# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Bipolar_disorder"
cohort = "GSE46449"

# Input paths
in_trait_dir = "../DATA/GEO/Bipolar_disorder"
in_cohort_dir = "../DATA/GEO/Bipolar_disorder/GSE46449"

# Output paths
out_data_file = "./output/z1/preprocess/Bipolar_disorder/GSE46449.csv"
out_gene_data_file = "./output/z1/preprocess/Bipolar_disorder/gene_data/GSE46449.csv"
out_clinical_data_file = "./output/z1/preprocess/Bipolar_disorder/clinical_data/GSE46449.csv"
json_path = "./output/z1/preprocess/Bipolar_disorder/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
import os
import re

# 1) Gene expression availability
is_gene_available = True  # Affymetrix microarray gene expression (not miRNA/methylation)

# 2) Variable availability (rows) inferred from sample characteristics
trait_row = 1        # 'genotype: bipolar patient' / 'genotype: control subject'
age_row = 2          # 'age: <number>'
gender_row = None    # Only 'gender: male' observed -> constant, not useful

# 2) Conversion functions
def _after_colon(x):
    if x is None:
        return None
    if not isinstance(x, str):
        return None
    parts = x.split(":", 1)
    val = parts[1] if len(parts) > 1 else parts[0]
    return val.strip()

def convert_trait(x):
    v = _after_colon(x)
    if v is None:
        return None
    vl = v.lower()
    if any(k in vl for k in ['control', 'healthy', 'normal']):
        return 0
    if ('bipolar' in vl) or ('bpd' in vl) or ('bp ' in vl) or (vl == 'bp') or ('patient' in vl) or ('case' in vl):
        return 1
    return None

def convert_age(x):
    v = _after_colon(x)
    if v is None:
        return None
    nums = re.findall(r'[-+]?\d*\.?\d+', v)
    if not nums:
        return None
    try:
        age_val = float(nums[0])
        if 0 <= age_val <= 120:
            return age_val
    except Exception:
        pass
    return None

def convert_gender(x):
    v = _after_colon(x)
    if v is None:
        return None
    vl = v.lower()
    if 'female' in vl or vl == 'f':
        return 0
    if 'male' in vl or vl == 'm':
        return 1
    return None

# 3) Save metadata (initial filtering)
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) Clinical feature extraction (only if trait is available)
if trait_row is not None:
    clinical_selected_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(clinical_selected_df)
    print(preview)

    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    clinical_selected_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
print("requires_gene_mapping = True")

# Step 5: Gene Annotation
# 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 6: Gene Identifier Mapping
# 1-2. Identify columns for probe IDs and gene symbols, then build mapping dataframe
probe_col = 'ID'  # Matches probe IDs in the expression data (e.g., '1007_s_at')
gene_symbol_col = 'Gene Symbol'  # Contains gene symbols (may include multiple symbols separated by delimiters)

mapping_df = get_gene_mapping(annotation=gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)

# 3. Apply mapping to convert probe-level data to gene-level expression
gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)

# Step 7: Data Normalization and Linking
import os
import pandas as pd

# 1. Normalize gene symbols and save gene expression data
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 the clinical and genetic data
try:
    clinical_df_link = clinical_selected_df
except NameError:
    clinical_df_link = pd.read_csv(out_clinical_data_file, index_col=0)

linked_data = geo_link_clinical_genetic_data(clinical_df_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
note = "INFO: Gender not available (constant) in source; not included."
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=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)