# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Asthma"
cohort = "GSE185658"

# Input paths
in_trait_dir = "../DATA/GEO/Asthma"
in_cohort_dir = "../DATA/GEO/Asthma/GSE185658"

# Output paths
out_data_file = "./output/preprocess/1/Asthma/GSE185658.csv"
out_gene_data_file = "./output/preprocess/1/Asthma/gene_data/GSE185658.csv"
out_clinical_data_file = "./output/preprocess/1/Asthma/clinical_data/GSE185658.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)
# 1) Gene Expression Data Availability
is_gene_available = True  # The background indicates Affymetrix microarrays for global gene expression

# 2) Variable Availability and Data Type Conversion
# Based on the sample characteristics dictionary, we only see a "group" field (row=1) that includes asthma vs healthy.
trait_row = 1
age_row = None
gender_row = None

# Define the conversion function for the trait (binary: 1 for Asthma, 0 for Healthy, None otherwise).
def convert_trait(value):
    parts = value.split(':')
    label = parts[-1].strip().lower()  # Take text after ':'
    if 'asthma' in label:
        return 1
    elif 'healthy' in label:
        return 0
    return None

# We do not have age or gender data, so these conversion functions are not used.
convert_age = None
convert_gender = None

# 3) Save Metadata (initial filtering)
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) 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,  # previously obtained DataFrame of sample characteristics
        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_dict = preview_df(selected_clinical_df)
    print("Preview of selected clinical features:", preview_dict)

    # Save the extracted clinical features to CSV
    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
# 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])
# Based on the numeric format (e.g., '7892501'), these are likely not standard human gene symbols.
# Therefore, we conclude that 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. Decide which columns in the gene_annotation dataframe correspond to the probe ID and the gene symbol text.
#    From the preview, "ID" appears to match the probe identifier (same as gene_data index),
#    and "gene_assignment" appears to contain the gene symbols (though in a messy string).

# 2. Build a mapping dataframe using these two columns.
mapping_df = get_gene_mapping(annotation=gene_annotation, prob_col="ID", gene_col="gene_assignment")

# 3. Convert the probe-level measurements to gene expression data using the mapping, 
#    distributing expression when a probe maps to multiple genes and summing the contributions for each gene.
gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
# STEP 7: Data Normalization and Linking

# 1) Normalize gene symbols
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) Link clinical and genetic data
#    We know from previous steps that we do have trait data in out_clinical_data_file.
clinical_df = pd.read_csv(out_clinical_data_file, header=0)
# The clinical CSV contains a single row with the trait values and columns as sample IDs.
# Label that row with the trait name, so that geo_link_clinical_genetic_data can handle it properly.
clinical_df.index = [trait]

linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)

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

# 4) Determine bias
trait_biased, linked_data = judge_and_remove_biased_features(df=linked_data, trait=trait)

# 5) Final dataset 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=linked_data,
    note="Completed data preprocessing and quality checks."
)

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