def subject_upload_process_controller():
file = request.files['file']
rows = get_row_values(file)
rows=rows[1:]
for column in rows:
new_subject = Subject(
id=generate_key(),
name=column[0])
new_subject.put()
return render_template('all_done.html')
def teacher_upload_process_controller():
file = request.files['file']
rows = get_row_values(file)
rows=rows[1:]
for column in rows:
new_teacher = Teacher(
id=generate_key(),
name=column[0],
email=column[1])
new_teacher.put()
return render_template('all_done.html')
def student_upload_process_controller():
file = request.files['file']
rows = get_row_values(file)
rows = rows[1:]
for column in rows:
new_student = Student(id=generate_key(),
name=column[0],
email=column[1],
username=column[2],
password=column[3])
new_student.put()
return render_template('all_done.html')
def main():
count_obj = 0
count_ser = 0
count_err = 0
all_values = {}
all_iso_info = False
last_object = {}
_obj = None
k_visited = []
current_area_code = 0
reroute = 0
last_line = []
last_area_code = 0
last_occ_code = 0
exclusions = []
my_states = {
"alabama": [1, "AL", "alabama"],
"alaska": [2, "AK", "alaska"],
"arizona": [3, "AZ", "arizona"],
"arkansas": [4, "AR", "arkansas"],
"california": [5, "CA", "california"],
"colorado": [6, "CO", "colorado"],
"connecticut": [7, "CT", "connecticut"],
"delaware": [8, "DE", "delaware"],
"district_of_columbia": [9, "DC", "district_of_columbia"],
"florida": [10, "FL", "florida"],
"georgia": [11, "GA", "georgia"],
"hawaii": [12, "HI", "hawaii"],
"idaho": [13, "ID", "idaho"],
"illinois": [14, "IL", "illinois"],
"indiana": [15, "IN", "indiana"],
"iowa": [16, "IA", "iowa"],
"kansas": [17, "KS", "kansas"],
"kentucky": [18, "KY", "kentucky"],
"louisiana": [19, "LA", "louisiana"],
"maine": [20, "ME", "maine"],
"maryland": [21, "MD", "maryland"],
"massachusetts": [22, "MA", "massachusetts"],
"michigan": [23, "MI", "michigan"],
"minnesota": [24, "MN", "minnesota"],
"mississippi": [25, "MS", "mississippi"],
"missouri": [26, "MO", "missouri"],
"montana": [27, "MT", "montana"],
"nebraska": [28, "NE", "nebraska"],
"nevada": [29, "NV", "nevada"],
"new_hampshire": [30, "NH", "new_hampshire"],
"new_jersey": [31, "NJ", "new_jersey"],
"new_mexico": [32, "NM", "new_mexico"],
"new_york": [33, "NY", "new_york"],
"north_carolina": [34, "NC", "north_carolina"],
"north_dakota": [35, "ND", "north_dakota"],
"ohio": [36, "OH", "ohio"],
"oklahoma": [37, "OK", "oklahoma"],
"oregon": [38, "OR", "oregon"],
"pennsylvania": [39, "PA", "pennsylvania"],
"puerto_rico": [40, "PR", "puerto_rico"],
"rhode_island": [41, "RI", "rhode_island"],
"south_carolina": [42, "SC", "south_carolina"],
"south_dakota": [43, "SD", "south_dakota"],
"tennessee": [44, "TN", "tennessee"],
"texas": [45, "TX", "texas"],
"utah": [46, "UT", "utah"],
"vermont": [47, "VT", "vermont"],
"virginia": [48, "VA", "virginia"],
"washington": [49, "WA", "washington"],
"west_virginia": [50, "WV", "west_virginia"],
"wisconsin": [51, "WI", "wisconsin"],
"wyoming": [52, "WY", "wyoming"]
}
vals_fp = open("./value_set.csv", "r")
build_values_into_json(vals_fp, all_values)
vals_fp.close()
file_stamp = 1
fw = open(f'./finals_out/series_data_{file_stamp}.json', "a+")
fw.write("[")
# 05/21/2020
# fw = open("bls_series_values.json", "w")
# fw.write(json.dumps(all_values, indent=4))
# fw.close()
# exit()
for filename in sorted(os.listdir("./split_series/")):
c_fp = open(f"./split_series/{filename}", "r")
print(f"Reading: {filename}")
for line in c_fp.readlines():
# if not reroute:
count_ser = count_ser + 1
c_line = line.split("\t")
area_code = c_line[7] # becomes redundant
state_code = c_line[6] # Redundant
series_id = c_line[0].strip(" ")
industry_code = c_line[3]
occupation_code = c_line[4]
sector_code = c_line[8]
title = c_line[9] # Parse the sh*t out of this
# if area_code in exclusions:
# continue
# vals = [area_code,state_code,series_id,industry_code,occupation_code,sector_code,title]
# with open("./debug_1.json", "a+") as fw_1:
# fw_1.write("--------------------------- NEW VALUES --------------------------------\n")
# fw_1.write(f"{vals}\n")
# fw_1.write(f"Current Area Code: {area_code}\nPrev Area Code: {last_area_code}\ncurrent occ code: {occupation_code}\nlast occ code: {last_occ_code}")
# fw_1.write("\n-----------------------------------------------------------------------\n")
try:
if last_area_code != area_code or last_occ_code != occupation_code:
count_obj = count_obj + 1
if count_obj % 600 == 0 and count_obj > 1:
fw.write(str(json.dumps(_obj, indent=4)))
fw.write("]")
fw.close()
fw = None
file_stamp = file_stamp + 1
elif _obj:
fw.write(str(json.dumps(_obj, indent=4)) + ",")
if fw == None:
fw = open(
f"./finals_out/series_data_{str(file_stamp)}.json",
"a+")
fw.write("[")
last_object = _obj
last_area_code = area_code
last_occ_code = occupation_code
k_visited = []
_obj = None
# Write to file
except Exception as e:
c_fp.close()
vals_fp.close()
fw.close()
print("Error: {e.message}")
exit()
# [1] Initialize this object
if _obj == None:
p_series = get_series(str(area_code))
# print(p_series)
# p_series {
# "t" : t,
# "state_id" : str(the_line[0]),
# "area_code" : str(code),
# "name" : str(the_line[3])
# }
if not p_series: # Improbability alert
c_fp.close()
vals_fp.close()
fw.close()
exit()
_obj = reset_structures(p_series["t"])
# Initialize immediately resolvable parameters
if p_series["t"] == "city":
_obj["ids"]["name"] = norm_name(p_series["name"][0])
_obj["ids"]["state_id"] = p_series["state_id"]
if p_series["t"] == "metro":
if _obj == None:
print("WTF???")
