# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Endometrioid_Cancer"
cohort = "GSE73637"

# Input paths
in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE73637"

# Output paths
out_data_file = "./output/z2/preprocess/Endometrioid_Cancer/GSE73637.csv"
out_gene_data_file = "./output/z2/preprocess/Endometrioid_Cancer/gene_data/GSE73637.csv"
out_clinical_data_file = "./output/z2/preprocess/Endometrioid_Cancer/clinical_data/GSE73637.csv"
json_path = "./output/z2/preprocess/Endometrioid_Cancer/cohort_info.json"


# Step 1: Initial Data Loading
from tools.preprocess import *
# 1. Identify the paths to the SOFT file and the matrix file
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# 2. Read the matrix file to obtain background information and sample characteristics data
background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)

# 3. Obtain the sample characteristics dictionary from the clinical dataframe
sample_characteristics_dict = get_unique_values_by_row(clinical_data)

# 4. Explicitly print out all the background information and the sample characteristics dictionary
print("Background Information:")
print(background_info)
print("Sample Characteristics Dictionary:")
print(sample_characteristics_dict)

# Step 2: Dataset Analysis and Clinical Feature Extraction
# 1. Gene Expression Data Availability
is_gene_available = True

# 2.1 Data Availability
trait_row = 3  # histopathology data contains endometrioid cancer information
age_row = None  # Not available for cell lines
gender_row = None  # Not available for cell lines

# 2.2 Data Type Conversion functions
def convert_trait(value):
    """Convert histopathology to binary endometrioid cancer status"""
    if value is None:
        return None
    # Extract value after colon
    if ':' in str(value):
        histology = str(value).split(':')[1].strip()
    else:
        histology = str(value).strip()
    
    # Check if it contains "Endometrioid" (including "Endometroid" variant)
    if "Endometrioid" in histology or "Endometroid" in histology:
        return 1
    else:
        return 0

def convert_age(value):
    """Age conversion function (not used since age_row is None)"""
    return None

def convert_gender(value):
    """Gender conversion function (not used since gender_row is None)"""
    return None

# 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:
    selected_clinical_data = geo_select_clinical_features(clinical_data, trait, trait_row, convert_trait,
                                                        age_row, convert_age, gender_row, convert_gender)
    print("Clinical data extracted:")
    print(preview_df(selected_clinical_data))
    
    # Save clinical data
    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    selected_clinical_data.to_csv(out_clinical_data_file)

# Step 3: Gene Data Extraction
# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
gene_data = get_genetic_data(matrix_file)

# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
print(gene_data.index[:20])

# Step 4: Gene Identifier Review
# Examine the gene identifiers from the previous step output
gene_identifiers = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20']

print("Sample gene identifiers:", gene_identifiers[:10])
print("These appear to be numeric probe/array identifiers, not human gene symbols")
print("Human gene symbols are typically alphanumeric like BRCA1, TP53, GAPDH, etc.")
print("These numeric identifiers will need to be mapped to actual gene symbols")

requires_gene_mapping = True

# Step 5: Gene Annotation
# First, let's examine the SOFT file structure to understand what we're dealing with
print("Examining SOFT file structure:")
with gzip.open(soft_file, 'rt') as f:
    lines = []
    for i, line in enumerate(f):
        lines.append(line.strip())
        if i >= 50:  # Read first 50 lines
            break

# Show first 20 lines to understand the structure
for i, line in enumerate(lines[:20]):
    print(f"Line {i}: {line[:100]}...")  # Show first 100 chars of each line

print("\n" + "="*50)

# Look for gene annotation section - typically starts after platform info
annotation_start = None
for i, line in enumerate(lines):
    if line.startswith('!platform_table_begin'):
        annotation_start = i + 1
        print(f"Found annotation section starting at line {annotation_start}")
        break

if annotation_start:
    print("Sample annotation lines:")
    for i in range(annotation_start, min(annotation_start + 10, len(lines))):
        if i < len(lines):
            print(f"Line {i}: {lines[i]}")

# Try alternative approach to get gene annotation
try:
    with gzip.open(soft_file, 'rt') as f:
        content = f.read()
    
    # Find the platform table section
    if '!platform_table_begin' in content and '!platform_table_end' in content:
        start_marker = '!platform_table_begin'
        end_marker = '!platform_table_end'
        start_idx = content.find(start_marker) + len(start_marker)
        end_idx = content.find(end_marker)
        
        table_content = content[start_idx:end_idx].strip()
        
        # Parse as CSV
        gene_annotation = pd.read_csv(io.StringIO(table_content), delimiter='\t', low_memory=False)
        
        print("\nSuccessfully extracted gene annotation data!")
        print("Gene annotation preview:")
        print(preview_df(gene_annotation))
        
    else:
        print("Could not find platform table markers in SOFT file")
        
except Exception as e:
    print(f"Alternative parsing also failed: {e}")
    # If all parsing fails, we may need to work without gene annotation
    gene_annotation = None

# Step 6: Gene Identifier Mapping
# 1. Identify the columns for gene identifiers and gene symbols
# The 'ID' column matches the gene expression data identifiers (numeric: 1, 2, 3, ...)
# The 'GeneSymbol' column contains the actual gene symbols (PRPF8, CAPNS1, etc.)

# 2. Get gene mapping dataframe
gene_mapping = get_gene_mapping(gene_annotation, 'ID', 'GeneSymbol')

# 3. Apply gene mapping to convert probe-level measurements to gene expression data
gene_data = apply_gene_mapping(gene_data, gene_mapping)

print(f"Gene expression data shape after mapping: {gene_data.shape}")
print(f"Sample gene symbols: {list(gene_data.index[:10])}")

# Step 7: Data Normalization and Linking
# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_data.to_csv(out_gene_data_file)

# 2. Link the clinical and genetic data with the 'geo_link_clinical_genetic_data' function from the library.
linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)

# 3. Handle missing values in the linked data
linked_data = handle_missing_values(linked_data, trait)

# 4. Determine whether the trait and some demographic features are severely biased, and remove biased features.
is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Conduct quality check and save the cohort information.
is_usable = validate_and_save_cohort_info(True, cohort, json_path, True, True, is_trait_biased, unbiased_linked_data)

# 6. If the linked data is usable, save it as a CSV file to 'out_data_file'.
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    unbiased_linked_data.to_csv(out_data_file)
    print(f"Successfully saved processed data to {out_data_file}")
else:
    print("Dataset is not usable for analysis - not saved")