# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Endometrioid_Cancer"
cohort = "GSE73614"

# Input paths
in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE73614"

# Output paths
out_data_file = "./output/preprocess/1/Endometrioid_Cancer/GSE73614.csv"
out_gene_data_file = "./output/preprocess/1/Endometrioid_Cancer/gene_data/GSE73614.csv"
out_clinical_data_file = "./output/preprocess/1/Endometrioid_Cancer/clinical_data/GSE73614.csv"
json_path = "./output/preprocess/1/Endometrioid_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 if the dataset contains gene expression data
is_gene_available = True  # Based on background info describing transcriptional profiling

# 2. Identify the availability of trait, age, and gender
#    From the sample characteristics dictionary, we only see {0: ['tissue: ovarian']}.
#    This has no variation (same value for all samples) and does not provide the Endometrioid_Cancer distinction.
#    Hence, there's no useful variable for trait, age, or gender.
trait_row = None
age_row = None
gender_row = None

# 2.2. Define type-conversion functions.
def convert_trait(value: str) -> Optional[int]:
    """
    Binary conversion for 'Endometrioid_Cancer':
    Return 1 if the value indicates endometrioid, 0 if indicates something else,
    None if unknown.
    """
    parts = value.split(':', 1)
    val = parts[-1].strip().lower() if len(parts) > 1 else value.strip().lower()
    if 'endometrioid' in val:
        return 1
    elif 'serous' in val or 'clear' in val or 'ovarian' in val:
        return 0
    return None

def convert_age(value: str) -> Optional[float]:
    """
    Continuous conversion for age:
    Try to parse a number from the string, return None if parsing fails.
    """
    parts = value.split(':', 1)
    val_str = parts[-1].strip() if len(parts) > 1 else value.strip()
    try:
        return float(val_str)
    except ValueError:
        return None

def convert_gender(value: str) -> Optional[int]:
    """
    Binary conversion for gender:
    Return 0 for female, 1 for male, None if unknown.
    """
    parts = value.split(':', 1)
    val = parts[-1].strip().lower() if len(parts) > 1 else value.strip().lower()
    if val in ['female', 'f']:
        return 0
    elif val in ['male', 'm']:
        return 1
    return None

# 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
)

# 4. Since trait_row is None, we skip the clinical feature extraction step
# 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])
requires_gene_mapping = True
# STEP5
import pandas as pd
import io

# 1. Extract the lines that do NOT start with '^', '!', or '#', but do NOT parse them into a DataFrame yet.
annotation_text, _ = filter_content_by_prefix(
    source=soft_file,
    prefixes_a=['^', '!', '#'],
    unselect=True,
    source_type='file',
    return_df_a=False,
    return_df_b=False
)

# 2. Manually parse the filtered text into a DataFrame, specifying engine="python" to avoid buffer overflow issues.
gene_annotation = pd.read_csv(
    io.StringIO(annotation_text),
    delimiter='\t',
    on_bad_lines='skip',
    engine='python'
)

print("Gene annotation preview:")
print(preview_df(gene_annotation))
# STEP: Gene Identifier Mapping

# 1. Determine which columns match the gene expression dataset and the gene symbols
probe_col = "ID"
symbol_col = "GENE_SYMBOL"

# 2. Extract the gene identifier and gene symbol columns to form a mapping dataframe
mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=symbol_col)

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

print("Gene mapping completed. Final gene_data shape:", gene_data.shape)
import os
import pandas as pd

# STEP7

# 1) Normalize gene symbols in our gene_data and save the result
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)

# 2) Check if the clinical data file (trait data) exists
if not os.path.exists(out_clinical_data_file):
    print(f"File not found: {out_clinical_data_file}. No trait data is available for this cohort.")
    # Perform an initial/partial validation because trait data is missing
    is_usable = validate_and_save_cohort_info(
        is_final=False,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=True,
        is_trait_available=False
    )
else:
    # 3) Trait data is present, so read it and link with gene data
    selected_clinical_df = pd.read_csv(out_clinical_data_file)
    # Rename the single row index to match the trait name
    selected_clinical_df.index = [trait]

    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

    # 4) Handle missing values, using the trait column name
    final_data = handle_missing_values(linked_data, trait_col=trait)

    # 5) Check whether the trait (and optional demographics) are severely biased
    trait_biased, final_data = judge_and_remove_biased_features(final_data, trait)

    # 6) Conduct final validation
    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=final_data,
        note="Trait data successfully extracted and processed."
    )

    # 7) If the dataset is deemed usable, save the final linked data
    if is_usable:
        final_data.to_csv(out_data_file)