def algo20(db,ref):
#Output if completion date < current date and activity status not like completed
status_col_loc = column_index_from_string(etl.find_in_header(db, 'Activity Status')) - 1
comp_col_loc = column_index_from_string(etl.find_in_header(db, 'DD - End')) - 1
bad_date = []
valid = True
comp = ""
for row in db.rows[1:]:
try:
comp = datetime(day = int(etl.xstr(row[comp_col_loc].value)), \
month = int(etl.xstr(row[comp_col_loc+1].value)), \
year = int(etl.xstr(row[comp_col_loc+2].value)))
except:
valid = False
if valid:
try:
if comp < datetime.now() and 'completed' not in row[status_col_loc].value.lower():
bad_date.append('%s row %i' % (str(comp),row[0].row))
except:
bad_date.append('%s row %i' % (str(comp),row[0].row))
return db, ref, return_message('Bad date or Completion Date has passed for ' ,bad_date)
def algo10(db, ref):
#column J: must be in reference>ActionDescription relative to Action_Type
#There are 4 action types each with their own self labeled column
db_type_loc = etl.find_in_header(db, 'Action type')
db_desc_loc = etl.find_in_header(db, 'Action description')
ref_col_loc = etl.find_in_header(ref, 'Action_Type')
#get ref action type tuples
act_types_tups = []
#go through action types and go through respected cols and make tuples
for row in ref.iter_rows(ref_col_loc + "2:" +
etl.find_last_value(ref, ref_col_loc, 'c')):
act = etl.xstr(row[0].value)
act_col = etl.find_in_header(ref, act)
for row in ref.iter_rows(act_col + "2:" +
etl.find_last_value(ref, act_col, 'c')):
act_types_tups.append((act, etl.xstr(row[0].value)))
#get tuples of dist actions
type_desc_tup = []
for row in db.iter_rows(db_type_loc + "2:" +
etl.find_last_value(db, db_desc_loc, 'c')):
type_desc_tup.append((etl.xstr(row[0].value), etl.xstr(row[1].value)))
malformed_acts = []
#go through dist actions and see if they're in ref tuples
for tup in type_desc_tup:
if tup not in act_types_tups:
malformed_acts.append(etl.xstr(tup))
return db, ref, return_message('Action descriptions not in action types:',
malformed_acts)
def algo7(db, ref):
#Column H: must be in Reference>VDC - relative to districy
#the district in which a vdc in the db is located must = the district in ref
db_dist_loc = etl.find_in_header(db, 'District')
db_vdc_loc = etl.find_in_header(db, 'VDC / Municipalities')
ref_dist_loc = etl.find_in_header(ref, 'Admin1_District')
ref_vdc_loc = etl.find_in_header(ref, 'Admin2_OCHA_VDC-Municipality')
#create tuple of vdc, dist for db and ref
db_tuple = []
for row in db.iter_rows(db_dist_loc + "2:" +
etl.find_last_value(db, db_vdc_loc, 'c')):
db_tuple.append((etl.xstr(row[0].value), etl.xstr(row[1].value)))
ref_tuple = []
#do it just for dist, then for vdc as they're not neighbors
for row in ref.iter_rows('E2:' + etl.find_last_value(ref, 'E', 'c')):
ref_tuple.append((etl.xstr(row[0].value), ))
it = 0
for row in ref.iter_rows(ref_vdc_loc + "2:" +
etl.find_last_value(ref, ref_vdc_loc, 'c')):
ref_tuple[it] += (etl.xstr(row[0].value.encode("utf8")), )
it += 1
#iterate through db_tuple and see if tuple is in ref
malformed_vdc = []
for tup in db_tuple:
if tup not in ref_tuple:
malformed_vdc.append(etl.xstr(tup))
return db, ref, return_message('VDCs in incorrect districts:',
malformed_vdc)
def algo16(db,ref):
