# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Schizophrenia"
cohort = "GSE120340"

# Input paths
in_trait_dir = "../DATA/GEO/Schizophrenia"
in_cohort_dir = "../DATA/GEO/Schizophrenia/GSE120340"

# Output paths
out_data_file = "./output/preprocess/1/Schizophrenia/GSE120340.csv"
out_gene_data_file = "./output/preprocess/1/Schizophrenia/gene_data/GSE120340.csv"
out_clinical_data_file = "./output/preprocess/1/Schizophrenia/clinical_data/GSE120340.csv"
json_path = "./output/preprocess/1/Schizophrenia/cohort_info.json"

# STEP1
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)
# 1. Gene Expression Data Availability
is_gene_available = True  # Affymetrix whole-genome expression microarray indicates gene expression data

# 2.1 Variable Availability
trait_row = 0     # Row with multiple disease states, including SCZ
age_row = None    # No age data in the dictionary
gender_row = None # No gender data in the dictionary

# 2.2 Data Type Conversion
def convert_trait(value: str):
    parts = value.split(':', 1)
    if len(parts) == 2:
        v = parts[1].strip().lower()
        # Mark SCZ as 1, others as 0
        return 1 if v == 'scz' else 0
    return None

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

def convert_gender(value: str):
    # No gender data is available
    return 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_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
    )
    # Observe output
    print(preview_df(selected_clinical_df))
    # Save extracted clinical features
    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 observed identifiers (e.g., 10002_at), these are Affymetrix probe set IDs, not standard human gene symbols.
# Therefore, a mapping step to human gene symbols is needed.
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 columns in gene_annotation that match the probe IDs and the gene symbols.
#    From the preview, "ID" matches the probe ID used in gene_data, and "Description" appears
#    to contain the gene's name/symbol information (albeit in a descriptive form).

mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Description')

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

# For confirmation, let's see the shape of the resulting gene_data
print("Mapped gene_data shape:", gene_data.shape)
# STEP7
# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)

# 2. Link the clinical and genetic data with the 'geo_link_clinical_genetic_data' function from the library.
#    Fixing the variable name from 'selected_clinical_data' to 'selected_clinical_df'
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

# 3. Handle missing values in the linked data
linked_data = handle_missing_values(linked_data, trait)

# 4. Determine whether the trait and some demographic features are severely biased, and remove biased features.
is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Conduct quality check and save the cohort information.
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=linked_data
)

# 6. If the linked data is usable, save it as a CSV file to 'out_data_file'.
if is_usable:
    unbiased_linked_data.to_csv(out_data_file)