# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Bladder_Cancer"
cohort = "GSE138118"

# Input paths
in_trait_dir = "../DATA/GEO/Bladder_Cancer"
in_cohort_dir = "../DATA/GEO/Bladder_Cancer/GSE138118"

# Output paths
out_data_file = "./output/preprocess/1/Bladder_Cancer/GSE138118.csv"
out_gene_data_file = "./output/preprocess/1/Bladder_Cancer/gene_data/GSE138118.csv"
out_clinical_data_file = "./output/preprocess/1/Bladder_Cancer/clinical_data/GSE138118.csv"
json_path = "./output/preprocess/1/Bladder_Cancer/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  # Based on the background "Expression profile...", it is likely gene-expression data

# 2. Variable Availability and Data Type Conversion

# Determine row indices
trait_row = 0     # Maps to "stage at sample..." which indicates healthy vs various tumor stages
age_row = 2       # This row consistently has age information (mostly "age: XX")
gender_row = None # No gender info found

# Define conversion functions
def convert_trait(value: Any) -> Optional[int]:
    """
    Convert the 'stage at sample' info into a binary variable:
    0 => Healthy/Neg
    1 => any 'G' stage
    None => unknown or missing
    """
    if not isinstance(value, str):
        return None

    parts = value.split(':', maxsplit=1)
    if len(parts) == 2:
        val_str = parts[1].strip().lower()
    else:
        val_str = value.strip().lower()

    if 'healthy' in val_str or 'neg' in val_str:
        return 0
    elif 'g' in val_str:
        return 1
    else:
        return None

def convert_age(value: Any) -> Optional[float]:
    """
    Convert strings of the form 'age: XX' to float. 
    If it doesn't match, returns None.
    """
    if not isinstance(value, str):
        return None

    parts = value.split(':', maxsplit=1)
    if len(parts) == 2 and 'age' in parts[0].lower():
        try:
            return float(parts[1].strip())
        except ValueError:
            return None
    return None

def convert_gender(value: Any) -> Optional[int]:
    """
    No gender data available, so always return None.
    """
    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_row is not None)
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 the extracted clinical features
    preview_data = preview_df(selected_clinical_df)
    print("Preview of clinical features:", preview_data)

    # Save clinical data as 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])
# Observing the gene identifiers, they appear to be numeric probe IDs rather than standard human gene symbols.
# Hence, they require mapping to known gene symbols.

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 correspond to probe IDs and gene symbols
#    From the preview, it appears 'ID' matches the probe IDs in the expression data (gene_data.index),
#    and 'gene_assignment' contains gene symbols in a messy string.

# 2. Get the gene mapping dataframe by extracting these two columns
mapping_df = get_gene_mapping(annotation=gene_annotation, prob_col='ID', gene_col='gene_assignment')

# 3. Apply the gene mapping to convert from probe-level to gene-level data
gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
# STEP 7

import pandas as pd

# 1) Normalize gene symbols with synonym information
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
#    After saving the clinical DataFrame with index=False, the row names (e.g., "Bladder_Cancer") might contain
#    trailing spaces or mismatches upon re-loading. Strip them before merging.
clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
clinical_df.index = clinical_df.index.str.strip()

linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)

# 3) Handle missing values (remove samples lacking trait, remove genes or samples with excess missingness, impute)
linked_data = handle_missing_values(linked_data, trait_col=trait)

# 4) Check if the trait (and optional covariates) is severely biased
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait=trait)

# 5) Final validation and metadata saving
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_biased,
    df=linked_data,
    note="Data processed successfully."
)

# 6) If the linked data is deemed usable, save it for downstream analysis
if is_usable:
    linked_data.to_csv(out_data_file, index=False)
# STEP 8

import pandas as pd

# 1) Normalize the gene symbols with synonym information
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)

# 2) Link clinical and genetic data
#    Load the clinical data so that rows = ["Bladder_Cancer","Age","Gender",...], columns = sample IDs.
clinical_df = pd.read_csv(out_clinical_data_file, header=0, index_col=0)
clinical_df.index = clinical_df.index.map(str).str.strip()

linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)

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

# 4) Evaluate bias in the trait and remove any biased covariates
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait=trait)

# 5) Final validation and metadata saving
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_biased,
    df=linked_data,
    note="Data processed successfully."
)

# 6) Save the final linked data if it is usable
if is_usable:
    linked_data.to_csv(out_data_file, index=False)