def test_get_valid_pincode(self):
current_result = pz.get(603203, "IN")
expected_result = {'state_short': '25', 'city': 'Kavanur', 'location': {'lat': 12.8336666, 'lng': 80.0199562}, 'state': 'Tamil Nadu', 'country': 'IN', 'postal_code': '603203', 'bounds': {'northeast': {'lat': 12.8769479, 'lng': 80.0572497}, 'southwest': {'lat': 12.7997355, 'lng': 79.9504465}}, 'county': 'Kanchipuram', 'location_type': 'APPROXIMATE'}
if sys.version_info[:2] <= (2, 7):
self.assertItemsEqual(current_result, expected_result)
else:
self.assertCountEqual(current_result, expected_result)
def test_ziptastic_valid_pincode_1(self):
current_result = pz.query_ziptastic_api(603203, "IN")
expected_result = {'postal_code': '603203', 'county': 'Kanchipuram', 'state_short': '25', 'state': 'Tamil Nadu', 'country': 'IN', 'city': 'Kavanur'}
if sys.version_info[:2] <= (2, 7):
self.assertItemsEqual(current_result, expected_result)
else:
self.assertCountEqual(current_result, expected_result)
def test_googlemaps_valid_pincode(self):
current_result = pz.query_google_api(603203, "IN")
expected_result = {'bounds': {'northeast': {'lng': 80.0572497, 'lat': 12.8769479}, 'southwest': {'lng': 79.9504465, 'lat': 12.7997355}}, 'location_type': 'APPROXIMATE', 'location': {'lng': 80.0199562, 'lat': 12.8336666}}
if sys.version_info[:2] <= (2, 7):
self.assertItemsEqual(current_result, expected_result)
else:
self.assertCountEqual(current_result, expected_result)
def test_ziptastic_valid_pincode_2(self):
current_result = pz.query_ziptastic_api(94305, "US")
expected_result = {'state_short': 'CA', 'state': 'California', 'city': 'Stanford', 'county': 'Santa Clara', 'country': 'US', 'postal_code': '94305'}
if sys.version_info[:2] <= (2, 7):
self.assertItemsEqual(current_result, expected_result)
else:
self.assertCountEqual(current_result, expected_result)
def test_get_valid_pincode(self):
current_result = pz.get(603203, "IN")
expected_result = {
'state_short': '25',
'city': 'Kavanur',
'location': {
'lat': 12.8336666,
'lng': 80.0199562
},
'state': 'Tamil Nadu',
'country': 'IN',
'postal_code': '603203',
'bounds': {
'northeast': {
'lat': 12.8769479,
'lng': 80.0572497
},
'southwest': {
'lat': 12.7997355,
'lng': 79.9504465
}
},
'county': 'Kanchipuram',
'location_type': 'APPROXIMATE'
}
if sys.version_info[:2] <= (2, 7):
self.assertItemsEqual(current_result, expected_result)
else:
self.assertCountEqual(current_result, expected_result)
def test_ziptastic_valid_pincode_2(self):
current_result = pz.query_ziptastic_api(94305, "US")
expected_result = {
'state_short': 'CA',
'state': 'California',
'city': 'Stanford',
'county': 'Santa Clara',
'country': 'US',
'postal_code': '94305'
}
if sys.version_info[:2] <= (2, 7):
self.assertItemsEqual(current_result, expected_result)
else:
self.assertCountEqual(current_result, expected_result)
def test_ziptastic_valid_pincode_1(self):
current_result = pz.query_ziptastic_api(603203, "IN")
expected_result = {
'postal_code': '603203',
'county': 'Kanchipuram',
'state_short': '25',
'state': 'Tamil Nadu',
'country': 'IN',
'city': 'Kavanur'
}
if sys.version_info[:2] <= (2, 7):
self.assertItemsEqual(current_result, expected_result)
else:
self.assertCountEqual(current_result, expected_result)
def sign_up(request):
if request.method == 'POST':
username = request.POST.get('username')
email = request.POST.get('email')
password = request.POST.get('password')
zipcode = request.POST.get('zipcode')
zip_object = pz.get(zipcode, "US")
city = zip_object['city']
state = zip_object['state']
if User.objects.filter(username=username).exists():
messages.error(request, 'Username is taken')
return render(request, 'join.html')
if User.objects.filter(email=email).exists():
messages.error(request, 'Email in use')
return render(request, 'join.html')
if not State.objects.filter(name=state).exists():
messages.error(request, 'This area is not supported yet. Here is a list: click here')
return render(request, 'join.html')
if Zipcode.objects.filter(number=zipcode).exists():
zipcode = Zipcode.objects.get(number=zipcode)
else:
if City.objects.filter(name=city).exists():
city = City.objects.get(name=city)
else:
state = State.objects.get(name=state)
city = City(name=city, state=state, slug='yes')
city.save()
zipcode = Zipcode(number=zipcode, city=city)
zipcode.save()
user = User.objects.create_user(username, email, password)
add_user = Customer(user=user, zipcode=zipcode)
add_user.save()
messages.success(request, 'Account created! Please sign in!')
return render(request, 'join.html')
else:
return redirect('/')
def test_googlemaps_valid_pincode(self):
current_result = pz.query_google_api(603203, "IN")
expected_result = {
'bounds': {
'northeast': {
'lng': 80.0572497,
'lat': 12.8769479
},
'southwest': {
'lng': 79.9504465,
'lat': 12.7997355
}
},
'location_type': 'APPROXIMATE',
'location': {
'lng': 80.0199562,
'lat': 12.8336666
}
}
if sys.version_info[:2] <= (2, 7):
self.assertItemsEqual(current_result, expected_result)
else:
self.assertCountEqual(current_result, expected_result)
def list(self, request):
"""
iPrice Trend Version 3.0 with new property types
1.'TownHouse',
2.'Multifamily'
3.'All'
and Date format (timestamp) in milliseconds.
