def collecGDPInfo(state_abbr=[], year_range=YEAR_RANGE):
"""
Collect GDP using 'getGDPInfo()' and write to csv file (This is metropolitan level)
:param state_abbr: List of selected states that the data will be collected and write to file
:param year_range: List of year that the data will be collected. This can be the year that data is not available
:return: None
"""
# Variables
gdp_info_header = ["area_id", "area_name", "area_type", "year"]
gdp_info_types = ["per_capita_gdp", "per_capita_gdp_percent_change"]
# Delete all old data and write header to new file
gdp_info_file_name = "gdp_info.csv"
with open(gdp_info_file_name, 'w') as f:
f.write(",".join(gdp_info_header + gdp_info_types) + '\n')
place_in_us_count = 0
gdp_info_count = 0 # How many places we found for gdp info
for state in list(states.keys()):
if len(state_abbr) > 0 and not state in state_abbr:
continue # If specify states
areas = getAreas(
state, ["region.msa"
]) # GDP appears only in Metro Area level, not city level
place_in_us_count += len(areas)
for area in areas:
for year in year_range:
print("Gathering - State: " + state + " Area: " +
area["name"] + " Year: " + str(year))
##### GDP Info
gdp_infos = getGDPInfo(area["id"], year)
if len(gdp_infos) > 0: # Not empty
gdp_info_count += 1
row = [
str(area["id"]), area["name"], area["type"],
str(year)
]
gdp_info_row = [""] * len(gdp_info_types)
for gdp_info in gdp_infos:
gdp_info_name = gdp_info[0]
for idx, gdp_info_type in enumerate(gdp_info_types):
if gdp_info_name == gdp_info_type:
gdp_info_row[idx] = str(gdp_info[1])
# Write to file
with open(gdp_info_file_name, 'a') as f:
row = list(map((lambda x: x.replace(",", "-")),
row)) # Remove ',' in area names
line = ",".join(row + gdp_info_row)
f.write(line + "\n")
# General Summary
print("\n########## General Summary ##########")
print("Place number in US: " + str(place_in_us_count))
print("Place with gdp_info number in US: " + str(gdp_info_count))
def main():
### build a dictionary to store the term score
sent_file = open(sys.argv[1])
scores = {}
for line in sent_file:
term, score = line.split("\t")
scores[term] = int(score)
# print scores.items()
## Load the tweet json data
stateScore = defaultdict(list)
with open(sys.argv[2]) as f:
for line in f:
tweet = json.loads(line, "utf-8")
#pprint(tweet)
#print(sentiment_tweet(scores,tweet))
s = sentiment_tweet(scores, tweet)
if "place" in tweet.keys():
if not tweet['place'] is None:
if tweet['place']['country_code'] == "US":
fullname = tweet['place']['full_name']
city, state = fullname.split(",")
#state=str(state)
state = state.strip()
if not state in states.keys():
continue
if state in stateScore.keys():
stateScore[state] = [s]
else:
stateScore[state].append(s)
## output
for st, lst in stateScore.items():
t = 0
for v in lst:
t = t + v
avgs = t / len(lst)
print(st + " " + str(avgs))
def on_success(self, data):
if 'text' in data:
candidate_name = get_candidate_name(data)
if candidate_name:
data['candidate_name'] = candidate_name
else:
return
user_id = data['user']['id']
if should_ignore_user(user_id):
return
############################################
print_err(str(data['geo']))
ori_tweet = data['text'].encode('utf-8')
tweet = tweet_text_clean_up(ori_tweet)
state_location = 'none'
if is_valid_tweet(tweet) and data['user']['lang']=='en':
print_err('-' * 50 + '\n' + tweet + '\n')
if data['user'].get('location'):
location = re.sub(r'[^A-Za-z\d ]','',str(data['user']['location'].encode('utf-8'))).lower()
print_err(location)
location = location.split(' ')
location = map(lambda x: x.lower().strip(), location)
print_err(str(location))
for state in states.keys():
vals = map(lambda x:x.lower().strip(),states[state])
