# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Allergies"
cohort = "GSE230164"

# Input paths
in_trait_dir = "../DATA/GEO/Allergies"
in_cohort_dir = "../DATA/GEO/Allergies/GSE230164"

# Output paths
out_data_file = "./output/preprocess/1/Allergies/GSE230164.csv"
out_gene_data_file = "./output/preprocess/1/Allergies/gene_data/GSE230164.csv"
out_clinical_data_file = "./output/preprocess/1/Allergies/clinical_data/GSE230164.csv"
json_path = "./output/preprocess/1/Allergies/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 "Gene expression profiling" title

# 2. Variable Availability and Data Type Conversion

# From the sample characteristics, we only see key=0 for "gender: female" and "gender: male".
# Therefore:
trait_row = None   # "Allergies" not found
age_row = None     # Age not found
gender_row = 0     # Found under key=0

# Conversion Functions
def convert_trait(value: str):
    # Since we don't have trait data, return None if called (function is here for completeness)
    return None

def convert_age(value: str):
    # Since we don't have age data, return None if called (function is here for completeness)
    return None

def convert_gender(value: str):
    # Split at ':' and pick the last portion, then convert to 0/1
    val = value.split(':')[-1].strip().lower()
    if val == 'female':
        return 0
    elif val == 'male':
        return 1
    return None

# 3. Initial Filtering and Saving Metadata
# trait_row is None => trait data is not available
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])
# These identifiers (e.g., ILMN_1343291) are Illumina probe IDs rather than standard gene symbols.
# Therefore, they need to be mapped 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))
# STEP: Gene Identifier Mapping

# 1. Select the columns from the gene_annotation dataframe for probe ID and gene symbol.
#    From the preview, the "ID" column matches the probe identifiers and "Symbol" stores the gene symbols.
mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Symbol")

# 2. Apply the mapping to convert probe-level data into gene-level data.
gene_data = apply_gene_mapping(gene_data, mapping_df)

# (Optional) Peek at the results
print("Gene expression dataframe shape:", gene_data.shape)
print("First 10 gene symbols:", list(gene_data.index[:10]))
import pandas as pd

# STEP 7: Data Normalization and Linking

# In this dataset, the trait is unavailable (trait_row was None), so we cannot proceed with linking or final processing
# that relies on clinical trait data. Instead, we record the dataset's unavailability without performing final validation.

# We still have a gene_data DataFrame from the previous steps. Let's normalize and save it.
# Although the clinical data is not usable (no trait), we can still provide the normalized gene data CSV
# for reference purposes.

# 1. Normalize gene symbols in the obtained gene expression data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file, index=True)
print(f"Saved normalized gene data to {out_gene_data_file}")

# 2. Since trait data is unavailable, we skip linking and downstream processing.

# 3. Record that the trait is missing via validate_and_save_cohort_info with is_final=False.
#    This avoids the requirement to provide 'df' and 'is_biased' parameters.
validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,  # We do have gene expression data
    is_trait_available=False,  # No trait data
    note="Trait data not available; further steps were skipped."
)

print("Trait data was missing, so final linking and downstream steps were skipped.")