url:https://insights.propmix.io:8004/iprice/v3/trend
---
# YAML (must be separated by `---`)
type:
schema : {zip_code_location: string, last_3_year_avg_l_s_ratio: string, last_3_months_trend_variation: string, last_3_months_price_trend_percentage: string, last_3_months_price_trend_change: string, last_3_month_data: string, l_s_ratio_recent: string}
zip_code_location:
required: true
type: set
sample:
nested:
required: true
type: object
last_3_year_avg_l_s_ratio:
required: false
type: string
last_3_months_trend_variation:
required: false
type: string
omit_serializer: false
parameters_strategy: merge
omit_parameters:
- path
parameters:
- name: zip
description: Postalcode which iPriceTrend to be calculated.
type: string
paramType: query
defaultValue: 33134
- name: type
description: Property types (or building types) fields to describe the kind of property for sale in Postal code.
paramType: query
responseMessages:
- code: 401
message: Not authenticated
- code : 404
message: Not Found
- code : ERROR
message : No Zip Specified or Invalid Zip
consumes:
- application/json
- application/xml
produces:
- application/json
- application/xml
"""
try:
if not request.query_params['zip'].isdigit():
return Response({'status': 'ERROR', 'error': 'INVALID_ZIP'})
if ('type' in request.query_params):
p_type = request.query_params['type']
else:
p_type = 'SingleFamily'
except:
return Response({'status': 'ERROR', 'error': 'NO_ZIP_SPECIFIED'})
db_client = MongoClient('mongo-master.propmix.io', 33017)
try:
zipcode = pz.get(str(request.query_params['zip']), "US")
except:
return Response({'status': 'ERROR', 'error': 'Invalid ZipCode'})
zip_code_location = ', '.join(
[zipcode['county'], zipcode['state_short']])
data = list(
db_client.iestimate.rets_standardized_data_nxmodel_01.find(
{"postalcode": str(request.query_params['zip'])}))
if len(data) == 0:
return Response({'status': 'ERROR', 'error': 'Insufficient data'})
else:
last_3_month_meta = data[0]['3_month_meta'][p_type][
'price_trend'] if data[0]['3_month_meta'][
p_type] is not None and data[0]['3_month_meta'][p_type][
'price_trend'] != {} else None
last_6_month_meta = data[0]['6_month_meta'][p_type][
'price_trend'] if data[0]['6_month_meta'][
p_type] is not None and data[0]['6_month_meta'][p_type][
'price_trend'] != {} else None
last_12_month_meta = data[0]['12_month_meta'][p_type][
'price_trend'] if data[0]['12_month_meta'][
p_type] is not None and data[0]['12_month_meta'][p_type][
'price_trend'] != {} else None
last_36_month_meta = data[0]['36_month_meta'][p_type][
'price_trend'] if data[0]['36_month_meta'][
p_type] is not None and data[0]['36_month_meta'][p_type][
'price_trend'] != {} else None
last_60_month_meta = data[0]['60_month_meta'][p_type][
'price_trend'] if data[0]['60_month_meta'][
p_type] is not None and data[0]['60_month_meta'][p_type][
'price_trend'] != {} else None
l_s_ratio_recent = data[0]['3_month_meta'][p_type]['price_trend'][
'lsratio'] if data[0]['3_month_meta'][
p_type] is not None and data[0]['3_month_meta'][p_type][
'price_trend'] != {} else None
last_3_month_avg_l_s_ratio = data[0]['3_month_meta'][p_type][
'price_trend']['avglsratio'] if data[0]['3_month_meta'][
p_type] is not None and data[0]['3_month_meta'][p_type][
'price_trend'] != {} else None
last_6_month_avg_l_s_ratio = data[0]['6_month_meta'][p_type][
'price_trend']['avglsratio'] if data[0]['6_month_meta'][
p_type] is not None and data[0]['6_month_meta'][p_type][
'price_trend'] != {} else None
last_1_year_avg_l_s_ratio = data[0]['12_month_meta'][p_type][
'price_trend']['avglsratio'] if data[0]['12_month_meta'][
p_type] is not None and data[0]['12_month_meta'][p_type][
'price_trend'] != {} else None
last_3_year_avg_l_s_ratio = data[0]['36_month_meta'][p_type][
'price_trend']['avglsratio'] if data[0]['36_month_meta'][
p_type] is not None and data[0]['36_month_meta'][p_type][
'price_trend'] != {} else None
last_5_year_avg_l_s_ratio = data[0]['60_month_meta'][p_type][
'price_trend']['avglsratio'] if data[0]['60_month_meta'][
p_type] is not None and data[0]['60_month_meta'][p_type][
'price_trend'] != {} else None
curr_open_listings = data[0]['3_month_meta'][p_type][
'price_trend']['openlistings'] if data[0]['3_month_meta'][
p_type] is not None and data[0]['3_month_meta'][p_type][
'price_trend'] != {} else None
curr_pending_sale = data[0]['3_month_meta'][p_type]['price_trend'][
'pendingsale'] if data[0]['3_month_meta'][
p_type] is not None and data[0]['3_month_meta'][p_type][
'price_trend'] != {} else None
last_3_month_total_txn = data[0]['3_month_meta'][p_type][
'price_trend']['totaltxn'] if data[0]['3_month_meta'][
p_type] is not None and data[0]['3_month_meta'][p_type][
'price_trend'] != {} else None
last_6_month_total_txn = data[0]['6_month_meta'][p_type][
'price_trend']['totaltxn'] if data[0]['6_month_meta'][
p_type] is not None and data[0]['6_month_meta'][p_type][