if any(map(lambda v: v in vals, location)):
state_location = state
data['state'] = state_location
print_err(state_location)
if data['user']['lang'] =='en':
print json.dumps(data)
else:
print_err(ori_tweet + '\n')
def main():
### build a dictionary to store the term score
sent_file = open(sys.argv[1])
scores = {}
for line in sent_file:
term, score = line.split("\t")
scores[term] = int(score)
# print scores.items()
## Load the tweet json data
stateScore = defaultdict(list)
with open(sys.argv[2]) as f:
for line in f:
tweet = json.loads(line,"utf-8");
#pprint(tweet)
#print(sentiment_tweet(scores,tweet))
s = sentiment_tweet(scores,tweet)
if "place" in tweet.keys():
if not tweet['place'] is None:
if tweet['place']['country_code'] == "US":
fullname = tweet['place']['full_name']
city,state = fullname.split(",")
#state=str(state)
state = state.strip()
if not state in states.keys():
continue
if state in stateScore.keys():
stateScore[state]=[s]
else:
stateScore[state].append(s)
## output
for st,lst in stateScore.items():
t=0
for v in lst:
t=t+v
avgs=t/len(lst)
print(st+" "+str(avgs))
class ChurchesSpider(scrapy.Spider):
name = 'churches'
allowed_domains = ['www.churchfinder.com']
start_urls = [
f'https://www.churchfinder.com/churches/{state.lower()}'
for state in states.keys()
]
def parse(self, response):
links = response.css('.field-content > a::attr(href)').extract()
for link in links:
yield scrapy.Request(response.urljoin(link), self.parse_city)
def parse_city(self, response):
next_page = response.css('.pager-next > a::attr(href)').extract_first()
if next_page:
yield scrapy.Request(response.urljoin(next_page), self.parse_city)
churches = response.css('#content .views-row')
for church in churches:
name = church.css('.views-field-title a::text').extract_first()
if name:
yield {
'city_url':
response.url,
'url':
church.css(
'.views-field-title a::attr(href)').extract_first(),
'name':
name,
'address':
church.css('.field-name-field-address .field-item::text').
extract_first(),
'denomination':
church.css('.field-name-field-specific-denomination::text'
).extract_first(),
}
def collectGraduationRates(state_abbr=[], year_range=YEAR_RANGE):
"""
Collect graduation rates using 'getGraduationRates()' and write to csv file
:param state_abbr: List of selected states that the data will be collected and write to file
:param year_range: List of year that the data will be collected. This can be the year that data is not available
:return: None
"""
# Variables
graduation_rates_header = ["area_id", "area_name", "area_type", "year"]
graduation_rate_types = [
"percent_associates_degree", "percent_bachelors_degree_or_higher",
"percent_graduate_or_professional_degree",
"percent_high_school_graduate_or_higher", "percent_less_than_9th_grade"
]
# Delete all old data and write header to new file
graduation_rates_file_name = "graduation_rates.csv"
with open(graduation_rates_file_name, 'w') as f:
f.write(",".join(graduation_rates_header + graduation_rate_types) +
'\n')
count = 0
graduation_rates_count = 0
for state in list(states.keys()):
if len(state_abbr) > 0 and not state in state_abbr:
continue # If specify states
areas = getAreas(state, ["region.place"])
count += len(areas)
for area in areas:
for year in year_range:
print("Gathering - State: " + state + " Area: " +
area["name"] + " Year: " + str(year))
##### Graduation Rates
graduation_rates = getGraduationRates(area["id"], year)
if len(graduation_rates) > 0: # Not empty
graduation_rates_count += 1
row = [
str(area["id"]), area["name"], area["type"],
str(year)
]
rates = [""] * len(graduation_rate_types)
for graduation_rate in graduation_rates:
rate_name = graduation_rate[0]
