Project: s41467-024-50317-w | Function: pivotCoord (python)

system: You are an expert Python programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow Python best practices and maintain consistent code style.
Return **only** the complete Python function code.

user: Complete this Python function:

**DESCRIPTION:** Implements pivot coordinate transformation for compositional data analysis, converting tissue substructure proportions into pivot coordinates to address the compositional dependence problem in statistical analysis

**KEY TERMS:** {
  "x": "compositional tissue proportion data",
  "D": "number of tissue substructures/pathologies",
  "x_ilr": "isometric log-ratio transformed coordinates",
  "gm": "geometric mean function",
  "norm_func": "normalization function (orthonormal/orthogonal)"
}

**CONTEXT CODE:** import numpy as np
import pandas as pd
from math import exp
from scipy.stats import gmean
import argparse


parser = argparse.ArgumentParser()
parser.add_argument("--tissue_name", type=str, default=None)
parser.add_argument("--idps_dir", type=str, default=None)
# Only parse args if run as main script
if __name__ == "__main__":
    args = parser.parse_args()
else:
    # Mock args for import
    args = argparse.Namespace(tissue_name=None, idps_dir=None)

def orthonormal(i, D):
    return np.sqrt((D - i) / (D - i + 1))

def orthogonal(i, D):
    return 1

norm_functions = {
    "orthonormal": orthonormal,
    "orthogonal": orthogonal
}

'''
    Function to compute pivot coordinates from a compositional dataset
'''
def pivotCoord(x, fast=False, method = "pivot", base = exp(1), norm = "orthonormal"):
    # MASKED: Implements pivot coordinate transformation for compositional data analysis, converting tissue substructure proportions into pivot coordinates to address the compositional dependence problem in statistical analysis
    raise NotImplementedError("Function has been masked for testing purposes")

if __name__ == "__main__":
    # Open compositional dataset
    df = pd.read_csv(f'{args.idps_dir}/{args.tissue_name}_compositional.csv', index_col=0)
    # set values < 1e-3 to 1e-3 to avoid zeros.
    df[df < 1e-3] = 1e-3
    copy = df.copy()
    out_df = df.copy()

    # iteration over df columns
    for col in df.columns.tolist():
        df = copy
        # move current column to the first position
        df.insert(0, col, df.pop(col))
        # compute pivot coordinates
        df_pivot = pivotCoord(df).set_index(df.index)
        # store pivot values in the output dataframe for the current idp
        out_df[col] = df_pivot[0]

    # check if infinite rows are present
    inf_rows = out_df.index[np.isinf(out_df).any(1)]

    # store pivot coordinates csv
    out_df.drop(inf_rows).to_csv(f'{args.idps_dir}/{args.tissue_name}_pivot.csv')


Complete the function implementation replacing "raise NotImplementedError".