Average + Fast + Very Fast +
+ + +
+
+
+
+
+
+
+
+
+ + +
+

Fuel Source

+ + +
+
+ +
+
+
Fats
+
{{ fat_percentage | default('33%') }}
+
+
+ +
+
+
Carbs
+
{{ carb_percentage | default('67%') }}
+
+
+
+ + +
+
+
Optimal
+ +
+
+ + +
+ 0 + 25 + 50 + 75 + 100 +
+ + +
+
+
+
+
+
+
+
+
+ + +
+

Caloric Intake

+ + +
+ +
+
{{ resting_calories | default('1386kCals') }}
+
+
Resting
+
Metabolic
+
+
+ + +
+
+ + +
+
{{ neat_calories | default('762kCals') }}
+
NEAT
+
+ + +
-
+ + +
+
{{ weight_loss_calories | default('423kCals') }}
+
+
to lose {{ weight_loss_rate | default('1.1lbs') }}
+
per week
+
+
+ + +
=
+ + +
+
{{ total_calories | default('~1725kCals') }}
+
+
+
+
+
+ + +
+
+
+ CONTACT: {{ contact_email | default('info@ishplabs.com') }} + WEBSITE: {{ website | default('www.ishplabs.com') }} + SOCIAL: {{ social | default('@ishplabs') }} +
+
+ {{ page_number | default('5') }} +
+
+
+ diff --git a/report_gen/page_6.html b/report_gen/page_6.html index e69de29..8d485b7 100644 --- a/report_gen/page_6.html +++ b/report_gen/page_6.html @@ -0,0 +1,246 @@ +
+ +
+
+
ISHP
+
+
+
+ Name: {{ patient_name | default('Keirstyn Moran') }} + Age: {{ age | default('34') }} + Height: {{ height | default('5\'4"') }} + Weight: {{ weight | default('123lbs') }} + Focus: {{ focus | default('Endurance') }} +
+
+ + +
+ +

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') }}
+
+
+
+
+ + +
+

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
+
+
+
+
+ + +
+
+
+ CONTACT: {{ contact_email | default('info@ishplabs.com') }} + WEBSITE: {{ website | default('www.ishplabs.com') }} + SOCIAL: {{ social | default('@ishplabs') }} +
+
+ {{ page_number | default('6') }} +
+
+
+ diff --git a/report_gen/page_7.html b/report_gen/page_7.html index e69de29..1413a4e 100644 --- a/report_gen/page_7.html +++ b/report_gen/page_7.html @@ -0,0 +1,180 @@ +
+ +
+
+
ISHP
+
+
+
+ Name: {{ patient_name | default('Keirstyn Moran') }} + Age: {{ age | default('34') }} + Height: {{ height | default('5\'4"') }} + Weight: {{ weight | default('123lbs') }} + Focus: {{ focus | default('Endurance') }} +
+
+ + +
+ +

Lung Analysis

+ + +

Spirometry Assessment

+

Spirometry is a diagnostic device that assesses how well a person breathes and how their lungs are functioning. Lung function is crucial for oxygen delivery during physical activity. Comparing results to expected/normal values can highlight potential limitations that would require additional lung training to improve overall physical activity.

+ + +
+ +
+
+
Lung Volume
+
LLN
+
+
+ +
+
+
+
+
+
+
+ +
+
Predicted
+ +
+ +
+ -5 + -4 + -3 + -2 + -1 + 0 + 1 + 2 + 3 +
+
+
+
FVC
+
{{ fvc_value | default('4.24L → 112.0%') }}
+
of predicted
+
+
+ + +
+
+
Lung Power
+
+
+ +
+
+
+
+
+
+
+ +
+ +
+ +
+ -5 + -4 + -3 + -2 + -1 + 0 + 1 + 2 + 3 +
+
+
+
FEV1
+
{{ fev1_value | default('3.26L → 103.3%') }}
+
of predicted
+
+
+ + +
+
+
Power/Volume
+
+
+ +
+
+
+
+
+
+
+ +
+ -5 + -4 + -3 + -2 + -1 + 0 + 1 + 2 + 3 +
+
+
+
FEV1/FVC
+
{{ fev1_fvc_ratio | default('76.89% → 91.8%') }}
+
of predicted
+
+
+
+ + +
+

Indications

+

{{ indication | default('No Respiratory Capacity Limitation') }}

+
+ + +
+

Respiratory

+ + +
+ Respiratory Analysis Chart +
+ + +
+

Peak VT

+

{{ peak_vt_value | default('2.38L/Breath which occurs at 172bpm (Zone 3)') }}

+

{{ peak_vt_percentage | default('73% of FEV1') }}

+
+
+
+ + +
+
+
+ CONTACT: {{ contact_email | default('info@ishplabs.com') }} + WEBSITE: {{ website | default('www.ishplabs.com') }} + SOCIAL: {{ social | default('@ishplabs') }} +
+
+ {{ page_number | default('7') }} +
+
+
+ diff --git a/report_gen/page_8.html b/report_gen/page_8.html index e69de29..24d874e 100644 --- a/report_gen/page_8.html +++ b/report_gen/page_8.html @@ -0,0 +1,225 @@ +
+ +
+
+
ISHP
+
+
+
+ Name: {{ patient_name | default('Keirstyn Moran') }} + Age: {{ age | default('34') }} + Height: {{ height | default('5\'4"') }} + Weight: {{ weight | default('123lbs') }} + Focus: {{ focus | default('Endurance') }} +
+
+ + +
+ +

Cardio Metrics

+ + +

Active Metabolic Rate Assessment

+

The active metabolic rate assessment is a key measure of aerobic fitness. It helps determine your specific heart rate zones and how well your body uses carbohydrates and fats as fuel while you exercise. It is also an indicator of overall health and wellbeing.

