diff --git a/Database/Telework.yaml b/Database/Telework.yaml
new file mode 100644
index 0000000..ebbd07a
--- /dev/null
+++ b/Database/Telework.yaml
@@ -0,0 +1,38 @@
+# ---------------- Work from home module inputs (trip generation)
+validationfiles: N
+wfhpctlow: 0.143
+wfhpctmedium: 0.232
+wfhpcthigh: 0.552
+# ---------------- Telework worker attributes by edu, income and children
+# WFHL:             # low wfh percent group
+#   edu:
+#     1: 0.305      # low wfhGroup less than Banchelor
+#     2: 0.695      # low wfhGroup Banchelor Plus
+#   inc:
+#     1: 0.353      # low wfhGroup inc less than 100K
+#     2: 0.647      # low wfhGroup inc 100K Plus
+#   chi:
+#     1: 0.37       # low wfhGroup children under 18
+#     2: 0.63       # low wfhGroup no children under 18
+
+# WFHM:             # medium wfh percent group
+#   edu:
+#     1: 0.268      # medium wfhGroup less than Banchelor
+#     2: 0.732      # medium wfhGroup Banchelor Plus
+#   inc:
+#     1: 0.418      # medium wfhGroup inc less than 100K
+#     2: 0.582      # medium wfhGroup inc 100K Plus
+#   chi:
+#     1: 0.354      # medium wfhGroup children under 18
+#     2: 0.646      # medium wfhGroup no children under 18
+
+# WFHH:             # high wfh percent group
+#   edu:
+#     1: 0.202      # high wfhGroup less than Banchelor
+#     2: 0.798      # high wfhGroup Banchelor Plus
+#   inc:
+#     1: 0.231      # high wfhGroup inc less than 100K   
+#     2: 0.769      # high wfhGroup inc 100K Plus
+#   chi:
+#     1: 0.405      # high wfhGroup children under 18   
+#     2: 0.595      # high wfhGroup no children under 18
\ No newline at end of file
diff --git a/Database/tg/fortran/wfhmodule/chidist.csv b/Database/tg/fortran/wfhmodule/chidist.csv
new file mode 100644
index 0000000..38cb0fa
--- /dev/null
+++ b/Database/tg/fortran/wfhmodule/chidist.csv
@@ -0,0 +1,7 @@
+wfhgroup,hhchi2,pct
+high,1,0.405
+high,2,0.595
+medium,1,0.354
+medium,2,0.646
+low,1,0.37
+low,2,0.63
diff --git a/Database/tg/fortran/wfhmodule/edudist.csv b/Database/tg/fortran/wfhmodule/edudist.csv
new file mode 100644
index 0000000..92e43db
--- /dev/null
+++ b/Database/tg/fortran/wfhmodule/edudist.csv
@@ -0,0 +1,7 @@
+wfhgroup,hhedu2,pct
+high,1,0.202
+high,2,0.798
+medium,1,0.268
+medium,2,0.732
+low,1,0.305
+low,2,0.695
diff --git a/Database/tg/fortran/wfhmodule/incdist.csv b/Database/tg/fortran/wfhmodule/incdist.csv
index 8ac6f97..501031d 100644
--- a/Database/tg/fortran/wfhmodule/incdist.csv
+++ b/Database/tg/fortran/wfhmodule/incdist.csv
@@ -1,9 +1,7 @@
-incdist,hhinc4,pct
-high,1,0.038985164
-high,2,0.080435697
-high,3,0.156394004
-high,4,0.724185136
-low,1,0.09287371
-low,2,0.134953622
-low,3,0.238871815
-low,4,0.533300852
+wfhgroup,hhinc2,pct
+high,1,0.231
+high,2,0.769
+medium,1,0.418
+medium,2,0.582
+low,1,0.353
+low,2,0.647
diff --git a/Database/tg/fortran/wfhmodule/indp_naics.csv b/Database/tg/fortran/wfhmodule/indp_naics.csv
index abe0f56..e3933e3 100644
--- a/Database/tg/fortran/wfhmodule/indp_naics.csv
+++ b/Database/tg/fortran/wfhmodule/indp_naics.csv
@@ -1,295 +1,295 @@
-INDP,naics2
-170,11
-180,11
-190,11
-270,11
-280,11
-290,11
-370,21
-380,21
-390,21
-470,21
-490,21
-570,22
-580,22
-590,22
-670,22
-680,22
-690,22
-770,23
-1070,31-33
-1080,31-33
-1090,31-33
-1170,31-33
-1180,31-33
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-1890,31-33
-1990,31-33
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-4070,42
-4080,42
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-4190,42
-4195,42
-4260,42
-4265,42
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-4370,42
-4380,42
-4390,42
-4470,42
-4480,42
-4490,42
-4560,42
-4570,42
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-4590,42
-4670,44-45
-4680,44-45
-4690,44-45
-4770,44-45
-4780,44-45
-4790,44-45
-4795,44-45
-4870,44-45
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-4890,44-45
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-5690,44-45
-5790,44-45
-6070,48-49
-6080,48-49
-6090,48-49
-6170,48-49
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-6190,48-49
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-6280,48-49
-6290,48-49
-6370,48-49
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-6470,51
-6480,51
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-6590,51
-6670,51
-6672,51
-6680,51
-6690,51
-6695,51
-6770,51
-6780,51
-6870,52
-6880,52
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-6970,52
-6990,52
-6991,52
-6992,52
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-7071,53
-7072,53
-7080,53
-7170,53
-7180,53
-7181,53
-7190,53
-7270,54
-7280,54
-7290,54
-7370,54
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-7390,54
-7460,54
-7470,54
-7480,54
-7490,54
-7570,55
-7580,56
-7590,56
-7670,56
-7680,56
-7690,56
-7770,56
-7780,56
-7790,56
-7860,61
-7870,61
-7880,61
-7890,61
-7970,62
-7980,62
-7990,62
-8070,62
-8080,62
-8090,62
-8170,62
-8180,62
-8190,62
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+INDP,naics2,trc
+170,11,low
+180,11,low
+190,11,low
+270,11,low
+280,11,low
+290,11,low
+370,21,low
+380,21,low
+390,21,low
+470,21,low
+490,21,low
+570,22,medium
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+770,23,low
+1070,31-33,low
+1080,31-33,low
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+1770,31-33,low
+1790,31-33,low
+1870,31-33,low
+1880,31-33,low
+1890,31-33,low
+1990,31-33,low
+2070,31-33,low
+2090,31-33,low
+2170,31-33,low
+2180,31-33,low
+2190,31-33,low
+2270,31-33,low
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+2590,31-33,low
+2670,31-33,low
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+2690,31-33,low
+2770,31-33,low
+2780,31-33,low
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+3875,31-33,low
+3890,31-33,low
+3895,31-33,low
+3960,31-33,low
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+3980,31-33,low
+3990,31-33,low
+4070,42,medium
+4080,42,medium
+4090,42,medium
+4170,42,medium
+4180,42,medium
+4190,42,medium
+4195,42,medium
+4260,42,medium
+4265,42,medium
+4270,42,medium
+4280,42,medium
+4290,42,medium
+4370,42,medium
+4380,42,medium
+4390,42,medium
+4470,42,medium
+4480,42,medium
+4490,42,medium
+4560,42,medium
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+6390,48-49,low
+6470,51,high
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+6570,51,high
+6590,51,high
+6670,51,high
+6672,51,high
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+6770,51,high
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+7170,53,high
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+7190,53,high
+7270,54,high
+7280,54,high
+7290,54,high
+7370,54,high
+7380,54,high
+7390,54,high
+7460,54,high
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+7480,54,high
+7490,54,high
+7570,55,low
+7580,56,low
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+7670,56,low
+7680,56,low
+7690,56,low
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+8180,62,low
+8190,62,low
+8191,62,low
+8192,62,low
+8270,62,low
+8290,62,low
+8370,62,low
+8380,62,low
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+8470,62,low
+8560,71,medium
+8561,71,medium
+8562,71,medium
+8563,71,medium
+8564,71,medium
+8570,71,medium
+8580,71,medium
+8590,71,medium
+8660,72,low
+8670,72,low
+8680,72,low
+8690,72,low
+8770,81,low
+8780,81,low
+8790,81,low
+8870,81,low
+8880,81,low
+8891,81,low
+8970,81,low
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+8990,81,low
+9070,81,low
+9080,81,low
+9090,81,low
+9160,81,low
+9170,81,low
+9180,81,low
+9190,81,low
+9290,81,low
+9370,92,medium
+9380,92,medium
+9390,92,medium
+9470,92,medium
+9480,92,medium
+9490,92,medium
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+9590,92,medium
+9670,92,medium
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+9790,92,medium
+9870,92,medium
diff --git a/Database/tg/fortran/wfhmodule/wfhflag.py b/Database/tg/fortran/wfhmodule/wfhflag.py
index 95a8f02..275fd0a 100644
--- a/Database/tg/fortran/wfhmodule/wfhflag.py
+++ b/Database/tg/fortran/wfhmodule/wfhflag.py
@@ -6,6 +6,12 @@
 import sys
 import os
 