#Column Q: must be a number>=0 and <= Column N (more conditions to follow)
db_cnt_loc = etl.find_in_header(db, 'Total Number Households')
db_vul_loc = etl.find_in_header(db, 'Vulnerable Caste / Ethnicity households ')
#get count values
cnt_vals = []
for row in db.iter_rows(db_cnt_loc + "2:" +
etl.find_last_value(db, db_cnt_loc, 'c')):
cnt_vals.append(etl.xstr(row[0].value))
#get female values
vul_vals = []
for row in db.iter_rows(db_vul_loc + "2:" +
etl.find_last_value(db, db_vul_loc, 'c')):
vul_vals.append(etl.xstr(row[0].value))
malformatted = []
#zip and check values
for vals in zip(cnt_vals, vul_vals):
if not (vals[0].isdigit() and vals[1].isdigit()):
malformatted.append(etl.xstr(vals))
elif int(vals[1]) > int(vals[0]):
malformatted.append(etl.xstr(vals))
return db, ref, return_message('Total #HH and Vulnerable #HH conflict or issue ' ,malformatted)
def algo16(db, ref):
#Column Q: must be a number>=0 and <= Column N (more conditions to follow)
db_cnt_loc = etl.find_in_header(db, 'Total Number Households')
db_vul_loc = etl.find_in_header(
db, 'Vulnerable Caste / Ethnicity households ')
#get count values
cnt_vals = []
for row in db.iter_rows(db_cnt_loc + "2:" +
etl.find_last_value(db, db_cnt_loc, 'c')):
cnt_vals.append(etl.xstr(row[0].value))
#get female values
vul_vals = []
for row in db.iter_rows(db_vul_loc + "2:" +
etl.find_last_value(db, db_vul_loc, 'c')):
vul_vals.append(etl.xstr(row[0].value))
malformatted = []
#zip and check values
for vals in zip(cnt_vals, vul_vals):
if not (vals[0].isdigit() and vals[1].isdigit()):
malformatted.append(etl.xstr(vals))
elif int(vals[1]) > int(vals[0]):
malformatted.append(etl.xstr(vals))
return db, ref, return_message(
'Total #HH and Vulnerable #HH conflict or issue ', malformatted)
def algo10(db,ref):
#column J: must be in reference>ActionDescription relative to Action_Type
#There are 4 action types each with their own self labeled column
db_type_loc = etl.find_in_header(db, 'Action type')
db_desc_loc = etl.find_in_header(db, 'Action description')
ref_col_loc = etl.find_in_header(ref,'Action_Type')
#get ref action type tuples
act_types_tups = []
#go through action types and go through respected cols and make tuples
for row in ref.iter_rows(ref_col_loc + "2:" +
etl.find_last_value(ref, ref_col_loc, 'c')):
act = etl.xstr(row[0].value)
act_col = etl.find_in_header(ref, act)
for row in ref.iter_rows(act_col + "2:" +
etl.find_last_value(ref, act_col, 'c')):
act_types_tups.append((act, etl.xstr(row[0].value)))
#get tuples of dist actions
type_desc_tup = []
for row in db.iter_rows(db_type_loc + "2:" +
etl.find_last_value(db, db_desc_loc, 'c')):
type_desc_tup.append((etl.xstr(row[0].value),etl.xstr(row[1].value)))
malformed_acts = []
#go through dist actions and see if they're in ref tuples
for tup in type_desc_tup:
if tup not in act_types_tups:
malformed_acts.append(etl.xstr(tup))
return db, ref, return_message('Action descriptions not in action types:' ,malformed_acts)
def algo5(db,ref):
#if Column C contains Column A: remove all spaces, commas, dashes and the matching substring in C
db_col_loc_a = etl.find_in_header(db, 'Implementing agency')
db_col_loc_c = etl.find_in_header(db, 'Local partner agency')
vals_changed = []
