# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Endometriosis"
cohort = "GSE51981"

# Input paths
in_trait_dir = "../DATA/GEO/Endometriosis"
in_cohort_dir = "../DATA/GEO/Endometriosis/GSE51981"

# Output paths
out_data_file = "./output/preprocess/1/Endometriosis/GSE51981.csv"
out_gene_data_file = "./output/preprocess/1/Endometriosis/gene_data/GSE51981.csv"
out_clinical_data_file = "./output/preprocess/1/Endometriosis/clinical_data/GSE51981.csv"
json_path = "./output/preprocess/1/Endometriosis/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)
# Step 1. Decide if gene expression data is available
# From the series summary, the samples were analyzed using whole genome microarrays (gene expression).
is_gene_available = True

# Step 2. Identify data availability and define conversion functions

# 2.1 Availability:
#   - trait (Endometriosis) corresponds to key=1, which has "Endometriosis" and "Non-Endometriosis"
#   - No clear key for 'age' or 'gender' was found. 
trait_row = 1
age_row = None
gender_row = None

# 2.2 Data Type Conversion
def convert_trait(value: str):
    """
    Convert endometriosis trait to binary:
    Endometriosis => 1
    Non-Endometriosis => 0
    """
    val = value.split(':')[-1].strip().lower()
    if "endometriosis" in val and "non" not in val:
        return 1
    elif "non-endometriosis" in val:
        return 0
    return None

# Since 'age' and 'gender' are not available, we won't use conversion,
# but we define placeholders in case needed by function signature.
convert_age = None
convert_gender = None

# Step 3. Conduct 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. Extract clinical features if trait_row is available
if trait_row is not None:
    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 = preview_df(selected_clinical_df)
    # Save the extracted clinical data
    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])
print("These identifiers are Affymetrix probe set IDs, not standard human 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. Decide which columns store the same identifiers as in the gene expression data and which store the gene symbols.
#    The "ID" column in the annotation matches the row index in our gene_data (e.g., "1007_s_at", "1053_at", etc.).
#    The "Gene Symbol" column contains the official gene symbols or multiple symbols separated by " /// ".

probe_col = "ID"
gene_symbol_col = "Gene Symbol"

# 2. Get the gene mapping dataframe
mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)

# 3. Convert probe-level measurements to gene-level expression data
gene_data = apply_gene_mapping(gene_data, mapping_df)
# STEP 7

import pandas as pd

# 1. Normalize the gene expression data to standard gene symbols.
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)
print("Normalized gene expression data saved to:", out_gene_data_file)

# 2. Read back the clinical data, reassign its single row index to the trait name, and link with genetic data.
selected_clinical_df = pd.read_csv(out_clinical_data_file, header=0)
selected_clinical_df.index = [trait]  # Ensure the clinical row is labeled by the trait
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

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

# 4. Determine whether the trait or demographic features are biased; remove biased demographic features.
trait_biased, df = judge_and_remove_biased_features(df, trait)

# 5. Perform final validation with full dataset information.
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=df,
    note="Final step with linking, missing-value handling, bias checks."
)

# 6. If the data is usable, save the final linked data.
if is_usable:
    df.to_csv(out_data_file)
    print(f"Final linked data saved to: {out_data_file}")
else:
    print("Dataset is not usable or severely biased. No final data saved.")