Source code for ogphl.input_output

import pandas as pd
import numpy as np
import os
from ogphl.constants import CONS_DICT, PROD_DICT


CUR_DIR = os.path.dirname(os.path.realpath(__file__))
"""
Read in Social Accounting Matrix (SAM) file
"""
# Read in SAM file
# SAM file:
sam_path = os.path.join(CUR_DIR, "data", "002_IFPRI_SAM_PHL_2018_SAM.csv")
SAM = pd.read_csv(sam_path, index_col=1, thousands=",")
# replace NaN with 0
SAM.fillna(0, inplace=True)




def get_alpha_c(sam=SAM, cons_dict=CONS_DICT):
    """
    Calibrate the alpha_c vector, showing the shares of household
    expenditures for each consumption category

    Args:
        sam (pd.DataFrame): SAM file
        cons_dict (dict): Dictionary of consumption categories

    Returns:
        alpha_c (dict): Dictionary of shares of household expenditures
    """
    hh_cols = [
        "hhd-r1",
        "hhd-r2",
        "hhd-r3",
        "hhd-r4",
        "hhd-r5",
        "hhd-u1",
        "hhd-u2",
        "hhd-u3",
        "hhd-u4",
        "hhd-u5",
    ]
    alpha_c = {}
    overall_sum = 0
    for key, value in cons_dict.items():
        # note the subtraction of the row to focus on domestic consumption
        category_total = (
            sam.loc[sam.index.isin(value), hh_cols].values.astype(float).sum()
        )
        alpha_c[key] = category_total
        overall_sum += category_total
    for key, value in cons_dict.items():
        alpha_c[key] = alpha_c[key] / overall_sum

    return alpha_c





def get_io_matrix(sam=SAM, cons_dict=CONS_DICT, prod_dict=PROD_DICT):
    """
    Calibrate the io_matrix array.  This array relates the share of each
    production category in each consumption category

    Args:
        sam (pd.DataFrame): SAM file
        cons_dict (dict): Dictionary of consumption categories
        prod_dict (dict): Dictionary of production categories

    Returns:
        io_df (pd.DataFrame): Dataframe of io_matrix
    """
    # Create initial matrix as dataframe of 0's to fill in
    io_dict = {}
    for key in prod_dict.keys():
        io_dict[key] = np.zeros(len(cons_dict.keys()))
    io_df = pd.DataFrame(io_dict, index=cons_dict.keys())
    # Fill in the matrix
    # Note, each cell in the SAM represents a payment from the columns
    # account to the row account
    # (see https://www.un.org/en/development/desa/policy/capacity/presentations/manila/6_sam_mams_philippines.pdf)
    # We are thus going to take the consumption categories from rows and
    # the production categories from columns
    for ck, cv in cons_dict.items():
        for pk, pv in prod_dict.items():
            io_df.loc[io_df.index == ck, pk] = (
                sam.loc[sam.index.isin(cv), pv].values.astype(float).sum()
            )
    # change from levels to share (where each row sums to one)
    io_df = io_df.div(io_df.sum(axis=1), axis=0)

    return io_df