# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Ovarian_Cancer"
cohort = "GSE135820"

# Input paths
in_trait_dir = "../DATA/GEO/Ovarian_Cancer"
in_cohort_dir = "../DATA/GEO/Ovarian_Cancer/GSE135820"

# Output paths
out_data_file = "./output/preprocess/3/Ovarian_Cancer/GSE135820.csv"
out_gene_data_file = "./output/preprocess/3/Ovarian_Cancer/gene_data/GSE135820.csv"
out_clinical_data_file = "./output/preprocess/3/Ovarian_Cancer/clinical_data/GSE135820.csv"
json_path = "./output/preprocess/3/Ovarian_Cancer/cohort_info.json"

# Get file paths for SOFT and matrix files
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data from the matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Create dictionary of unique values for each feature
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print the information
print("Dataset Background Information:")
print(background_info)
print("\nSample Characteristics:")
for feature, values in unique_values_dict.items():
    print(f"\n{feature}:")
    print(values)
# 1. Gene Expression Data Availability
# This dataset contains gene expression data based on NanoString panel
is_gene_available = True

# 2.1 Data Availability
# Trait info in sample characteristics row 0 (diagnosis)
trait_row = 0

# Age info in sample characteristics row 3
age_row = 3

# Gender is not available in sample characteristics
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(val):
    """Convert HGSOC vs non-HGSOC to binary"""
    if not isinstance(val, str):
        return None
    val = val.split(': ')[-1].strip().upper()
    if val == 'HGSOC':
        return 1 
    elif val == 'NON-HGSOC':
        return 0
    return None

def convert_age(val):
    """Convert age at diagnosis to continuous value"""
    if not isinstance(val, str):
        return None
    try:
        age = int(val.split(': ')[-1])
        return age
    except:
        return None

def convert_gender(val):
    """Placeholder function since gender is not available"""
    return None

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

# 4. Extract Clinical Features
clinical_df = 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)

# Preview the extracted features
preview_result = preview_df(clinical_df)
print("Preview of clinical features:")
print(preview_result)

# Save clinical data
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
clinical_df.to_csv(out_clinical_data_file)
# Extract genetic data matrix
genetic_data = get_genetic_data(matrix_file_path)

# Print first few rows with column names to examine data structure
print("Data preview:")
print("\nColumn names:")
print(list(genetic_data.columns)[:5])
print("\nFirst 5 rows:")
print(genetic_data.head())
print("\nShape:", genetic_data.shape)

# Verify this is gene expression data and check identifiers
is_gene_available = True

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

# Save gene expression data 
genetic_data.to_csv(out_gene_data_file)
# Looking at gene identifiers from the DataFrame index
# We can see formats like "NM_000038.3:6850" which are RefSeq transcript IDs
# These need to be mapped to HGNC gene symbols for standardization
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file_path) 

# Preview column names and first few values
preview = preview_df(gene_metadata)
print("\nGene annotation columns and sample values:")
print(preview)
# 1. From the preview, we can see that 'ID' contains the same format of identifiers as gene expression data
# and 'ORF' contains gene symbols

# 2. Extract identifier-to-symbol mapping
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='ORF')

# 3. Convert probe-level data to gene-level data using the mapping
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Preview gene-level expression data
print("Gene-level expression data preview:")
print("\nFirst 5 rows:")
print(gene_data.head())
print("\nShape:", gene_data.shape)

# Save converted gene expression data
gene_data.to_csv(out_gene_data_file)
# 1. Normalize gene symbols and save gene data
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 clinical and genetic data  
clinical_features = pd.read_csv(out_clinical_data_file, index_col=0).T
clinical_features.columns = [trait, 'Age']
linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)

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

# 4. Judge bias in features and remove biased ones
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save metadata
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available, 
    is_trait_available=True,
    is_biased=trait_biased,
    df=linked_data,
    note="The dataset contains NanoString gene expression measurements from high-grade serous ovarian cancer patients, with binary comparison between HGSOC vs non-HGSOC."
)

# 6. Save linked data if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)
# Get file paths for SOFT and matrix files
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data from the matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Create dictionary of unique values for each feature
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print the information
print("Dataset Background Information:")
print(background_info)
print("\nSample Characteristics:")
for feature, values in unique_values_dict.items():
    print(f"\n{feature}:")
    print(values)
# 1. Gene Expression Data Availability
# Yes, from background info this is a gene expression study with NanoString panel
is_gene_available = True

# 2. Variable Availability and Data Type Conversion

# 2.1 Data Availability

# Trait (HGSOC) data available in row 0
trait_row = 0

# Age data available in row 3
age_row = 3 

# Gender data not available
gender_row = None

# 2.2 Data Type Conversion Functions

def convert_trait(x):
    # Extract value after colon and strip whitespace
    val = x.split(':')[1].strip()
    # Convert HGSOC to 1, non-HGSOC to 0
    if 'HGSOC' == val:
        return 1
    elif 'non-HGSOC' == val:
        return 0
    return None

def convert_age(x):
    # Extract value after colon and strip whitespace
    val = x.split(':')[1].strip()
    try:
        # Convert to integer
        return int(val)
    except:
        return None

def convert_gender(x):
    return None  # No gender data available

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

# 4. Clinical Feature Extraction
# Extract clinical features since trait_row is not None
clinical_df = 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)

# Preview extracted data
preview = preview_df(clinical_df)
print("Preview of clinical data:")
print(preview)

# Save clinical data
clinical_df.to_csv(out_clinical_data_file)
# Extract genetic data matrix
genetic_data = get_genetic_data(matrix_file_path)

# Print first few rows with column names to examine data structure
print("Data preview:")
print("\nColumn names:")
print(list(genetic_data.columns)[:5])
print("\nFirst 5 rows:")
print(genetic_data.head())
print("\nShape:", genetic_data.shape)

# Verify this is gene expression data and check identifiers
is_gene_available = True

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

# Save gene expression data 
genetic_data.to_csv(out_gene_data_file)
# Examining the gene identifiers - they appear to be transcript IDs in RefSeq and Ensembl format
# Need to map these to standard human gene symbols for consistency
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file_path) 

# Preview column names and first few values
preview = preview_df(gene_metadata)
print("\nGene annotation columns and sample values:")
print(preview)
# 1. From the preview, we can see that 'ID' contains the same format of identifiers as gene expression data
# and 'ORF' contains gene symbols

# 2. Extract identifier-to-symbol mapping
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='ORF')

# 3. Convert probe-level data to gene-level data using the mapping
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Preview gene-level expression data
print("Gene-level expression data preview:")
print("\nFirst 5 rows:")
print(gene_data.head())
print("\nShape:", gene_data.shape)

# Save converted gene expression data
gene_data.to_csv(out_gene_data_file)
# 1. Normalize gene symbols and save gene data
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 clinical and genetic data
clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
clinical_features.columns = [trait, 'Age'] 
linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)

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

# 4. Judge bias in features and remove biased ones
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save metadata
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available, 
    is_trait_available=True,
    is_biased=trait_biased,
    df=linked_data,
    note="The dataset contains NanoString gene expression measurements from high-grade serous ovarian cancer patients, with binary comparison between HGSOC vs non-HGSOC."
)

# 6. Save linked data if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)
# Gene expression data availability check
is_gene_available = False  # Cannot determine without previous output

# Variable availability - cannot determine without data
trait_row = None  
age_row = None
gender_row = None

def convert_trait(x):
    return None

def convert_age(x):
    return None

def convert_gender(x):
    return None

# Save initial metadata
_ = validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,  
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=(trait_row is not None)
)