In [17]:

    

import pandas as pd
import os
# this is where I unzipped the MTC data
SRCDIR = "/Users/ffoti/data/activitysim"
# and where it's going to
TGTFILE = "../example/data/mtc_asim.h5"


    

In [18]:

    

store = pd.HDFStore(TGTFILE, "w")


    

In [19]:

    

col_map = {
    "HHID": "household_id",
    "AGE": "age",
    "TOTHH": "total_households",
    "TOTEMP": "total_employment",
    "TOTACRE": "total_acres",
    "COUNTY": "county_id",
    "hworkers": "workers",
    "HINC": "income"
}


    

In [20]:

    

df = pd.read_csv(os.path.join(SRCDIR, "landuse", "tazData.csv"), index_col="ZONE")
df.columns = [col_map.get(s, s) for s in df.columns]
store["land_use/taz_data"] = df


    

In [21]:

    

df = pd.read_csv(os.path.join(SRCDIR, "skims", "accessibility.csv"), index_col="taz")
df.columns = [col_map.get(s, s) for s in df.columns]
store["skims/accessibility"] = df


    

In [22]:

    

df = pd.read_csv(os.path.join(SRCDIR, "popsyn", "hhFile.p2011s3a1.2010.csv"), index_col="HHID")
df.columns = [col_map.get(s, s) for s in df.columns]
store["households"] = df


    

In [23]:

    

df = pd.read_csv(os.path.join(SRCDIR, "popsyn", "personFile.p2011s3a1.2010.csv"), index_col="PERID")
df.columns = [col_map.get(s, s) for s in df.columns]
store["persons"] = df


    

In [24]:

    

store.close()


    

In [24]: