# ORIG_JOB
# transfer proposal stovepipe jobs (month_form) to long_form via index
# (empkey) alignment...
ds['orig_job'] = df_proposal['orig_job']
# grab long_form indexed stovepipe jobs (int)
orig = np.array(ds['orig_job'])
if cf.compute_with_job_changes:
table = f.job_gain_loss_table(nonret_each_month.size,
num_of_job_levels,
jcnts_arr,
j_changes)
job_change_months = f.get_job_change_months(j_changes)
reduction_months = f.get_job_reduction_months(j_changes)
df_align = ds[['eg', 'twa', 'fur', 'orig_job']].copy()
jobs_and_counts = f.assign_jobs_nbnf_job_changes(df_align, np_low_limits,
cumulative, all_months,
table[0], table[1],
job_change_months,
reduction_months,
imp_month,
proposal_name,
fur_return=cf.recall)
nbnf = jobs_and_counts[0]
# if job_changes, replace original fur column...
def main():
script, *conditions = argv
input_skel = 'skeleton'
pre, suf = 'dill/', '.pkl'
skeleton_path_string = (pre + input_skel + suf)
try:
ds = pd.read_pickle(skeleton_path_string)
except OSError:
print('\nSkeleton file not found. ' +
'Run build_program_files script?\n\n' +
'Standalone build failed.\n\n' +
' >>> exiting routine.\n')
exit()
if os.path.isdir('dill/'):
try:
os.remove('dill/standalone.pkl')
except OSError:
pass
sdict = pd.read_pickle('dill/dict_settings.pkl')
tdict = pd.read_pickle('dill/dict_job_tables.pkl')
num_of_job_levels = sdict['num_of_job_levels']
egs = np.unique(ds.eg)
start_month = 0
# make prex True or False
# (for input to assign_standalone_job_changes function)
prex = 'prex' in conditions
table = tdict['s_table']
jcnts_arr = tdict['jcnts_arr']
j_changes = tdict['j_changes']
job_change_months = f.get_job_change_months(j_changes)
job_reduction_months = f.get_job_reduction_months(j_changes)
# sort the skeleton by employee group, month, and index
# (preserves each group's list order)
ds.sort_values(['eg', 'mnum', 'idx'])
ds_dict = {}
short_ds_dict = {}
for grp in egs:
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 eg in egs:
df_long = ds_dict[eg]
df_short = short_ds_dict[eg]
jcnts = jcnts_arr[0][eg - 1]
short_len = len(df_short)
# ORIG_JOB*
cmonths_this_ds = \
f.career_months(df_short, sdict['starting_date'])
this_ds_nonret_each_month = f.count_per_month(cmonths_this_ds)
high_limits = this_ds_nonret_each_month.cumsum()
low_limits = f.make_lower_slice_limits(high_limits)
all_months = np.sum(this_ds_nonret_each_month)
this_eg_table = f.add_zero_col(table[0][eg - 1])
this_eg_month_counts = table[1][eg - 1]
df_align_cols = ['fur']
if 'sg' in df_long:
df_align_cols.append('sg')
df_align = df_long[df_align_cols]
# pre-existing employee group special job assignment is included within
# the job assignment function below...
results = f.assign_standalone_job_changes(eg,
df_align,
low_limits,
high_limits,
all_months,
this_eg_table,
this_eg_month_counts,
this_ds_nonret_each_month,
job_change_months,
job_reduction_months,
start_month,
sdict,
tdict,
apply_sg_cond=prex)
jnums = results[0]
count_col = results[1]
held = results[2]
fur = results[3]
orig_jobs = results[4]
# HELD JOB
# job from previous month
df_long['held'] = held
# JOB_COUNT
df_long['job_count'] = count_col
# ORIG_JOB
df_short['orig_job'] = orig_jobs
df_long['orig_job'] = df_short['orig_job']
# ASSIGN JOBS - (stovepipe method only since only
# assigning within each employee group separately)
# JNUM
df_long['jnum'] = jnums
# SNUM, SPCNT, LNUM, LSPCNT
monthly_job_counts = table[1][eg - 1]
lspcnt_calc = sdict['lspcnt_calc_on_remaining_population']
df_long['snum'], df_long['spcnt'], \
df_long['lnum'], df_long['lspcnt'] = \
f.create_snum_and_spcnt_arrays(jnums, num_of_job_levels,
this_ds_nonret_each_month,
monthly_job_counts,
lspcnt_calc)
# RANK in JOB
df_long['rank_in_job'] = \
df_long.groupby(['mnum', 'jnum']).cumcount() + 1
# JOBP
# make last percentage position in each job category .99999 vs 1.0
# so that jobp calculations are correct
jpcnt = (df_long.rank_in_job / df_long.job_count).values
np.put(jpcnt, np.where(jpcnt == 1.0)[0], .99999)
df_long['jobp'] = df_long['jnum'] + jpcnt
# PAY - merge with pay table - provides monthly pay
if sdict['compute_pay_measures']:
if sdict['discount_longev_for_fur']:
