# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Ankylosing_Spondylitis"
cohort = "GSE73754"

# Input paths
in_trait_dir = "../DATA/GEO/Ankylosing_Spondylitis"
in_cohort_dir = "../DATA/GEO/Ankylosing_Spondylitis/GSE73754"

# Output paths
out_data_file = "./output/preprocess/1/Ankylosing_Spondylitis/GSE73754.csv"
out_gene_data_file = "./output/preprocess/1/Ankylosing_Spondylitis/gene_data/GSE73754.csv"
out_clinical_data_file = "./output/preprocess/1/Ankylosing_Spondylitis/clinical_data/GSE73754.csv"
json_path = "./output/preprocess/1/Ankylosing_Spondylitis/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)
# Step 1. Determine gene expression data availability
is_gene_available = True  # Based on the background (differential gene expression analysis), we consider this dataset to have gene data

# Step 2. Identify data availability for trait, age, and gender
# According to the sample characteristics dictionary:
#   0 -> 'Sex: Male', 'Sex: Female'
#   1 -> 'age (yr): 53', ...
#   3 -> 'disease: Ankylosing Spondylitis', 'disease: healthy control'
trait_row = 3
age_row = 1
gender_row = 0

# Step 2.2 Define conversion functions
def convert_trait(x: str):
    # e.g., "disease: Ankylosing Spondylitis" -> 1, "disease: healthy control" -> 0
    # parse out the value after the colon
    try:
        val = x.split(":", 1)[1].strip().lower()
    except IndexError:
        return None
    if "ankylosing spondylitis" in val:
        return 1
    elif "healthy control" in val:
        return 0
    else:
        return None

def convert_age(x: str):
    # e.g., "age (yr): 53" -> 53
    try:
        val = x.split(":", 1)[1].strip()
        return float(val)
    except:
        return None

def convert_gender(x: str):
    # e.g., "Sex: Male" -> 1, "Sex: Female" -> 0
    try:
        val = x.split(":", 1)[1].strip().lower()
    except IndexError:
        return None
    if "male" in val:
        return 1
    elif "female" in val:
        return 0
    else:
        return None

# Step 3. Initial filtering and 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
)

# Step 4. Clinical feature extraction (only if trait_row is not None)
if trait_row is not None:
    # 'clinical_data' is assumed to be the DataFrame previously obtained for sample characteristics
    selected_clinical_df = geo_select_clinical_features(
        clinical_df=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 result
    print("Preview of clinical features:")
    print(preview_df(selected_clinical_df, n=5))

    # Save to CSV
    selected_clinical_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])
# The observed gene identifiers (ILMN_####) are Illumina microarray probe IDs.
# They are not standard human gene symbols and require mapping to official gene symbols.
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 the annotation columns for mapping
#    - The gene expression data uses 'ILMN_####' as identifiers, which match the 'ID' column in the annotation.
#    - The gene symbols are in the 'Symbol' column.

# 2. Extract the gene mapping dataframe using the library function
gene_mapping_df = get_gene_mapping(annotation=gene_annotation, prob_col='ID', gene_col='Symbol')

# 3. Convert probe-level measurements to gene expression data
gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=gene_mapping_df)

# Print shape of the new gene_data to confirm processing
print("Gene data shape after mapping:", gene_data.shape)
# 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. 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, removing or imputing as instructed
linked_data = handle_missing_values(linked_data, trait)

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

# 5. 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,   # We do have a trait column
    is_biased=trait_biased,
    df=linked_data,
    note="Cohort data successfully processed with trait-based analysis."
)

# 6. 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.")