def handle(self, *args, **options):
ai = AdamImport()
ifile = 'F:\\adamexports\\adamcache\Incar\Data\INVEN.DBF'
ofile = 'F:\\adamexports\csvfiles\INVEN.csv'
out_type = 'csv'
ai.DBFConverter(ifile, ofile, out_type)
inv = pd.read_csv(ofile)
ext = inv.ONHAND * inv.COST
ext = sum(ext)
try:
r = PartsInv(invvalue=ext)
r.save()
print "updated inventory value to %s" % ext
except:
print "There was an error updating invvalue"
ifile = 'F:\\adamexports\\adamcache\Sicar\Data\\rofile.dbf'
ofile = 'F:\\adamexports\csvfiles\\rofile.csv'
out_type = 'csv'
ai.DBFConverter(ifile, ofile, out_type)
rof = pd.read_csv(ofile)
cutoff_date = datetime.date.today() + datetime.timedelta(-30)
ttlcount = rof.RO_NUM.count()
custsum = rof.CP_TOTAL.sum()
intsum = rof.IN_TOTAL.sum()
warsum = rof.WP_TOTAL.sum()
extsum = rof.XP_TOTAL.sum()
pcount = rof[rof['STATUS'].str.contains('P')]
pcount = pcount['STATUS'].count()
rof['DATE_IN'] = pd.to_datetime(rof['DATE_IN'])
oldcount = rof[rof['DATE_IN'] < pd.to_datetime(cutoff_date)]
oldcount = oldcount['RO_NUM'].count()
try:
r = ServiceRO(
totalrocount=ttlcount,
oldrocount=oldcount,
printedrocount=pcount,
ro_custpay=custsum,
ro_intpay=intsum,
ro_warpay=warsum,
ro_extpay=extsum,
)
r.save()
print "updated service RO values"
print ttlcount, pcount, oldcount, custsum, intsum, warsum, extsum
except:
print "There was an error updating Service Ro Values"
def do_conversion(in_file, out_file):
ai = AdamImport()
ifile = ''.join([ADAM_PATH, in_file])
ofile = ''.join([ADAM_EXPORT_PATH, out_file])
out_type = 'csv'
ai.DBFConverter(ifile, ofile, out_type)
print "ran conversion %s to %s" % (in_file, out_file)
def sv_Get_RO_Count(type):
"""
type:valid options are totalcount, oldcount
"""
ai = AdamImport()
ifile = 'F:\\adamexports\\adamcache\Sicar\Data\\rofile.dbf'
ofile = 'F:\\adamexports\csvfiles\\rofile.csv'
out_type = 'csv'
if need_refresh(ofile):
ai.DBFConverter(ifile, ofile, out_type)
rof = pd.read_csv(ofile, engine='python')
cutoff_date = datetime.date.today() + datetime.timedelta(-30)
ttlcount = rof.RO_NUM.count()
rof['DATE_IN'] = pd.to_datetime(rof['DATE_IN'])
oldcount = rof[rof['DATE_IN'] < pd.to_datetime(cutoff_date)]
oldcount = oldcount['RO_NUM'].count()
if type == 'totalcount':
return ttlcount
elif type == 'oldcount':
return oldcount
def do_conversion(ifile, ofile):
ai = AdamImport()
ifile = ''.join([ADAM_PATH, ifile])
ofile = ''.join([ADAM_EXPORT_PATH, ofile])
out_type = 'csv'
#if need_refresh(ofile):
ai.DBFConverter(ifile, ofile, out_type)
print "conversion completed"
def pa_Get_Inventory_Value():
ai = AdamImport()
ifile = 'F:\\adamexports\\adamcache\Incar\Data\INVEN.DBF'
ofile = 'F:\\adamexports\csvfiles\INVEN.csv'
out_type = 'csv'
if need_refresh(ofile):
ai.DBFConverter(ifile, ofile, out_type)
inv = pd.read_csv(ofile, engine='python')
ext = inv.ONHAND * inv.COST
ext = sum(ext)
return ext
def export(request, path_id):
# view to convert a DBF file on the fly and export it to a CSV
ai = AdamImport()
# get the id from the path and pull the correspoding DBF
p = ADAMFiles.objects.get(id=path_id)
p = str(p)
ai.DBFConverter(p, 'output.csv', 'csv')
f = open('output.csv')
ofile = os.path.basename(p)
ofile = ofile.replace('.dbf', '')
ofile = ofile.replace('.DBF', '')
response = HttpResponse(f, content_type="text/csv")
params = 'attachment; filename=%s.csv' % ofile
response['Content-Disposition'] = params
f.close()
return response
def detail(request, path_id):
# view for the adam/<id> page returns an html rendered dataframe
# create the object to convert DBF to pandas
# this view is mainly for previewing data
ai = AdamImport()
# get the id from the path and pull the correspoding DBF
p = ADAMFiles.objects.get(id=path_id)
