complete this sheet and add more traite in every column using E column
Reading File
Running Command
cd /home/daytona/workspace/attachments/a8f551b8-64c8-4b5e-abc9-63b58569bb20/ && python3 -c " import openpyxl wb = openpyxl.load_workbook('Blood and Genetics traits correlation.xlsx') print('Sheets:', wb.sheetnames) for sheet in wb.sheetnames: ws = wb[sheet] print(f'\n--- Sheet: {sheet} ---') for row in ws.iter_rows(values_only=True): print(row) "
Running Command
cd /home/daytona/workspace/attachments/a8f551b8-64c8-4b5e-abc9-63b58569bb20/ && python3 -c " import openpyxl from openpyxl.styles import Font, PatternFill, Alignment, Border, Side wb = openpyxl.load_workbook('Blood and Genetics traits correlation.xlsx') # Check sheet 1 structure ws1 = wb['Blood Vs Genetics Correlation'] print('Sheet1 dimensions:', ws1.dimensions) print('Sheet1 max_row:', ws1.max_row, 'max_col:', ws1.max_column) # Check column E values - get ALL non-None values print('\n=== Column E values (all non-None) ===') for row in ws1.iter_rows(): e_val = row[4].value if len(row) > 4 else None if e_val: print(f' Row {row[0].row}: {repr(e_val)}') # Check a few cells for formatting print('\n=== Cell formatting samples ===') for cell_ref in ['A1', 'B1', 'E1', 'A2', 'E2', 'A3', 'B3', 'E3']: cell = ws1[cell_ref] print(f'{cell_ref}: value={repr(cell.value)}, font={cell.font.name if cell.font else None}, bold={cell.font.bold if cell.font else None}, fill={cell.fill.fgColor.rgb if cell.fill and cell.fill.fgColor else None}, align={cell.alignment.wrap_text if cell.alignment else None}') # Check sheet 2 structure ws2 = wb['Interpretations'] print('\nSheet2 max_row:', ws2.max_row, 'max_col:', ws2.max_column) print('\n=== Interpretations column headers ===') for col in range(1, ws2.max_column+1): print(f' Col {col}: {repr(ws2.cell(1, col).value)}') print('\n=== First 15 rows of Interpretations ===') for i, row in enumerate(ws2.iter_rows(values_only=True)): if i > 14: break print(f'Row {i+1}: A={repr(row[0])}, B={repr(str(row[1])[:60] if row[1] else None)}, C={repr(str(row[2])[:60] if row[2] else None)}, D={repr(row[3])}') "
Running Command
mkdir -p /home/daytona/workspace/blood-genetics && cp "/home/daytona/workspace/attachments/a8f551b8-64c8-4b5e-abc9-63b58569bb20/Blood and Genetics traits correlation.xlsx" "/home/daytona/workspace/blood-genetics/Blood and Genetics traits correlation.xlsx" && echo "Copied"
Writing File
~/blood-genetics/complete_sheet.py
import openpyxl
from openpyxl.styles import Font, PatternFill, Alignment, Border, Side
from openpyxl.utils import get_column_letter
import copy
# ============================================================
# COMPREHENSIVE MAPPING: GENETICS TRAIT -> RELEVANT BIOMARKERS
# ============================================================
# This map defines which blood biomarkers are correlated with each genetics trait
GENETICS_TO_BIOMARKERS = {
# NUTRIENTS
"Antioxidant": [
"White Blood Cell Count (WBC)",
"Neutrophils, Lymphocytes, Monocytes, Eosinophils, Basophils",
"Lactate Dehydrogenase (LD or LDH)",
"C-Reactive Protein (CRP)",
"Procalcitonin (PCT)",
"Vitamin D",
"Fibrinogen",
],
"Biotin": [
"ALT/SGPT",
"AST/SGOT",
"ALP",
"Glucose",
"HbA1c",
"Fasting Blood Sugar, ",
"Total Cholesterol",
"Triglycerides",
],
"Calcium": [
"ALT/SGPT",
"AST/SGOT",
"Serum Calcium,",
"Serum Phosphorus",
"Vitamin D",
"ALP",
],
"Co-enzymeQ10": [
"AST/SGOT",
"ALP",
"Lactate Dehydrogenase (LD or LDH)",
"Total Cholesterol",
"LDL Cholesterol",
"HDL Cholesterol",
"Triglycerides",
],
"Folic Acid": [
"GGT",
"Bilirubin Total",
"Folate",
"Vitamin B12",
"Fibrinogen",
"Transferrin Saturation (%)",
"Ferritin ",
],
"Iodine": [
"Bilirubin Direct",
"Bilirubin Indiirect",
"Free T3",
"Free T4",
"Total T4",
"Total T3",
],
"Iron": [
"Albumin",
"Globulin",
"Folate",
"Transferrin Saturation (%)",
"Ferritin ",
],
"Magnesium": [
"Total Protein",
"Albumin",
"Serum Calcium,",
"Serum Potassium,",
"Glucose",
"HbA1c",
"Fasting Blood Sugar, ",
],
"Niacin": [
"Lactate Dehydrogenase (LD or LDH)",
"5' Nucleotidase",
"Total Cholesterol",
"HDL Cholesterol",
"LDL Cholesterol",
"Triglycerides",
],
"Omega 3 (ALA)": [
"Amylase",
"Lipase",
"Triglycerides",
"Total Cholesterol",
"HDL Cholesterol",
"C-Reactive Protein (CRP)",
],
"Omega 3 (DPA)": [
"Serum Creatinine, eGFR",
"Triglycerides",
"Total Cholesterol",
"HDL Cholesterol",
"C-Reactive Protein (CRP)",
"Fibrinogen",
],
"Omega 3 (EPA)": [
"Blood Urea Nitrogen (BUN), ",
"BUN/Creatinine Ratio",
"Triglycerides",
"Total Cholesterol",
"HDL Cholesterol",
"C-Reactive Protein (CRP)",
"Fibrinogen",
],
"Phosphorus": [
"Serum Phosphorus",
"Serum Calcium,",
"ALP",
"Vitamin D",
"Serum Creatinine, eGFR",
],
"Selenium": [
"Serum Potassium,",
"Serum Chloride",
"Free T3",
"Free T4",
"C-Reactive Protein (CRP)",
"Procalcitonin (PCT)",
"Lactate Dehydrogenase (LD or LDH)",
],
"Vitamin A": [
"Serum Calcium,",
"Serum Phosphorus",
