# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Cervical_Cancer"
cohort = "GSE107754"

# Input paths
in_trait_dir = "../DATA/GEO/Cervical_Cancer"
in_cohort_dir = "../DATA/GEO/Cervical_Cancer/GSE107754"

# Output paths
out_data_file = "./output/preprocess/1/Cervical_Cancer/GSE107754.csv"
out_gene_data_file = "./output/preprocess/1/Cervical_Cancer/gene_data/GSE107754.csv"
out_clinical_data_file = "./output/preprocess/1/Cervical_Cancer/clinical_data/GSE107754.csv"
json_path = "./output/preprocess/1/Cervical_Cancer/cohort_info.json"

# STEP1
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)
# 1) Determine gene expression data availability
is_gene_available = True  # Based on the series summary, it uses whole human genome microarrays

# 2) Determine availability and data type for trait, age, and gender
#    Map them to the appropriate row indices in the sample characteristics dictionary.
trait_row = 2  # Row 2 contains "tissue: Cervix cancer" and "tissue: Cervical cancer"
age_row = None  # No explicit or inferred age data
gender_row = 0  # Row 0 contains "gender: Male" and "gender: Female"

# Define conversion functions for each variable
def convert_trait(x: str):
    parts = x.split(':', 1)
    if len(parts) < 2:
        return None
    val = parts[1].strip().lower()
    # Convert to binary: 1 if "cervix" or "cervical" is mentioned, else 0
    if 'cervix' in val or 'cervical' in val:
        return 1
    return 0

# Age is not available, so we won't use a real conversion function
def convert_age(x: str):
    return None

def convert_gender(x: str):
    parts = x.split(':', 1)
    if len(parts) < 2:
        return None
    val = parts[1].strip().lower()
    # Convert to binary: female -> 0, male -> 1
    if val == 'female':
        return 0
    elif val == 'male':
        return 1
    return None

# 3) Conduct initial filtering and save metadata
is_trait_available = (trait_row is not None)
filter_result = 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) If trait data is available, proceed with clinical feature extraction
if trait_row is not None:
    clinical_features_df = geo_select_clinical_features(
        clinical_data,
        trait,
        trait_row,
        convert_trait,
        age_row=age_row,
        convert_age=None,
        gender_row=gender_row,
        convert_gender=convert_gender
    )
    # Preview the extracted clinical features
    preview = preview_df(clinical_features_df, n=5, max_items=200)
    print(preview)

    # Save the clinical features to a CSV file
    clinical_features_df.to_csv(out_clinical_data_file, index=False)
# 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])
# Based on the observed identifiers (e.g. A_23_P100001), they appear to be microarray probe IDs and not human gene symbols.
# Therefore, they 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) Inspecting the preview, the "ID" column in the annotation matches the probe identifiers
#    from our gene_data index, and "GENE_SYMBOL" will be used as the gene symbol.
probe_col = "ID"
symbol_col = "GENE_SYMBOL"

# 2) Get the gene mapping dataframe
mapping_df = get_gene_mapping(gene_annotation, probe_col, symbol_col)

# 3) Apply the mapping to convert probe-level data to gene-level expression data
gene_data = apply_gene_mapping(gene_data, mapping_df)
# STEP 7

# Ensure "selected_clinical_df" is defined before use
selected_clinical_df = clinical_features_df

# 1. Normalize gene symbols in the gene_data, then save to CSV.
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)

# 2. Link the clinical and genetic data on sample IDs
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, 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 demographic features are biased
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Conduct final validation and save cohort info
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="Trait is available. Completed linking and QC steps."
)

# 6. If the dataset is usable, save the final linked data
if is_usable:
    linked_data.to_csv(out_data_file)