'price_trend'] != {} else None
last_1_year_total_txn = data[0]['12_month_meta'][p_type][
'price_trend']['totaltxn'] if data[0]['12_month_meta'][
p_type] is not None and data[0]['12_month_meta'][p_type][
'price_trend'] != {} else None
last_3_year_total_txn = data[0]['36_month_meta'][p_type][
'price_trend']['totaltxn'] if data[0]['36_month_meta'][
p_type] is not None and data[0]['36_month_meta'][p_type][
'price_trend'] != {} else None
last_5_year_total_txn = data[0]['60_month_meta'][p_type][
'price_trend']['totaltxn'] if data[0]['60_month_meta'][
p_type] is not None and data[0]['60_month_meta'][p_type][
'price_trend'] != {} else None
last_3_month_avg_txn_per_week = data[0]['3_month_meta'][p_type][
'price_trend']['avgtxn'] if data[0]['3_month_meta'][
p_type] is not None and data[0]['3_month_meta'][p_type][
'price_trend'] != {} else None
last_6_month_avg_txn_per_week = data[0]['6_month_meta'][p_type][
'price_trend']['avgtxn'] if data[0]['6_month_meta'][
p_type] is not None and data[0]['6_month_meta'][p_type][
'price_trend'] != {} else None
last_1_year_avg_txn_per_week = data[0]['12_month_meta'][p_type][
'price_trend']['avgtxn'] if data[0]['12_month_meta'][
p_type] is not None and data[0]['12_month_meta'][p_type][
'price_trend'] != {} else None
last_3_year_avg_txn_per_week = data[0]['36_month_meta'][p_type][
'price_trend']['avgtxn'] if data[0]['36_month_meta'][
p_type] is not None and data[0]['36_month_meta'][p_type][
'price_trend'] != {} else None
last_5_year_avg_txn_per_week = data[0]['60_month_meta'][p_type][
'price_trend']['avgtxn'] if data[0]['60_month_meta'][
p_type] is not None and data[0]['60_month_meta'][p_type][
'price_trend'] != {} else None
week_52_high = data[0]['12_month_meta'][p_type]['price_trend'][
'week_52_high'] if data[0]['12_month_meta'][
p_type] is not None and data[0]['12_month_meta'][p_type][
'price_trend'] != {} else None
week_52_low = data[0]['12_month_meta'][p_type]['price_trend'][
'week_52_low'] if data[0]['12_month_meta'][
p_type] is not None and data[0]['12_month_meta'][p_type][
'price_trend'] != {} else None
import datetime
epoch = datetime.datetime.utcfromtimestamp(0)
def unix_time_millis(dt):
return (dt - epoch).total_seconds() * 1000.0
if last_3_month_meta is not None:
last_3_month_meta["series"] = [{
key:
sorted(map(
lambda k: [
unix_time_millis(
datetime.datetime.strptime(k[0], '%d/%m/%Y')),
k[1]
], item[key]),
key=lambda k: k[0])
} for item in last_3_month_meta["series"]
for key in item.keys()]
if last_6_month_meta is not None:
last_6_month_meta["series"] = [
{
key:
sorted(map(
lambda k: [
unix_time_millis(
datetime.datetime.strptime(
k[0], '%d/%m/%Y')), k[1]
], item[key]),
key=lambda k: k[0])
} for item in last_6_month_meta["series"]
for key in item.keys()
] if last_6_month_meta is not None else None
if last_12_month_meta is not None:
last_12_month_meta["series"] = [
{
key:
sorted(map(
lambda k: [
unix_time_millis(
datetime.datetime.strptime(
k[0], '%d/%m/%Y')), k[1]
], item[key]),
key=lambda k: k[0])
} for item in last_12_month_meta["series"]
for key in item.keys()
] if last_12_month_meta is not None else None
if last_36_month_meta is not None:
last_36_month_meta["series"] = [
{
key:
sorted(map(
lambda k: [
unix_time_millis(
datetime.datetime.strptime(
k[0], '%d/%m/%Y')), k[1]
], item[key]),
key=lambda k: k[0])
} for item in last_36_month_meta["series"]
for key in item.keys()
] if last_36_month_meta is not None else None
if last_60_month_meta is not None:
last_60_month_meta["series"] = [
{
key:
sorted(map(
lambda k: [
unix_time_millis(
datetime.datetime.strptime(
k[0], '%d/%m/%Y')), k[1]
], item[key]),
key=lambda k: k[0])
} for item in last_60_month_meta["series"]
for key in item.keys()
] if last_60_month_meta is not None else None
return Response({
"zip_code_location":
zip_code_location,
"last_3_months_price_per_square_feet":
last_3_month_meta["series"][0]['price_per_square_feet'][-1][1]
if last_3_month_meta is not None else None,
"last_3_months_trend_variation":
abs(last_3_month_meta["series"][0]['price_per_square_feet'][0]
[1] -
last_3_month_meta["series"][0]['price_per_square_feet'][-1]
[1]) if last_3_month_meta is not None else None,
"last_3_months_price_trend_percentage":
float(
abs(last_3_month_meta["series"][0]['price_per_square_feet']
[0][1] - last_3_month_meta["series"][0]
['price_per_square_feet'][-1][1])) /
float(last_3_month_meta["series"][0]['price_per_square_feet']
[0][1]) if last_3_month_meta is not None else None,
"last_3_months_price_trend_change":
("+" if last_3_month_meta["series"][0]['price_per_square_feet']
[-1][1] -
last_3_month_meta["series"][0]['price_per_square_feet'][0][1]
>= 0 else "-") if last_3_month_meta is not None else None,
"last_3_month_data":
last_3_month_meta["series"]
if last_3_month_meta is not None else None,
"last_6_month_data":
last_6_month_meta["series"]
if last_6_month_meta is not None else None,
"last_1_year_data":