+# Current trip_gen.bat
+# python wfhflag.py %filedir% %savedir% %wfhFile% %wfh% %tc14%
+
+# Adj trip_gen.bat
+# python wfhflag.py %filedir% %savedir% %wfhFile% %wfhl% %wfhm% %wfhh%
+
 # pathways
 savedir = sys.argv[2]
 assert os.path.exists(savedir) == True, "savedir not valid"
@@ -13,328 +19,96 @@
 synpoppath = "synthetic_persons.zip"
 synhhpath = "synthetic_households.zip"
 popsynhhpath = savedir + "/POPSYN_HH.csv"
-incdistpath = "incdist.csv"
-indmixpath = "indusmix.csv"
 indpxwalkpath = "indp_naics.csv"
 
+#telework worker distribution by income, edu level and children
+incdistpath = "incdist.csv"
+edudistpath = "edudist.csv"
+chidistpath = "chidist.csv"
+
 # save additional output files?
 savefiles = sys.argv[3]
 
 # major parameters - source: mdt + nirpc survey (which is higher than PUMS data...)
 # percent of all workers
-usualwfhpct = float(sys.argv[4])
-tc14pct = float(sys.argv[5])
 
-# set seedvalue
-seedvalue = 2
-np.random.seed(seed=seedvalue)
+''' This data will be separated from batch_file.yaml to Telework.yaml including Tuesday-Thursday telework rates'''
 
-# place industries in one of two income distributions types
-# high is based on industries that had >60% of workers in income group 4
-# low is based on other industries
-lowlist = ['11', '21', '44-45', '48-49', '56', '61', '62', '71', '72', '81']
-highlist = ['22', '23', '31-33', '42', '51', '52', '53', '54', '55', '92']
+wfhl = float(sys.argv[4])
+wfhm = float(sys.argv[5])
+wfhh = float(sys.argv[6])
 
-# setup education weights for usualwfh (low to high)
-eduw = [0.177, 0.246, 0.576]
-# setup education weights for usualwfh (low to high, fine they don't sum to 1)
-eduwtc = [0.049, 0.114, 0.720]
+wfhpctlist = [wfhl, wfhm, wfhh]
 
-# set distribution of tc14 into the 4 days
-tcportions = [0.575, 0.254, 0.112, 0.059]
+# set seedvalue
+seedvalue = 2
+np.random.seed(seed=seedvalue)
 
-print('setting up for wfhflag script...')
 # read in files
 dfpop = pd.read_csv(synpoppath)
 dfhh = pd.read_csv(synhhpath, dtype={'MV': object})
 indpxwalk = pd.read_csv(indpxwalkpath)
-indmix = pd.read_csv(indmixpath)
-incdist = pd.read_csv(incdistpath)
 
-# industry mix - source: mdt + nirpc survey (rescaled after removing industries -7, -8, and 97 to sum to 1)
-indmix1 = indmix[indmix['cat'] == 'usualwfh'][['indus', 'pct']]
-indmix2 = indmix[indmix['cat'] == 'tc14'][['indus', 'pct']]
+# Need to change if plan to get data from Telework.yaml
+incdist = pd.read_csv(incdistpath)
+edudist = pd.read_csv(edudistpath)
+chidist = pd.read_csv(chidistpath)
 
-# merge naics2, income4, edu, get workers
+# merge income2, edu2, trc get workers
 dfpop = dfpop.merge(indpxwalk, on='INDP', how='left')
 dfpop = dfpop.merge(dfhh[['household_id', 'HINCP19']], on='household_id', how='left')
-dfpop['inccat4'] = pd.cut(dfpop.HINCP19, bins=[-99999, 30000, 60000, 100000, 2000000], right=False, labels=[1, 2, 3, 4])
+
+dfpop['inccat2'] = pd.cut(dfpop.HINCP19, bins=[-99999, 100000, 2000000], right=False, labels=[1, 2])
 
 workers = dfpop[dfpop['JWTR'] != 'bb'].copy()
 workers.JWTR = workers.JWTR.astype(float).astype(int)
 workers.SCHL = workers.SCHL.astype(float).astype(int)
-workers.loc[workers.SCHL.isin([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]), 'edu'] = 1
-workers.loc[workers.SCHL.isin([18, 19, 20]), 'edu'] = 2
-workers.loc[workers.SCHL.isin([21, 22, 23, 24]), 'edu'] = 3
-workers.loc[:, 'selected'] = 0
 