for row in db.iter_rows(db_col_loc_a + "2:" +
etl.find_last_value(db, db_col_loc_c, 'c')):
if etl.xstr(row[0].value) in etl.xstr(row[2].value) and etl.xstr(row[0].value) != etl.xstr(row[2].value):
vals_changed.append(row[2].value)
row[2].value = row[2].value.replace(row[0].value,'').replace(' ','').replace(',','').replace('-','')
return db, ref, return_message('IA contained in LPA for the following:' ,vals_changed)
def algo12(db,ref):
#Column M: must be a number>=0 and (more conditions to follow)
db_col_loc = etl.find_in_header(db, '# Items / # Man-hours / NPR')
bad_vals = []
for row in db.iter_rows(db_col_loc + "2:" +
etl.find_last_value(db, db_col_loc, 'c')):
if not etl.xstr(row[0].value).isdigit():
if row[0].value == None:
bad_vals.append('Blank @ cell' + row[0].coordinate)
else:
bad_vals.append(etl.xstr(row[0].value))
return db, ref, return_message('Invalid numbers for # Items / # Man-hours / NPR' ,bad_vals)
def algo13(db,ref):
#Column N: must be a number>=0 and (more conditions to follow)
db_col_loc = etl.find_in_header(db, 'Total Number Households')
bad_vals = []
for row in db.iter_rows(db_col_loc + "2:" +
etl.find_last_value(db, db_col_loc, 'c')):
if not etl.xstr(row[0].value).isdigit():
if row[0].value == None:
bad_vals.append('Blank@ ' + row[0].coordinate)
else:
bad_vals.append(etl.xstr(row[0].value))
return db, ref, return_message('Invalid numbers for Total Number Households' ,bad_vals)
def algo4(db,ref):
#if Column C == Column A
#Clear column C
db_col_loc_a = etl.find_in_header(db, 'Implementing agency')
db_col_loc_c = etl.find_in_header(db, 'Local partner agency')
vals_changed = []
for row in db.iter_rows(db_col_loc_a + "2:" +
etl.find_last_value(db, db_col_loc_c, 'c')):
if etl.xstr(row[0].value) == etl.xstr(row[2].value):
vals_changed.append(etl.xstr(row[2].value))
row[2].value = ''
return db, ref, return_message('Local Partner Agencies matched Implementing and were cleared' ,vals_changed)
def algo14(db, ref):
#Column O: must be a number>=0 and (more conditions to follow)
db_col_loc = etl.find_in_header(db, 'Average cost per households (NPR)')
bad_vals = []
for row in db.iter_rows(db_col_loc + "2:" +
etl.find_last_value(db, db_col_loc, 'c')):
if not etl.xstr(row[0].value).isdigit():
if row[0].value == None:
bad_vals.append('Blank@ ' + row[0].coordinate)
else:
bad_vals.append(etl.xstr(row[0].value))
return db, ref, return_message(
'Invalid numbers for Average cost per households (NPR)', bad_vals)
def algo12(db, ref):
#Column M: must be a number>=0 and (more conditions to follow)
db_col_loc = etl.find_in_header(db, '# Items / # Man-hours / NPR')
bad_vals = []
for row in db.iter_rows(db_col_loc + "2:" +
etl.find_last_value(db, db_col_loc, 'c')):
if not etl.xstr(row[0].value).isdigit():
if row[0].value == None:
bad_vals.append('Blank @ cell' + row[0].coordinate)
else:
bad_vals.append(etl.xstr(row[0].value))
return db, ref, return_message(
'Invalid numbers for # Items / # Man-hours / NPR', bad_vals)
def algo18(db,ref):
#Column S: must be a date>= 25/04/2015
date_col_loc = column_index_from_string(etl.find_in_header(db, 'DD - Start')) - 1
cur = ""
bad_date = []
for row in db.rows[1:]:
try:
cur = datetime(day = int(etl.xstr(row[date_col_loc].value)), \
month = int(etl.xstr(row[date_col_loc+1].value)), \