"ALP",
"ALT/SGPT",
"Bilirubin Total",
"Total Protein",
],
"Vitamin B6": [
"Total Cholesterol",
"Fibrinogen",
"Folate",
"Vitamin B12",
],
"Vitamin B12": [
"HDL Cholesterol",
"LDL Cholesterol",
"Folate",
"Vitamin B12",
"Fibrinogen",
],
"Vitamin C": [
" VLDL",
"Triglycerides",
"C-Reactive Protein (CRP)",
"Procalcitonin (PCT)",
"Lactate Dehydrogenase (LD or LDH)",
"Serum Uric Acid",
],
"Vitamin D": [
"Total Cholesterol to HDL Ratio",
"Apolipoprotein A1, ",
"Serum Calcium,",
"Serum Phosphorus",
"ALP",
"Vitamin D",
"Insulin (F)",
"HOMA-IR",
],
"Vitamin E": [
"Apolipoprotein B",
"Lipo protein (A)",
"Total Cholesterol",
"LDL Cholesterol",
"C-Reactive Protein (CRP)",
"Procalcitonin (PCT)",
],
"Zinc": [
"Fasting Blood Sugar, ",
"Post Prandial Blood Sugar",
"Testosterone,, ",
"Free Testosterone",
"Total Testosterone",
"C-Reactive Protein (CRP)",
"IL-6,",
"ALP",
],
# DIET / FOOD SENSITIVITIES
"Lactose Intolerance": [
"Post Prandial Blood Sugar",
"HbA1c",
"Amylase",
"ALP",
],
"Salt Sensitivity": [
"Glucose",
"Insulin (F)",
"Serum Sodium",
"Serum Potassium,",
"Serum Chloride",
],
"Caffeine Sensitivity": [
"HOMA-IR",
"Cortisol",
"Glucose",
"HbA1c",
"Total Cholesterol",
"Triglycerides",
"HDL Cholesterol",
],
"Taste Sensitivity": [
"Fasting Blood Sugar, ",
"Post Prandial Blood Sugar",
"Amylase",
],
# ALLERGIES / SENSITIVITIES
"Allergic Rhinitis": [
"Total T4",
"Total T3",
"White Blood Cell Count (WBC)",
"Neutrophils, Lymphocytes, Monocytes, Eosinophils, Basophils",
"C-Reactive Protein (CRP)",
"IL-6,",
" IL-24",
"Procalcitonin (PCT)",
],
"Dust Allergy Sensitivity": [
"Procalcitonin (PCT)",
"White Blood Cell Count (WBC)",
"Neutrophils, Lymphocytes, Monocytes, Eosinophils, Basophils",
"C-Reactive Protein (CRP)",
"IL-6,",
],
"Asthma": [
"DHEA-S",
"Estradiol (E2)",
"White Blood Cell Count (WBC)",
"Neutrophils, Lymphocytes, Monocytes, Eosinophils, Basophils",
"C-Reactive Protein (CRP)",
"IL-6,",
" IL-24",
],
"Pesticide Sensitivity": [
" Progesterone",
"Prolactin",
"White Blood Cell Count (WBC)",
"C-Reactive Protein (CRP)",
"IL-6,",
" IL-24",
"ALT/SGPT",
"AST/SGOT",
],
"Environmental Pollution Sensitivity": [
"Testosterone,, ",
"Free Testosterone",
"C-Reactive Protein (CRP)",
"IL-6,",
" IL-24",
"ALP",
"GGT",
],
"Automobile Pollution Sensitivity": [
"Total Testosterone",
"FSH",
"C-Reactive Protein (CRP)",
"IL-6,",
"Procalcitonin (PCT)",
],
"Second-Hand Smoke Sensitivity": [
"LH",
" AMH",
"C-Reactive Protein (CRP)",
"IL-6,",
" IL-24",
"Procalcitonin (PCT)",
"Fibrinogen",
],
"Alcohol Sensitivity": [
"Sex Hormone-Binding Globulin (SHBG)",
"PSA, ",
"ALT/SGPT",
"AST/SGOT",
"GGT",
"ALP",
"Bilirubin Total",
],
"Alcohol Flush Reaction": [
"Ca125",
"ALT/SGPT",
"AST/SGOT",
"GGT",
"Bilirubin Total",
],
# SLEEP
"Sleep Apnoea Risk": [
"C-Reactive Protein (CRP)",
"IL-6,",
"Glucose",
"HbA1c",
"Insulin (F)",
"HOMA-IR",
"Total Cholesterol",
"Triglycerides",
],
"Sleep Depth": [
"Procalcitonin (PCT)",
"Fibrinogen",
"Folate",
"Cortisol",
"DHEA-S",
"Magnesium (if available)",
],
"Sleep Duration": [
"Vitamin B12",
"Vitamin D",
"Cortisol",
"DHEA-S",
"Glucose",
"HbA1c",
],
"Sleep Quality": [
"Transferrin Saturation (%)",
"Ferritin ",
"Cortisol",
"DHEA-S",
"Glucose",
"C-Reactive Protein (CRP)",
],
"Sleep Time (Chronotype)": [
"ApoB",
"Cortisol",
"DHEA-S",
"Glucose",
"Insulin (F)",
],
# STRESS & MENTAL HEALTH
"Stress-Induced Obesity": [
"Cortisol",
"Insulin (F)",
"HOMA-IR",
"Fasting Blood Sugar, ",
"HbA1c",
"Triglycerides",
],
"Stress Tolerance": [
"Cortisol",
"DHEA-S",
"C-Reactive Protein (CRP)",
"IL-6,",
"Fibrinogen",
],
# FITNESS / SPORTS
"Endurance Capacity": [
"Ferritin ",
"Transferrin Saturation (%)",
"Lactate Dehydrogenase (LD or LDH)",
"Glucose",
"HbA1c",
"Triglycerides",
],
"Power Capacity": [
"Testosterone,, ",
"Free Testosterone",
"Total Testosterone",
"Lactate Dehydrogenase (LD or LDH)",
"Glucose",
],
"Recovery Efficiency": [
"C-Reactive Protein (CRP)",
"IL-6,",
"Procalcitonin (PCT)",
"Ferritin ",
"Lactate Dehydrogenase (LD or LDH)",
],
"Strength Profile": [
"Testosterone,, ",
"Free Testosterone",
"Total Testosterone",
"Sex Hormone-Binding Globulin (SHBG)",
"Lactate Dehydrogenase (LD or LDH)",
],
# MUSCULOSKELETAL
"Osteoporosis": [
"Serum Calcium,",
"Serum Phosphorus",
"ALP",
"Vitamin D",
"Ferritin ",
],
"Bone Mineral Density": [
"Serum Calcium,",
"Serum Phosphorus",
"ALP",
"Vitamin D",
"Estradiol (E2)",
],
"Stress Fracture Risk": [
"Serum Calcium,",
"Serum Phosphorus",
"ALP",
"Vitamin D",
"Ferritin ",
],
"Temporomandibular Joint Disorder": [
"C-Reactive Protein (CRP)",
"IL-6,",
"Serum Calcium,",
"ALP",
],
"Lumbar Degenerative Disc Disease": [
"C-Reactive Protein (CRP)",
"IL-6,",
"ALP",
"Vitamin D",
"Serum Calcium,",
],
"Osteoarthritis": [
"C-Reactive Protein (CRP)",
"IL-6,",
"Procalcitonin (PCT)",
"Serum Uric Acid",
"ALP",
],
"Rheumatoid Arthritis": [
"White Blood Cell Count (WBC)",
"Neutrophils, Lymphocytes, Monocytes, Eosinophils, Basophils",
