# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Intellectual_Disability"
cohort = "GSE158385"

# Input paths
in_trait_dir = "../DATA/GEO/Intellectual_Disability"
in_cohort_dir = "../DATA/GEO/Intellectual_Disability/GSE158385"

# Output paths
out_data_file = "./output/preprocess/3/Intellectual_Disability/GSE158385.csv"
out_gene_data_file = "./output/preprocess/3/Intellectual_Disability/gene_data/GSE158385.csv"
out_clinical_data_file = "./output/preprocess/3/Intellectual_Disability/clinical_data/GSE158385.csv"
json_path = "./output/preprocess/3/Intellectual_Disability/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)

# Get unique values for each clinical feature 
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background information
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
is_gene_available = True  # Yes, this appears to be gene expression data based on the background which studies effects in human amniocytes

# 2. Variable Availability and Data Type Conversion

# 2.1 Key identification
trait_row = 2  # Karyotype indicates T21 (Trisomy 21) status which represents Intellectual Disability
age_row = None  # No age data available 
gender_row = None  # Although gender info is embedded in karyotype, we can't reliably extract it since some patients could have multiple records

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    if pd.isna(value):
        return None
    value = value.split(': ')[-1].strip()
    if '47' in value and 'T21' in value:  # Trisomy 21 cases
        return 1
    elif '46' in value and '2N' in value:  # Normal karyotype
        return 0
    return None

convert_age = None  # No age data
convert_gender = None  # No reliable gender data

# 3. Save Metadata 
is_trait_available = trait_row is not None
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. Clinical Feature Extraction
if trait_row is not None:
    clinical_features = geo_select_clinical_features(clinical_data,
                                                   trait=trait,
                                                   trait_row=trait_row,
                                                   convert_trait=convert_trait)
    print("Preview of clinical features:")
    print(preview_df(clinical_features))
    
    clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data from the matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 row IDs
print("First 20 row IDs:")
print(genetic_data.index[:20].tolist())
# The identifiers in the gene expression data appear to be Affymetrix transcript cluster IDs (TC.....hg.1)
# These are probe set IDs that need to be mapped to human gene symbols for analysis
requires_gene_mapping = True
# Identify all platform sections in the SOFT file
with gzip.open(soft_file_path, 'rt') as f:
    platform_sections = []
    current_platform = None
    for line in f:
        if line.startswith('^PLATFORM'):
            if current_platform:
                platform_sections.append(current_platform)
            current_platform = {'id': line.strip()}
        elif current_platform is not None and line.startswith('!Platform_title'):
            current_platform['title'] = line.strip()
            if 'human' in line.lower() or 'homo sapiens' in line.lower():
                current_platform['is_human'] = True
        elif not line.startswith('^'):  # End of platform section
            if current_platform:
                platform_sections.append(current_platform)
                current_platform = None
    if current_platform:  # Handle last platform if exists
        platform_sections.append(current_platform)

print("Found Platform Sections:")
for platform in platform_sections:
    print(platform)

# Look for human gene annotations
with gzip.open(soft_file_path, 'rt') as f:
    human_data = []
    is_human_section = False
    for line in f:
        if line.startswith('^PLATFORM'):
            is_human_section = False
            platform_id = line.strip()
        elif line.startswith('!Platform_title') and ('human' in line.lower() or 'homo sapiens' in line.lower()):
            is_human_section = True
            print(f"\nFound human platform section: {platform_id}")
            print(f"Platform title: {line.strip()}")
        elif is_human_section and not line.startswith(('!', '#', '^')):
            human_data.append(line)

if human_data:
    # Convert human annotation data to dataframe
    human_annotation_df = pd.read_csv(io.StringIO(''.join(human_data)), sep='\t')
    
    print("\nColumn names:")
    print(human_annotation_df.columns.tolist())
    
    print("\nData shape:", human_annotation_df.shape)
    
    print("\nPreview of the annotation data:")
    print(json.dumps(preview_df(human_annotation_df), indent=2))
else:
    print("\nNo human gene annotation data found in the SOFT file.")
# Extract probe and gene mapping from annotation data
prob_col = 'ID'  # The gene expression data uses TC.....hg.1 identifiers, which match the ID column
gene_col = 'gene_assignment'  # This column contains gene symbol information

# Get initial mapping between probes and genes
mapping_df = get_gene_mapping(human_annotation_df, prob_col, gene_col)

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

# Normalize gene symbols to ensure consistency
gene_data = normalize_gene_symbols_in_index(gene_data)

# Preview the result
print("\nGene expression data shape:", gene_data.shape)
print("\nFirst few gene symbols:", gene_data.index[:5].tolist())

# Save gene expression data
gene_data.to_csv(out_gene_data_file)
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# Get clinical features 
clinical_features = geo_select_clinical_features(
    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
)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)

# 3. Handle missing values
linked_data = handle_missing_values(linked_data, trait)

# Early exit if trait values are all NaN
if linked_data[trait].isna().all():
    is_biased = True
    linked_data = None
else:
    # 4. Judge whether features are biased and remove biased demographic features
    is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save metadata
note = "Dataset contains gene expression data from pediatric AML samples, focusing on Down syndrome cases versus other AML types."
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=is_biased,
    df=linked_data,
    note=note
)

# 6. Save the linked data only if it's usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)