# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Sickle_Cell_Anemia"
cohort = "GSE46471"

# Input paths
in_trait_dir = "../DATA/GEO/Sickle_Cell_Anemia"
in_cohort_dir = "../DATA/GEO/Sickle_Cell_Anemia/GSE46471"

# Output paths
out_data_file = "./output/preprocess/1/Sickle_Cell_Anemia/GSE46471.csv"
out_gene_data_file = "./output/preprocess/1/Sickle_Cell_Anemia/gene_data/GSE46471.csv"
out_clinical_data_file = "./output/preprocess/1/Sickle_Cell_Anemia/clinical_data/GSE46471.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)
import pandas as pd
import os
import json

# 1. Determine if gene expression data is available
is_gene_available = True  # This appears to be a microarray-based gene expression dataset

# 2. Determine data availability
# Based on the sample characteristics, no keys provide trait/age/gender info.
trait_row = None
age_row = None
gender_row = None

# 2.2 Define dummy conversion functions
def convert_trait(x: str):
    # No trait data is actually available; return None
    return None

def convert_age(x: str):
    # No age data is actually available; return None
    return None

def convert_gender(x: str):
    # No gender data is actually available; return None
    return None

# 3. Save metadata (initial filtering)
# Trait data availability is determined by 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 is skipped because trait_row is None
# 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 format of the identifiers (e.g., '1', '2', '3', etc.),
# these are not standard human gene symbols and thus require mapping.

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 which columns in the gene annotation correspond to the expression data ID and the gene symbols.
#    From our observations: "ID" in the annotation matches the probe IDs in gene_data, 
#    and "GENE_SYMBOL" stores the gene symbols (even though initial rows are NAs, real mapping info might appear further down).

# 2. Generate the gene mapping dataframe from the annotation.
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')

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

# (Optional) Quickly show the shape of the resulting gene_data for reference.
print("Mapped gene expression data shape:", gene_data.shape)
# STEP7

# 1. Normalize the obtained gene data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)

# Since the previous steps indicated 'trait_row = None', there is no clinical data
# for linking or analysis. We therefore skip linking, missing-value handling, and bias checks.
# Instead, finalize the dataset availability information to mark the dataset as not usable.

import pandas as pd

# Create a dummy DataFrame for final validation
dummy_df = pd.DataFrame()

# 5. Final quality validation
# Mark is_trait_available=False. For is_biased, we can set True to fully exclude it from usage.
# The library function requires both 'df' and 'is_biased' when is_final=True.
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False,
    is_biased=True,  # Marks dataset as not usable
    df=dummy_df,
    note="No trait data was found, so the dataset is not usable."
)

# 6. If the dataset is usable, save it (but here is_usable will be False, so no saving)
if is_usable:
    # If trait were available and everything passed, we'd link & save the final CSV here.
    pass