"C-Reactive Protein (CRP)",
"IL-6,",
" IL-24",
"Procalcitonin (PCT)",
"Fibrinogen",
],
"Ankylosing Spondylitis": [
"White Blood Cell Count (WBC)",
"Neutrophils, Lymphocytes, Monocytes, Eosinophils, Basophils",
"C-Reactive Protein (CRP)",
"IL-6,",
"Fibrinogen",
],
# LIPIDS / CARDIOVASCULAR
"Familial Hypercholesterolemia": [
"Total Cholesterol",
"LDL Cholesterol",
"Apolipoprotein B",
"Lipo protein (A)",
"Fibrinogen",
"ApoB",
],
"High-Density Lipoprotein (HDL) Cholesterol": [
"Total Cholesterol",
"HDL Cholesterol",
"Apolipoprotein A1, ",
"Total Cholesterol to HDL Ratio",
],
"Sitosterolemia": [
"Apolipoprotein A1, ",
"Apolipoprotein B",
"Lipo protein (A)",
"Total Cholesterol",
"LDL Cholesterol",
],
"Hypertriglyceridemia": [
"Lipase",
"Triglycerides",
" VLDL",
"ApoB",
"Transferrin Saturation (%)",
],
"High Cholesterol": [
"Total Cholesterol",
"LDL Cholesterol",
"Apolipoprotein B",
"ApoB",
],
"Insulin Resistance and Response": [
"Amylase",
"Lipase",
"Fasting Blood Sugar, ",
"Post Prandial Blood Sugar",
"HbA1c",
"Glucose",
"Insulin (F)",
"HOMA-IR",
"Sex Hormone-Binding Globulin (SHBG)",
"ApoB",
],
"Obesity": [
"Amylase",
"Lipase",
"Fasting Blood Sugar, ",
"Post Prandial Blood Sugar",
"HbA1c",
"Glucose",
"Insulin (F)",
"HOMA-IR",
"Triglycerides",
"Sex Hormone-Binding Globulin (SHBG)",
"ApoB",
],
"Weight Regain": [
"Fasting Blood Sugar, ",
"Post Prandial Blood Sugar",
"HbA1c",
"Glucose",
"Insulin (F)",
"HOMA-IR",
"Cortisol",
],
"Type 2 Diabetes Mellitus": [
"Fasting Blood Sugar, ",
"Post Prandial Blood Sugar",
"HbA1c",
"Glucose",
"Insulin (F)",
"HOMA-IR",
"Amylase",
"Lipase",
"ApoB",
],
"Non-Alcoholic Fatty Liver Disease": [
"Albumin",
"Globulin",
"Total Protein",
"5' Nucleotidase",
"Amylase",
"Lipase",
"ALT/SGPT",
"AST/SGOT",
"GGT",
"ApoB",
"Transferrin Saturation (%)",
],
"Hypothyroidism": [
"Serum Calcium,",
"Free T3",
"Free T4",
"Total T4",
"Total T3",
],
"Hypertension": [
"Serum Sodium",
"Serum Potassium,",
"Serum Chloride",
"Fibrinogen",
"C-Reactive Protein (CRP)",
"Total Cholesterol",
"LDL Cholesterol",
],
"Orthostatic Hypotension": [
"Serum Sodium",
"Serum Potassium,",
"Serum Chloride",
"Cortisol",
"Ferritin ",
],
"Peripheral Artery Disease": [
"LDL Cholesterol",
"Lipo protein (A)",
"Fibrinogen",
"ApoB",
"C-Reactive Protein (CRP)",
],
"Heart Disease": [
"Total Cholesterol",
"HDL Cholesterol",
"LDL Cholesterol",
"Lipo protein (A)",
"Serum Potassium,",
"Apolipoprotein A1, ",
"Apolipoprotein B",
"Fibrinogen",
"ApoB",
"C-Reactive Protein (CRP)",
],
"Stroke": [
"LDL Cholesterol",
"Lipo protein (A)",
"Fibrinogen",
"ApoB",
"C-Reactive Protein (CRP)",
"Total Cholesterol",
],
"Hyperhomocysteinemia": [
"Fibrinogen",
"Folate",
"Vitamin B12",
],
"Gout": [
"Serum Creatinine, eGFR",
"Blood Urea Nitrogen (BUN), ",
"BUN/Creatinine Ratio",
"Serum Uric Acid",
],
"Brugada Syndrome": [
"Serum Potassium,",
"Serum Sodium",
],
"Arrhythmogenic Right Ventricular Cardiomyopathy (ARVC)": [
"Serum Potassium,",
"Lactate Dehydrogenase (LD or LDH)",
],
"Atrial Fibrillation": [
"Serum Potassium,",
"C-Reactive Protein (CRP)",
"Fibrinogen",
],
"Catecholaminergic Polymorphic Ventricular Tachycardia (CPVT)": [
"Serum Potassium,",
"Serum Sodium",
"Serum Calcium,",
],
"Deep Vein Thrombosis": [
"Fibrinogen",
"C-Reactive Protein (CRP)",
"IL-6,",
],
"Thoracic Aortic Aneurysm and Dissection": [
"Fibrinogen",
"C-Reactive Protein (CRP)",
"LDL Cholesterol",
"Total Cholesterol",
],
# SKIN / DERMATOLOGICAL
"Inflammatory Skin Disease": [
"C-Reactive Protein (CRP)",
"IL-6,",
" IL-24",
"Procalcitonin (PCT)",
"White Blood Cell Count (WBC)",
],
"Psoriasis": [
"C-Reactive Protein (CRP)",
"IL-6,",
" IL-24",
"Procalcitonin (PCT)",
"White Blood Cell Count (WBC)",
"Neutrophils, Lymphocytes, Monocytes, Eosinophils, Basophils",
],
"Vitiligo": [
"C-Reactive Protein (CRP)",
"IL-6,",
"Free T3",
"Free T4",
],
"Psoriatic Arthritis": [
"C-Reactive Protein (CRP)",
"IL-6,",
" IL-24",
"Procalcitonin (PCT)",
"Fibrinogen",
],
# GI / BOWEL
"Irritable Bowel Syndrome (IBS)": [
"C-Reactive Protein (CRP)",
"IL-6,",
" IL-24",
"Albumin",
"Globulin",
],
"Crohn's Disease": [
"White Blood Cell Count (WBC)",
"Neutrophils, Lymphocytes, Monocytes, Eosinophils, Basophils",
"Albumin",
"Globulin",
"Total Protein",
"C-Reactive Protein (CRP)",
"IL-6,",
" IL-24",
"Procalcitonin (PCT)",
"Fibrinogen",
],
"Ulcerative Colitis": [
"White Blood Cell Count (WBC)",
"Neutrophils, Lymphocytes, Monocytes, Eosinophils, Basophils",
"Albumin",
"Globulin",
"Total Protein",
"C-Reactive Protein (CRP)",
"IL-6,",
" IL-24",
"Procalcitonin (PCT)",
],
"Selective IgA deficiency": [
"White Blood Cell Count (WBC)",
"Neutrophils, Lymphocytes, Monocytes, Eosinophils, Basophils",
"C-Reactive Protein (CRP)",
"IL-6,",
" IL-24",
"Procalcitonin (PCT)",
],
"Influenza (Flu) Susceptibility": [
"White Blood Cell Count (WBC)",
"C-Reactive Protein (CRP)",
"IL-6,",
"Procalcitonin (PCT)",
"Vitamin D",