+ + +
+

VO2 Max - {{ vo2_max_value | default('49.5') }} ({{ vo2_max_percentile | default('100th percentile') }})

+ + +
+ + + + + + + + + + + + + + + + + + + + + + + +
Age (F)Very PoorPoorFairGoodExcellent + Superior + +
+ +
+
{{ age_range | default('30-39') }}{{ very_poor_range | default('19.0-24.1') }}{{ poor_range | default('24.1-28.2') }}{{ fair_range | default('28.2-32.2') }}{{ good_range | default('32.2-35.7') }}{{ excellent_range | default('35.7-45.8') }}{{ superior_range | default('45.8+') }}
+
+
+ + +
+

Personalized Heart Rate Zones

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Zone 1Zone 2Zone 3Zone 4Zone 5
+
Improves health and recovery capacity
+ 		+
Improves endurance and fat burning
+ 		+
Improves Aerobic fitness
+ 		+
Improves maximum performance capacity
+ 		+
Develops maximum performance and speed
+
{{ zone1_percentage | default('55-65% of Max Heart Rate') }}{{ zone2_percentage | default('65-75% of Max Heart Rate') }}{{ zone3_percentage | default('80-85% of Max Heart Rate') }}{{ zone4_percentage | default('85-88% of Max Heart Rate') }}{{ zone5_percentage | default('90% of Max Heart Rate') }}
{{ zone1_bpm | default('81-96bpm') }}{{ zone2_bpm | default('96-100bpm') }}{{ zone3_bpm | default('100-178bpm') }}{{ zone4_bpm | default('178-188bpm') }}{{ zone5_bpm | default('188-198bpm') }}
+
{{ zone1_speed | default('3.5mph') }}
+
{{ zone1_incline | default('2% Incline') }}
+ 		+
{{ zone2_speed | default('3.5-4.0mph') }}
+
{{ zone2_incline | default('2% Incline') }}
+ 		+
{{ zone3_speed | default('4.0-6.5mph') }}
+
{{ zone3_incline | default('2% Incline') }}
+ 		+
{{ zone4_speed | default('6.5-7.0mph') }}
+
{{ zone4_incline | default('2% Incline') }}
+ 		+
{{ zone5_speed | default('7.0-8.0mph') }}
+
{{ zone5_incline | default('2% Incline') }}
+
{{ zone1_pace | default('10:39min/km Pace') }}{{ zone2_pace | default('10:39-9:19min/km Pace') }}{{ zone3_pace | default('9:19-5:44min/km Pace') }}{{ zone4_pace | default('5:44-5:20min/km Pace') }}{{ zone5_pace | default('5:20-4:40min/km Pace') }}
+
Avg:
+
{{ zone1_calories | default('4.4kcals/minute') }}
+ 		+
Avg:
+
{{ zone2_calories | default('5.9kcals/minute') }}
+ 		+
Avg:
+
{{ zone3_calories | default('9.4kcals/minute') }}
+ 		+
Avg:
+
{{ zone4_calories | default('12.5kcals/minute') }}
+ 		+
Avg:
+
{{ zone5_calories | default('12.8kcals/minute') }}
+
{{ zone1_carb | default('Avg: 0.4g/min Carb Utilization') }}{{ zone2_carb | default('Avg: 0.6g/min Carb Utilization') }}{{ zone3_carb | default('Avg: 1.9g/min Carb Utilization') }}{{ zone4_carb | default('Avg: 2.9g/min Carb Utilization') }}{{ zone5_carb | default('Avg: 3.1g/min Carb Utilization') }}
+
{{ zone1_breaths | default('Avg: 27 breaths') }}
+
{{ zone1_breath_range | default('Ideal Range: 15-20 breaths') }}
+ 		+
{{ zone2_breaths | default('Avg: 28 breaths') }}
+
{{ zone2_breath_range | default('Ideal Range: 20-25 breaths') }}
+ 		+
{{ zone3_breaths | default('Avg: 31 breaths') }}
+
{{ zone3_breath_range | default('Ideal Range: 25-30 breaths') }}
+ 		+
{{ zone4_breaths | default('Avg: 42 breaths') }}
+
{{ zone4_breath_range | default('Ideal Range: 30-35 breaths') }}
+ 		+
{{ zone5_breaths | default('Avg: 51 breaths') }}
+
{{ zone5_breath_range | default('Ideal Range: 40+ breaths') }}
+
+
+
+ + +
+
+
+ CONTACT: {{ contact_email | default('info@ishplabs.com') }} + WEBSITE: {{ website | default('www.ishplabs.com') }} + SOCIAL: {{ social | default('@ishplabs') }} +
+
+ {{ page_number | default('8') }} +
+
+
+ diff --git a/report_gen/page_9.html b/report_gen/page_9.html index e69de29..94f4ac3 100644 --- a/report_gen/page_9.html +++ b/report_gen/page_9.html @@ -0,0 +1,48 @@ +
+ +
+
+
ISHP
+
+
+
+ Name: {{ patient_name | default('Keirstyn Moran') }} + Age: {{ age | default('34') }} + Height: {{ height | default('5\'4"') }} + Weight: {{ weight | default('123lbs') }} + Focus: {{ focus | default('Endurance') }} +
+
+ + +
+ +
+ Fuel Utilization Report - Institute of Science, Health and Performance +
+ + +
+

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

+
+
+ + +
+
+
+ CONTACT: {{ contact_email | default('info@ishplabs.com') }} + WEBSITE: {{ website | default('www.ishplabs.com') }} + SOCIAL: {{ social | default('@ishplabs') }} +
+
+ {{ page_number | default('9') }} +
+
+
+