last_12_month_meta["series"]
if last_12_month_meta is not None else None,
"last_3_year_data":
last_36_month_meta["series"]
if last_36_month_meta is not None else None,
"last_5_year_data":
last_60_month_meta["series"]
if last_60_month_meta is not None else None,
"start_to_end_data":
last_60_month_meta["series"]
if last_60_month_meta is not None else None,
"l_s_ratio_recent":
l_s_ratio_recent,
"last_3_month_avg_l_s_ratio":
last_3_month_avg_l_s_ratio,
"last_6_month_avg_l_s_ratio":
last_6_month_avg_l_s_ratio,
"last_1_year_avg_l_s_ratio":
last_1_year_avg_l_s_ratio,
"last_3_year_avg_l_s_ratio":
last_3_year_avg_l_s_ratio,
"last_5_year_avg_l_s_ratio":
last_5_year_avg_l_s_ratio,
"curr_open_listings":
curr_open_listings,
"curr_pending_sale":
curr_pending_sale,
"last_3_month_total_txn":
last_3_month_total_txn,
"last_6_month_total_txn":
last_6_month_total_txn,
"last_1_year_total_txn":
last_1_year_total_txn,
"last_3_year_total_txn":
last_3_year_total_txn,
"last_5_year_total_txn":
last_5_year_total_txn,
"last_3_month_avg_txn_per_week":
last_3_month_avg_txn_per_week,
"last_6_month_avg_txn_per_week":
last_6_month_avg_txn_per_week,
"last_1_year_avg_txn_per_week":
last_1_year_avg_txn_per_week,
"last_3_year_avg_txn_per_week":
last_3_year_avg_txn_per_week,
"last_5_year_avg_txn_per_week":
last_5_year_avg_txn_per_week,
"week_52_low":
week_52_low,
"week_52_high":
week_52_high,
"start_to_end_total_txs":
last_5_year_total_txn,
"start_to_end_avg_txs_per_week":
last_5_year_avg_txn_per_week,
"start_to_end_avg_l_s_ratio":
last_5_year_avg_l_s_ratio,
})
def test_get_invalid_pincode(self):
current_result = pz.get(33044)
self.assertFalse(current_result)
def test_ziptastic_invalid_pincode(self):
current_result = pz.query_ziptastic_api(33044)
self.assertFalse(current_result)
def list(self, request):
"""
iPrice Trend Version 1.0 with property types
and response data is in monthly format.
url:https://insights.propmix.io:8004/iprice/v1/trend
---
# YAML (must be separated by `---`)
type:
zipcode:
required: true
type: string
type:
required: false
type: string
parameters:
- name: zip
description: Postalcode which iPriceTrend to be calculated.
required: true
type: string
paramType: query
defaultValue: 33134
- name: type
description: Property types (or building types) fields to describe the kind of property for sale in Postal code.
paramType: query
responseMessages:
- code: 401
message: Not authenticated
- code : 404
message: Not Found
- code : ERROR
message : No Zip Specified or Invalid Zip
consumes:
- application/json
- application/xml
produces:
- application/json
- application/xml
"""
try:
if not request.query_params['zip'].isdigit():
return Response({'status': 'ERROR', 'error': 'INVALID_ZIP'})
if ('type' in request.query_params):
p_type = request.query_params['type']
else:
p_type = 'SingleFamily'
except:
return Response({'status': 'ERROR', 'error': 'NO_ZIP_SPECIFIED'})
db_client = MongoClient('mongo-master.propmix.io', 33017)
try:
zipcode = pz.get(str(request.query_params['zip']), "US")
except:
return Response({'status': 'ERROR', 'error': 'Invalid ZipCode'})
zip_code_location = ', '.join(
[zipcode['county'], zipcode['state_short']])
data = list(
db_client.iestimate.mls_data_new_nxmodel2.find(
{"postalcode": str(request.query_params['zip'])}))
if len(data) == 0:
return Response({'status': 'ERROR', 'error': 'Insufficient data'})
else:
last_3_month_meta = data[0]['3_month_meta'][p_type][
'price_trend'] if data[0]['3_month_meta'][
p_type] is not None else None
last_6_month_meta = data[0]['6_month_meta'][p_type][
'price_trend'] if data[0]['6_month_meta'][
p_type] is not None else None
last_12_month_meta = data[0]['12_month_meta'][p_type][
'price_trend'] if data[0]['12_month_meta'][
p_type] is not None else None
last_36_month_meta = data[0]['36_month_meta'][p_type][
'price_trend'] if data[0]['36_month_meta'][
p_type] is not None else None
last_60_month_meta = data[0]['60_month_meta'][p_type][
'price_trend'] if data[0]['60_month_meta'][
p_type] is not None else None
l_s_ratio_recent = data[0]['3_month_meta'][p_type]['price_trend'][
'lsratio'] if data[0]['3_month_meta'][
p_type] is not None else None
last_3_month_avg_l_s_ratio = data[0]['3_month_meta'][p_type][
'price_trend']['avglsratio'] if data[0]['3_month_meta'][
p_type] is not None else None
last_6_month_avg_l_s_ratio = data[0]['6_month_meta'][p_type][
'price_trend']['avglsratio'] if data[0]['6_month_meta'][
p_type] is not None else None
last_1_year_avg_l_s_ratio = data[0]['12_month_meta'][p_type][
'price_trend']['avglsratio'] if data[0]['12_month_meta'][
p_type] is not None else None
last_3_year_avg_l_s_ratio = data[0]['36_month_meta'][p_type][
'price_trend']['avglsratio'] if data[0]['36_month_meta'][
p_type] is not None else None
last_5_year_avg_l_s_ratio = data[0]['60_month_meta'][p_type][
'price_trend']['avglsratio'] if data[0]['60_month_meta'][
p_type] is not None else None
