# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Asthma"
cohort = "GSE270312"

# Input paths
in_trait_dir = "../DATA/GEO/Asthma"
in_cohort_dir = "../DATA/GEO/Asthma/GSE270312"

# Output paths
out_data_file = "./output/z1/preprocess/Asthma/GSE270312.csv"
out_gene_data_file = "./output/z1/preprocess/Asthma/gene_data/GSE270312.csv"
out_clinical_data_file = "./output/z1/preprocess/Asthma/clinical_data/GSE270312.csv"
json_path = "./output/z1/preprocess/Asthma/cohort_info.json"


# Step 1: Initial Data Loading
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 2: Dataset Analysis and Clinical Feature Extraction
# Step 1: Determine data availability
is_gene_available = True  # Nanostring RNA transcriptome profiles indicate gene expression data is available.

# Step 2: Identify rows for variables based on the Sample Characteristics Dictionary
trait_row = 3     # 'asthma status: Yes/No'
age_row = None    # Age not available in the provided dictionary
gender_row = 2    # 'gender: Male/Female'

# Step 2.2: Define conversion functions
def _extract_after_colon(x):
    if x is None:
        return None
    try:
        # Handle values like 'asthma status: Yes'
        parts = str(x).split(":")
        val = parts[-1].strip() if len(parts) > 1 else str(x).strip()
        return val if val not in {"", "NA", "NaN", "None", "nan", "N/A", "Unknown"} else None
    except Exception:
        return None

def convert_trait(x):
    v = _extract_after_colon(x)
    if v is None:
        return None
    v_low = v.strip().lower()
    if v_low in {"yes", "y", "asthma", "asthmatic", "case", "1"}:
        return 1
    if v_low in {"no", "n", "non-asthma", "control", "0"}:
        return 0
    return None

def convert_gender(x):
    v = _extract_after_colon(x)
    if v is None:
        return None
    v_low = v.strip().lower()
    # Female -> 0, Male -> 1
    if v_low in {"female", "f", "woman", "girl"}:
        return 0
    if v_low in {"male", "m", "man", "boy"}:
        return 1
    return None

convert_age = None  # Not used since age_row is 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 data 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
    )
    clinical_preview = preview_df(selected_clinical_df)
    print(clinical_preview)
    # Save clinical data
    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    selected_clinical_df.to_csv(out_clinical_data_file)

# Step 3: Gene Data Extraction
# 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])

# Step 4: Gene Identifier Review
# The provided identifiers (e.g., ABCF1, ACE, ACKR2) are standard human gene symbols (HGNC-approved).
requires_gene_mapping = False
print(f"requires_gene_mapping = {requires_gene_mapping}")

# Step 5: Data Normalization and Linking
import os
import pandas as pd

# 1. Normalize gene symbols and save
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
try:
    clinical_df_to_link = selected_clinical_df  # from Step 2, if still in memory
except NameError:
    # Fallback: load from disk
    clinical_df_to_link = pd.read_csv(out_clinical_data_file, index_col=0)

linked_data = geo_link_clinical_genetic_data(clinical_df_to_link, normalized_gene_data)

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

# 4. Determine bias and remove biased demographic features
is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save cohort info
is_gene_available_flag = (normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0)
is_trait_available_flag = (trait in clinical_df_to_link.index) or (trait in linked_data.columns)
note = "INFO: HGNC gene symbols provided by matrix; age not available in sample characteristics."

is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available_flag,
    is_trait_available=is_trait_available_flag,
    is_biased=is_trait_biased,
    df=unbiased_linked_data,
    note=note
)

# 6. Save linked data if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    unbiased_linked_data.to_csv(out_data_file)