# skel provides non-discounted scale data
# flip ones and zeros...
df_long['non_fur'] = 1 - fur
df_long['fur'] = fur
non_fur = \
(df_long.groupby([pd.Grouper('empkey')])
['non_fur'].cumsum().values)
df_long.pop('non_fur')
starting_mlong = df_long.s_lmonths.values
cum_active_months = non_fur + starting_mlong
df_long['mlong'] = cum_active_months
df_long['ylong'] = df_long['mlong'] / 12
df_long['scale'] = \
np.clip((cum_active_months / 12) + 1, 1,
sdict['top_of_scale']).astype(int)
# SCALE
df_pt_index = pd.DataFrame(
index=(df_long['scale'] * 100) + df_long['jnum'] +
(df_long['year'] * 100000))
if sdict['enhanced_jobs']:
df_pt = pd.read_pickle(
'dill/pay_table_enhanced.pkl')
else:
df_pt = pd.read_pickle(
'dill/pay_table_basic.pkl')
df_pt_index['monthly'] = df_pt['monthly']
df_long['monthly'] = df_pt_index.monthly.values
# MPAY
# adjust monthly pay for any raise and last month pay percent if
# applicable
df_long['mpay'] = (
(df_long['pay_raise'] *
df_long['mth_pcnt'] *
df_long['monthly'])) / 1000
df_long.pop('monthly')
# CPAY
df_long['cpay'] = df_long.groupby('idx')['mpay'].cumsum()
ds = pd.concat([ds, df_long], ignore_index=True)
ds.sort_values(by=['mnum', 'idx'], inplace=True)
ds.set_index('empkey', drop=False, verify_integrity=False, inplace=True)
# CAT_ORDER
# global job ranking
if sdict['compute_job_category_order']:
table = tdict['table']
ds['cat_order'] = f.make_cat_order(ds, table[0])
# save to file
if sdict['save_to_pickle']:
ds.to_pickle('dill/standalone.pkl')
imp_month = 0
# ORIG_JOB
# transfer proposal stovepipe jobs (month_form) to long_form via index
# (empkey) alignment...
ds['orig_job'] = df_proposal['orig_job']
# grab long_form indexed stovepipe jobs (int)
orig = np.array(ds['orig_job'])
if cf.compute_with_job_changes:
table = f.job_gain_loss_table(nonret_each_month.size, num_of_job_levels,
jcnts_arr, j_changes)
job_change_months = f.get_job_change_months(j_changes)
reduction_months = f.get_job_reduction_months(j_changes)
df_align = ds[['eg', 'twa', 'fur', 'orig_job']].copy()
jobs_and_counts = f.assign_jobs_nbnf_job_changes(df_align,
np_low_limits,
cumulative,
all_months,
table[0],
table[1],
job_change_months,
reduction_months,
imp_month,
proposal_name,
fur_return=cf.recall)
def main():
script, *conditions = argv
input_skel = 'skeleton'
pre, suf = 'dill/', '.pkl'
skeleton_path_string = (pre + input_skel + suf)
try:
ds = pd.read_pickle(skeleton_path_string)
except OSError:
print('\nSkeleton file not found. ' +
'Run build_program_files script?\n\n' +
'Standalone build failed.\n\n' + ' >>> exiting routine.\n')
exit()
if os.path.isdir('dill/'):
try:
os.remove('dill/standalone.pkl')
except OSError:
pass
sdict = pd.read_pickle('dill/dict_settings.pkl')
tdict = pd.read_pickle('dill/dict_job_tables.pkl')
num_of_job_levels = sdict['num_of_job_levels']
egs = np.unique(ds.eg)
start_month = 0
# make prex True or False
# (for input to assign_standalone_job_changes function)
prex = 'prex' in conditions
table = tdict['s_table']
jcnts_arr = tdict['jcnts_arr']
j_changes = tdict['j_changes']
job_change_months = f.get_job_change_months(j_changes)
job_reduction_months = f.get_job_reduction_months(j_changes)
# sort the skeleton by employee group, month, and index
# (preserves each group's list order)
ds.sort_values(['eg', 'mnum', 'idx'])
ds_dict = {}
short_ds_dict = {}
for grp in egs:
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 eg in egs:
df_long = ds_dict[eg]
df_short = short_ds_dict[eg]
jcnts = jcnts_arr[0][eg - 1]
short_len = len(df_short)
# ORIG_JOB*
cmonths_this_ds = \
f.career_months(df_short, sdict['starting_date'])
this_ds_nonret_each_month = f.count_per_month(cmonths_this_ds)
high_limits = this_ds_nonret_each_month.cumsum()
low_limits = f.make_lower_slice_limits(high_limits)
all_months = np.sum(this_ds_nonret_each_month)
this_eg_table = f.add_zero_col(table[0][eg - 1])
this_eg_month_counts = table[1][eg - 1]
df_align_cols = ['fur']
if 'sg' in df_long:
df_align_cols.append('sg')
df_align = df_long[df_align_cols]
