# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Autoinflammatory_Disorders"
cohort = "GSE43553"

# Input paths
in_trait_dir = "../DATA/GEO/Autoinflammatory_Disorders"
in_cohort_dir = "../DATA/GEO/Autoinflammatory_Disorders/GSE43553"

# Output paths
out_data_file = "./output/z1/preprocess/Autoinflammatory_Disorders/GSE43553.csv"
out_gene_data_file = "./output/z1/preprocess/Autoinflammatory_Disorders/gene_data/GSE43553.csv"
out_clinical_data_file = "./output/z1/preprocess/Autoinflammatory_Disorders/clinical_data/GSE43553.csv"
json_path = "./output/z1/preprocess/Autoinflammatory_Disorders/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 pandas as pd

# 1) Gene expression availability
is_gene_available = True  # Microarray-based gene expression profiling stated in background

# 2) Variable availability and converters
# Based on Sample Characteristics Dictionary:
# - Use key 1 ('genotype: ...') to infer Autoinflammatory_Disorders vs healthy controls
trait_row = 1
age_row = None
gender_row = None

def _after_colon(value):
    if pd.isna(value):
        return None
    s = str(value)
    parts = s.split(":", 1)
    v = parts[1] if len(parts) > 1 else parts[0]
    v = v.strip()
    return v if v else None

def convert_trait(value):
    v = _after_colon(value)
    if v is None:
        return None
    vl = v.lower()
    # Controls
    if "healthy" in vl or "control" in vl:
        return 0
    # Known autoinflammatory-related genotype descriptors
    keywords = ["mutation", "carrier", "mvk", "nlrp3", "pstpip1", "tnfrsf1a"]
    if any(k in vl for k in keywords):
        return 1
    # Default to case if genotype is not explicitly healthy/control
    return 1

def convert_age(value):
    return None

def convert_gender(value):
    return None

# 3) Initial filtering metadata save
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:
    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)
    print(preview)

    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
requires_gene_mapping = True
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 the appropriate columns for probe IDs and gene symbols, then create the mapping dataframe
# From the annotation preview, probe IDs are in 'ID' and gene symbols are in 'Gene Symbol'
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')

# 3. Apply the 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
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)

# Ensure clinical data is available (reuse in-memory or reload from disk)
try:
    selected_clinical_df
except NameError:
    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

# 2) Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

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

# 4) Bias assessment and removal of biased demographic features
is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)

# Availability flags
is_gene_available = bool((normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0))
is_trait_available = bool((trait in linked_data.columns) and (linked_data[trait].notna().sum() > 0))

# Prepare a brief note
try:
    trait_counts = linked_data[trait].value_counts(dropna=True).to_dict()
except Exception:
    trait_counts = {}
note = (
    f"INFO: Post-QC samples={len(unbiased_linked_data)}; "
    f"trait_counts={trait_counts}; "
    f"has_age={'Age' in linked_data.columns}; "
    f"has_gender={'Gender' in linked_data.columns}."
)

# 5) Final validation and save cohort info
# Ensure df has plain string column names to avoid any non-serializable types downstream
df_for_validation = unbiased_linked_data.copy()
df_for_validation.columns = [str(c) for c in list(df_for_validation.columns)]

is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=bool(is_gene_available),
    is_trait_available=bool(is_trait_available),
    is_biased=bool(is_trait_biased),
    df=df_for_validation,
    note=note
)

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