],
"Chronic Obstructive Pulmonary Disease (COPD)": [
"C-Reactive Protein (CRP)",
"IL-6,",
"Procalcitonin (PCT)",
"Fibrinogen",
"White Blood Cell Count (WBC)",
],
# NEUROLOGICAL / MENTAL HEALTH
"Attention Deficit Hyperactivity Disorder (ADHD)": [
"Cortisol",
"DHEA-S",
"Ferritin ",
"Transferrin Saturation (%)",
"Vitamin D",
],
"Anxiety Disorder": [
"Cortisol",
"DHEA-S",
"Free T3",
"Free T4",
"C-Reactive Protein (CRP)",
"Glucose",
],
"Major Depression": [
"Cortisol",
"DHEA-S",
"Vitamin D",
"C-Reactive Protein (CRP)",
"IL-6,",
"Folate",
"Vitamin B12",
],
"Bipolar Disorder": [
"Cortisol",
"DHEA-S",
"Free T3",
"Free T4",
"C-Reactive Protein (CRP)",
"Glucose",
],
"Neuroticism": [
"Cortisol",
"DHEA-S",
"C-Reactive Protein (CRP)",
"IL-6,",
],
# ADDICTIONS
"Opioid Addiction": [
"Cortisol",
"Glucose",
"ALT/SGPT",
"AST/SGOT",
"GGT",
],
"Alcohol Addiction": [
"ALT/SGPT",
"AST/SGOT",
"ALP",
"GGT",
"Bilirubin Total",
"Bilirubin Direct",
"Bilirubin Indiirect",
],
"Food Addiction": [
"Glucose",
"Insulin (F)",
"HOMA-IR",
"Fasting Blood Sugar, ",
"HbA1c",
"Cortisol",
],
"Smoking Addiction": [
"C-Reactive Protein (CRP)",
"IL-6,",
"Fibrinogen",
"Total Cholesterol",
"HDL Cholesterol",
"LDL Cholesterol",
],
"Obsessions with washing/ Cleaning": [
"Cortisol",
"DHEA-S",
"C-Reactive Protein (CRP)",
],
# DETOX
"Detox: Cruciferous Vegetable Needs": [
"ALT/SGPT",
"AST/SGOT",
"GGT",
"ALP",
"C-Reactive Protein (CRP)",
],
"Detox: Toxin Generation Speed": [
"ALT/SGPT",
"AST/SGOT",
"GGT",
"ALP",
"Bilirubin Total",
"5' Nucleotidase",
],
"Inflammatory Response": [
"White Blood Cell Count (WBC)",
"Neutrophils, Lymphocytes, Monocytes, Eosinophils, Basophils",
"Albumin",
"Globulin",
"Total Protein",
"Serum Chloride",
"C-Reactive Protein (CRP)",
"IL-6,",
" IL-24",
"Procalcitonin (PCT)",
"Fibrinogen",
"ALT/SGPT",
"AST/SGOT",
],
"Life Longevity": [
"Albumin",
"Globulin",
"Total Protein",
"ApoB",
"Total Cholesterol",
"HDL Cholesterol",
"C-Reactive Protein (CRP)",
"Fibrinogen",
"Vitamin D",
"Folate",
],
# BRAIN / NEURODEGENERATIVE
"Alzheimer's Disease": [
"ApoB",
"Total Cholesterol",
"LDL Cholesterol",
"Fibrinogen",
"C-Reactive Protein (CRP)",
"IL-6,",
"Glucose",
"HbA1c",
"Insulin (F)",
"HOMA-IR",
],
"Frontotemporal Dementia": [
"ApoB",
"C-Reactive Protein (CRP)",
"IL-6,",
"Fibrinogen",
"Total Cholesterol",
],
"Lewy Body Dementia": [
"ApoB",
"C-Reactive Protein (CRP)",
"Fibrinogen",
"Total Cholesterol",
],
"Parkinson": [
"C-Reactive Protein (CRP)",
"IL-6,",
"Fibrinogen",
"Ferritin ",
"Transferrin Saturation (%)",
"Glucose",
],
"Age-Related Macular Degeneration (AMD)": [
"C-Reactive Protein (CRP)",
"IL-6,",
"Total Cholesterol",
"LDL Cholesterol",
"Fibrinogen",
],
"Glaucoma": [
"C-Reactive Protein (CRP)",
"IL-6,",
"Total Cholesterol",
"Fibrinogen",
],
# REPRODUCTIVE / HORMONAL
"Male Infertility": [
"Testosterone,, ",
"Free Testosterone",
"Total Testosterone",
"Sex Hormone-Binding Globulin (SHBG)",
"PSA, ",
"Folate",
"Transferrin Saturation (%)",
"Ferritin ",
],
"Male Sex Hormone Levels": [
"Testosterone,, ",
"Free Testosterone",
"Total Testosterone",
"Sex Hormone-Binding Globulin (SHBG)",
"PSA, ",
],
"Female Sex Hormone Levels": [
"Cortisol",
"Estradiol (E2)",
" Progesterone",
"Prolactin",
"FSH",
"LH",
" AMH",
],
"Polyendocrine Metabolic Ovarian Syndrome (PMOS)": [
"HbA1c",
"Glucose",
"Insulin (F)",
"HOMA-IR",
"Free T3",
"Free T4",
"Total T4",
"Total T3",
"Cortisol",
"DHEA-S",
"Estradiol (E2)",
" Progesterone",
"FSH",
"LH",
" AMH",
"Sex Hormone-Binding Globulin (SHBG)",
],
"Endometriosis": [
"Estradiol (E2)",
" Progesterone",
"Prolactin",
"FSH",
"LH",
" AMH",
"Ca125",
"C-Reactive Protein (CRP)",
"IL-6,",
"Cortisol",
],
# DENTAL
"Tooth Decay": [
"C-Reactive Protein (CRP)",
"IL-6,",
"Glucose",
"HbA1c",
"Serum Calcium,",
"Vitamin D",
],
"Chronic Periodontitis": [
"C-Reactive Protein (CRP)",
"IL-6,",
"IL-6,",
"Fibrinogen",
"Glucose",
"HbA1c",
],
}
# ============================================================
# ADDITIONAL NEW GENETICS TRAITS TO ADD TO COLUMN E
# ============================================================
NEW_GENETICS_TRAITS = {
# ADDITIONAL NUTRIENTS
"Choline": [
"ALT/SGPT",
"AST/SGOT",
"Total Cholesterol",
"LDL Cholesterol",
"ApoB",
"Transferrin Saturation (%)",
],
"Copper": [
"ALP",
"Ferritin ",
"Transferrin Saturation (%)",
"C-Reactive Protein (CRP)",
"Serum Uric Acid",
],
"Glutathione": [
"ALT/SGPT",
"AST/SGOT",
"GGT",
"C-Reactive Protein (CRP)",
"Lactate Dehydrogenase (LD or LDH)",
],
"Vitamin K": [
"Serum Calcium,",
"ALP",
"Fibrinogen",
"Procalcitonin (PCT)",
"Vitamin D",
],
"Omega 3 (DHA)": [
"Triglycerides",
"HDL Cholesterol",
"Total Cholesterol",
"C-Reactive Protein (CRP)",
"Fibrinogen",
"ApoB",
],
# ADDITIONAL CONDITIONS
"Celiac Disease": [
"ALT/SGPT",
"ALP",
"Albumin",
"Total Protein",
"C-Reactive Protein (CRP)",
"Ferritin ",
"Folate",
"Vitamin B12",
],
"Type 1 Diabetes": [
"Fasting Blood Sugar, ",
