# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Asthma"
cohort = "GSE182798"

# Input paths
in_trait_dir = "../DATA/GEO/Asthma"
in_cohort_dir = "../DATA/GEO/Asthma/GSE182798"

# Output paths
out_data_file = "./output/preprocess/1/Asthma/GSE182798.csv"
out_gene_data_file = "./output/preprocess/1/Asthma/gene_data/GSE182798.csv"
out_clinical_data_file = "./output/preprocess/1/Asthma/clinical_data/GSE182798.csv"
json_path = "./output/preprocess/1/Asthma/cohort_info.json"

# STEP 1

# 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("\nSample Characteristics Dictionary:")
print(sample_characteristics_dict)
def convert_trait(x):
    if not isinstance(x, str):
        return None
    # Split only once, to ensure we keep the part after the colon.
    parts = x.split(':', 1)
    if len(parts) < 2:
        return None
    val = parts[1].strip().lower()
    # Convert to a binary indicator: 1 if adult-onset asthma, else 0
    # (other categories like IEI or healthy => 0)
    if 'adult-onset asthma' in val:
        return 1
    else:
        return 0

def convert_age(x):
    if not isinstance(x, str):
        return None
    parts = x.split(':', 1)
    if len(parts) < 2:
        return None
    try:
        return float(parts[1].strip())
    except ValueError:
        return None

def convert_gender(x):
    if not isinstance(x, str):
        return None
    parts = x.split(':', 1)
    if len(parts) < 2:
        return None
    val = parts[1].strip().lower()
    if val in ['female', 'f']:
        return 0
    elif val in ['male', 'm']:
        return 1
    return None

# 1. Check gene expression data availability
is_gene_available = True  # Based on the transcriptomic profiling background

# 2.1 Identify row indices for trait, age, and gender
trait_row = 0   # "diagnosis: adult-onset asthma", etc. => available
age_row = 2     # "age: 33.42", "age: 46.08", ... => available
# Row 1 (gender) has only one unique value => treat it as not available
gender_row = None

# 3. Metadata: initial filtering
#   trait_row != None => trait is available
is_trait_available = (trait_row is not None)
is_usable = 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. If trait is available, extract clinical features
if trait_row is not None:
    selected_clinical_df = geo_select_clinical_features(
        clinical_data,
        trait=trait,
        trait_row=trait_row,
        convert_trait=convert_trait,
        age_row=age_row,
        convert_age=convert_age,
        gender_row=gender_row,   # None
        convert_gender=convert_gender
    )
    preview_result = preview_df(selected_clinical_df)
    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
    print(preview_result)
# 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])
# These IDs (e.g., 'A_19_P00315452') appear to be array probe identifiers rather than standard gene symbols.
# Therefore, gene mapping is required.
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) Identify the appropriate columns in the gene annotation
#    - The probe ID column in the annotation that matches the expression data index is "ID"
#    - The gene symbol column is "GENE_SYMBOL"

# 2) Get a dataframe mapping probe IDs to gene symbols
mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="GENE_SYMBOL")

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

# (Optional) Print the shape or a small preview of the resulting gene_data
print("Gene-level expression data shape:", gene_data.shape)
print("Gene-level expression data (head):")
print(gene_data.head())
# STEP 7: Data Normalization and Linking

# 1) Normalize gene symbols in the obtained gene expression data;
#    remove unrecognized symbols and average duplicates.
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)
print(f"Saved normalized gene data to {out_gene_data_file}")

# 2) Read previously saved clinical data. Because we saved it in Step 2 with index=False and each row representing
#    a feature (trait or age), we need to transpose it so that the samples become rows and features become columns.
clinical_df = pd.read_csv(out_clinical_data_file, header=0)
clinical_df = clinical_df.T
# Rename the columns so they match the variables we want
clinical_df.columns = [trait, "Age"]

# 3) Link clinical with genetic data
linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)

# 4) Handle missing values in the linked data:
#    remove samples with missing trait, remove genes with >20% missing,
#    remove samples with >5% missing genes, then impute for the rest.
linked_data = handle_missing_values(linked_data, trait)

# 5) Check for severe bias in the trait and remove biased demographic features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 6) Conduct final quality validation and save metadata
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=linked_data,
    note="Processed with trait and gene data successfully."
)

# 7) If the dataset is usable, save the final linked data to CSV
if is_usable:
    linked_data.to_csv(out_data_file)
    print(f"Saved final linked data to {out_data_file}")
else:
    print("Data not usable. No final linked file was saved.")