# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Asthma"
cohort = "GSE184382"

# Input paths
in_trait_dir = "../DATA/GEO/Asthma"
in_cohort_dir = "../DATA/GEO/Asthma/GSE184382"

# Output paths
out_data_file = "./output/preprocess/1/Asthma/GSE184382.csv"
out_gene_data_file = "./output/preprocess/1/Asthma/gene_data/GSE184382.csv"
out_clinical_data_file = "./output/preprocess/1/Asthma/clinical_data/GSE184382.csv"
json_path = "./output/preprocess/1/Asthma/cohort_info.json"

# STEP 1

# 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("\nSample Characteristics Dictionary:")
print(sample_characteristics_dict)
# 1. Gene Expression Data Availability
is_gene_available = True  # This dataset includes transcriptome microarray data.

# 2. Variable Availability
trait_row = None   # No row indicates "asthma" or similar
age_row = None     # No row for age
gender_row = None  # No row for gender

# 2.2 Data Type Conversion
def convert_trait(value: str) -> int:
    """
    Convert raw trait string to a binary indicator (0 or 1).
    Since trait_row is None, this function won't be used.
    """
    # Placeholder implementation
    return None

def convert_age(value: str) -> float:
    """
    Convert raw age string to a float (continuous).
    Since age_row is None, this function won't be used.
    """
    # Placeholder implementation
    return None

def convert_gender(value: str) -> int:
    """
    Convert raw gender string to 0 (female) or 1 (male).
    Since gender_row is None, this function won't be used.
    """
    # Placeholder implementation
    return None

# 3. Save Metadata
# Determine trait availability
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. Clinical Feature Extraction
# Since trait_row is None, we skip feature extraction.
# STEP3
# 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])
# These identifiers (e.g., "A_19_P...", "(+)E1A_r60_...", "3xSLv1") are not standard human gene symbols.
# They appear to be array or custom IDs that require mapping to gene symbols.

requires_gene_mapping = True
# STEP5
# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
gene_annotation = get_gene_annotation(soft_file)

# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
print("Gene annotation preview:")
print(preview_df(gene_annotation))
# STEP: Gene Identifier Mapping

# 1 & 2. Identify the columns in the annotation corresponding to the gene expression IDs and the gene symbols
#    Here, 'ID' holds probe identifiers matching those in 'gene_data'
#    and 'GENE_SYMBOL' holds the corresponding gene symbols.

gene_mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="GENE_SYMBOL")

# 3. Convert probe-level measurements to gene-level data using the mapping.
gene_data = apply_gene_mapping(gene_data, gene_mapping_df)
# STEP 7: Data Normalization and Linking

# We know from prior steps:
# - Trait is NOT available (trait_row = None), so no clinical CSV was saved.
# - We do have gene data, so we will at least normalize it.

# 1) Normalize gene symbols
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)
print(f"Saved normalized gene data to {out_gene_data_file}")

# 2) Since trait is not available, we cannot link or handle clinical data.
#    We'll set up placeholders for final validation.
is_trait_available = False
trait_biased = False   # Arbitrarily set; the library requires a boolean.

# 3) We have no clinical data to integrate; skip missing value handling.

# 4) With no trait, we cannot check bias meaningfully. Skipped.

# 5) Final dataset validation
#    The library requires df and is_biased if is_final=True, so we provide an empty DataFrame.
#    This ensures it records the dataset as not usable.
empty_df = pd.DataFrame()
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,   # Gene data is available
    is_trait_available=False, # Trait is not available
    is_biased=trait_biased,
    df=empty_df,
    note="No trait data; final record."
)

# 6) If the linked data were usable, we would save it. But here, is_usable will be False.
if is_usable:
    # This block won't run in our scenario, but included for completeness
    empty_df.to_csv(out_data_file)
    print(f"Saved final linked data to {out_data_file}")
else:
    print("Data not usable (no trait). No final linked file was saved.")