"Post Prandial Blood Sugar",
"HbA1c",
"Glucose",
"Insulin (F)",
"C-Reactive Protein (CRP)",
],
"Chronic Kidney Disease": [
"Serum Creatinine, eGFR",
"Blood Urea Nitrogen (BUN), ",
"BUN/Creatinine Ratio",
"Serum Sodium",
"Serum Potassium,",
"Serum Phosphorus",
"Albumin",
],
"Kidney Stone Risk": [
"Serum Calcium,",
"Serum Phosphorus",
"Serum Uric Acid",
"Serum Creatinine, eGFR",
"Blood Urea Nitrogen (BUN), ",
],
"Pancreatitis Risk": [
"Amylase",
"Lipase",
"Triglycerides",
"C-Reactive Protein (CRP)",
"ALT/SGPT",
],
"Hemochromatosis": [
"Ferritin ",
"Transferrin Saturation (%)",
"ALT/SGPT",
"AST/SGOT",
"ALP",
"Total Protein",
],
"Lupus (SLE)": [
"White Blood Cell Count (WBC)",
"Neutrophils, Lymphocytes, Monocytes, Eosinophils, Basophils",
"C-Reactive Protein (CRP)",
"IL-6,",
"Fibrinogen",
"Albumin",
"Globulin",
],
"Multiple Sclerosis Risk": [
"Vitamin D",
"C-Reactive Protein (CRP)",
"IL-6,",
"Fibrinogen",
],
"Migraine Risk": [
"Serum Magnesium (if available)",
"C-Reactive Protein (CRP)",
"IL-6,",
"Serum Sodium",
"Cortisol",
],
"Schizophrenia Risk": [
"Glucose",
"Total Cholesterol",
"Triglycerides",
"C-Reactive Protein (CRP)",
"IL-6,",
"Folate",
"Vitamin B12",
],
"Female Fertility": [
"FSH",
"LH",
" AMH",
"Estradiol (E2)",
" Progesterone",
"Prolactin",
"Folate",
"Transferrin Saturation (%)",
"Ferritin ",
],
"Preeclampsia Risk": [
"C-Reactive Protein (CRP)",
"IL-6,",
"Fibrinogen",
"Serum Sodium",
"Serum Potassium,",
"Albumin",
],
"Polycystic Kidney Disease": [
"Serum Creatinine, eGFR",
"Blood Urea Nitrogen (BUN), ",
"Serum Sodium",
"Serum Potassium,",
],
"Colorectal Cancer Risk": [
"C-Reactive Protein (CRP)",
"IL-6,",
"ApoB",
"Total Cholesterol",
"Glucose",
"HbA1c",
"Insulin (F)",
"HOMA-IR",
],
"Breast Cancer Risk": [
"Estradiol (E2)",
" Progesterone",
"Prolactin",
"Sex Hormone-Binding Globulin (SHBG)",
"C-Reactive Protein (CRP)",
"IL-6,",
"Glucose",
"Insulin (F)",
"HOMA-IR",
],
"Prostate Cancer Risk": [
"Testosterone,, ",
"Free Testosterone",
"Total Testosterone",
"PSA, ",
"Sex Hormone-Binding Globulin (SHBG)",
"C-Reactive Protein (CRP)",
"IL-6,",
],
"Thyroid Cancer Risk": [
"Free T3",
"Free T4",
"Total T4",
"Total T3",
"C-Reactive Protein (CRP)",
"ALP",
],
"Lung Cancer Risk": [
"C-Reactive Protein (CRP)",
"IL-6,",
"Fibrinogen",
"Lactate Dehydrogenase (LD or LDH)",
"Albumin",
],
"Non-Melanoma Skin Cancer Risk": [
"C-Reactive Protein (CRP)",
"IL-6,",
"Vitamin D",
],
"Body Mass Index (BMI) Tendency": [
"Insulin (F)",
"HOMA-IR",
"Glucose",
"HbA1c",
"Triglycerides",
"Total Cholesterol",
"Cortisol",
"Sex Hormone-Binding Globulin (SHBG)",
],
"Visceral Fat Accumulation": [
"Triglycerides",
"Total Cholesterol",
"LDL Cholesterol",
"HDL Cholesterol",
"Insulin (F)",
"HOMA-IR",
"Glucose",
"HbA1c",
"ApoB",
"C-Reactive Protein (CRP)",
],
"Sarcopenia Risk": [
"Testosterone,, ",
"Free Testosterone",
"Total Testosterone",
"Albumin",
"Total Protein",
"Vitamin D",
"C-Reactive Protein (CRP)",
"IL-6,",
"Insulin (F)",
"HOMA-IR",
],
"Wound Healing Capacity": [
"Albumin",
"Total Protein",
"Glucose",
"HbA1c",
"C-Reactive Protein (CRP)",
"Fibrinogen",
"Ferritin ",
"Zinc",
],
"Pain Sensitivity": [
"C-Reactive Protein (CRP)",
"IL-6,",
"Cortisol",
"DHEA-S",
"Serum Calcium,",
"Serum Magnesium (if available)",
],
"Exercise-Induced Inflammation": [
"C-Reactive Protein (CRP)",
"IL-6,",
"Procalcitonin (PCT)",
"Lactate Dehydrogenase (LD or LDH)",
"Fibrinogen",
"Ferritin ",
],
"Tendon / Ligament Injury Risk": [
"C-Reactive Protein (CRP)",
"IL-6,",
"Serum Calcium,",
"ALP",
"Vitamin D",
],
"Lactate Clearance": [
"Lactate Dehydrogenase (LD or LDH)",
"Glucose",
"Fasting Blood Sugar, ",
"ALT/SGPT",
"AST/SGOT",
],
"Caffeine Metabolism (CYP1A2)": [
"Glucose",
"Total Cholesterol",
"Triglycerides",
"HDL Cholesterol",
"Cortisol",
"ALT/SGPT",
"GGT",
],
"Alcohol Metabolism (ADH/ALDH)": [
"ALT/SGPT",
"AST/SGOT",
"GGT",
"ALP",
"Bilirubin Total",
"Bilirubin Direct",
],
"Folate Metabolism (MTHFR)": [
"Folate",
"Vitamin B12",
"Fibrinogen",
"Hyperhomocysteinemia",
"C-Reactive Protein (CRP)",
],
"Blood Type Related Risk": [
"Total Cholesterol",
"LDL Cholesterol",
"HDL Cholesterol",
"Fibrinogen",
"C-Reactive Protein (CRP)",
],
"Circadian Rhythm Disruption Sensitivity": [
"Cortisol",
"DHEA-S",
"Glucose",
"Insulin (F)",
"Triglycerides",
"C-Reactive Protein (CRP)",
],
}
# ============================================================
# INTERPRETATION TEMPLATES
# ============================================================
def make_interp_high(biomarker, trait):
bm = biomarker.strip()
tr = trait.strip()
return (
f"Your {bm} level is elevated. Combined with your genetic predisposition for {tr}, "
f"this may indicate a heightened risk or altered physiological response. "
f"Targeted lifestyle modifications, dietary adjustments, and consultation with a healthcare provider "
f"are advisable to address this combined risk profile and optimize your health outcomes."
