def map():
global total, search_term
search_term = request.form['search']
numbers = norm_vals(job_search(search_term))
total = numbers.pop()
return render_template("demand_map.html", job_totals = numbers[0], date_avgs = numbers[1], numbers_list = numbers[2], total = total, query = search_term)
def job_info(job_name):
## Initialize global variables
date_list, state_list, expired_list, city_list, lat_list, long_list = [], [], [], [], [], []
query_name = job_search(job_name)
## Get a count of the total number of relevant jobs found
job_total = num_pages(query_name)
page_total = job_total/25
count = 1
for pages in range(page_total):
# #for now, the extract_data will only extract state_list
state_list += extract_data(query_name, str(count), str(count * 25), date_list, state_list, expired_list, city_list, lat_list, long_list)
count += 25
## Separate the states by uniqueness and count the number of each state with a job post
unique_states = list(set(state_list))
state_counts = {i: state_list.count(i) for i in unique_states}
state_info = [unique_states, state_counts]
return state_info
def top_cities():
global total, search_term
state = request.form['state_name']
if state == 'MI1' or state == 'MI2':
cities = fifteen_per_state("MI")
state = "MI"
else:
cities = fifteen_per_state(state)
search_string = job_search(search_term)
number_jobs = city_numbers(search_string, cities, state)
sorted_city_indices = sorted(range(len(number_jobs)), key=lambda k: number_jobs[k])
job_list, city_list = [], []
for indices in sorted_city_indices:
job_list.append(number_jobs[indices])
city_list.append(cities[indices])
return render_template("top_cities.html", state = state, total = total,
jobnum_list = job_list, list_of_cities = city_list, query = search_term, search_string = search_string)
import urllib2
import simplejson as json
from python_testing import job_search, norm_vals
from city_practice import city_numbers, fifteen_per_state
search = "executive chef"
number_jobs = norm_vals(job_search(search))
print number_jobs
