# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Endometriosis"
cohort = "GSE73622"

# Input paths
in_trait_dir = "../DATA/GEO/Endometriosis"
in_cohort_dir = "../DATA/GEO/Endometriosis/GSE73622"

# Output paths
out_data_file = "./output/preprocess/1/Endometriosis/GSE73622.csv"
out_gene_data_file = "./output/preprocess/1/Endometriosis/gene_data/GSE73622.csv"
out_clinical_data_file = "./output/preprocess/1/Endometriosis/clinical_data/GSE73622.csv"
json_path = "./output/preprocess/1/Endometriosis/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 series description indicating transcriptome analysis, we consider this dataset to contain gene expression data.
is_gene_available = True

# 2) Variable Availability and Data Type Conversion
# From the sample characteristics dictionary:
#  - The disease status ("Endometriosis" or "No Endometriosis") is at key 0
#  - The age information is at key 3
#  - No gender information is observed in the dictionary
trait_row = 0
age_row = 3
gender_row = None

# Define data conversion functions
def convert_trait(value: str):
    parts = value.split(":")
    if len(parts) > 1:
        val = parts[1].strip().lower()
        if val == "endometriosis":
            return 1
        elif val == "no endometriosis":
            return 0
    return None

def convert_age(value: str):
    parts = value.split(":")
    if len(parts) > 1:
        val = parts[1].strip()
        try:
            return float(val)
        except ValueError:
            return None
    return None

def convert_gender(value: str):
    # Not used here, but defined for completeness
    parts = value.split(":")
    if len(parts) > 1:
        val = parts[1].strip().lower()
        if val in ["female", "f"]:
            return 0
        elif val in ["male", "m"]:
            return 1
    return None

# 3) Save Metadata
# Determine if trait data is available (based on whether trait_row is None)
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 proceed if trait data is available
if trait_row is not None:
    selected_clinical_df = geo_select_clinical_features(
        clinical_data,
        trait,
        trait_row,
        convert_trait,
        age_row=age_row,
        convert_age=convert_age,
        gender_row=gender_row,
        convert_gender=convert_gender
    )
    preview_result = preview_df(selected_clinical_df, n=5)
    print("Preview of extracted clinical data:", preview_result)
    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, these appear to be probe IDs rather than standard human gene symbols.
# They likely require mapping to 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))
# STEP6: Gene Identifier Mapping

# 1. Use "ID" as the probe column and "gene_assignment" as the gene symbol column.
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')

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

# Just to observe the first few row indices of the newly formed gene_data for verification
print("Mapped gene_data preview:", gene_data.index[:20])
# STEP 7

import pandas as pd

# 1. Normalize the gene expression data to standard gene symbols.
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)
print("Normalized gene expression data saved to:", out_gene_data_file)

# 2. Read back the clinical data, then assign the appropriate row index to match how it was saved.
#    From the step 2 output, we know there are exactly 2 rows in the CSV:
#    Row 0 => trait (Endometriosis), Row 1 => Age
selected_clinical_df = pd.read_csv(out_clinical_data_file, header=0)
selected_clinical_df.index = [trait, "Age"]  # Convert the 2-rows into a row index
# Now columns are sample IDs, rows are [Endometriosis, Age].

# Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

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

# 4. Determine whether the trait or demographic features are biased; remove biased demographic features.
trait_biased, df = judge_and_remove_biased_features(df, trait)

# 5. Perform final validation with full dataset 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=trait_biased,
    df=df,
    note="Final step with linking, missing-value handling, bias checks."
)

# 6. If the data is usable, save the final linked data.
if is_usable:
    df.to_csv(out_data_file)
    print(f"Final linked data saved to: {out_data_file}")
else:
    print("Dataset is not usable or severely biased. No final data saved.")