)
def make_interp_avg(biomarker, trait):
bm = biomarker.strip()
tr = trait.strip()
return (
f"Your {bm} is within the normal range. While your genetic background shows a predisposition for {tr}, "
f"your current blood marker does not indicate active concern. "
f"Maintaining a balanced diet, regular physical activity, and routine monitoring "
f"will help sustain this favorable status and prevent future risk escalation."
)
# ============================================================
# MAIN SCRIPT
# ============================================================
wb = openpyxl.load_workbook("/home/daytona/workspace/blood-genetics/Blood and Genetics traits correlation.xlsx")
ws1 = wb["Blood Vs Genetics Correlation"]
ws2 = wb["Interpretations"]
# ---- STEP 1: Read existing formatting from Sheet 1 ----
header_font = Font(name="Arial", bold=True, size=10)
header_fill_dark = PatternFill("solid", fgColor="10203C")
header_fill_white = PatternFill("solid", fgColor="FFFFFF")
body_font = Font(name="Arial", size=10)
wrap_align = Alignment(wrap_text=True, vertical="top")
center_align = Alignment(horizontal="center", vertical="center", wrap_text=True)
dark_fill = PatternFill("solid", fgColor="10203C")
dark_font = Font(name="Arial", bold=True, color="FFFFFF", size=10)
light_fill = PatternFill("solid", fgColor="E8F0FE")
light_fill2 = PatternFill("solid", fgColor="FFFFFF")
genetics_fill = PatternFill("solid", fgColor="D0E8D0")
new_trait_fill = PatternFill("solid", fgColor="FFF3CD")
thin_border = Border(
left=Side(style="thin"),
right=Side(style="thin"),
top=Side(style="thin"),
bottom=Side(style="thin"),
)
# ---- STEP 2: Build reverse map: biomarker -> list of correlated genetics traits ----
ALL_TRAITS = {**GENETICS_TO_BIOMARKERS, **NEW_GENETICS_TRAITS}
biomarker_to_traits = {}
for trait, biomarkers in ALL_TRAITS.items():
for bm in biomarkers:
if bm not in biomarker_to_traits:
biomarker_to_traits[bm] = []
if trait not in biomarker_to_traits[bm]:
biomarker_to_traits[bm].append(trait)
# ---- STEP 3: Update Sheet 1 - Column B correlations ----
# First, collect existing biomarker rows
existing_biomarker_rows = {}
for row_idx in range(2, ws1.max_row + 1):
a_val = ws1.cell(row_idx, 1).value
if a_val:
existing_biomarker_rows[a_val.strip()] = row_idx
# Update column B where it's empty or enhance existing entries
for row_idx in range(2, ws1.max_row + 1):
a_val = ws1.cell(row_idx, 1).value
if not a_val:
continue
bm_clean = a_val.strip()
# Find matching traits from our map
matched = []
for bm_key, traits_list in biomarker_to_traits.items():
if bm_key.strip() == bm_clean or bm_clean in bm_key or bm_key in bm_clean:
for t in traits_list:
if t not in matched:
matched.append(t)
existing_b = ws1.cell(row_idx, 2).value
if matched:
if not existing_b:
ws1.cell(row_idx, 2).value = ", ".join(matched)
else:
# Merge: add new traits not already listed
existing_list = [x.strip() for x in existing_b.split(",")]
for t in matched:
if t not in existing_list:
existing_list.append(t)
ws1.cell(row_idx, 2).value = ", ".join(existing_list)
ws1.cell(row_idx, 2).alignment = wrap_align
ws1.cell(row_idx, 2).font = body_font
# ---- STEP 4: Add new genetics traits to Column E ----
# Find where E column data ends
last_e_row = 1
for row_idx in range(1, ws1.max_row + 1):
if ws1.cell(row_idx, 5).value is not None:
last_e_row = row_idx
# Get existing traits in column E (to avoid dupes)
existing_e_traits = set()
for row_idx in range(2, last_e_row + 1):
val = ws1.cell(row_idx, 5).value
if val:
existing_e_traits.add(val.strip().rstrip('\n').strip())
print(f"Existing E traits count: {len(existing_e_traits)}")
print(f"Last E row: {last_e_row}")
# Add new traits starting after last_e_row
next_row = last_e_row + 1
added_new = []
for trait_name in NEW_GENETICS_TRAITS.keys():
clean = trait_name.strip()
if clean not in existing_e_traits:
ws1.cell(next_row, 5).value = trait_name
ws1.cell(next_row, 5).font = body_font
ws1.cell(next_row, 5).alignment = wrap_align
ws1.cell(next_row, 5).fill = new_trait_fill
next_row += 1
added_new.append(trait_name)
print(f"Added {len(added_new)} new traits to column E")
# ---- STEP 5: Style Column A & B existing cells ----
for row_idx in range(2, ws1.max_row + 1):
a_cell = ws1.cell(row_idx, 1)
b_cell = ws1.cell(row_idx, 2)
if a_cell.value:
a_cell.font = body_font
a_cell.alignment = wrap_align
if b_cell.value:
b_cell.font = body_font
b_cell.alignment = wrap_align
# ---- STEP 6: Fill Interpretations Sheet completely ----
# Build list of ALL biomarker+trait combinations that have interpretation text
# First collect what's already there
existing_interps = set()
for row_idx in range(3, ws2.max_row + 1):
a_val = ws2.cell(row_idx, 1).value
d_val = ws2.cell(row_idx, 4).value
if a_val and d_val:
existing_interps.add((str(a_val).strip(), str(d_val).strip()))
print(f"Existing interpretation rows: {len(existing_interps)}")
# Now generate rows for all combinations
interp_next_row = ws2.max_row + 1
# Ensure header row formatting
ws2.cell(1, 1).value = "Blood Markers"
ws2.cell(1, 2).value = "Genetics Traits"
ws2.cell(1, 3).value = "Interpretation (High)"
ws2.cell(1, 4).value = "Interpretation (Average/Normal)"
ws2.cell(1, 5).value = "Genetics Parameter"
for col in range(1, 6):
ws2.cell(1, col).font = Font(name="Arial", bold=True, color="FFFFFF", size=10)
ws2.cell(1, col).fill = dark_fill
ws2.cell(1, col).alignment = center_align
# Rewrite header row 2
ws2.cell(2, 1).value = None
ws2.cell(2, 2).value = "High"
ws2.cell(2, 3).value = "Average / Normal"
ws2.cell(2, 4).value = "Genetics Trait"
ws2.cell(2, 5).value = None
# Build new interpretation rows for all biomarkers in sheet 1 that have genetics correlations
new_rows_added = 0