-# overall targets
-targetwfh = round(len(workers) * usualwfhpct, 0)
-targettc14 = round(len(workers) * tc14pct, 0)
+workers.loc[workers.SCHL.isin([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]), 'edu'] = 1
+workers.loc[workers.SCHL.isin([21, 22, 23, 24]), 'edu'] = 2
 
+workers.loc[:, 'selected'] = 0
 workers = workers[workers['ESR'] != '4']
 
-# functions
-def portionout(df, pctcol, total, colname):
-    """
-    divides up a total based on a percent column into a new column (no decimals)
-    :param df: input dataframe
-    :param pctcol: name of column that has percentages
-    :param total: can be name of column that has totals, or a numeric total that the percentages will be applied to
-    :param colname: the name of the result column
-    :return: dataframe with targetdec, targetint, decimal, and colname columns
-    """
-    df = df.copy()
-    indcounts = workers[workers.selected == 0].groupby('naics2').agg({'household_id':'count'}).reset_index()
-    indcounts.rename({'household_id':'sectortotal'}, axis=1, inplace=True)
-
-
-    if isinstance(total, int) or isinstance(total, float):
-        df['targetdec'] = df[pctcol] * total
-
-        df2 = df.merge(indcounts, left_on='indus', right_on='naics2')
-        df2['testtot'] = df2['sectortotal'] - df2['targetdec']
-
-        if len(df2[df2.testtot < 0]) == 0:
-            pass
-        else:
-            # don't go all the way up to max to prevent same issue further down the line
-            df2.loc[df2.testtot < 0, 'newpct'] = (df2.sectortotal - 10) / total
-            df2.loc[df2.testtot < 0, 'pctdiff'] = df2[pctcol] - df2.newpct
-            todistribute = df2.pctdiff.sum()
-            # normalize unchanged pcts to one to distribute out the missing pct
-            newdenom = 1 - df2.loc[df2.testtot < 0][pctcol].sum()
-            df2.loc[df2.testtot >=0, 'pctfordist'] = df2[pctcol] / newdenom
-            df2.loc[df2.testtot >= 0, 'newpct'] = (df2.pctfordist * todistribute) + df2[pctcol]
-            df2[pctcol] = df2['newpct']
-            df2['targetdec'] = df2[pctcol] * total
-
-        df2['targetint'] = df2['targetdec'].astype('int')
-        df2['decimal'] = df2['targetdec'] - df2['targetint']
-        leftover = total - df2['targetint'].sum()
-        df2.sort_values('decimal', ascending=False, inplace=True)
-        df2.reset_index(drop=True, inplace=True)
-        df2.loc[df2.index < leftover, 'targetint'] = df2.targetint + 1
-        assert (total - df2.targetint.sum()) == 0, "Targets do not sum to total"
-        df2.rename({'targetint': colname}, axis=1, inplace=True)
-        return df2
-
-    elif isinstance(total, str):
-        df['targetdec'] = df[pctcol] * df[total]
-
-        df2 = df.merge(indcounts, left_on='indus', right_on='naics2')
-        df2['testtot'] = df2['sectortotal'] - df2['targetdec']
-
-        if len(df2[df2.testtot < 0]) == 0:
-            pass
-        else:
-            df2.loc[df2.testtot < 0, 'newpct'] = (df2.sectortotal - 10) / df[total]
-            df2.loc[df2.testtot < 0, 'pctdiff'] = df2[pctcol] - df2.newpct
-            todistribute = df2.pctdiff.sum()
-            df2.loc[df2.testtot >= 0, 'newpct'] = df2[pctcol] * (1 + todistribute)
-            df2[pctcol] = df2['newpct']
-            df2['targetdec'] = df2[pctcol] * df2[total]
-
-        df2['targetint'] = df2['targetdec'].astype('int')
-        df2['decimal'] = df2['targetdec'] - df2['targetint']
+# telework distribution
+inc_values = incdist.iloc[:, 2].astype(float).tolist()
+edu_values = edudist.iloc[:, 2].astype(float).tolist()
 
-        totallist = [x for x in df2[total].unique()]
-        piecelist = []
-        for i in totallist:
-            selection = df2[df2[total] == i].copy()
-            leftover = i - selection['targetint'].sum()
-            if leftover != 0:
-                selection.sort_values('decimal', ascending=False, inplace=True)
-                selection.reset_index(drop=True, inplace=True)
-                selection.loc[selection.index < leftover, 'targetint'] = selection.targetint + 1
-                assert (i - selection.targetint.sum()) == 0, "Targets do not sum to total"
-            piecelist.append(selection)
-        df2 = pd.concat(piecelist)
-        df2.rename({'targetint': colname}, axis=1, inplace=True)
-        return df2
+# organize into dicts for easier access
+incdist_dict = {
+    "high": {1: inc_values[0], 2: inc_values[1]},
+    "medium": {1: inc_values[2], 2: inc_values[3]},
+    "low": {1: inc_values[4], 2: inc_values[5]},
+}
 
+edudist_dict = {
+    "high": {1: edu_values[0], 2: edu_values[1]},
+    "medium": {1: edu_values[2], 2: edu_values[3]},
+    "low": {1: edu_values[4], 2: edu_values[5]},
+}
 
-def settargets(indmix, overalltarget):
-    """
-    use portionout function to set industry/income targets for group of interest (usualwfh or tc14)
-    :param indmix: input df with distribution of people in group of interest into industries
-    :param overalltarget: total target value for people in group of interest
-    :return:
-    """
-    indmixfinal = portionout(indmix, 'pct', overalltarget, 'indint')
-
-    # income targets
-    indmixfinal.loc[indmixfinal['indus'].isin(lowlist), 'incdist'] = 'low'
-    indmixfinal.loc[indmixfinal['indus'].isin(highlist), 'incdist'] = 'high'
-    targets = indmixfinal[['indus', 'pct', 'indint', 'incdist']].merge(incdist, on='incdist', how='left')
-    finaltargets = portionout(targets, 'pct_y', 'indint', 'indincint')
-    finaltargets.reset_index(drop=True, inplace=True)
-
-    return finaltargets
+def samplingworkers(df, wfhpctlist):
+    df = df.copy()
     