curr_open_listings = data[0]['3_month_meta'][p_type][
'price_trend']['openlistings'] if data[0]['3_month_meta'][
p_type] is not None else None
curr_pending_sale = data[0]['3_month_meta'][p_type]['price_trend'][
'pendingsale'] if data[0]['3_month_meta'][
p_type] is not None else None
last_3_month_total_txn = data[0]['3_month_meta'][p_type][
'price_trend']['totaltxn'] if data[0]['3_month_meta'][
p_type] is not None else None
last_6_month_total_txn = data[0]['6_month_meta'][p_type][
'price_trend']['totaltxn'] if data[0]['6_month_meta'][
p_type] is not None else None
last_1_year_total_txn = data[0]['12_month_meta'][p_type][
'price_trend']['totaltxn'] if data[0]['12_month_meta'][
p_type] is not None else None
last_3_year_total_txn = data[0]['36_month_meta'][p_type][
'price_trend']['totaltxn'] if data[0]['36_month_meta'][
p_type] is not None else None
last_5_year_total_txn = data[0]['60_month_meta'][p_type][
'price_trend']['totaltxn'] if data[0]['60_month_meta'][
p_type] is not None else None
last_3_month_avg_txn_per_week = data[0]['3_month_meta'][p_type][
'price_trend']['avgtxn'] if data[0]['3_month_meta'][
p_type] is not None else None
last_6_month_avg_txn_per_week = data[0]['6_month_meta'][p_type][
'price_trend']['avgtxn'] if data[0]['6_month_meta'][
p_type] is not None else None
last_1_year_avg_txn_per_week = data[0]['12_month_meta'][p_type][
'price_trend']['avgtxn'] if data[0]['12_month_meta'][
p_type] is not None else None
last_3_year_avg_txn_per_week = data[0]['36_month_meta'][p_type][
'price_trend']['avgtxn'] if data[0]['36_month_meta'][
p_type] is not None else None
last_5_year_avg_txn_per_week = data[0]['60_month_meta'][p_type][
'price_trend']['avgtxn'] if data[0]['60_month_meta'][
p_type] is not None else None
week_52_high = data[0]['12_month_meta'][p_type]['price_trend'][
'week_52_high'] if data[0]['12_month_meta'][
p_type] is not None else None
week_52_low = data[0]['12_month_meta'][p_type]['price_trend'][
'week_52_low'] if data[0]['12_month_meta'][
p_type] is not None else None
return Response({
"zip_code_location":
zip_code_location,
# "property_type":type,
"last_3_months_price_per_square_feet":
last_3_month_meta["series"][0]['price_per_square_feet'][-1][1]
if last_3_month_meta is not None else None,
"last_3_months_trend_variation":
abs(last_3_month_meta["series"][0]['price_per_square_feet'][0]
[1] -
last_3_month_meta["series"][0]['price_per_square_feet'][-1]
[1]) if last_3_month_meta is not None else None,
"last_3_months_price_trend_percentage":
float(
abs(last_3_month_meta["series"][0]['price_per_square_feet']
[0][1] - last_3_month_meta["series"][0]
['price_per_square_feet'][-1][1])) /
float(last_3_month_meta["series"][0]['price_per_square_feet']
[0][1]) if last_3_month_meta is not None else None,
"last_3_months_price_trend_change":
("+" if last_3_month_meta["series"][0]['price_per_square_feet']
[-1][1] -
last_3_month_meta["series"][0]['price_per_square_feet'][0][1]
>= 0 else "-") if last_3_month_meta is not None else None,
"last_3_month_data":
last_3_month_meta["series"]
if last_3_month_meta is not None else None,
"last_6_month_data":
last_6_month_meta["series"]
if last_6_month_meta is not None else None,
"last_1_year_data":
last_12_month_meta["series"]
if last_12_month_meta is not None else None,
"last_3_year_data":
last_36_month_meta["series"]
if last_36_month_meta is not None else None,
"last_5_year_data":
last_60_month_meta["series"]
if last_60_month_meta is not None else None,
"start_to_end_data":
last_60_month_meta["series"]
if last_60_month_meta is not None else None,
"l_s_ratio_recent":
l_s_ratio_recent,
"last_3_month_avg_l_s_ratio":
last_3_month_avg_l_s_ratio,
"last_6_month_avg_l_s_ratio":
last_6_month_avg_l_s_ratio,
"last_1_year_avg_l_s_ratio":
last_1_year_avg_l_s_ratio,
"last_3_year_avg_l_s_ratio":
last_3_year_avg_l_s_ratio,
"last_5_year_avg_l_s_ratio":
last_5_year_avg_l_s_ratio,
"curr_open_listings":
curr_open_listings,
"curr_pending_sale":
curr_pending_sale,
"last_3_month_total_txn":
last_3_month_total_txn,
"last_6_month_total_txn":
last_6_month_total_txn,
"last_1_year_total_txn":
last_1_year_total_txn,
"last_3_year_total_txn":
last_3_year_total_txn,
"last_5_year_total_txn":
last_5_year_total_txn,
"last_3_month_avg_txn_per_week":
last_3_month_avg_txn_per_week,
"last_6_month_avg_txn_per_week":
last_6_month_avg_txn_per_week,
"last_1_year_avg_txn_per_week":
last_1_year_avg_txn_per_week,
"last_3_year_avg_txn_per_week":
last_3_year_avg_txn_per_week,
"last_5_year_avg_txn_per_week":
last_5_year_avg_txn_per_week,
"week_52_low":
week_52_low,
"week_52_high":
week_52_high,
"start_to_end_total_txs":
last_5_year_total_txn,
"start_to_end_avg_txs_per_week":
last_5_year_avg_txn_per_week,
"start_to_end_avg_l_s_ratio":
last_5_year_avg_l_s_ratio
})
def list(self, request):
"""
iPrice Trend MLSLite
Will respond back with Price Trend on particular postalcode for different property types.
