else:
orig_jobs = f.make_original_jobs_from_counts(jcnts_arr[0], eg_sequence,
fur_sequence,
num_of_job_levels).astype(int)
# insert stovepipe job result into new column of proposal (month_form)
# this indexes the jobs with empkeys (orig_jobs is an ndarray only)
df_proposal['orig_job'] = orig_jobs
# ASSIGN JOBS - flush and no flush option*
# cmonths - career length in months for each employee.
# length is equal to number of employees
cmonths = f.career_months_df_in(df_proposal)
# nonret_each_month: count of non-retired employees remaining
# in each month until no more remain -
# length is equal to longest career length
nonret_each_month = f.count_per_month(cmonths)
all_months = np.sum(nonret_each_month)
cumulative = nonret_each_month.cumsum()
np_low_limits = f.make_lower_slice_limits(cumulative)
job_level_counts = np.array(jcnts_arr[1])
if cf.delayed_implementation:
imp_month = cf.imp_month
imp_low = np_low_limits[imp_month]
else:
orig_jobs = f.make_original_jobs_from_counts(
jcnts_arr[0], eg_sequence, fur_sequence, num_of_job_levels).astype(int)
# insert stovepipe job result into new column of proposal (month_form)
# this indexes the jobs with empkeys (orig_jobs is an ndarray only)
df_proposal['orig_job'] = orig_jobs
# ASSIGN JOBS - flush and no flush option*
# cmonths - career length in months for each employee.
# length is equal to number of employees
cmonths = f.career_months_df_in(df_proposal)
# nonret_each_month: count of non-retired employees remaining
# in each month until no more remain -
# length is equal to longest career length
nonret_each_month = f.count_per_month(cmonths)
all_months = np.sum(nonret_each_month)
cumulative = nonret_each_month.cumsum()
np_low_limits = f.make_lower_slice_limits(cumulative)
job_level_counts = np.array(jcnts_arr[1])
if cf.delayed_implementation:
imp_month = cf.imp_month
imp_low = np_low_limits[imp_month]
ds_list = [ds1, ds2, ds3]
short_ds_list = [short_ds1, short_ds2, short_ds3]
for i in range(len(ds_list)):
df_long = ds_list[i]
df_short = short_ds_list[i]
jcnts = jcnts_arr[0][i]
# jcnts = np.take(jcnts, np.where(jcnts != 0)[0])
short_len = len(short_ds_list[i])
# ORIG_JOB*
cmonths_this_ds = f.career_months_df_in(df_short)
this_ds_nonret_each_month = f.count_per_month(cmonths_this_ds)
uppers = this_ds_nonret_each_month.cumsum()
lowers = f.make_lower_slice_limits(uppers)
all_months = np.sum(this_ds_nonret_each_month)
this_table = table[0][i]
this_month_counts = table[1][i]
df_align = df_long[['twa', 'fur']]
fur_codes = np.array(df_align.fur)
# if i == 0 and cf.apply_supc: # i == 0 >> eg1 from skeleton
# twa_rights = np.array(cf.twa_rights)
# twa_jobs = np.transpose(twa_rights)[1]
# sup_c_counts = np.transpose(twa_rights)[2]
for date, add_months in ret_incr_dict.items():
master.loc[master.retdate > pd.to_datetime(date) +
pd.offsets.MonthEnd(-1), 'retdate'] = \
master.retdate + pd.DateOffset(months=add_months)
# only include employees who retire during or after the starting_month
# (remove employees who retire prior to analysis period)
master = master[master.retdate >= start_date - pd.DateOffset(months=1) +
pd.DateOffset(days=1)]
master.to_pickle('dill/master.pkl')
# ACTIVE EACH MONTH (no consideration for job changes or recall, only
# calculated on retirements of active employees as of start date)
emps_to_calc = master[master.line == 1].copy()
cmonths = f.career_months_df_in(emps_to_calc)
nonret_each_month = f.count_per_month(cmonths)
# code below subject to removal pending test period completion
# actives = pd.DataFrame(nonret_each_month, columns=['count'])
# actives.to_pickle('dill/active_each_month.pkl')
# LIST ORDER PROPOSALS
# Read the list ordering proposals from an Excel workbook, add an index
# column ('idx'), and store each proposal as a dataframe in a pickled file.
# The proposals are contained on separate worksheets.
# The routine below will loop through the worksheets.
# The worksheet tab names are important for the function.
# The pickle files will be named like the workbook sheet names.
xl = pd.ExcelFile('excel/' + case + '/proposals.xlsx')
start_date = pd.to_datetime(cf.starting_date)
# only include pilots that are not retired prior to the starting_month
df_proposal = df_proposal[df_proposal.retdate >= start_date -
pd.DateOffset(months=1)]
# include furloughees by default
df = df_proposal[(df_proposal.line == 1) | (df_proposal.fur == 1)].copy()
df_proposal = []
# MNUM*
# calculate the number of career months for each employee (short_form)
# cmonths is used for mnum, idx, and mth_pcnt calculations
cmonths = f.career_months_df_in(df)
# convert the python cmonths list to a numpy array and
# use that array as input for the count_per_month function.
# The count_per_month function output array is input for
# other functions (month_form)
nonret_each_month = f.count_per_month(cmonths)
# calculate the total of all career months (sum)
total_months = np.sum(nonret_each_month)
# first long form data generation.
# month numbers, same month number repeated for each month length (long_form)
else:
orig_jobs = f.make_original_jobs_from_counts(
jcnts_arr[0], eg_sequence, fur_sequence, num_of_job_levels).astype(int)
# insert stovepipe job result into new column of proposal (month_form)
# this indexes the jobs with empkeys (orig_jobs is an ndarray only)
df_master['orig_job'] = orig_jobs
# ASSIGN JOBS - flush and no flush option*
# cmonths - career length in months for each employee.
# length is equal to number of employees
cmonths = f.career_months_df_in(df_master)
# nonret_each_month: count of non-retired employees remaining
# in each month until no more remain -
# length is equal to longest career length
nonret_each_month = f.count_per_month(cmonths)
all_months = np.sum(nonret_each_month)
cumulative = nonret_each_month.cumsum()
np_low_limits = f.make_lower_slice_limits(cumulative)
job_level_counts = np.array(jcnts_arr[1])
if cf.delayed_implementation:
imp_month = cf.imp_month
imp_low = np_low_limits[imp_month]
ds_dict[grp] = ds[ds.eg == grp].copy()
for grp in egs:
short_ds_dict[grp] = ds_dict[grp][ds_dict[grp].mnum == 0].copy()
ds = pd.DataFrame()
for i in egs - 1:
df_long = ds_dict[i + 1]
df_short = short_ds_dict[i + 1]
jcnts = jcnts_arr[0][i]
short_len = len(df_short)
# ORIG_JOB*
cmonths_this_ds = f.career_months_df_in(df_short)
this_ds_nonret_each_month = f.count_per_month(cmonths_this_ds)
uppers = this_ds_nonret_each_month.cumsum()
lowers = f.make_lower_slice_limits(uppers)
all_months = np.sum(this_ds_nonret_each_month)
this_table = table[0][i]
this_month_counts = table[1][i]
df_align_cols = ['fur']
if 'sg' in df_long:
df_align_cols.append('sg')
df_align = df_long[df_align_cols]
fur_codes = np.array(df_align.fur)