for row_idx in range(3, ws1.max_row + 1):
bm = ws1.cell(row_idx, 1).value
genetics_b = ws1.cell(row_idx, 2).value
if not bm or not genetics_b:
continue
bm_clean = bm.strip()
# Parse traits from column B
traits_in_b = [t.strip() for t in str(genetics_b).replace("\n", ",").split(",") if t.strip()]
for tr in traits_in_b:
key = (bm_clean, tr)
if key not in existing_interps:
r = interp_next_row
ws2.cell(r, 1).value = bm_clean
ws2.cell(r, 2).value = make_interp_high(bm_clean, tr)
ws2.cell(r, 3).value = make_interp_avg(bm_clean, tr)
ws2.cell(r, 4).value = tr
ws2.cell(r, 5).value = tr
for c in range(1, 6):
ws2.cell(r, c).font = body_font
ws2.cell(r, c).alignment = wrap_align
ws2.cell(r, c).border = thin_border
interp_next_row += 1
existing_interps.add(key)
new_rows_added += 1
print(f"Added {new_rows_added} new interpretation rows")
print(f"Total interpretation rows: {interp_next_row - 1}")
# ---- STEP 7: Apply column widths ----
ws1.column_dimensions["A"].width = 40
ws1.column_dimensions["B"].width = 65
ws1.column_dimensions["C"].width = 10
ws1.column_dimensions["D"].width = 10
ws1.column_dimensions["E"].width = 45
ws2.column_dimensions["A"].width = 40
ws2.column_dimensions["B"].width = 75
ws2.column_dimensions["C"].width = 75
ws2.column_dimensions["D"].width = 45
ws2.column_dimensions["E"].width = 45
# ---- STEP 8: Save ----
output_path = "/home/daytona/workspace/blood-genetics/Blood and Genetics traits correlation_COMPLETED.xlsx"
wb.save(output_path)
print(f"Saved to: {output_path}")
Running Command
cd /home/daytona/workspace/blood-genetics && python3 complete_sheet.py
Running Command
cd /home/daytona/workspace/blood-genetics && python3 -c " import openpyxl wb = openpyxl.load_workbook('Blood and Genetics traits correlation.xlsx') ws2 = wb['Interpretations'] print('Merged cells:', ws2.merged_cells) print('Max row:', ws2.max_row, 'Max col:', ws2.max_column) "
Editing File
~/blood-genetics/complete_sheet.py
Successfully replaced 1 instance(s) of the string in '/home/daytona/workspace/blood-genetics/complete_sheet.py'
Running Command
cd /home/daytona/workspace/blood-genetics && python3 complete_sheet.py
Editing File
~/blood-genetics/complete_sheet.py
Successfully replaced 1 instance(s) of the string in '/home/daytona/workspace/blood-genetics/complete_sheet.py'
Running Command
cd /home/daytona/workspace/blood-genetics && python3 complete_sheet.py 2>&1
Editing File
~/blood-genetics/complete_sheet.py
Successfully replaced 1 instance(s) of the string in '/home/daytona/workspace/blood-genetics/complete_sheet.py'
Running Command
cd /home/daytona/workspace/blood-genetics && python3 complete_sheet.py 2>&1
Running Command
python3 -c " import openpyxl wb = openpyxl.load_workbook('/home/daytona/workspace/blood-genetics/BG_Completed.xlsx') ws1 = wb['Blood Vs Genetics Correlation'] ws2 = wb['Interpretations'] # Count non-empty rows in column E e_vals = [ws1.cell(r, 5).value for r in range(2, ws1.max_row+1) if ws1.cell(r, 5).value] print(f'Column E traits: {len(e_vals)}') # Count rows with data in col A and B ab_rows = sum(1 for r in range(2, ws1.max_row+1) if ws1.cell(r, 1).value and ws1.cell(r, 2).value) print(f'Biomarker rows with correlations (A+B): {ab_rows}') # Show sample of new traits in E print('Last 5 E column traits:') for v in e_vals[-5:]: print(f' {v}') # Interpretations sheet total_interp = sum(1 for r in range(3, ws2.max_row+1) if ws2.cell(r, 1).value) print(f'Interpretation rows: {total_interp}') # Show sample print('Sample interpretation (row 15):') print(f' A: {ws2.cell(15,1).value}') print(f' B (High): {str(ws2.cell(15,2).value)[:100] if ws2.cell(15,2).value else None}') print(f' D (Trait): {ws2.cell(15,4).value}') print(f'File size: {__import__(\"os\").path.getsize(\"/home/daytona/workspace/blood-genetics/BG_Completed.xlsx\")} bytes') "
The completed file is ready:
BG Completed
Spreadsheet · XLSX
Here's a summary of everything that was done:
Sheet 1 - "Blood Vs Genetics Correlation"
| What changed | Details |
|---|---|
| Column B completions | All 70 biomarker rows now have their genetics trait correlations filled in and enriched - previously empty cells were populated and existing entries had missing traits added |
| Column E - New traits added | 37 new genetics parameters added (highlighted in yellow), bringing the total from 229 to 266 genetics traits |
New genetics traits added to Column E include:
- Nutrients: Choline, Copper, Glutathione, Vitamin K, Omega 3 (DHA)
- Diseases: Celiac Disease, Type 1 Diabetes, Chronic Kidney Disease, Kidney Stone Risk, Pancreatitis Risk, Hemochromatosis, Lupus (SLE), Multiple Sclerosis Risk, Migraine Risk, Schizophrenia Risk
- Cancer risks: Colorectal, Breast, Prostate, Thyroid, Lung, Non-Melanoma Skin Cancer
- Hormonal: Female Fertility, Preeclampsia Risk
- Body composition: Body Mass Index Tendency, Visceral Fat Accumulation, Sarcopenia Risk
- Fitness: Wound Healing, Pain Sensitivity, Exercise-Induced Inflammation, Tendon/Ligament Injury Risk, Lactate Clearance
- Metabolism: Caffeine Metabolism (CYP1A2), Alcohol Metabolism (ADH/ALDH), Folate Metabolism (MTHFR), Blood Type Related Risk, Circadian Rhythm Disruption Sensitivity
Sheet 2 - "Interpretations"
- Expanded from 9 rows to 1,089 rows covering every biomarker + genetics trait combination
- Each row has a personalized "High" interpretation and an "Average/Normal" interpretation explaining the combined blood marker + genetic predisposition context
- Headers reformatted: Blood Marker | Interpretation (High) | Interpretation (Average/Normal) | Genetics Trait | Genetics Parameter
Not able yo open this file please reshare.
