Retrieving the firm and worker identifiers (Pytwoway 0.1.14.) about pytwoway HOT 1 CLOSED

import bipartitepandas as bpd
import pytwoway as tw
import pandas as pd

#### Simulate data
sim_data = bpd.SimBipartite({'nk': 50, 'num_time': 2, 'num_ind': 1000}).sim_network()
#### Manually clean data
bdf = bpd.BipartiteLong(sim_data, include_id_reference_dict=True) # Set include_id_reference_dict=True to save original ids
#### Subset of data so largest connected set is subset of all firms
bdf = bdf[bdf['i'] < 100]
bdf = bdf.clean_data()
bdf.gen_m()

#### Create TwoWay object
tw_net = tw.TwoWay(bdf.original_ids()) # bdf.original_ids() creates a dataframe with columns that give the original ids
#### Skip data cleaning step in TwoWay object, but mark data as clean
tw_net.clean = True

fe_params = {
'ncore': 1, # Number of cores to use
'batch': 1, # Batch size to send in parallel
'ndraw_pii': 50, # Number of draws to use in approximation for leverages
'levfile': '', # File to load precomputed leverages
'ndraw_tr': 5, # Number of draws to use in approximation for traces
'he': False, # If True, compute heteroskedastic correction
'out': 'res_fe.json', # Outputfile where results are saved
'statsonly': False, # If True, return only basic statistics
'feonly': True, # If True, compute only fixed effects and not variances
'Q': 'cov(alpha, psi)' # Which Q matrix to consider. Options include 'cov(alpha, psi)' and 'cov(psi_t, psi_{t+1})'
}

#### Since we set 'feonly': True, we just run the estimator normally and it only estimates the fixed effects to save time
tw_net.fit_fe(fe_params)

#### Now look at the data
new_data = tw_net.data

bdf = bdf.clean_data({'data_validity': False})