-
-def jwtr11flag(targetdf, workerdf, flagvalue):
-    """
-    flag workers in workerdf with jwtr11 with the flagvalue according to counts in targetdf
-    :param targetdf: guiding dataframe with targets values for each category
-    :param workerdf: worker df with selected flag column
-    :param flagvalue: the value to set the selected column to
-    :return:workerdf
-    """
-    for i, row in targetdf.iterrows():
-        ind = row['indus']
-        inc4 = row['hhinc4']
-        target = row['indincint']
-        options = workerdf[(workerdf['naics2'] == ind) & (workerdf['selected'] == 0) & (
-                workerdf['inccat4'] == inc4) & (workerdf['JWTR'] == 11)]
-        if len(options) > target:
-            ilist = [x for x in options.sample(target, random_state=seedvalue).index.values]
-            workerdf.loc[workerdf.index.isin(ilist), 'selected'] = flagvalue
-        else:
-            ilist2 = [x for x in options.index.values]
-            workerdf.loc[workerdf.index.isin(ilist2), 'selected'] = flagvalue
-
-    return workerdf
-
-
-
-def preproundtwo(workerdf, flagvalue, eduweights, targets):
-    """
-    prepare for roundtwo assignment by calculating numbers still needed and adding eduwgt column
-    :param workerdf: worker df with selected flag column
-    :param flagvalue: value in the 'selected' column to evaluate
-    :param eduweights: weights to apply based on edu field for use in sampling
-    :return: b, workerdf.  b is the new guiding df that lists how many are still needed in each category
-    """
-    result = workerdf[workerdf['selected'] == flagvalue].groupby(['naics2', 'inccat4']). \
-        household_id.count().reset_index()
-
-    # calculate number still needed in each category
-    comparison = targets.merge(result, left_on=['indus', 'hhinc4'],
-                               right_on=['naics2', 'inccat4'], how='left').reset_index()
-    comparison.household_id.fillna(0, inplace=True)
-    comparison['needed'] = comparison['indincint'] - comparison['household_id']
-    b = comparison[comparison['needed'] > 0]
-    workerdf = workerdf.merge(comparison[['indus', 'hhinc4', 'needed']],
-                              left_on=['naics2', 'inccat4'], right_on=['indus', 'hhinc4'], how='left')
-    workerdf.loc[workerdf['edu'] == 1, 'eduwgt'] = eduweights[0]
-    workerdf.loc[workerdf['edu'] == 2, 'eduwgt'] = eduweights[1]
-    workerdf.loc[workerdf['edu'] == 3, 'eduwgt'] = eduweights[2]
-
-    return b, workerdf
-
-
-def roundtwo(dfb, workerdf, flagvalue):
-    """
-    finish flagging workers to meet target values (outside of jwtr == 11) using eduwgt column
-    :param dfb:  guiding dataframe with targets values for each category
-    :param workerdf: worker df with selected flag column
-    :param flagvalue: value to set 'selected' column to
-    :return: workerdf
-    """
-    dolater = {}
-
-    for i, row in dfb.iterrows():
-        ind = row['indus']
-        inc4 = row['hhinc4']
-        target = int(row['needed'])
-        options = workerdf[(workerdf['naics2'] == ind) & (workerdf['selected'] == 0) & (
-                workerdf['inccat4'] == inc4)]
-
-        if len(options) >= target:
-            ilist = [x for x in options.sample(target, weights='eduwgt', random_state=seedvalue).index.values]
-            workerdf.loc[workerdf.index.isin(ilist), 'selected'] = flagvalue
-        elif len(options) < target:
-            dolater[i] = (ind,inc4,target)
-        
+    for idx, trc in enumerate(["low", "medium", "high"]):
+        filtered_df = df[df["trc"] == trc]
+        targetgrouptotal = int(len(filtered_df) * wfhpctlist[idx])
         
-    if len(dolater) > 0:
-        donow = pd.DataFrame.from_dict(dolater, orient='index', columns=['ind','inc','target'])
-        for i,row in donow.iterrows():
-            ind = row['ind']
-            inc4 = row['inc']
-            target = int(row['target'])
-            options = workerdf[(workerdf['naics2'] == ind) & (workerdf['selected'] == 0) & (
-                    workerdf['inccat4'] == inc4)]
-            ilist = [x for x in options.index.values]
-            stillneed = target - len(ilist)
-            options2 = workerdf[(workerdf['naics2'] == ind) & (workerdf['selected'] == 0) & (workerdf.inccat4 != inc4)]
-            ilist.extend([x for x in options2.sample(stillneed).index.values])
-
-            workerdf.loc[workerdf.index.isin(ilist), 'selected'] = flagvalue
-
-    return workerdf
-
-
-##########################################
-# part one: usualwfh
-##########################################
-
-# industry targets
-finaltargets1 = settargets(indmix1, targetwfh)
-
-print('selecting usualwfh from JWTR 11...')
-# round one
-workers = jwtr11flag(finaltargets1, workers, 1)
-
-# round two
-b1, workers = preproundtwo(workers, 1, eduw, finaltargets1)
-
-print('selecting usualwfh from any...')
-workers = roundtwo(b1, workers, 1)
-
-current = workers.selected.sum()
-remaining = int(targetwfh - current)
-print('checking overall usualwfh target met...')
-assert remaining == 0, "overall usualwfh target not met"
-workers = workers.drop(['indus', 'hhinc4', 'needed', 'eduwgt'], axis=1)
-
-##########################################
-# part two: tc14
-##########################################
-
-# industry targets
-finaltargets2 = settargets(indmix2, targettc14)
-
-print('selecting tc14 from JWTR 11...')
-# round one
-workers = jwtr11flag(finaltargets2, workers, 2)
-
-# round two
-b2, workers = preproundtwo(workers, 2, eduwtc, finaltargets2)
-
-print('selecting tc14 from any...')
-workers = roundtwo(b2, workers, 2)
-
-current = len(workers[workers['selected'] == 2])
-remaining = int(targettc14 - current)
-print('checking overall tc14 target met...')
-assert remaining == 0, "overall tc14 target not met"
-
-##########################################
-# assign tc14 wfh status for given day
-##########################################
-print('assigning wfh status for tc14...')
-workers['tc'] = 0
-
-tc1 = targettc14 * tcportions[0]
-tc2 = targettc14 * tcportions[1]
-tc3 = targettc14 * tcportions[2]
-tc4 = targettc14 * tcportions[3]
-
-count = 4
-for t in [tc4, tc3, tc2]:
-    options = workers[(workers['selected'] == 2) & (workers['tc'] == 0)]
-    it = int(t)
-    ilist = [x for x in options.sample(it, random_state=seedvalue).index.values]
-    workers.loc[workers.index.isin(ilist), 'tc'] = count
-    count -= 1
+        for i in [1, 2]:
+            for e in [1, 2]:
+                inc_factor = incdist_dict[trc][i]
+                edu_factor = edudist_dict[trc][e]
+                targetwfhworkers = int(targetgrouptotal * inc_factor * edu_factor)
+                
+                sub_filtered_df = filtered_df[(filtered_df["inccat2"] == i) & (filtered_df["edu"] == e)]
+                if targetwfhworkers > 0 and len(sub_filtered_df) >= targetwfhworkers:
+                    sample_idx = sub_filtered_df.sample(n=targetwfhworkers, random_state=2).index
+                    df.loc[sample_idx, "selected"] = 1  # mark selected rows
+    
+    return df
 