# pre-existing employee group special job assignment is included within
# the job assignment function below...
results = f.assign_standalone_job_changes(eg,
df_align,
low_limits,
high_limits,
all_months,
this_eg_table,
this_eg_month_counts,
this_ds_nonret_each_month,
job_change_months,
job_reduction_months,
start_month,
sdict,
tdict,
apply_sg_cond=prex)
jnums = results[0]
count_col = results[1]
held = results[2]
fur = results[3]
orig_jobs = results[4]
# HELD JOB
# job from previous month
df_long['held'] = held
# JOB_COUNT
df_long['job_count'] = count_col
# ORIG_JOB
df_short['orig_job'] = orig_jobs
df_long['orig_job'] = df_short['orig_job']
# ASSIGN JOBS - (stovepipe method only since only
# assigning within each employee group separately)
# JNUM
df_long['jnum'] = jnums
# SNUM, SPCNT, LNUM, LSPCNT
monthly_job_counts = table[1][eg - 1]
lspcnt_calc = sdict['lspcnt_calc_on_remaining_population']
df_long['snum'], df_long['spcnt'], \
df_long['lnum'], df_long['lspcnt'] = \
f.create_snum_and_spcnt_arrays(jnums, num_of_job_levels,
this_ds_nonret_each_month,
monthly_job_counts,
lspcnt_calc)
# RANK in JOB
df_long['rank_in_job'] = \
df_long.groupby(['mnum', 'jnum']).cumcount() + 1
# JOBP
# make last percentage position in each job category .99999 vs 1.0
# so that jobp calculations are correct
jpcnt = (df_long.rank_in_job / df_long.job_count).values
np.put(jpcnt, np.where(jpcnt == 1.0)[0], .99999)
df_long['jobp'] = df_long['jnum'] + jpcnt
# PAY - merge with pay table - provides monthly pay
if sdict['compute_pay_measures']:
if sdict['discount_longev_for_fur']:
# skel provides non-discounted scale data
# flip ones and zeros...
df_long['non_fur'] = 1 - fur
df_long['fur'] = fur
non_fur = \
(df_long.groupby([pd.Grouper('empkey')])
['non_fur'].cumsum().values)
df_long.pop('non_fur')
starting_mlong = df_long.s_lmonths.values
cum_active_months = non_fur + starting_mlong
df_long['mlong'] = cum_active_months
df_long['ylong'] = df_long['mlong'] / 12
df_long['scale'] = \
np.clip((cum_active_months / 12) + 1, 1,
sdict['top_of_scale']).astype(int)
# SCALE
df_pt_index = pd.DataFrame(index=(df_long['scale'] * 100) +
df_long['jnum'] +
(df_long['year'] * 100000))
if sdict['enhanced_jobs']:
df_pt = pd.read_pickle('dill/pay_table_enhanced.pkl')
else:
df_pt = pd.read_pickle('dill/pay_table_basic.pkl')
df_pt_index['monthly'] = df_pt['monthly']
df_long['monthly'] = df_pt_index.monthly.values
# MPAY
# adjust monthly pay for any raise and last month pay percent if
# applicable
df_long['mpay'] = ((df_long['pay_raise'] * df_long['mth_pcnt'] *
df_long['monthly'])) / 1000
df_long.pop('monthly')
# CPAY
df_long['cpay'] = df_long.groupby('idx')['mpay'].cumsum()
ds = pd.concat([ds, df_long], ignore_index=True)
ds.sort_values(by=['mnum', 'idx'], inplace=True)
ds.set_index('empkey', drop=False, verify_integrity=False, inplace=True)
# CAT_ORDER
# global job ranking
if sdict['compute_job_category_order']:
table = tdict['table']
ds['cat_order'] = f.make_cat_order(ds, table[0])
# save to file
if sdict['save_to_pickle']:
ds.to_pickle('dill/standalone.pkl')