year = int(etl.xstr(row[date_col_loc+2].value)))
if cur < parse('4-25-2015'):
bad_date.append(str(cur) + ' @ row ' + str(row[0].row))
except :
bad_date.append(str(cur) + ' @ row ' + str(row[0].row))
return db, ref, return_message('Malformatted date before EQ date', bad_date)
def algo19(db,ref):
#Column T: must be a date>= 25/04/2015 and >=Column S
start_col_loc = column_index_from_string(etl.find_in_header(db, 'DD - Start')) - 1
comp_col_loc = column_index_from_string(etl.find_in_header(db, 'DD - End')) - 1
bad_date = []
start = ""
comp = ""
for row in db.rows[1:]:
try:
start = datetime(day = int(etl.xstr(row[start_col_loc].value)), \
month = int(etl.xstr(row[start_col_loc+1].value)), \
year = int(etl.xstr(row[start_col_loc+2].value)))
comp = datetime(day = int(etl.xstr(row[comp_col_loc].value)), \
month = int(etl.xstr(row[comp_col_loc+1].value)), \
year = int(etl.xstr(row[comp_col_loc+2].value)))
if comp < start:
bad_date.append(str(start) + ' @ row ' + str(row[0].row))
except:
bad_date.append(str(start) + ' @ row ' + str(row[0].row))
return db, ref, return_message('Malformatted date or before Start Date', bad_date)
def algo2(db,ref):
#Column B: If == Implementing Agency set Column B=Internal
#assumes they are next to each other
db_col_loc_impl = etl.find_in_header(db, 'Implementing agency')
db_col_loc_source = etl.find_in_header(db, 'Sourcing Agency')
vals_changed = []
for row in db.iter_rows(db_col_loc_impl + "2:" +
etl.find_last_value(db, db_col_loc_source, 'c')):
if row[0].value == row[1].value:
vals_changed.append(etl.xstr(row[1].value))
row[1].value = 'INTERNAL'
return db, ref, return_message('Sourcing Agencies set as INTERNAL:' ,vals_changed)
def algo7(db,ref):
#Column H: must be in Reference>VDC - relative to districy
#the district in which a vdc in the db is located must = the district in ref
db_dist_loc = etl.find_in_header(db, 'District')
db_vdc_loc = etl.find_in_header(db, 'VDC / Municipalities')
ref_dist_loc = etl.find_in_header(ref, 'Admin1_District')
ref_vdc_loc = etl.find_in_header(ref, 'Admin2_OCHA_VDC-Municipality')
#create tuple of vdc, dist for db and ref
db_tuple = []
for row in db.iter_rows(db_dist_loc + "2:" +
etl.find_last_value(db, db_vdc_loc, 'c')):
db_tuple.append((etl.xstr(row[0].value), etl.xstr(row[1].value)))
ref_tuple = []
#do it just for dist, then for vdc as they're not neighbors
for row in ref.iter_rows('E2:' +
etl.find_last_value(ref, 'E', 'c')):
ref_tuple.append((etl.xstr(row[0].value),))
it = 0
for row in ref.iter_rows(ref_vdc_loc + "2:" +
etl.find_last_value(ref, ref_vdc_loc, 'c')):
ref_tuple[it]+=(etl.xstr(row[0].value.encode("utf8")),)
it+=1
#iterate through db_tuple and see if tuple is in ref
malformed_vdc = []
for tup in db_tuple:
if tup not in ref_tuple:
malformed_vdc.append(etl.xstr(tup))
return db, ref, return_message('VDCs in incorrect districts:' ,malformed_vdc)
def gen_pk(r, locs):
return etl.xstr(r[locs["imp_agency"]-1].value)+etl.xstr(r[locs["local_partner"]-1].value)+etl.xstr(r[locs["district"]-1].value)+etl.xstr(r[locs["vdc"]-1].value)+etl.xstr(r[locs["ward"]-1].value)+etl.xstr(r[locs["act_type"]-1].value)+etl.xstr(r[locs["act_desc"]-1].value)+etl.xstr(r[locs["quantity"]-1].value)+etl.xstr(r[locs["total_hh"]-1].value)