---
# YAML (must be separated by `---`)
type:
schema : {zip_code_location: string, last_3_year_avg_l_s_ratio: string, last_3_months_trend_variation: string, last_3_months_price_trend_percentage: string, last_3_months_price_trend_change: string, last_3_month_data: string, l_s_ratio_recent: string}
zip_code_location:
required: true
type: set
sample:
nested:
required: true
type: object
last_3_year_avg_l_s_ratio:
required: false
type: string
last_3_months_trend_variation:
required: false
type: string
omit_serializer: false
parameters_strategy: merge
omit_parameters:
- path
parameters:
- name: zip
description: Postalcode which iPriceTrend to be calculated.
type: string
paramType: query
defaultValue: 33134
- name: type
description: Property types (should be one of 'TownHouse', 'Multifamily', 'Single Family', 'All')
paramType: query
responseMessages:
- code: 401
message: Not Authorized
- code : 404
message: Not Found
consumes:
- application/json
- application/xml
produces:
- application/json
- application/xml
"""
try:
if not request.query_params['zip'].isdigit():
return Response({'status': 'ERROR', 'error': 'INVALID_ZIP'})
if ('type' in request.query_params):
p_type = request.query_params['type']
else:
p_type = 'SingleFamily'
except:
return Response({'status': 'ERROR', 'error': 'NO_ZIP_SPECIFIED'})
try:
zipcode = pz.get(str(request.query_params['zip']), "US")
if not zipcode:
return Response({'status': 'ERROR',
'error': 'Invalid ZipCode'})
except:
return Response({'status': 'ERROR', 'error': 'Invalid ZipCode'})
db_client = MongoClient('mongo-master.propmix.io', 33017)
db_client.infodb.authenticate(**DATABASE_ACCESS)
zip_status_list =\
db_client['infodb']['mlslite_unique_data_zipstatus.s']\
.find_one({"version": "v3"})['ministatus']
for status in zip_status_list:
if status['postalcode'] == str(request.query_params['zip']):
break
else:
return Response({'status':'ERROR',
'error':'NO DATA FOR POSTALCODE'})
zip_code_location = ', '.join([zipcode['county'],
zipcode['state_short']])
db_client.mlmodeldb.authenticate(**DATABASE_ACCESS)
data = list(db_client['mlmodeldb']['mlslite_unique_data_nxmodel.s']
.find({"postalcode": str(request.query_params['zip'])}))
db_client.close()
if len(data) == 0:
return Response({'status': 'ERROR', 'error': 'Insufficient data'})
else:
last_3_month_meta =\
data[0]['3_month_meta'][p_type]['price_trend']\
if '3_month_meta' in data[0]\
and data[0]['3_month_meta'][p_type] is not None\
and 'price_trend' in data[0]['3_month_meta'][p_type]\
and data[0]['3_month_meta'][p_type]['price_trend'] != {}\
else None
last_6_month_meta =\
data[0]['6_month_meta'][p_type]['price_trend']\
if '6_month_meta' in data[0]\
and data[0]['6_month_meta'][p_type] is not None\
and 'price_trend' in data[0]['6_month_meta'][p_type]\
and data[0]['6_month_meta'][p_type]['price_trend'] != {}\
else None
last_12_month_meta =\
data[0]['12_month_meta'][p_type]['price_trend']\
if '12_month_meta' in data[0]\
and data[0]['12_month_meta'][p_type] is not None\
and 'price_trend' in data[0]['12_month_meta'][p_type]\
and data[0]['12_month_meta'][p_type]['price_trend'] != {}\
else None
last_36_month_meta =\
data[0]['36_month_meta'][p_type]['price_trend']\
if '36_month_meta'in data[0]\
and data[0]['36_month_meta'][p_type] is not None\
and 'price_trend' in data[0]['36_month_meta'][p_type]\
and data[0]['36_month_meta'][p_type]['price_trend'] != {}\
else None
last_60_month_meta =\
data[0]['60_month_meta'][p_type]['price_trend']\
if '60_month_meta' in data[0]\
and data[0]['60_month_meta'][p_type] is not None\
and 'price_trend' in data[0]['60_month_meta'][p_type]\
and data[0]['60_month_meta'][p_type]['price_trend'] != {}\
else None
l_s_ratio_recent =\
data[0]['3_month_meta'][p_type]['price_trend']['lsratio']\
if '3_month_meta' in data[0]\
and data[0]['3_month_meta'][p_type] is not None\
and 'price_trend' in data[0]['3_month_meta'][p_type]\
and data[0]['3_month_meta'][p_type]['price_trend'] != {}\
else None
last_3_month_avg_l_s_ratio =\
data[0]['3_month_meta'][p_type]['price_trend']['avglsratio']\
if '3_month_meta' in data[0]\
and data[0]['3_month_meta'][p_type] is not None\
and 'price_trend' in data[0]['3_month_meta'][p_type]\
and data[0]['3_month_meta'][p_type]['price_trend'] != {}\
else None
last_6_month_avg_l_s_ratio =\
data[0]['6_month_meta'][p_type]['price_trend']['avglsratio']\
if '6_month_meta' in data[0]\
and data[0]['6_month_meta'][p_type] is not None\
and 'price_trend' in data[0]['6_month_meta'][p_type]\
and data[0]['6_month_meta'][p_type]['price_trend'] != {}\
else None
last_1_year_avg_l_s_ratio =\
data[0]['12_month_meta'][p_type]['price_trend']['avglsratio']\
if '12_month_meta' in data[0]\
and data[0]['12_month_meta'][p_type] is not None\