p = str(p)
# conver the DBF to a dataframe
datafr = ai.DBFConverter(p, 'output.csv', 'pandas')
# *******************
# modify the dataframe here before converting to HTML
# be careful not to return too many rows or the http req will time out
# *******************
s = datafr.head()
# *******************
# *******************
s_trunk = s.to_html()
return HttpResponse(s_trunk)
from dbftopandas import AdamImport ai = AdamImport() i = 'f:\\adamexports\\adamcache\Sicar\Data\\rofile.dbf' o = 'f:\\adamexports\csvfiles\\rofile.csv' t = 'csv' ai.DBFConverter(i, o, t) #t = 'pandas' #pd = ai.DBFConverter(i,o,t) #print pd.head() #headers = ai.GetColNames(i) #print headers #i = 'f:\\adamexports\\adamcache\Apcar\Data\\apinv.dbf' #headers = ai.GetColNames(i) #print headers #data_types = ai.GetColNamesAndTypes(i) #for d in data_types: # if data_types[d] == 'NoneType': # print d, data_types[d]
def pa_Get_Parts_Count(type, start_days, end_days, field, cost=1500):
"""
type takes either total, detail, detail_stock (this returns detail including stock parts)
total retuns in int, sum of ONHAND
detail return a dataframe obj with detailed records
field can take DATEPURC or DATESOLD
cost should be expressed as an integer in cents
start_days should be a negative integer
end_days should be a negative integer greater than start days
"""
#parts needed to be in stock for DPA
stock_file = ''.join([ADAM_EXPORT_PATH, 'Extract.csv'])
stock = pd.read_csv(stock_file)
stock.columns = ['Ford', 'Alternate', 'QOH', 'Days1', 'Days2']
ford = stock[['Ford']].dropna()
ford['Alternate'] = ford['Ford']
stock = stock[['Alternate']].dropna()
stock = stock.append(ford['Alternate'], ignore_index=True)
#pull the latest parts inventory from ADAM
ai = AdamImport()
ifile = ''.join([ADAM_PATH, '\Incar\Data\INVEN.DBF'])
ofile = ''.join([ADAM_EXPORT_PATH, 'INVEN.csv'])
out_type = 'csv'
#refresh the data in necessary
if need_refresh(ofile):
ai.DBFConverter(ifile, ofile, out_type)
#read the CSV in a dataframe and convert the dates
inv = pd.read_csv(ofile, engine='python')
startdate = datetime.date.today() + datetime.timedelta(start_days)
enddate = datetime.date.today() + datetime.timedelta(end_days)
#initialize the parameters from the query string
fdDate = str(field)
#change cost to an integer and multiply by 100 to make it in dollars
intCost = int(cost) / 100
#filter out the data
inv = inv[inv['ONHAND'] > 0]
inv = inv[inv['COST'] > intCost]
inv[fdDate] = pd.to_datetime(inv[fdDate])
inv = inv[(inv[fdDate] < pd.to_datetime(startdate))
& (inv[fdDate] > pd.to_datetime(enddate))]
inv['ext'] = inv['ONHAND'] * inv['COST']
#if the type is total just give a total parts value
if type == "total":
inv_sum = inv['ONHAND'].sum()
return inv_sum
#if the type is detail prepare the data for detailed export
elif type == "detail":
#create a full part number field to filter on
inv.SUFFIX = inv.SUFFIX.fillna('')
inv['FULLPN'] = inv['PREFIX'] + inv['PARTNO'] + inv['SUFFIX']
#print inv['PREFIX'],inv['PARTNO'],inv['SUFFIX'],inv['FULLPN']
#create the filter to exclude stock parts
invx = inv['FULLPN'].isin(stock['Alternate'])
#print stock['Alternate'].loc[20:30]
print invx
#create a new inv dataframe with out the stock parts
inv = inv[~invx]
inv_detail = inv[[
'PREFIX', 'PARTNO', 'SUFFIX', 'DESC', 'ONHAND', 'DATEPURC',
'DATESOLD', 'COST', 'ext', 'LOCATION'
]]
inv_detail = inv_detail.sort(fdDate)
return inv_detail
elif type == "detail_stock":
#in this case, return everything including stock parts
inv_detail = inv[[
'PREFIX', 'PARTNO', 'SUFFIX', 'DESC', 'ONHAND', 'DATEPURC',
'DATESOLD', 'COST', 'ext', 'LOCATION'
]]
inv_detail = inv_detail.sort(fdDate)
return inv_detail