# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Anxiety_disorder"
cohort = "GSE78104"

# Input paths
in_trait_dir = "../DATA/GEO/Anxiety_disorder"
in_cohort_dir = "../DATA/GEO/Anxiety_disorder/GSE78104"

# Output paths
out_data_file = "./output/preprocess/1/Anxiety_disorder/GSE78104.csv"
out_gene_data_file = "./output/preprocess/1/Anxiety_disorder/gene_data/GSE78104.csv"
out_clinical_data_file = "./output/preprocess/1/Anxiety_disorder/clinical_data/GSE78104.csv"
json_path = "./output/preprocess/1/Anxiety_disorder/cohort_info.json"

# STEP 1

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("\nSample Characteristics Dictionary:")
print(sample_characteristics_dict)
# 1. Gene Expression Data Availability
is_gene_available = True  # From the background info (lncRNA + mRNA microarray), it's likely gene expression data is available.

# 2. Variable Availability and Data Type Conversion
#    Based on the sample characteristics dictionary:
#        Row 1 => "disease state: Obsessive-Compulsive Disorder" or "normal control"
#                 We interpret OCD as part of "Anxiety_disorder" (1) vs normal (0).
trait_row = 1
#        Row 2 => "gender: male" or "gender: female"
gender_row = 2
#        Row 3 => "age: 25y", "23y", "18y", etc.
age_row = 3

# Define conversion functions
def convert_trait(value: str):
    """Convert a disease state value into binary, mapping OCD to 1 and normal control to 0."""
    if ":" in value:
        value = value.split(":", 1)[1].strip().lower()
    if "compulsive" in value:
        return 1
    elif "normal" in value:
        return 0
    return None

def convert_age(value: str):
    """Convert age string like 'age: 25y' into an integer."""
    if ":" in value:
        value = value.split(":", 1)[1].strip().lower()
    value = value.replace("y", "")  # remove trailing 'y'
    try:
        return float(value)
    except ValueError:
        return None

def convert_gender(value: str):
    """Convert gender string like 'gender: male'/'gender: female' into binary, female->0, male->1."""
    if ":" in value:
        value = value.split(":", 1)[1].strip().lower()
    if value == "male":
        return 1
    elif value == "female":
        return 0
    return None

# 3. Save Metadata (Initial filtering)
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 (only if trait data is available)
if trait_row is not None:
    clinical_selected = 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 = preview_df(clinical_selected)
    print("Preview of selected clinical features:")
    print(preview)
    clinical_selected.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 identifiers, they do not appear to be standard human gene symbols.
# Therefore, they likely need to be mapped.

print("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. Identify which annotation columns match the gene expression data's "ID" and which contain the gene symbol.
#    From our observation, the annotation column "ID" matches the gene expression data "ID",
#    and "GeneSymbol" contains the gene symbol information.

# 2. Get a mapping dataframe with those columns.
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GeneSymbol')

# 3. Convert probe-level measurements to gene-level measurements.
gene_data = apply_gene_mapping(gene_data, mapping_df)
# STEP 7: Data Normalization and Linking

# 1. Normalize gene symbols in the obtained gene expression data
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. Ensure sample IDs in clinical and gene data match
def unify_sample_ids(df):
    df.columns = df.columns.astype(str).str.strip().str.strip('"')
    df.columns = df.columns.str.replace(r'\.CEL.*$', '', regex=True)
    return df

selected_clinical = unify_sample_ids(clinical_selected)
normalized_gene_data = unify_sample_ids(normalized_gene_data)

common_samples = set(selected_clinical.columns).intersection(normalized_gene_data.columns)
if len(common_samples) == 0:
    print("Warning: No matching sample IDs were found. The dataset may be misaligned.")
selected_clinical = selected_clinical.loc[:, list(common_samples)]
normalized_gene_data = normalized_gene_data.loc[:, list(common_samples)]

# 3. Link the clinical and genetic data on sample IDs
linked_data = geo_link_clinical_genetic_data(selected_clinical, normalized_gene_data)

# 4. Handle missing values, removing or imputing as instructed
linked_data = handle_missing_values(linked_data, trait)

# 5. Determine whether the trait (and potentially other features) is severely biased.
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 6. Conduct final quality validation and save metadata
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="Cohort data processed with ID alignment fix."
)

# 7. If the dataset is usable, save the final linked data
if is_usable:
    linked_data.to_csv(out_data_file, index=True)
    print(f"Saved final linked data to {out_data_file}")
else:
    print("The dataset is not usable for trait-based association. Skipping final output.")