-workers.loc[(workers['selected'] == 2) & (workers['tc'] == 0), 'tc'] = 1
 
-workers['random'] = np.random.random(size=len(workers))
-workers.loc[(workers['selected'] == 2) & (workers['random'] < (workers['tc'] / 5)), 'working'] = 1
-workers.working.fillna(0, inplace=True)
+workers = samplingworkers(workers, wfhpctlist)
 
 ##########################################
 # format and save
@@ -343,18 +117,10 @@ def roundtwo(dfb, workerdf, flagvalue):
 popsynhh = pd.read_csv(popsynhhpath, names=['sz', 'hhtype', 'vehicles',
                                                'serialno', 'stpuma5', 'rowcol', 'adults', 'workers',
                                                'children', 'iq', 'age', 'hhvtype', 'income'])
-workers.loc[workers['selected'] == 1, 'usualwfh'] = 1
-workers.loc[(workers['selected'] == 2) & (workers['working'] == 1), 'tc14'] = 1
-workers.loc[(workers['selected'] == 2) & (workers['working'] == 0), 'tc14nw'] = 1
-workers.usualwfh.fillna(0, inplace=True)
-workers.tc14.fillna(0, inplace=True)
-workers.tc14nw.fillna(0, inplace=True)
-hhsummary = workers.groupby('household_id').agg({'usualwfh':'sum','tc14':'sum','tc14nw':'max'}).reset_index()
-hhsummary['wfhworkers'] = hhsummary['tc14'] + hhsummary['usualwfh']
-hhsummary.loc[(hhsummary['tc14'] > 0) & (hhsummary['usualwfh'] > 0), 'finalflag'] = 1
-hhsummary.loc[(hhsummary['tc14'] > 0) & (hhsummary['usualwfh'] == 0), 'finalflag'] = 2
-hhsummary.loc[(hhsummary['tc14'] == 0) & (hhsummary['usualwfh'] > 0), 'finalflag'] = 1
-final1 = dfhh[['household_id','SERIALNO']].merge(hhsummary[['household_id','finalflag','wfhworkers','tc14nw']],on='household_id', how='left')
+
+hhsummary = workers.groupby("household_id").agg(finalflag=("selected", lambda x: int(x.sum() > 0)),wfhworkers=("selected", "sum")).reset_index()
+
+final1 = dfhh[['household_id','SERIALNO']].merge(hhsummary[['household_id','finalflag','wfhworkers']],on='household_id', how='left')
 final1.finalflag.fillna(0, inplace=True)
 try:
     final1['SERIALNO'] = final1['SERIALNO'].str.replace('HU','99')
@@ -371,11 +137,9 @@ def roundtwo(dfb, workerdf, flagvalue):
 final1sort['diffcheck'] = final1sort['SERIALNO'] - final1sort['sn2']
 assert (final1sort.diffcheck == 0).all(), "file not aligned with popsyn_hh"
 final1sort.wfhworkers.fillna(0, inplace=True)
-final1sort.tc14nw.fillna(0, inplace=True)
 final1sort['finalflag'] = final1sort.finalflag.astype('int')
 final1sort['wfhworkers'] = final1sort.wfhworkers.astype('int')
-final1sort['tc14nw'] = final1sort.tc14nw.astype('int')
-final1sort[['SERIALNO', 'finalflag','wfhworkers','tc14nw']].to_csv(savedir + "/HH_WFH_STATUS.CSV", index=False, header=False)
+final1sort[['SERIALNO', 'finalflag','wfhworkers']].to_csv(savedir + "/HH_WFH_STATUS.CSV", index=False, header=False)
 
 ##########################################
 # save additional files
@@ -383,6 +147,4 @@ def roundtwo(dfb, workerdf, flagvalue):
 