and 'price_trend' in data[0]['12_month_meta'][p_type]\
and data[0]['12_month_meta'][p_type]['price_trend'] != {}\
else None
last_3_year_avg_l_s_ratio =\
data[0]['36_month_meta'][p_type]['price_trend']['avglsratio']\
if '36_month_meta'in data[0]\
and data[0]['36_month_meta'][p_type] is not None\
and 'price_trend' in data[0]['36_month_meta'][p_type]\
and data[0]['36_month_meta'][p_type]['price_trend'] != {}\
else None
last_5_year_avg_l_s_ratio =\
data[0]['60_month_meta'][p_type]['price_trend']['avglsratio']\
if '60_month_meta' in data[0]\
and data[0]['60_month_meta'][p_type] is not None\
and 'price_trend' in data[0]['60_month_meta'][p_type]\
and data[0]['60_month_meta'][p_type]['price_trend'] != {}\
else None
curr_open_listings =\
data[0]['3_month_meta'][p_type]['price_trend']['openlistings']\
if '3_month_meta' in data[0]\
and data[0]['3_month_meta'][p_type] is not None\
and 'price_trend' in data[0]['3_month_meta'][p_type]\
and data[0]['3_month_meta'][p_type]['price_trend'] != {}\
else None
curr_pending_sale =\
data[0]['3_month_meta'][p_type]['price_trend']['pendingsale']\
if '3_month_meta' in data[0]\
and data[0]['3_month_meta'][p_type] is not None\
and 'price_trend' in data[0]['3_month_meta'][p_type]\
and data[0]['3_month_meta'][p_type]['price_trend'] != {}\
else None
last_3_month_total_txn =\
data[0]['3_month_meta'][p_type]['price_trend']['totaltxn']\
if '3_month_meta' in data[0]\
and data[0]['3_month_meta'][p_type] is not None\
and 'price_trend' in data[0]['3_month_meta'][p_type]\
and data[0]['3_month_meta'][p_type]['price_trend'] != {}\
else None
last_6_month_total_txn =\
data[0]['6_month_meta'][p_type]['price_trend']['totaltxn']\
if '6_month_meta' in data[0]\
and data[0]['6_month_meta'][p_type] is not None\
and 'price_trend' in data[0]['6_month_meta'][p_type]\
and data[0]['6_month_meta'][p_type]['price_trend'] != {}\
else None
last_1_year_total_txn =\
data[0]['12_month_meta'][p_type]['price_trend']['totaltxn']\
if '12_month_meta' in data[0]\
and data[0]['12_month_meta'][p_type] is not None\
and 'price_trend' in data[0]['12_month_meta'][p_type]\
and data[0]['12_month_meta'][p_type]['price_trend'] != {}\
else None
last_3_year_total_txn =\
data[0]['36_month_meta'][p_type]['price_trend']['totaltxn']\
if '36_month_meta'in data[0]\
and data[0]['36_month_meta'][p_type] is not None\
and 'price_trend' in data[0]['36_month_meta'][p_type]\
and data[0]['36_month_meta'][p_type]['price_trend'] != {}\
else None
last_5_year_total_txn =\
data[0]['60_month_meta'][p_type]['price_trend']['totaltxn']\
if '60_month_meta' in data[0]\
and data[0]['60_month_meta'][p_type] is not None\
and 'price_trend' in data[0]['60_month_meta'][p_type]\
and data[0]['60_month_meta'][p_type]['price_trend'] != {}\
else None
last_3_month_avg_txn_per_week =\
data[0]['3_month_meta'][p_type]['price_trend']['avgtxn']\
if '3_month_meta' in data[0]\
and data[0]['3_month_meta'][p_type] is not None\
and 'price_trend' in data[0]['3_month_meta'][p_type]\
and data[0]['3_month_meta'][p_type]['price_trend'] != {}\
else None
last_6_month_avg_txn_per_week =\
data[0]['6_month_meta'][p_type]['price_trend']['avgtxn']\
if '6_month_meta' in data[0]\
and data[0]['6_month_meta'][p_type] is not None\
and 'price_trend' in data[0]['6_month_meta'][p_type]\
and data[0]['6_month_meta'][p_type]['price_trend'] != {}\
else None
last_1_year_avg_txn_per_week =\
data[0]['12_month_meta'][p_type]['price_trend']['avgtxn']\
if '12_month_meta' in data[0]\
and data[0]['12_month_meta'][p_type] is not None\
and 'price_trend' in data[0]['12_month_meta'][p_type]\
and data[0]['12_month_meta'][p_type]['price_trend'] != {}\
else None
last_3_year_avg_txn_per_week =\
data[0]['36_month_meta'][p_type]['price_trend']['avgtxn']\
if '36_month_meta'in data[0]\
and data[0]['36_month_meta'][p_type] is not None\
and 'price_trend' in data[0]['36_month_meta'][p_type]\
and data[0]['36_month_meta'][p_type]['price_trend'] != {}\
else None
last_5_year_avg_txn_per_week =\
data[0]['60_month_meta'][p_type]['price_trend']['avgtxn']\
if '60_month_meta' in data[0]\
and data[0]['60_month_meta'][p_type] is not None\
and 'price_trend' in data[0]['60_month_meta'][p_type]\
and data[0]['60_month_meta'][p_type]['price_trend'] != {}\
else None
week_52_high =\
data[0]['12_month_meta'][p_type]['price_trend']['week_52_high']\
if '12_month_meta' in data[0]\
and data[0]['12_month_meta'][p_type] is not None\
and 'price_trend' in data[0]['12_month_meta'][p_type]\
and data[0]['12_month_meta'][p_type]['price_trend'] != {}\
else None
week_52_low =\
data[0]['12_month_meta'][p_type]['price_trend']['week_52_low']\
if '12_month_meta' in data[0]\
and data[0]['12_month_meta'][p_type] is not None\
and 'price_trend' in data[0]['12_month_meta'][p_type]\