Running Command
ls -lh /home/daytona/workspace/blood-genetics/ && python3 -c " import openpyxl, io wb = openpyxl.load_workbook('/home/daytona/workspace/blood-genetics/BG_Completed.xlsx') print('Sheets:', wb.sheetnames) print('File loads OK') "
Running Command
python3 -c " import openpyxl, io, shutil # Load the completed file wb = openpyxl.load_workbook('/home/daytona/workspace/blood-genetics/BG_Completed.xlsx') # Save fresh copy with new name buf = io.BytesIO() wb.save(buf) data = buf.getvalue() out = '/home/daytona/workspace/blood-genetics/Blood_Genetics_Completed_v2.xlsx' with open(out, 'wb') as f: f.write(data) import os size = os.path.getsize(out) print(f'Written: {out}') print(f'Size: {size} bytes ({size//1024} KB)') # Verify it opens wb2 = openpyxl.load_workbook(out) ws1 = wb2['Blood Vs Genetics Correlation'] ws2 = wb2['Interpretations'] print(f'Sheet1 rows: {ws1.max_row}, Sheet2 rows: {ws2.max_row}') print('Verification OK') "
Here is the refreshed file:
Blood Genetics Completed v2
Spreadsheet · XLSX
If that still doesn't open, the issue may be with your local Excel version or a download problem. A few things to try:
- Right-click the link and choose "Save link as" to download it directly
- Open with Google Sheets or LibreOffice Calc as an alternative
- Make sure the file downloads fully before opening (it's 78 KB)
Generate a CSV version of the completed data for easier import
Running Command
python3 -c " import openpyxl, csv, os wb = openpyxl.load_workbook('/home/daytona/workspace/blood-genetics/Blood_Genetics_Completed_v2.xlsx') out_dir = '/home/daytona/workspace/blood-genetics' # --- Sheet 1: Blood Vs Genetics Correlation --- ws1 = wb['Blood Vs Genetics Correlation'] csv1 = os.path.join(out_dir, 'Blood_Genetics_Correlation.csv') with open(csv1, 'w', newline='', encoding='utf-8') as f: writer = csv.writer(f, quoting=csv.QUOTE_ALL) for row in ws1.iter_rows(values_only=True): # Only write rows that have at least one non-None value if any(v is not None for v in row): clean = [str(v).strip().replace('\n', ' ').replace('\t', ' ') if v is not None else '' for v in row] writer.writerow(clean) print('Sheet1 CSV written:', csv1) # --- Sheet 2: Interpretations --- ws2 = wb['Interpretations'] csv2 = os.path.join(out_dir, 'Blood_Genetics_Interpretations.csv') with open(csv2, 'w', newline='', encoding='utf-8') as f: writer = csv.writer(f, quoting=csv.QUOTE_ALL) for row in ws2.iter_rows(values_only=True): if any(v is not None for v in row): clean = [str(v).strip().replace('\n', ' ').replace('\t', ' ') if v is not None else '' for v in row] writer.writerow(clean) print('Sheet2 CSV written:', csv2) # Stats import subprocess r1 = sum(1 for _ in open(csv1, encoding='utf-8')) r2 = sum(1 for _ in open(csv2, encoding='utf-8')) s1 = os.path.getsize(csv1) s2 = os.path.getsize(csv2) print(f'Correlation CSV: {r1} rows, {s1//1024} KB') print(f'Interpretations CSV: {r2} rows, {s2//1024} KB') "
Running Command
python3 -c " import csv print('=== Correlation CSV - first 5 rows ===') with open('/home/daytona/workspace/blood-genetics/Blood_Genetics_Correlation.csv', encoding='utf-8') as f: for i, row in enumerate(csv.reader(f)): if i >= 5: break print(row[:2]) # cols A and B only (col E is index 4) print() print('=== Interpretations CSV - first 4 rows ===') with open('/home/daytona/workspace/blood-genetics/Blood_Genetics_Interpretations.csv', encoding='utf-8') as f: for i, row in enumerate(csv.reader(f)): if i >= 4: break print([c[:80] if len(c)>80 else c for c in row[:4]]) "
Both CSVs look clean. Here are your downloads:
CSV 1 - Biomarker to Genetics Correlations (267 rows, 31 KB)
Blood Genetics Correlation
CSV File · CSV
CSV 2 - Interpretations (1,091 rows, 884 KB)
Blood Genetics Interpretations
CSV File · CSV
What each file contains:
| File | Columns | Rows |
|---|---|---|
| Blood_Genetics_Correlation.csv | Biomarker, Genetics Traits, (cols C/D blank), Genetics Parameter | 267 |
| Blood_Genetics_Interpretations.csv | Blood Marker, Interpretation (High), Interpretation (Average/Normal), Genetics Trait, Genetics Parameter | 1,091 |
Both files are UTF-8 encoded with all fields quoted, so they'll import cleanly into Excel, Google Sheets, any database, or data pipeline without encoding issues.
please share this file in google sheet
I'm not able to directly upload or share files to Google Sheets - I don't have access to your Google account or the ability to create Google Sheets links on your behalf.
Here's how you can do it in under a minute:
Option 1 - Import the CSV (easiest):
- Go to sheets.google.com and create a new sheet
- Click File → Import
- Upload
Blood_Genetics_Correlation.csvorBlood_Genetics_Interpretations.csv - Choose "Replace spreadsheet" and click Import data
- Repeat for the second CSV on a new sheet
Option 2 - Import the Excel file directly:
- Go to drive.google.com
- Click + New → File upload
- Upload
Blood_Genetics_Completed_v2.xlsx - Once uploaded, right-click it → Open with → Google Sheets
- Google Sheets will automatically convert it and preserve both tabs
Option 3 - Fastest via Google Drive:
- Drag and drop the
.xlsxfile directly into your Google Drive browser window - Double-click it → it opens in Google Sheets with both sheets intact
Option 2 or 3 is recommended since it keeps both sheets ("Blood Vs Genetics Correlation" and "Interpretations") in one file with all formatting preserved.
this file is not having data . Please reshare
Your credits are exhausted. You can purchase a top-up or subscribe to a plan to continue using Orris. If you're already subscribed, your daily credits will reset tomorrow.
Create a summary document of what was completed
Your credits are exhausted. You can purchase a top-up or subscribe to a plan to continue using Orris. If you're already subscribed, your daily credits will reset tomorrow.
This is a shared conversation. Sign in to Orris to start your own chat.