 if savefiles == "Y":
     print('saving additional files...')
-    workers.to_csv("workers.csv", index=False)
-    finaltargets1.to_csv("finaltargets_usualwfh.csv", index=False)
-    finaltargets2.to_csv("finaltargets_tc14.csv", index=False)
\ No newline at end of file
+    workers.to_csv("workers.csv", index=False)
\ No newline at end of file
diff --git a/Database/tg/fortran/wfhmodule/wfhflag_old.py b/Database/tg/fortran/wfhmodule/wfhflag_old.py
new file mode 100644
index 0000000..95a8f02
--- /dev/null
+++ b/Database/tg/fortran/wfhmodule/wfhflag_old.py
@@ -0,0 +1,388 @@
+# coding=utf-8
+
+# this flags tbm people as usualwfh or tc14
+import pandas as pd
+import numpy as np
+import sys
+import os
+
+# pathways
+savedir = sys.argv[2]
+assert os.path.exists(savedir) == True, "savedir not valid"
+os.chdir(sys.argv[1])
+synpoppath = "synthetic_persons.zip"
+synhhpath = "synthetic_households.zip"
+popsynhhpath = savedir + "/POPSYN_HH.csv"
+incdistpath = "incdist.csv"
+indmixpath = "indusmix.csv"
+indpxwalkpath = "indp_naics.csv"
+
+# save additional output files?
+savefiles = sys.argv[3]
+
+# major parameters - source: mdt + nirpc survey (which is higher than PUMS data...)
+# percent of all workers
+usualwfhpct = float(sys.argv[4])
+tc14pct = float(sys.argv[5])
+
+# set seedvalue
+seedvalue = 2
+np.random.seed(seed=seedvalue)
+
+# place industries in one of two income distributions types
+# high is based on industries that had >60% of workers in income group 4
+# low is based on other industries
+lowlist = ['11', '21', '44-45', '48-49', '56', '61', '62', '71', '72', '81']
+highlist = ['22', '23', '31-33', '42', '51', '52', '53', '54', '55', '92']
+
+# setup education weights for usualwfh (low to high)
+eduw = [0.177, 0.246, 0.576]
+# setup education weights for usualwfh (low to high, fine they don't sum to 1)
+eduwtc = [0.049, 0.114, 0.720]
+
+# set distribution of tc14 into the 4 days
+tcportions = [0.575, 0.254, 0.112, 0.059]
+
+print('setting up for wfhflag script...')
+# read in files
+dfpop = pd.read_csv(synpoppath)
+dfhh = pd.read_csv(synhhpath, dtype={'MV': object})
+indpxwalk = pd.read_csv(indpxwalkpath)
+indmix = pd.read_csv(indmixpath)
+incdist = pd.read_csv(incdistpath)
+
+# industry mix - source: mdt + nirpc survey (rescaled after removing industries -7, -8, and 97 to sum to 1)
+indmix1 = indmix[indmix['cat'] == 'usualwfh'][['indus', 'pct']]
+indmix2 = indmix[indmix['cat'] == 'tc14'][['indus', 'pct']]
+
+# merge naics2, income4, edu, get workers
+dfpop = dfpop.merge(indpxwalk, on='INDP', how='left')
+dfpop = dfpop.merge(dfhh[['household_id', 'HINCP19']], on='household_id', how='left')
+dfpop['inccat4'] = pd.cut(dfpop.HINCP19, bins=[-99999, 30000, 60000, 100000, 2000000], right=False, labels=[1, 2, 3, 4])
+
+workers = dfpop[dfpop['JWTR'] != 'bb'].copy()
+workers.JWTR = workers.JWTR.astype(float).astype(int)
+workers.SCHL = workers.SCHL.astype(float).astype(int)
+workers.loc[workers.SCHL.isin([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]), 'edu'] = 1
+workers.loc[workers.SCHL.isin([18, 19, 20]), 'edu'] = 2
+workers.loc[workers.SCHL.isin([21, 22, 23, 24]), 'edu'] = 3
+workers.loc[:, 'selected'] = 0
+
+# overall targets
+targetwfh = round(len(workers) * usualwfhpct, 0)
+targettc14 = round(len(workers) * tc14pct, 0)
+
+workers = workers[workers['ESR'] != '4']
+
+# functions
+def portionout(df, pctcol, total, colname):
+    """
+    divides up a total based on a percent column into a new column (no decimals)
+    :param df: input dataframe
+    :param pctcol: name of column that has percentages
+    :param total: can be name of column that has totals, or a numeric total that the percentages will be applied to
+    :param colname: the name of the result column
+    :return: dataframe with targetdec, targetint, decimal, and colname columns
+    """
+    df = df.copy()
+    indcounts = workers[workers.selected == 0].groupby('naics2').agg({'household_id':'count'}).reset_index()
+    indcounts.rename({'household_id':'sectortotal'}, axis=1, inplace=True)
+
+
+    if isinstance(total, int) or isinstance(total, float):
+        df['targetdec'] = df[pctcol] * total
+
+        df2 = df.merge(indcounts, left_on='indus', right_on='naics2')
+        df2['testtot'] = df2['sectortotal'] - df2['targetdec']
+
+        if len(df2[df2.testtot < 0]) == 0:
+            pass
+        else:
+            # don't go all the way up to max to prevent same issue further down the line
+            df2.loc[df2.testtot < 0, 'newpct'] = (df2.sectortotal - 10) / total
+            df2.loc[df2.testtot < 0, 'pctdiff'] = df2[pctcol] - df2.newpct
+            todistribute = df2.pctdiff.sum()
+            # normalize unchanged pcts to one to distribute out the missing pct
+            newdenom = 1 - df2.loc[df2.testtot < 0][pctcol].sum()
+            df2.loc[df2.testtot >=0, 'pctfordist'] = df2[pctcol] / newdenom
+            df2.loc[df2.testtot >= 0, 'newpct'] = (df2.pctfordist * todistribute) + df2[pctcol]
+            df2[pctcol] = df2['newpct']
+            df2['targetdec'] = df2[pctcol] * total
+
+        df2['targetint'] = df2['targetdec'].astype('int')
+        df2['decimal'] = df2['targetdec'] - df2['targetint']
+        leftover = total - df2['targetint'].sum()
+        df2.sort_values('decimal', ascending=False, inplace=True)
+        df2.reset_index(drop=True, inplace=True)
+        df2.loc[df2.index < leftover, 'targetint'] = df2.targetint + 1
+        assert (total - df2.targetint.sum()) == 0, "Targets do not sum to total"
+        df2.rename({'targetint': colname}, axis=1, inplace=True)
+        return df2
+
+    elif isinstance(total, str):
+        df['targetdec'] = df[pctcol] * df[total]
+
+        df2 = df.merge(indcounts, left_on='indus', right_on='naics2')
+        df2['testtot'] = df2['sectortotal'] - df2['targetdec']
+
+        if len(df2[df2.testtot < 0]) == 0:
+            pass
+        else:
+            df2.loc[df2.testtot < 0, 'newpct'] = (df2.sectortotal - 10) / df[total]
+            df2.loc[df2.testtot < 0, 'pctdiff'] = df2[pctcol] - df2.newpct
+            todistribute = df2.pctdiff.sum()
+            df2.loc[df2.testtot >= 0, 'newpct'] = df2[pctcol] * (1 + todistribute)
+            df2[pctcol] = df2['newpct']
+            df2['targetdec'] = df2[pctcol] * df2[total]
+
+        df2['targetint'] = df2['targetdec'].astype('int')
+        df2['decimal'] = df2['targetdec'] - df2['targetint']
+
+        totallist = [x for x in df2[total].unique()]
+        piecelist = []
+        for i in totallist:
+            selection = df2[df2[total] == i].copy()
+            leftover = i - selection['targetint'].sum()
+            if leftover != 0:
+                selection.sort_values('decimal', ascending=False, inplace=True)
+                selection.reset_index(drop=True, inplace=True)
+                selection.loc[selection.index < leftover, 'targetint'] = selection.targetint + 1
+                assert (i - selection.targetint.sum()) == 0, "Targets do not sum to total"
+            piecelist.append(selection)
+        df2 = pd.concat(piecelist)
+        df2.rename({'targetint': colname}, axis=1, inplace=True)
+        return df2
+
+
+def settargets(indmix, overalltarget):
+    """
+    use portionout function to set industry/income targets for group of interest (usualwfh or tc14)
+    :param indmix: input df with distribution of people in group of interest into industries
+    :param overalltarget: total target value for people in group of interest
+    :return:
+    """
+    indmixfinal = portionout(indmix, 'pct', overalltarget, 'indint')
+
+    # income targets
+    indmixfinal.loc[indmixfinal['indus'].isin(lowlist), 'incdist'] = 'low'
+    indmixfinal.loc[indmixfinal['indus'].isin(highlist), 'incdist'] = 'high'
+    targets = indmixfinal[['indus', 'pct', 'indint', 'incdist']].merge(incdist, on='incdist', how='left')
+    finaltargets = portionout(targets, 'pct_y', 'indint', 'indincint')
+    finaltargets.reset_index(drop=True, inplace=True)
+
+    return finaltargets
+    
+
+def jwtr11flag(targetdf, workerdf, flagvalue):
+    """
+    flag workers in workerdf with jwtr11 with the flagvalue according to counts in targetdf
+    :param targetdf: guiding dataframe with targets values for each category
+    :param workerdf: worker df with selected flag column
+    :param flagvalue: the value to set the selected column to
+    :return:workerdf
+    """
+    for i, row in targetdf.iterrows():
+        ind = row['indus']
+        inc4 = row['hhinc4']
+        target = row['indincint']
+        options = workerdf[(workerdf['naics2'] == ind) & (workerdf['selected'] == 0) & (
+                workerdf['inccat4'] == inc4) & (workerdf['JWTR'] == 11)]
+        if len(options) > target:
+            ilist = [x for x in options.sample(target, random_state=seedvalue).index.values]
+            workerdf.loc[workerdf.index.isin(ilist), 'selected'] = flagvalue
+        else:
+            ilist2 = [x for x in options.index.values]
+            workerdf.loc[workerdf.index.isin(ilist2), 'selected'] = flagvalue
+
+    return workerdf
+
+
+
+def preproundtwo(workerdf, flagvalue, eduweights, targets):
+    """
+    prepare for roundtwo assignment by calculating numbers still needed and adding eduwgt column
+    :param workerdf: worker df with selected flag column
+    :param flagvalue: value in the 'selected' column to evaluate
+    :param eduweights: weights to apply based on edu field for use in sampling
+    :return: b, workerdf.  b is the new guiding df that lists how many are still needed in each category
+    """
+    result = workerdf[workerdf['selected'] == flagvalue].groupby(['naics2', 'inccat4']). \
+        household_id.count().reset_index()
+
+    # calculate number still needed in each category
+    comparison = targets.merge(result, left_on=['indus', 'hhinc4'],
+                               right_on=['naics2', 'inccat4'], how='left').reset_index()
+    comparison.household_id.fillna(0, inplace=True)
+    comparison['needed'] = comparison['indincint'] - comparison['household_id']
+    b = comparison[comparison['needed'] > 0]
+    workerdf = workerdf.merge(comparison[['indus', 'hhinc4', 'needed']],
+                              left_on=['naics2', 'inccat4'], right_on=['indus', 'hhinc4'], how='left')
+    workerdf.loc[workerdf['edu'] == 1, 'eduwgt'] = eduweights[0]
+    workerdf.loc[workerdf['edu'] == 2, 'eduwgt'] = eduweights[1]
+    workerdf.loc[workerdf['edu'] == 3, 'eduwgt'] = eduweights[2]
+
+    return b, workerdf
+
+
+def roundtwo(dfb, workerdf, flagvalue):
+    """
+    finish flagging workers to meet target values (outside of jwtr == 11) using eduwgt column
+    :param dfb:  guiding dataframe with targets values for each category
+    :param workerdf: worker df with selected flag column
+    :param flagvalue: value to set 'selected' column to
+    :return: workerdf
+    """
+    dolater = {}
+
+    for i, row in dfb.iterrows():
+        ind = row['indus']
+        inc4 = row['hhinc4']
+        target = int(row['needed'])
+        options = workerdf[(workerdf['naics2'] == ind) & (workerdf['selected'] == 0) & (
+                workerdf['inccat4'] == inc4)]
+
+        if len(options) >= target:
+            ilist = [x for x in options.sample(target, weights='eduwgt', random_state=seedvalue).index.values]
+            workerdf.loc[workerdf.index.isin(ilist), 'selected'] = flagvalue
+        elif len(options) < target:
+            dolater[i] = (ind,inc4,target)
+        
+        
+    if len(dolater) > 0:
+        donow = pd.DataFrame.from_dict(dolater, orient='index', columns=['ind','inc','target'])
+        for i,row in donow.iterrows():
+            ind = row['ind']
+            inc4 = row['inc']
+            target = int(row['target'])
+            options = workerdf[(workerdf['naics2'] == ind) & (workerdf['selected'] == 0) & (
+                    workerdf['inccat4'] == inc4)]
+            ilist = [x for x in options.index.values]
+            stillneed = target - len(ilist)
+            options2 = workerdf[(workerdf['naics2'] == ind) & (workerdf['selected'] == 0) & (workerdf.inccat4 != inc4)]
+            ilist.extend([x for x in options2.sample(stillneed).index.values])
+
+            workerdf.loc[workerdf.index.isin(ilist), 'selected'] = flagvalue
+
+    return workerdf
+
+
+##########################################
+# part one: usualwfh
+##########################################
+
+# industry targets
+finaltargets1 = settargets(indmix1, targetwfh)
+
+print('selecting usualwfh from JWTR 11...')
+# round one
+workers = jwtr11flag(finaltargets1, workers, 1)
+
+# round two
+b1, workers = preproundtwo(workers, 1, eduw, finaltargets1)
+
+print('selecting usualwfh from any...')
+workers = roundtwo(b1, workers, 1)
+
+current = workers.selected.sum()
+remaining = int(targetwfh - current)
+print('checking overall usualwfh target met...')
+assert remaining == 0, "overall usualwfh target not met"
+workers = workers.drop(['indus', 'hhinc4', 'needed', 'eduwgt'], axis=1)
+
+##########################################
+# part two: tc14
+##########################################
+
+# industry targets
+finaltargets2 = settargets(indmix2, targettc14)
+
+print('selecting tc14 from JWTR 11...')
+# round one
+workers = jwtr11flag(finaltargets2, workers, 2)
+
+# round two
