# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Sickle_Cell_Anemia"
cohort = "GSE84634"

# Input paths
in_trait_dir = "../DATA/GEO/Sickle_Cell_Anemia"
in_cohort_dir = "../DATA/GEO/Sickle_Cell_Anemia/GSE84634"

# Output paths
out_data_file = "./output/preprocess/1/Sickle_Cell_Anemia/GSE84634.csv"
out_gene_data_file = "./output/preprocess/1/Sickle_Cell_Anemia/gene_data/GSE84634.csv"
out_clinical_data_file = "./output/preprocess/1/Sickle_Cell_Anemia/clinical_data/GSE84634.csv"
json_path = "./output/preprocess/1/Sickle_Cell_Anemia/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. Decide if this dataset contains gene expression data
is_gene_available = True  # Based on the background info stating "Gene expression ... were analyzed"

# 2. Determine variable availability and build conversion functions
#    From the sample characteristics {0: [...], 1: [...], 2: ['disease: sickle cell disease']},
#    we see only one unique "disease" value. Per instructions, constant features are not available.
trait_row = None
age_row = None
gender_row = None

# Define data type conversion functions even if no corresponding row is found (for consistency).
def convert_trait(value: str):
    parts = value.split(':', 1)
    if len(parts) < 2:
        return None
    v = parts[1].strip().lower()
    if "sickle cell" in v:
        return 1
    return 0

def convert_age(value: str):
    parts = value.split(':', 1)
    if len(parts) < 2:
        return None
    try:
        return float(parts[1].strip())
    except ValueError:
        return None

def convert_gender(value: str):
    parts = value.split(':', 1)
    if len(parts) < 2:
        return None
    v = parts[1].strip().lower()
    if v in ["male", "m"]:
        return 1
    elif v in ["female", "f"]:
        return 0
    return None

# Since trait is constant in this dataset, treat it as unavailable
is_trait_available = False

# 3. Conduct the initial filtering and save metadata
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. Since trait_row is None, we skip clinical feature extraction.
#    (No further action taken here.)
# 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 numeric probe-like identifiers, these are not standard gene symbols.
# Therefore, they likely need to be mapped 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 6: Gene Identifier Mapping

# 1. Identify the columns in the annotation corresponding to the probe identifiers (ID), 
#    using "mrna_assignment" for the gene symbols as decided.
mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="mrna_assignment")

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

# (Optional debugging output)
print("Gene expression data mapped to symbols. Data shape:", gene_data.shape)
print("First 10 gene symbols:", gene_data.index[:10].tolist())
# STEP7

import pandas as pd

# 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. Since no trait data is available (trait_row=None), we cannot link it to clinical data nor perform a proper final analysis.
#    We'll finalize the metadata indicating that the dataset is not usable for trait-based analysis.
empty_df = pd.DataFrame()  # Empty placeholder since we must pass a non-None df for final validation
is_trait_available = False
is_biased = True  # Arbitrarily set True to signal that it's not suitable for final use

# 3. Perform final validation with an empty df; this will mark the dataset as not usable.
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=is_trait_available,
    is_biased=is_biased,
    df=empty_df,
    note="No trait data provided in this cohort"
)

# 4. We do not save any final linked data because it is not usable.
if is_usable:
    pass