def hastag_tweets():
try:
if request.method =='GET':
return render_template('search_form.html')
elif request.method == 'POST':
#tweets
form_text = request.form["text"] # get form text
search_term = form_text.upper() # process form text
data_set = api.get_tweets(search_term, 10)
#create chart
neutral = 0
positive = 0
negative = 0
print(range(len(data_set)))
legend = 'Number of tweets'
labels = ['Positive', 'Negative', 'Neutral']
for i in range(len(data_set)):
if data_set[i]['sentiment'] == 'neutral':
neutral += 1
elif data_set[i]['sentiment'] == 'positive':
positive += 1
elif data_set[i]['sentiment'] == 'negative':
negative += 1
values = [positive, negative, neutral]
print(positive)
print(negative)
print(neutral)
geolocation(search_term)
if os.path.exists('templates/heatmap_result.html'):
os.remove('templates/heatmap_result.html')
shutil.move('heatmap_result.html', 'templates')
else:
shutil.move('heatmap_result.html', 'templates')
return render_template(
'chart.html', response=data_set, values=values, labels=labels, legend=legend
)
except:
print('Function: hashtag_tweets failed to run')
def __init__(self,options):
self.DEBUG = True
self.geo_utils = geo_utils()
self.geoloc = geolocation()
self.method = 'tdoa' # method {'tdoa'|'custom'}
self.tdoa_iterator = 1
self.t_sleep = 0.01
self.user = '******'
self.passwd = 'sdrc_pass'
self.kml_file_number = 1
self.state = 1
self.dead_loop = 0
self.loop_escape = 5
#beacon packet number for extracting from db
self.bpn = 0
self.bpn_max = 0
self.db_host = '192.168.42.200'
self.db = 'sdrc_db'
self.t1 = 'data_table'+options.number
self.t1_fields = '(rpt_pkt_num, rpt_team_id, rpt_location, rpt_timestamp, beacon_id, beacon_pkt_num)'
self.t1_field1 = '(rpt_location)'
self.t1_field2 = '(rpt_timestamp)'
self.x_results = []
self.y_results = []
def find_closest_forecast_location(address):
"""address in format['location', 'area', 'state', 'country'], returns closes weather URL"""
address_location = geolocation(address)
############CHANGE OUT OF ARCHIVE WHEN COMPLETE##########
filename = r"C:\Users\thoma\Desktop\Python\TH Projects\Port\tools\weather\weather_programs\forecast_working_files\Archive\forecast_location.txt"
with open(filename) as f:
forecast_location_dict = json.load(f)
distance_list = []
url_list = []
for key, items in forecast_location_dict.items():
forecast_lat = items[0]
forecast_lon = items[1]
distance = coordinate_distance(address_location[0],
address_location[1], forecast_lat,
forecast_lon)
distance_list.append(distance)
url_list.append([distance, key, items])
ranked_list = sorted(distance_list)
best_ranking = ranked_list[0]
answer_url = []
for url_entry in url_list:
if url_entry[0] == best_ranking:
answer_url.append(url_entry)
print("Found these urls and using the first entry: " + str(answer_url))
return answer_url[0][1]
def __call__(self):
try:
location = geolocation(self.address)
lat = location[0]
lon = location[1]
answer = [self.url, lat, lon, self.address]
print(answer)
except Exception as error:
print(error)
answer = [self.url, 'fail', 'fail', self.address]
print(answer)
return answer
def find_nearest_noaa_station(address, weather_type):
location = geolocation(address)
lat1 = location[0]
lon1 = location[1]
if weather_type == "T":
weather_type = "TAVG"
elif weather_type == "P":
weather_type = "PRCP"
else:
print("weather type argument spelt wrong - P or T")
filename = r"C:\Users\thoma\Desktop\Python\TH Projects\Port\tools\weather\weather_databases\ghcnd-inventory_processed.txt"
with open(filename) as f:
station_list = json.load(f)
adj_station_list = []
for i in range(len(station_list)):
if str(station_list[i][3]) == str(weather_type):
adj_station_list.append(station_list[i])
else:
None
if len(adj_station_list) == 0:
print("weather type argument spelt wrong - P or T")
distance_dict = {}
for i in range(len(adj_station_list)):
station = adj_station_list[i]
station_code = station[0]
lat2 = station[1]
lon2 = station[2]
key = coordinate_distance(lat1, lon1, lat2, lon2)
distance_dict[key] = station_code
sorted_locations = []
for key in distance_dict.keys():
sorted_locations.append(float(key))
sorted_locations = sorted(sorted_locations)
eligible_dict = {}
for key in sorted_locations[:30]:
eligible_dict[key] = distance_dict[key]
return eligible_dict
def main():
t0 = time.perf_counter()
processed_forecast_location = r'C:\Users\thoma\Desktop\Python\TH Projects\Port\tools\weather\weather_programs\forecast_working_files\processed_forecast_location.txt'
with open(processed_forecast_location) as f:
processed_dict = json.load(f)
print(" dict loaded")
local_dict = {}
for key, items in processed_dict.items():
local_dict[key] = items
#print(local_dict)
total_no = 0
for key in local_dict.keys():
total_no += 1
done_no = 0
location_dict = {}
reject_dict = {}
for key, items in local_dict.items():
t2 = time.perf_counter()
try:
raw_address = items.split(",")
address = []
for location in raw_address:
address.append(location.strip())
print(address)
country = address[len(address) - 2]
town = address[0]
region = address[1]
state = address[2]
answer = geolocation(
town, region, state,
country) #[lat, lon, #of entries - above 1 bad]
lat = answer[0]
lon = answer[1]
except Exception:
lat = "Not found"
lon = "Not found"
if lat != "Not found":
location_dict[key] = [lat, lon, items]
print([lat, lon, items])
else:
reject_dict[key] = items
print('Not found')
done_no += 1
t1 = time.perf_counter()
time_taken = t1 - t0
time_prediction = ((time_taken) * (total_no / done_no) - t1) / 3600
print("On #" + str(done_no) + "/" + str(total_no) +
" - estimated time to completion in hours is " +
str(time_prediction) + "and the last opeation took " +
str(t1 - t2))
file1 = r'C:\Users\thoma\Desktop\Python\TH Projects\Port\tools\weather\weather_programs\forecast_working_files\forecast_location.txt'
file2 = r'C:\Users\thoma\Desktop\Python\TH Projects\Port\tools\weather\weather_programs\forecast_working_files\reject_location.txt'
with open(file1, 'w') as f:
json.dump(location_dict, f)
with open(file2, 'w') as f:
json.dump(reject_dict, f)