# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Amyotrophic_Lateral_Sclerosis"
cohort = "GSE68607"

# Input paths
in_trait_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis"
in_cohort_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis/GSE68607"

# Output paths
out_data_file = "./output/preprocess/1/Amyotrophic_Lateral_Sclerosis/GSE68607.csv"
out_gene_data_file = "./output/preprocess/1/Amyotrophic_Lateral_Sclerosis/gene_data/GSE68607.csv"
out_clinical_data_file = "./output/preprocess/1/Amyotrophic_Lateral_Sclerosis/clinical_data/GSE68607.csv"
json_path = "./output/preprocess/1/Amyotrophic_Lateral_Sclerosis/cohort_info.json"

# STEP 1

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("\nSample Characteristics Dictionary:")
print(sample_characteristics_dict)
# Step 1: Determine gene expression data availability
is_gene_available = True  # Based on the series description, it measures gene expression, not miRNA or methylation.

# Step 2: Identify data availability and define data type conversion functions

# The trait is found at key=1 with multiple distinct values (Control, ALS).
trait_row = 1

# No age or gender data found.
age_row = None
gender_row = None

# Define conversion functions
def convert_trait(value: str):
    if value is None:
        return None
    # Extract value after the colon (if any)
    if ":" in value:
        value = value.split(":", 1)[1].strip().lower()
    # Convert to binary (0 = Control, 1 = ALS)
    if value == "control":
        return 0
    elif value == "als due to mtc9orf72" or value == "als not due to mtc9orf72":
        return 1
    return None

def convert_age(value: str):
    # No age data is available, so always return None
    return None

def convert_gender(value: str):
    # No gender data is available, so always return None
    return None

# Step 3: Conduct 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: If trait data is available, extract clinical features, preview, and save
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,
        convert_gender=convert_gender
    )
    preview = preview_df(selected_clinical_df, n=5)
    print("Preview of selected clinical features:", preview)
    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])
# These identifiers ("ENST...") are Ensembl transcript IDs and are not standard human gene symbols.
# Therefore, they require additional mapping to gene symbols.
print("\nrequires_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))
# Gene Identifier Mapping

# 1 & 2. Decide which columns in the gene_annotation correspond to the transcript identifiers
#    and which columns correspond to the gene symbols. Here, "ID" matches the ENST... IDs
#    and "ORF" holds the desired gene symbols.
mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="ORF")

# 3. Convert probe-level data to gene-level data by applying the mapping.
gene_data = apply_gene_mapping(gene_data, mapping_df)

# (Optional) Print a small portion of the new index to verify changes in identifiers.
print("New gene_data index (first 20):")
print(gene_data.index[:20])
# STEP 7: Data Normalization and Linking

# 1. Normalize gene symbols in the obtained gene expression data
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 the clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

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

# 4. Determine whether the trait (and demographic features) are severely biased
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. 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="Trait data is ALS vs. control; age and gender are not available."
)

# 6. If the dataset is usable, save the final linked data
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 for association; skipping final output.")