and data[0]['12_month_meta'][p_type]['price_trend'] != {}\
else None
import datetime
epoch = datetime.datetime.utcfromtimestamp(0)
def unix_time_millis(dt):
return (dt - epoch).total_seconds() * 1000.0
if last_3_month_meta is not None:
last_3_month_meta["series"] =\
[{key: sorted(
map(lambda k: [
unix_time_millis(
datetime.datetime
.strptime(k[0], '%d/%m/%Y')),
k[1]], item[key]), key=lambda k: k[0])}
for item in last_3_month_meta["series"]
for key in item.keys()]
if last_6_month_meta is not None:
last_6_month_meta["series"] =\
[{key: sorted(
map(lambda k: [
unix_time_millis(
datetime.datetime.strptime(k[0], '%d/%m/%Y')),
k[1]], item[key]), key=lambda k: k[0])}
for item in last_6_month_meta["series"]
for key in item.keys()]\
if last_6_month_meta is not None else None
if last_12_month_meta is not None:
last_12_month_meta["series"] =\
[{key: sorted(map(lambda k:
[unix_time_millis(
datetime.datetime
.strptime(k[0], '%d/%m/%Y')),
k[1]], item[key]), key=lambda k: k[0])}
for item in last_12_month_meta["series"]
for key in item.keys()]\
if last_12_month_meta is not None else None
if last_36_month_meta is not None:
last_36_month_meta["series"] =\
[{key: sorted(map(lambda k:
[unix_time_millis(
datetime.datetime
.strptime(k[0], '%d/%m/%Y')), k[1]],
item[key]), key=lambda k: k[0])}
for item in last_36_month_meta["series"]
for key in item.keys()]\
if last_36_month_meta is not None else None
if last_60_month_meta is not None:
last_60_month_meta["series"] =\
[{key: sorted(map(lambda k:
[unix_time_millis(
datetime.datetime
.strptime(k[0], '%d/%m/%Y')), k[1]],
item[key]), key=lambda k: k[0])}
for item in last_60_month_meta["series"]
for key in item.keys()]\
if last_60_month_meta is not None else None
return Response({
"zip_code_location": zip_code_location,
# "property_type":type,
"last_3_months_price_per_square_feet":
last_3_month_meta["series"][0]
['price_per_square_feet'][-1][1]\
if last_3_month_meta is not None else None,
"last_3_months_trend_variation":
abs(last_3_month_meta["series"][0]
['price_per_square_feet'][0][1]\
- last_3_month_meta["series"][0]
['price_per_square_feet'][-1][1])\
if last_3_month_meta is not None else None,
"last_3_months_price_trend_percentage":
float(abs(last_3_month_meta["series"][0]
['price_per_square_feet'][0][1]\
- last_3_month_meta["series"][0]
['price_per_square_feet'][-1][1]))\
/ float(last_3_month_meta["series"][0]
['price_per_square_feet'][0][1])\
if last_3_month_meta is not None else None,
"last_3_months_price_trend_change":
("+" if last_3_month_meta["series"][0]
['price_per_square_feet'][-1][1]\
- last_3_month_meta["series"][0]
['price_per_square_feet'][0][1] >= 0 else "-")\
if last_3_month_meta is not None else None,
"last_3_month_data":
last_3_month_meta["series"]\
if last_3_month_meta is not None else None,
"last_6_month_data":
last_6_month_meta["series"]\
if last_6_month_meta is not None else None,
"last_1_year_data":
last_12_month_meta["series"]\
if last_12_month_meta is not None else None,
"last_3_year_data":
last_36_month_meta["series"]\
if last_36_month_meta is not None else None,
"last_5_year_data":
last_60_month_meta["series"]\
if last_60_month_meta is not None else None,
"start_to_end_data":
last_60_month_meta["series"]\
if last_60_month_meta is not None else None,
"l_s_ratio_recent": l_s_ratio_recent,
"last_3_month_avg_l_s_ratio":
last_3_month_avg_l_s_ratio,
"last_6_month_avg_l_s_ratio":
last_6_month_avg_l_s_ratio,
"last_1_year_avg_l_s_ratio":
last_1_year_avg_l_s_ratio,
"last_3_year_avg_l_s_ratio":
last_3_year_avg_l_s_ratio,
"last_5_year_avg_l_s_ratio":
last_5_year_avg_l_s_ratio,
"curr_open_listings": curr_open_listings,
"curr_pending_sale": curr_pending_sale,
"last_3_month_total_txn": last_3_month_total_txn,
"last_6_month_total_txn": last_6_month_total_txn,
"last_1_year_total_txn": last_1_year_total_txn,
"last_3_year_total_txn": last_3_year_total_txn,
"last_5_year_total_txn": last_5_year_total_txn,
"last_3_month_avg_txn_per_week":
last_3_month_avg_txn_per_week,
"last_6_month_avg_txn_per_week":
last_6_month_avg_txn_per_week,
"last_1_year_avg_txn_per_week":
last_1_year_avg_txn_per_week,
"last_3_year_avg_txn_per_week":
last_3_year_avg_txn_per_week,
"last_5_year_avg_txn_per_week":
last_5_year_avg_txn_per_week,
"week_52_low": week_52_low,
"week_52_high": week_52_high,
"start_to_end_total_txs": last_5_year_total_txn,
"start_to_end_avg_txs_per_week":
last_5_year_avg_txn_per_week,
"start_to_end_avg_l_s_ratio":
last_5_year_avg_l_s_ratio,
})
def test_ziptastic_invalid_pincode(self):
current_result = pz.query_ziptastic_api(33044)
self.assertFalse(current_result)
def test_get_invalid_pincode(self):
current_result = pz.get(33044)
self.assertFalse(current_result)
