# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Cervical_Cancer"
cohort = "GSE163114"

# Input paths
in_trait_dir = "../DATA/GEO/Cervical_Cancer"
in_cohort_dir = "../DATA/GEO/Cervical_Cancer/GSE163114"

# Output paths
out_data_file = "./output/preprocess/1/Cervical_Cancer/GSE163114.csv"
out_gene_data_file = "./output/preprocess/1/Cervical_Cancer/gene_data/GSE163114.csv"
out_clinical_data_file = "./output/preprocess/1/Cervical_Cancer/clinical_data/GSE163114.csv"
json_path = "./output/preprocess/1/Cervical_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)
# Step 1: Gene Expression Data Availability
# Based on the background information ("Ki-67 promotes carcinogenesis by enabling global transcriptional programmes")
# and the use of the HeLa cell line, it is likely that this dataset contains gene expression data.
is_gene_available = True

# Step 2: Variable Availability and Data Type Conversion

# From the sample characteristics dictionary:
# {0: ['cell line: HeLa'], 1: ['lentivirus: shRNA control', 'lentivirus: shRNA Ki-67']}
# - All samples come from the HeLa cell line, which is derived from cervical cancer, but this is a constant feature (no variation).
# - There's no row providing age or gender information.
# Hence, no variable has meaningful variation. We set rows to None.

trait_row = None   # No row captures a varying cervical cancer trait
age_row = None     # No row for age
gender_row = None  # No row for gender

# Even though these functions won't be used (since trait_row, age_row, gender_row = None),
# we provide them per instructions.

def convert_trait(value: str):
    """
    Convert the trait to the chosen type. 
    Not applicable here, but defined for completeness.
    """
    if not value or ':' not in value:
        return None
    val = value.split(':', 1)[-1].strip()
    return val if val else None

def convert_age(value: str):
    """
    Convert age data to a continuous type.
    Not applicable here, but defined for completeness.
    """
    if not value or ':' not in value:
        return None
    val = value.split(':', 1)[-1].strip()
    # We do not actually have numeric values, so just return None.
    return None

def convert_gender(value: str):
    """
    Convert gender data to binary.
    Not applicable here, but defined for completeness.
    """
    if not value or ':' not in value:
        return None
    val = value.split(':', 1)[-1].strip().lower()
    if val in ['male', 'm']:
        return 1
    elif val in ['female', 'f']:
        return 0
    return None

# Step 3: Save Metadata
# If trait_row is None, trait data is considered unavailable.
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
)

# Step 4: Since trait_row is None, we skip geo_select_clinical_features.
# No clinical data extraction is performed because the trait is not available.
# 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 IDs (1,2,3,...), they do not appear to be standard human gene symbols.
# They seem like probe identifiers or some form of numeric reference that would require mapping.
print("These numeric IDs likely need mapping to standard 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. Decide which key in the gene annotation dataframe stores the gene identifiers
#    matching the gene expression data. From the preview, the 'ID' column in gene_annotation
#    corresponds to the numeric probe IDs in gene_data. For gene symbols, we use 'GENE_SYMBOL'.

# 2. Get a gene mapping dataframe
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')

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

# Print a quick check of the mapped dataframe
print("Mapped gene_data shape:", gene_data.shape)
print("Head of mapped gene_data:")
print(gene_data.head())
# STEP 7

# 1. Normalize gene symbols in the gene_data, then save to CSV.
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)

# Since trait_row was None in earlier steps, there is no actual trait data available.
# We cannot link clinical data or do trait-based QC. However, the library requires a final
# validation with a DataFrame and a Boolean for is_biased.

# Create an empty DataFrame as a placeholder, and declare is_biased=False by default.
placeholder_df = pd.DataFrame()
trait_biased = False

# 2. Perform final validation, marking trait as unavailable but providing the required arguments.
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,       # Gene data is present
    is_trait_available=False,     # Trait is not available
    is_biased=trait_biased,
    df=placeholder_df,            # Provide a placeholder DataFrame
    note="No trait data in this series. Final validation with placeholder DataFrame."
)

# 3. If the dataset were usable (it won't be without trait), we would save final linked data.
if is_usable:
    # Typically we would link data and save CSV, but trait is absent. Skipping.
    pass