def forecast_update(site_key):
site = Site.get_by_key_name(site_key)
if site is None:
return Response(status=404)
forecast_url = "http://www.metoffice.gov.uk/public/data/PWSCache/BestForecast/Forecast/%s?format=application/json" % site_key
result = urlfetch.fetch(forecast_url)
if result.status_code == 200:
forecast = parse_forecast(result.content)
issued_date = parse_date(forecast["@dataDate"])
for date, day in days(forecast):
forecast_day = ForecastDay.get_by_key_name(
make_key_name(site, date))
if forecast_day is None:
forecast_day = ForecastDay(key_name=make_key_name(site, date),
forecast_date=date,
site=site)
forecast_day.site = site
for timestep, data in day_timesteps(day):
w = Forecast()
w.issued = issued_date
for k, v in data.items():
prop_name = snake_case(k)
if hasattr(w, prop_name):
if v == "missing":
v = None
setattr(w, prop_name, v)
forecast_day.forecasts.add(timestep, w)
forecast_day.save()
site.save()
return Response(status=204)
def forecast_update(site_key):
site = Site.get_by_key_name(site_key)
if site is None:
return Response(status = 404)
forecast_url = "http://www.metoffice.gov.uk/public/data/PWSCache/BestForecast/Forecast/%s?format=application/json" % site_key
result = urlfetch.fetch(forecast_url)
if result.status_code == 200:
forecast = parse_forecast(result.content)
issued_date = parse_date(forecast["@dataDate"])
for date, day in days(forecast):
forecast_day = ForecastDay.get_by_key_name(make_key_name(site,date))
if forecast_day is None:
forecast_day = ForecastDay(key_name=make_key_name(site,date), forecast_date = date, site = site)
forecast_day.site = site
for timestep, data in day_timesteps(day):
w = Forecast()
w.issued = issued_date
for k,v in data.items():
prop_name = snake_case(k)
if hasattr(w, prop_name):
if v == "missing":
v = None
setattr(w, prop_name, v)
forecast_day.forecasts.add(timestep,w)
forecast_day.save()
site.save()
return Response(status = 204)
def save_forecast(user_id, game_id, forecast, team_host_goals, team_guest_goals):
forecast_ = db.session.query(Forecast).\
filter(Forecast.user_id == user_id).\
filter(Forecast.game_id == game_id).\
first()
if not forecast_:
forecast_ = Forecast(user_id=user_id, game_id=game_id)
forecast_.forecast = forecast
forecast_.team_host_goals = team_host_goals
forecast_.team_guest_goals = team_guest_goals
db.session.add(forecast_)
db.session.commit()
def raw_forecast_to_domain_forecast(raw_forecast) -> Forecast:
TIME_CURRENT = 'currently'
raw_weather = raw_forecast[TIME_CURRENT]['icon']
weather = Weather.from_value(raw_weather)
return Forecast(weather)
def _parse_forecast(self, request_url):
weatherbitio_reponse = requests.get(request_url)
weatherbitio_reponse.raise_for_status()
json = weatherbitio_reponse.json()
headers = weatherbitio_reponse.headers
return Forecast(json, weatherbitio_reponse, headers)
def get_forecast(self, data, date_from, date_to):
forecasts = []
for data_forecast in data['data']:
forecast = Forecast(data_forecast['date'], data_forecast['temperature']['max'], data_forecast['temperature']['min'], data_forecast['rain']['probability'], data_forecast['rain']['precipitation'], data['id'])
forecasts.append(forecast)
return forecasts
def add_forecasts():
# 5-day 3-h forecast (free)
url = 'http://api.openweathermap.org/data/2.5/forecast?id=6167865&APPID=631d59f50ab1841ba7af0f0f706e1505'
response = requests.get(url, verify=True)
if response:
r = response.json()
session = db.session # creates a SQLAlchemy Session
this_hour = datetime.datetime.now().replace(microsecond=0,
second=0,
minute=0)
Forecast.query.filter(
Forecast.id < this_hour -
datetime.timedelta(hours=150)).delete() # deletes old forecasts
for dt in r['list']:
data = Forecast(
id=dt['dt_txt'],
retrieval_time=datetime.datetime.now().replace(microsecond=0,
second=0,
minute=0),
drybulb=dt['main']['temp'] - 273.15, # Kelvin to Celcius
relative_humidity=dt['main']['humidity'])
session.merge(data) # adds new forecasts
session.commit()
else:
print('Unable to add forecasts!')