# There are 5 graduation rate types in general
for idx, graduation_rate_type in enumerate(
graduation_rate_types):
if rate_name == graduation_rate_type:
rates[idx] = str(graduation_rate[1])
# Write to file
with open(graduation_rates_file_name, 'a') as f:
row = list(map((lambda x: x.replace(",", "-")),
row)) # Remove ',' in area names
line = ",".join(row + rates)
f.write(line + "\n")
# General Summary
print("\n########## General Summary ##########")
print("Place number in US: " + str(count))
print("Place with graduation_rates number in US: " +
str(graduation_rates_count))
def collectEarningInfo(state_abbr=[], year_range=YEAR_RANGE):
"""
Collect Earning information using 'getEarningInfo()' and write to csv file
:param state_abbr: List of selected states that the data will be collected and write to file
:param year_range: List of year that the data will be collected. This can be the year that data is not available
:return: None
"""
# Variables
earning_info_header = ["area_id", "area_name", "area_type", "year"]
earning_info_types = [
"female_full_time_median_earnings", "female_median_earnings",
"male_full_time_median_earnings", "male_median_earnings",
"median_earnings", "median_earnings_bachelor_degree",
"median_earnings_graduate_or_professional_degree",
"median_earnings_high_school", "median_earnings_less_than_high_school",
"median_earnings_some_college_or_associates",
"percent_with_earnings_10000_to_14999",
"percent_with_earnings_15000_to_24999",
"percent_with_earnings_1_to_9999",
"percent_with_earnings_25000_to_34999",
"percent_with_earnings_35000_to_49999",
"percent_with_earnings_50000_to_64999",
"percent_with_earnings_65000_to_74999",
"percent_with_earnings_75000_to_99999",
"percent_with_earnings_over_100000"
]
# Delete all old data and write header to new file
earning_info_file_name = "earning_info.csv"
with open(earning_info_file_name, 'w') as f:
f.write(",".join(earning_info_header + earning_info_types) + '\n')
place_in_us_count = 0
earning_info_count = 0 # How many places we found for earning info
for state in list(states.keys()):
if len(state_abbr) > 0 and not state in state_abbr:
continue # If specify states
areas = getAreas(state, ["region.place"])
place_in_us_count += len(areas)
for area in areas:
for year in year_range:
print("Gathering - State: " + state + " Area: " +
area["name"] + " Year: " + str(year))
##### Earning Info
earning_infos = getEarningInfo(area["id"], year)
if len(earning_infos) > 0: # Not empty
earning_info_count += 1
row = [
str(area["id"]), area["name"], area["type"],
str(year)
]
earning_info_row = [""] * len(earning_info_types)
for earning_info in earning_infos:
earning_info_name = earning_info[0]
for idx, earning_info_type in enumerate(
earning_info_types):
if earning_info_name == earning_info_type:
earning_info_row[idx] = str(earning_info[1])
# Write to file
with open(earning_info_file_name, 'a') as f:
row = list(map((lambda x: x.replace(",", "-")),
row)) # Remove ',' in area names
line = ",".join(row + earning_info_row)
f.write(line + "\n")
# General Summary
print("\n########## General Summary ##########")
print("Place number in US: " + str(place_in_us_count))
print("Place with earning_info number in US: " + str(earning_info_count))
def collectCrimeRates(state_abbr=[], year_range=YEAR_RANGE):
"""
Collect crime rates using 'getCrimeRates()' and write to csv file
:param state_abbr: List of selected states that the data will be collected and write to file
:param year_range: List of year that the data will be collected. This can be the year that data is not available
:return: None
"""
# Variables
crime_rates_header = ["area_id", "area_name", "area_type", "year"]
crime_rate_types = [
"Aggravated assault", "All Crimes", "Burglary", "Larceny",
