# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Endometrioid_Cancer"
cohort = "GSE73551"

# Input paths
in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE73551"

# Output paths
out_data_file = "./output/preprocess/1/Endometrioid_Cancer/GSE73551.csv"
out_gene_data_file = "./output/preprocess/1/Endometrioid_Cancer/gene_data/GSE73551.csv"
out_clinical_data_file = "./output/preprocess/1/Endometrioid_Cancer/clinical_data/GSE73551.csv"
json_path = "./output/preprocess/1/Endometrioid_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
# Based on the background information, this dataset is likely gene expression data
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# From the sample characteristics, row 0 has multiple "cell type" entries
# that include "ENDOMETRIOID". We'll treat that as the trait row for Endometrioid_Cancer.
trait_row = 0

# No age or gender information is available or inferred from the sample characteristics
age_row = None
gender_row = None

# For the trait, we convert "ENDOMETRIOID" to 1 and all other cell types to 0.
def convert_trait(value: str):
    parts = value.split(':', 1)
    val = parts[1].strip().lower() if len(parts) > 1 else parts[0].strip().lower()
    return 1 if val == "endometrioid" else 0

# Since age and gender data are not available, these functions will return None.
def convert_age(value: str):
    return None

def convert_gender(value: str):
    return None

# 3. Save Metadata (Initial filtering)
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
)

# 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,
        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 selected clinical features
    preview = preview_df(selected_clinical_df)
    print("Preview of selected clinical features:", preview)

    # Save 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 listed identifiers (1,2,3, etc.), they are not standard human gene symbols.
# These identifiers likely need to be mapped to recognized gene symbols.
print("requires_gene_mapping = True")
# STEP5
import pandas as pd
import io

# 1. Extract the lines that do NOT start with '^', '!', or '#', but do NOT parse them into a DataFrame yet.
annotation_text, _ = filter_content_by_prefix(
    source=soft_file,
    prefixes_a=['^', '!', '#'],
    unselect=True,
    source_type='file',
    return_df_a=False,
    return_df_b=False
)

# 2. Manually parse the filtered text into a DataFrame, specifying engine="python" to avoid buffer overflow issues.
gene_annotation = pd.read_csv(
    io.StringIO(annotation_text),
    delimiter='\t',
    on_bad_lines='skip',
    engine='python'
)

print("Gene annotation preview:")
print(preview_df(gene_annotation))
# STEP: Gene Identifier Mapping

# 1 & 2. Identify the columns in the gene_annotation that correspond to the gene expression row IDs ("ID")
#    and the human gene symbols ("GeneSymbol"). Extract them into a mapping dataframe.
mapping_df = get_gene_mapping(
    annotation=gene_annotation,
    prob_col='ID',         # Matches the row IDs in our gene_data
    gene_col='GeneSymbol'  # Stores the human gene symbols
)

# 3. Convert probe-level measurements to gene-level expression data using apply_gene_mapping
gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)

# Just display the first few rows for observation
print(gene_data.head(5))
import pandas as pd

# STEP7

# 1) Normalize gene symbols and save
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)

# Read back the clinical dataframe saved in Step 2. 
# According to Step 2, we saved 1 row (the trait) × N columns (the samples) without the row index.
selected_clinical_df = pd.read_csv(out_clinical_data_file)  # shape: (1, number_of_samples)
# Rename the row index to the trait (e.g., "Eczema")
selected_clinical_df.index = [trait]

# 2) Link the clinical and gene expression data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

# 3) Handle missing values using the trait column
final_data = handle_missing_values(linked_data, trait_col=trait)

# 4) Evaluate bias in the trait (and remove biased demographic features if they existed)
trait_biased, final_data = judge_and_remove_biased_features(final_data, trait)

# 5) Final validation. Since we do have trait data, set is_trait_available=True
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=final_data,
    note="Trait data successfully extracted from Step 2."
)

# 6) If the dataset is deemed usable, save final linked data
if is_usable:
    final_data.to_csv(out_data_file)