# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Osteoarthritis"
cohort = "GSE98460"

# Input paths
in_trait_dir = "../DATA/GEO/Osteoarthritis"
in_cohort_dir = "../DATA/GEO/Osteoarthritis/GSE98460"

# Output paths
out_data_file = "./output/preprocess/3/Osteoarthritis/GSE98460.csv"
out_gene_data_file = "./output/preprocess/3/Osteoarthritis/gene_data/GSE98460.csv"
out_clinical_data_file = "./output/preprocess/3/Osteoarthritis/clinical_data/GSE98460.csv"
json_path = "./output/preprocess/3/Osteoarthritis/cohort_info.json"

# Get file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")

# Get dictionary of unique values per row 
unique_values_dict = get_unique_values_by_row(clinical_data)
for row, values in unique_values_dict.items():
    print(f"\n{row}:")
    print(values)
# 1. Gene Expression Data Availability
is_gene_available = True  # RNA microarray data indicated in background info

# 2. Variable Availability and Data Type
# Trait (OA) - can be inferred from diagnosis field
trait_row = 1
def convert_trait(x):
    if not x or ':' not in x:
        return None
    value = x.split(':')[1].strip().lower()
    if 'osteoarthritis' in value or 'oa' in value:
        return 1
    return 0

# Age - available in field 2
age_row = 2  
def convert_age(x):
    if not x or ':' not in x:
        return None
    try:
        return float(x.split(':')[1].strip().split()[0])
    except:
        return None

# Gender - available in field 3
gender_row = 3
def convert_gender(x):
    if not x or ':' not in x:
        return None
    value = x.split(':')[1].strip().lower()
    if 'female' in value:
        return 0
    elif 'male' in value:
        return 1
    return None

# 3. Save metadata
is_trait_available = trait_row is not None
is_usable = validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=is_trait_available
)

# 4. Extract clinical features
if trait_row is not None:
    selected_clinical = geo_select_clinical_features(
        clinical_df=clinical_data,
        trait=trait,
        trait_row=trait_row,
        convert_trait=convert_trait,
        age_row=age_row,
        convert_age=convert_age,
        gender_row=gender_row,
        convert_gender=convert_gender
    )
    print("Preview of selected clinical features:")
    print(preview_df(selected_clinical))
    selected_clinical.to_csv(out_clinical_data_file)
# Get gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Examine data structure
print("Data structure and head:")
print(genetic_data.head())

print("\nShape:", genetic_data.shape)

print("\nFirst 20 row IDs (gene/probe identifiers):")
print(list(genetic_data.index)[:20])

# Get a few column names to verify sample IDs
print("\nFirst 5 column names:")
print(list(genetic_data.columns)[:5])
# Examining gene identifiers
# The IDs look like custom platform probe IDs (e.g. 16650001, 16650003)
# These are not standard human gene symbols (which would be like BRCA1, TP53, etc.)
# We will need to map these probe IDs to gene symbols

requires_gene_mapping = True
# Look at more content in SOFT file to find gene annotation section
with gzip.open(soft_file_path, 'rt') as f:
    platform_found = False
    table_start = False
    first_row = None
    gene_rows = []
    
    for line in f:
        if '!Platform_table_begin' in line:
            table_start = True
            continue
        elif '!Platform_table_end' in line:
            break
        elif table_start:
            if first_row is None:
                first_row = line.strip()
            else:
                gene_rows.append(line.strip())

# Create dataframe from the platform table data
import io
header = first_row.split('\t')
gene_data = '\n'.join(gene_rows)
gene_annotation = pd.read_csv(io.StringIO(gene_data), sep='\t', names=header)

print("Column names:")
print(gene_annotation.columns)

print("\nPreview of gene annotation data:")
print(preview_df(gene_annotation))
# First examine more content in SOFT file to locate gene symbol information
with gzip.open(soft_file_path, 'rt') as f:
    found_table = False
    header = None
    first_five_rows = []
    for line in f:
        if '!Platform_title' in line:
            print("Platform title:", line.strip())
        elif '!Platform_organism' in line:
            print("Platform organism:", line.strip())
        elif '!Platform_table_begin' in line:
            found_table = True
            continue
        elif found_table:
            if header is None:
                header = line.strip()
                print("\nPlatform table header:")
                print(header)
            elif len(first_five_rows) < 5:
                first_five_rows.append(line.strip())
            else:
                break

print("\nFirst few rows:")
for row in first_five_rows:
    print(row)

# Now try using tabs as delimiter to see full column structure
print("\nSplitting first row by tabs to check all fields:")
if first_five_rows:
    print(first_five_rows[0].split('\t'))

# Based on examination results, extract complete platform data
platform_data = pd.read_csv(gzip.open(soft_file_path, 'rt'), 
                          sep='\t',
                          skiprows=lambda x: x == 0 or not found_table,
                          comment='!')

print("\nFull column names found:")
print(platform_data.columns.tolist())

print("\nPreview of complete annotation data:")
print(preview_df(platform_data))
# Extract gene annotation using library function
gene_annotation = get_gene_annotation(soft_file_path)

# Print available columns to identify correct names
print("Available columns:", gene_annotation.columns.tolist())

# First examine the column names 
probe_data = gene_annotation.head()
print("\nFirst few rows:")
print(preview_df(probe_data))

# Create mapping after seeing actual column names
mapping_df = get_gene_mapping(gene_annotation, 
                            prob_col='ID', 
                            gene_col='Gene Title')  

# Convert probe-level measurements to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_df)

print("\nPreview of gene expression data after mapping:")
print(preview_df(gene_data))
# Load clinical data
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

# Normalize gene symbols and save gene expression data
genetic_data = normalize_gene_symbols_in_index(genetic_data) 
genetic_data.to_csv(out_gene_data_file)

# Link clinical and genetic data using library function
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_data)

# Handle missing values systematically  
linked_data = handle_missing_values(linked_data, trait)

# Check for bias in trait and demographic features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# Final validation and information saving
note = "This dataset contains cartilage tissue samples from OA patients, with gene expression data and demographic information."
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=True,
    is_biased=trait_biased,
    df=linked_data,
    note=note
)

# Save linked data only if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)
# First examine platform information in SOFT file
print("Examining platform information in SOFT file...")
with gzip.open(soft_file_path, 'rt') as f:
    platform_lines = []
    capture = False
    for line in f:
        if line.startswith(('!Platform_title', '!Platform_organism', '!Platform_technology')):
            print(line.strip())
        elif '!platform_table_begin' in line.lower():
            capture = True
            continue
        elif '!platform_table_end' in line.lower():
            break
        elif capture:
            platform_lines.append(line.strip())

# Now extract complete annotation with pandas
print("\nExtracting complete platform annotation...")
platform_df = pd.read_csv(io.StringIO('\n'.join(platform_lines)), sep='\t')

print("\nFound columns:")
print(platform_df.columns.tolist())

print("\nPreview of annotation data:")
print(preview_df(platform_df))