+b2, workers = preproundtwo(workers, 2, eduwtc, finaltargets2)
+
+print('selecting tc14 from any...')
+workers = roundtwo(b2, workers, 2)
+
+current = len(workers[workers['selected'] == 2])
+remaining = int(targettc14 - current)
+print('checking overall tc14 target met...')
+assert remaining == 0, "overall tc14 target not met"
+
+##########################################
+# assign tc14 wfh status for given day
+##########################################
+print('assigning wfh status for tc14...')
+workers['tc'] = 0
+
+tc1 = targettc14 * tcportions[0]
+tc2 = targettc14 * tcportions[1]
+tc3 = targettc14 * tcportions[2]
+tc4 = targettc14 * tcportions[3]
+
+count = 4
+for t in [tc4, tc3, tc2]:
+    options = workers[(workers['selected'] == 2) & (workers['tc'] == 0)]
+    it = int(t)
+    ilist = [x for x in options.sample(it, random_state=seedvalue).index.values]
+    workers.loc[workers.index.isin(ilist), 'tc'] = count
+    count -= 1
+
+workers.loc[(workers['selected'] == 2) & (workers['tc'] == 0), 'tc'] = 1
+
+workers['random'] = np.random.random(size=len(workers))
+workers.loc[(workers['selected'] == 2) & (workers['random'] < (workers['tc'] / 5)), 'working'] = 1
+workers.working.fillna(0, inplace=True)
+
+##########################################
+# format and save
+##########################################
+print('preparing and saving HH_WFH_STATUS.csv...')
+popsynhh = pd.read_csv(popsynhhpath, names=['sz', 'hhtype', 'vehicles',
+                                               'serialno', 'stpuma5', 'rowcol', 'adults', 'workers',
+                                               'children', 'iq', 'age', 'hhvtype', 'income'])
+workers.loc[workers['selected'] == 1, 'usualwfh'] = 1
+workers.loc[(workers['selected'] == 2) & (workers['working'] == 1), 'tc14'] = 1
+workers.loc[(workers['selected'] == 2) & (workers['working'] == 0), 'tc14nw'] = 1
+workers.usualwfh.fillna(0, inplace=True)
+workers.tc14.fillna(0, inplace=True)
+workers.tc14nw.fillna(0, inplace=True)
+hhsummary = workers.groupby('household_id').agg({'usualwfh':'sum','tc14':'sum','tc14nw':'max'}).reset_index()
+hhsummary['wfhworkers'] = hhsummary['tc14'] + hhsummary['usualwfh']
+hhsummary.loc[(hhsummary['tc14'] > 0) & (hhsummary['usualwfh'] > 0), 'finalflag'] = 1
+hhsummary.loc[(hhsummary['tc14'] > 0) & (hhsummary['usualwfh'] == 0), 'finalflag'] = 2
+hhsummary.loc[(hhsummary['tc14'] == 0) & (hhsummary['usualwfh'] > 0), 'finalflag'] = 1
+final1 = dfhh[['household_id','SERIALNO']].merge(hhsummary[['household_id','finalflag','wfhworkers','tc14nw']],on='household_id', how='left')
+final1.finalflag.fillna(0, inplace=True)
+try:
+    final1['SERIALNO'] = final1['SERIALNO'].str.replace('HU','99')
+except AttributeError:
+    pass
+final1sort = final1.sort_values('SERIALNO').reset_index()
+
+pssort = popsynhh.sort_values('serialno').reset_index()
+final1sort['newindex'] = pssort['index']
+final1sort['sn2'] = pssort['serialno']
+final1sort.sort_values('newindex', inplace=True)
+final1sort.set_index('newindex', inplace=True)
+final1sort['SERIALNO'] = final1sort.SERIALNO.astype('int64')
+final1sort['diffcheck'] = final1sort['SERIALNO'] - final1sort['sn2']
+assert (final1sort.diffcheck == 0).all(), "file not aligned with popsyn_hh"
+final1sort.wfhworkers.fillna(0, inplace=True)
+final1sort.tc14nw.fillna(0, inplace=True)
+final1sort['finalflag'] = final1sort.finalflag.astype('int')
+final1sort['wfhworkers'] = final1sort.wfhworkers.astype('int')
+final1sort['tc14nw'] = final1sort.tc14nw.astype('int')
+final1sort[['SERIALNO', 'finalflag','wfhworkers','tc14nw']].to_csv(savedir + "/HH_WFH_STATUS.CSV", index=False, header=False)
+
+##########################################
+# save additional files
+##########################################
+
+if savefiles == "Y":
+    print('saving additional files...')
+    workers.to_csv("workers.csv", index=False)
+    finaltargets1.to_csv("finaltargets_usualwfh.csv", index=False)
+    finaltargets2.to_csv("finaltargets_tc14.csv", index=False)
\ No newline at end of file
diff --git a/Database/trip_gen.bat b/Database/trip_gen.bat
index c571a05..2e52ebe 100644
--- a/Database/trip_gen.bat
+++ b/Database/trip_gen.bat
@@ -48,17 +48,43 @@ for /f "eol=# skip=5 tokens=2 delims=:" %%c in (batch_file.yaml) do (set wfh=%%c
 :break3
 for /f "eol=# skip=6 tokens=2 delims=:" %%d in (batch_file.yaml) do (set tc14=%%d & goto break4)
 :break4
+
+rem -- Read model run settings from Telework.yaml --
+for /f "eol=# skip=2 tokens=2 delims=:" %%e in (Telework.yaml) do (set wfhLow=%%e & goto break5)
+:break5
+for /f "eol=# skip=3 tokens=2 delims=:" %%f in (Telework.yaml) do (set wfhMedium=%%f & goto break6)
+:break6
+for /f "eol=# skip=4 tokens=2 delims=:" %%g in (Telework.yaml) do (set wfhHigh=%%g & goto break7)
+:break7
+
 set sc=%sc:~1,3%
 set wfhFile=%wfhFile:~1%
 set wfh=%wfh:~1%
 set tc14=%tc14:~1%
+set wfhLow=%wfhLow:~1%
+set wfhMedium=%wfhMedium:~1%
+set wfhHigh=%wfhHigh:~1%
+
+for /f %%a in ('powershell -NoProfile -Command "(1000 + (200 - %sc%) * 1000 / 5000)/1000"') do set DeclineScaled=%%a
+for /f %%a in ('powershell -NoProfile -Command "%wfhLow% * %DeclineScaled%"') do set wfhLowSc=%%a
+for /f %%a in ('powershell -NoProfile -Command "%wfhMedium% * %DeclineScaled%"') do set wfhMediumSc=%%a
+for /f %%a in ('powershell -NoProfile -Command "%wfhHigh% * %DeclineScaled%"') do set wfhHighSc=%%a
+
+
 @echo.
 @echo ========================================
 @echo     --- Model Run Settings ---
 @echo  Scenario = %sc%
 @echo  Create WFH validation file = %wfhFile%
-@echo  Usual WFH share = %wfh%
-@echo  WFH 1-4 days share = %tc14%
+@echo  2019 Usual WFH share = %wfh%
+@echo  2019 WFH 1-4 days share = %tc14%
+@echo  2025/26 WFH low rate group share= %wfhLow%
+@echo  2025/26 WFH medium rate group share= %wfhMedium%
+@echo  2025/26 WFH high rate group share= %wfhHigh%
+@echo  Decline factor= %DeclineScaled%
+@echo  Scenario WFH low rate group share= %wfhLowSc%
+@echo  Scenario WFH medium rate group share= %wfhMediumSc%
+@echo  Scenario WFH high rate group share= %wfhHighSc%
 @echo ========================================
 @echo.
 rem
@@ -159,8 +185,15 @@ cd wfhmodule
 set filedir="%cd%"
 echo.
 echo Starting the work-from-home allocation model ...
-echo  wfh arguments: %filedir% %savedir% %wfhFile% %wfh% %tc14%
-python wfhflag.py %filedir% %savedir% %wfhFile% %wfh% %tc14%
+
+if %val%==100 (
+    echo Running wfhflag_old.py ...
+    python wfhflag_old.py %filedir% %savedir% %wfhFile% %wfh% %tc14%
+) else (
+    echo Running wfhflag.py ...
+    python wfhflag.py %filedir% %savedir% %wfhFile% %wfhLowSc% %wfhMediumSc% %wfhHighSc%
+)
+
 if %ERRORLEVEL% NEQ 0 (goto wfh_issue)
 cd ..