def detailed_results(request):
# The function for our detailed results page
# Matches the house id with the selected house and adds the data to the dictionary
# Creates and saves a graph
c = {}
house_id=request.POST.get("house_id")
if not house_id:
return render(request, 'search/error.html', c)
with open(HOUSE_PATH+"/attributes.csv", "r") as f:
header=f.readline()
reader=csv.reader(f)
for row in reader:
if int(row[0])==int(house_id):
c["current_lat"]=row[5]
c["current_long"]=row[6]
c["current_bedroom"]=row[3]
c["current_bathroom"]=row[4]
c["current_price"]=row[2]
c["current_address"]=row[1]
c["current_house_id"]=house_id
c["current_link"] = row[8]
break
c['current_distance'] = request.POST.get('distance', 1200)
data=[]
all_crimes={}
line_styles=[".r-", ".b-", ".g-", ".y-"]
# The following code both saves a graph using matlibplot and stores chart data to use in charts
graph_data_raw = []
c['pie_data']=[]
for j in DATABASE_CATEGORIES:
all_crimes[j]={}
with open(HOUSE_PATH+"/{}/{}.csv".format(house_id.strip(),j), "r") as f:
header=f.readline()
reader=csv.reader(f)
for row in reader:
date=row[0]
month_year=date[:7]
all_crimes[j][month_year]=all_crimes[j].get(month_year, 0)+1
t_labels=list(all_crimes[j].keys())
t_labels.sort()
t=range(len(t_labels))
s=[all_crimes[j][k] for k in t_labels]
# Stores the pie data for crime
c['pie_data'].append((j, sum(s)))
if len(t)>15:
step=(len(t)//15)+1
for k in range(len(t_labels)):
if k%step!=0:
t_labels[k]=""
# Stores the graph data for crime
graph_data_raw.append((j,t_labels,s))
plt.xticks(t, t_labels, rotation=30)
plt.plot(t, s, line_styles.pop(), label =j)
plt.xlabel("Date YYYY-MM")
plt.ylabel("Number of crimes")
plt.title("Crime within {}m of this property".format(c["current_distance"]))
plt.legend()
plt.grid(True)
plt.savefig(HOUSE_PATH+"/{}/historical_crime.png".format(house_id.strip()))
plt.clf()
#c["crime_graph"]=HOUSE_PATH+"/{}/historical_crime.png".format(house_id.strip())
# Changes graph_data_raw into correct format
crime_list = []
for date in graph_data_raw[0][1]:
crime_list.append([date])
for i in range(len(graph_data_raw)):
crime_data = graph_data_raw[i][2]
for j in range(len(crime_data)):
crime_list[j].append(crime_data[j])
c['graph_data'] = crime_list
# Passes in the database categories for the pie chart
c['database_cat'] = DATABASE_CATEGORIES
# Gets the category selected, not selecting is the equivalent of no filter for category
current_cat = request.POST.get('cat')
if not current_cat:
current_cat = ""
# Passes the yelp categories to use in the form
c['categories'] = ["restaurants", "active",
"arts", "education", "health", "nightlife", "shopping"]
distance = float(c["current_distance"])
units = request.POST.get("distance_type")
# Corrects distance for units and limit (Yelp limit is 40000)
if units == "miles":
distance *= 1609.34
if distance > 40000:
distance = 40000
c["current_term"] =request.POST.get("term", "food")
page = request.POST.get('page',1)
c['current_page'] = page
# Stores and passes in the Yelp results
c['results'], total = Yelp.yelp_search((c["current_lat"], c["current_long"]),
distance, c['current_term'], category_filter = current_cat, offset = (int(page)-1)*20)
# Calculates the number of pages the user can request
c['pages'] = list(range(1,math.ceil(total/20)))
return render(request, 'search/detailed_results.html', c)