exit()
_obj["ids"]["name"] = norm_name(p_series["name"][0])
_obj["ids"]["state_id"] = p_series["state_id"]
_obj["ids"]["netro_uid"] = p_series["area_code"]
if p_series["t"] == "state":
_obj["ids"]["name"] = norm_name(p_series["name"][0])
_obj["ids"]["state_id"] = p_series["state_id"]
if p_series["t"] == "nation":
_obj["ids"]["name"] = "united_states_of_america"
# [2] Initialize object general info
if len(_obj['ww_info']) == 0:
next_step = {
"series_id": series_id,
"industry_code": industry_code,
"occupation_code": occupation_code,
"sector_code": sector_code,
"title": title
}
# return {
# "sector_info" : [code, name], "occupation_info" : [code, name], "industry_info" : [code, name]
# }
all_info = get_all_infos(next_step["sector_code"], \
next_step["occupation_code"], \
next_step["industry_code"])
# Final "ww_info" values √
ww_info = {
"sec": all_info['sector_info'],
"occ": all_info['occupation_info'],
"ind": all_info['industry_info'],
"ae": area_code
}
_obj["ww_info"] = ww_info
# Ex. "[per_tile, hrly, atfp]"
cur_key_info = generate_key(title)
k_buffer = []
for k in cur_key_info:
if k != None:
k_buffer.append(k)
ser_value = all_values[series_id]["val"]
if ser_value == "-":
ser_value = "NOTE_" + all_values[series_id]['note'].rstrip(
'\n')
if len(k_buffer) == 2:
_obj[k_buffer[0]][k_buffer[1]] = ser_value
if len(k_buffer) == 3:
_obj[k_buffer[0]][k_buffer[1]][k_buffer[2]] = ser_value
if "".join(k_buffer) not in k_visited:
k_visited.append("".join(k_buffer))
else:
print("--------- STOP ----------")
count_err = count_err + 1
print(
f"Duplicate: {''.join(k_buffer)}\nSeries: {series_id}\nError: {count_err}\n"
)
k_visited = []
k_visited.append("".join(k_buffer))
print(f"counted {count_obj} objects")
print(f"counted {count_ser} series")
print(f"counted {count_err} errors")
exit()
c_fp.close()
# Reference Chunk
# Key Title
# [emp] Employment for Management Occupations in All Industries in Abilene, TX
# [e_std_err] Employment percent relative standard error for Management Occupations in All Industries in Abilene, TX
# [hmean] Hourly mean wage for Management Occupations in All Industries in Abilene, TX
# [amean] Annual mean wage for Management Occupations in All Industries in Abilene, TX
# [wp_std_err] Wage percent relative standard error for Management Occupations in All Industries in Abilene, TX
# [htp] Hourly 10th percentile wage for Management Occupations in All Industries in Abilene, TX
# [htfp] Hourly 25th percentile wage for Management Occupations in All Industries in Abilene, TX
# [hmp] Hourly median wage for Management Occupations in All Industries in Abilene, TX
# [hsfp] Hourly 75th percentile wage for Management Occupations in All Industries in Abilene, TX
# [hnp] Hourly 90th percentile wage for Management Occupations in All Industries in Abilene, TX
# [atp] Annual 10th percentile wage for Management Occupations in All Industries in Abilene, TX
# [atfp] Annual 25th percentile wage for Management Occupations in All Industries in Abilene, TX
# [amp] Annual median wage for Management Occupations in All Industries in Abilene, TX
# [asfp] Annual 75th percentile wage for Management Occupations in All Industries in Abilene, TX
# [anp] Annual 90th percentile wage for Management Occupations in All Industries in Abilene, TX
# [pk] Employment per 1,000 jobs for Management Occupations in All Industries in Abilene, TX
# [quo] Location Quotient for Management Occupations in All Industries in Abilene, TX
# Write the last object
fw.write(str(json.dumps(_obj, indent=4)))
fw.write("]")
vals_fp.close()
fw.close()
print(f"counted {count_obj} objects")
print(f"counted {count_ser} series")
print(f"counted {count_err} errors")