"Motor vehicle theft", "Murder and nonnegligent manslaughter",
"Property crime", "Rape (revised definition)", "Robbery",
"Violent crime"
]
# Delete all old data and write header to new file
crime_rates_file_name = "crime_rates.csv"
with open(crime_rates_file_name, 'w') as f:
f.write(",".join(crime_rates_header + crime_rate_types) + '\n')
place_in_us_count = 0
crime_rates_count = 0 # How many places we found for crime rates
for state in list(states.keys()):
if len(state_abbr) > 0 and not state in state_abbr:
continue # If specify states
areas = getAreas(state, ["region.place"])
place_in_us_count += len(areas)
for area in areas:
for year in year_range:
print("Gathering - State: " + state + " Area: " +
area["name"] + " Year: " + str(year))
##### Crime Rates
crime_rates = getCrimeRates(area["id"], year)
if len(crime_rates) > 0: # Not empty
crime_rates_count += 1
row = [
str(area["id"]), area["name"], area["type"],
str(year)
]
crime_rates_row = [""] * len(crime_rate_types)
for crime_rate in crime_rates:
crime_name = crime_rate[0]
for idx, crime_rate_type in enumerate(
crime_rate_types):
if crime_name == crime_rate_type:
crime_rates_row[idx] = str(crime_rate[1])
# Write to file
with open(crime_rates_file_name, 'a') as f:
row = list(map((lambda x: x.replace(",", "-")),
row)) # Remove ',' in area names
line = ",".join(row + crime_rates_row)
f.write(line + "\n")
# General Summary
print("\n########## General Summary ##########")
print("Place number in US: " + str(place_in_us_count))
print("Place with crime_rates number in US: " + str(crime_rates_count))
"""
"""
colors = sns.color_palette(n_colors=3, palette='bright')
sns.distplot(geo_partisan_vec, ax=ax, color=colors[0], hist=False, label='Geographic Distance', rug=False, kde_kws={'bw':.2, 'gridsize':150, 'clip': clip, 'shade':False})
sns.distplot(final_partisan_vec, ax=ax, color=colors[1], hist=False, label='Geographic and Demographic Distance', rug=False, kde_kws={'bw':.2, 'gridsize':150, 'clip': clip, 'shade':False})
sns.distplot(original_partisan_vec, ax=ax, color=colors[2], hist=False, label='Original Districts', rug=False, kde_kws={'bw':.2, 'gridsize':150, 'clip': clip, 'shade':False})
ax.tick_params(axis='both', which='major', labelsize=15)
ax.tick_params(axis='both', which='minor', labelsize=15)
ax.set_xlim(clip)
ax.legend([], frameon=False)
if __name__ == "__main__":
state_list = list(states.keys())
state_list.sort(key=itemgetter(0))
state_list.remove('CO')
fig, axes = plt.subplots(len(state_list), 1,figsize=(7, 17), sharex=False)
for state, ax in zip(state_list, axes):
with open('../maps/'+state+'_10pct/static/kde_data_'+state+'.p', 'rb') as f:
[geo_partisan_vec, final_partisan_vec, original_partisan_vec, geo_demog_vec, final_demog_vec, original_demog_vec] = pickle.load(f)
clip = (0, 1)
make_histograms(geo_partisan_vec, final_partisan_vec, original_partisan_vec, ax, clip)
ax.set_ylabel(state, fontsize=15)
ax.set_yticks([])
ax.set_yticklabels([])
"""
)
quizFile.write(
(' ' * 20) + f'State Capitals Quiz (Form {quizNum + 1}) \n'
)
# Create answer file
ansFile = open(f'quizesAns/quizAns{quizNum + 1}.rtf', 'a')
# A Header For Every Answers File
ansFile.write(
(' ' * 20) +
f'State Capitals Quiz Model Answer (Form {quizNum + 1}) \n'
)
# Shuffle the order of the states
states = list(capitals.keys())
random.shuffle(states) # States list is shuffled evertime
# Questions in the files
for count, state in enumerate(states):
# # # creating choices
capitalsValues = list(capitals.values())
random.shuffle(capitalsValues)
choices = []
choices.append(capitals[state])
for i in range(2):
choices.append(capitalsValues[i])
random.shuffle(choices)
# # # end