def get_forecast_in_location(lat, lon, days=3):
"""Generic method to get the forecast in a specified location for a variable number of days
The weather is returned in JSON format. More info about the responses can be found in the official API documentation
site (http://openweathermap.org/current)"""
# Checking that the message contains actually a location
if (lon, lat) == (-1, -1):
raise AttributeError # Use a custom exception in the future
TOKEN = get_OpenWeatherAPI_token()
# Asking for the weather
current_weather_url = "http://api.openweathermap.org/data/2.5/forecast"
params = {'appid': TOKEN, 'lon': lon, 'lat': lat}
rq = requests.get(current_weather_url, params=params)
return Forecast(api_response=rq.text, days=days)
def get(self):
args = self.parser.parse_args()
username = _request_ctx_stack.top.current_user['sub']
if len(args['group']) > 0:
username = '******'.format(
username)
to_coord = _split_coordinates('to', args['to'])
from_coord = _split_coordinates('from', args['from'])
group_users = username.split(',')
gm_routes = get_all_routes(from_coord, to_coord, limit=8)
ds_forecasts = get_forecast(from_coord, to_coord)
gm_routes = _prioritize(gm_routes, ds_forecasts, group_users)
directions = map(
lambda x: Directions(
mode=x['_mode'], data=x, priority=x['_priority']), gm_routes)
directions = Directions.objects.bulk_create(list(directions))
for idx in range(len(gm_routes)):
gm_routes[idx]['_id'] = str(directions[idx])
forecasts = map(
lambda f: Forecast(lat=f['latitude'], lng=f['longitude'], data=f),
ds_forecasts)
forecasts = Forecast.objects.bulk_create(list(forecasts))
recommendations = Recommendations(available=directions,
forecast=forecasts,
user=username,
created_on=datetime.utcnow())
recommendations_id = recommendations.save()
return {
'to': to_coord,
'from': from_coord,
'directions': gm_routes,
'forecast': ds_forecasts,
'recommendation_id': str(recommendations_id.pk)
}
def get_forecasts_for_city(city_id):
url = "http://apiadvisor.climatempo.com.br/api/v1/forecast/locale/%s/days/15?token=%s" % (
city_id, WeatherService._api_user_token)
response = requests.get(url)
if response.status_code != 200:
return None
else:
response_data = response.json()
city = CityRepository.save_city(
City(city_id, response_data["name"], response_data["state"],
response_data["country"]))
return [
ForecastRepository.save_forecast(
Forecast(city, data["date"], data["rain"]["probability"],
data["rain"]["precipitation"],
data["temperature"]["min"],
data["temperature"]["max"]))
for data in response_data["data"]
]
def make_forecast(response):
return Forecast(response.json(), response, response.headers)
def __init__(self, team_name, remaining_issues=None):
self.model = ForecastModel(team_name)
self.remaining_issues = remaining_issues
class Forecast:
def __init__(self, team_name, remaining_issues=None):
self.model = ForecastModel(team_name)
self.remaining_issues = remaining_issues
def mk_throughput_line(self):
df = self.model.throughput_df()
fig = go.Figure(data=go.Scatter(
x=df['end'].tolist(),
y=df['throughput'].tolist(),
name='throughput'
))
if self.remaining_issues:
self.add_uncertaintity_cone(df, fig)
return fig
def add_uncertaintity_cone(self, throughput_df, fig):
current_throughput = throughput_df.iloc[-1]['throughput']
target = current_throughput + self.remaining_issues
quick, slow = self.model.uncertainty_cone_coords(target)
x_a, y_a, x_b, y_b = quick
fig.add_trace(go.Scatter(
x=[x_a, x_b],
y=[y_a, y_b],
name='optimistic',
mode='lines',
line={
'color': 'green',
'dash': 'dash',
'width': 1
},
legendgroup='optimistic'
))
fig.add_trace(go.Scatter(
x=[x_b, x_b],
y=[y_b, 0],
mode='lines',
line={
'color': 'green',
'dash': 'dash',
'width': 1
},
showlegend=False,
legendgroup='optimistic'
))
x_a, y_a, x_b, y_b = slow
fig.add_trace(go.Scatter(
x=[x_a, x_b],
y=[y_a, y_b],
name='pesimistic',
mode='lines',
line={
'color': 'red',
'dash': 'dash',
'width': 1
},
legendgroup='optimistic'
))
fig.add_trace(go.Scatter(
x=[x_b, x_b],
y=[y_b, 0],
mode='lines',
line={
'color': 'red',
'dash': 'dash',
'width': 1
},
showlegend=False,
legendgroup='pesimistic'
))
def mk_story_point_scatter(self):
self.model.throughput_df()
return px.scatter(
self.model.historic_df, x="story_points", y="days_taken")
def mk_time_per_issue_scatter(self):
percent_80 = self.model.historic_df['days_taken'].quantile(0.8)
percent_50 = self.model.historic_df['days_taken'].quantile(0.5)
issue_min = self.model.historic_df['end_time'].min(numeric_only=False)
issue_max = self.model.historic_df['end_time'].max(numeric_only=False)
quantiles_df = pd.DataFrame({
'x': [issue_min, issue_max, issue_min, issue_max],
'y': [percent_50, percent_50, percent_80, percent_80],
'name': [
f"50% {round(percent_50)} days",
f"50% {round(percent_50)} days",
f"80% {round(percent_80)} days",
f"80% {round(percent_80)} days"]
})
fig = px.scatter(
self.model.historic_df, x='end_time', y="days_taken",
hover_name="name",
hover_data={'end_time': False, 'days_taken': True})
for trace in px.line(
quantiles_df, x='x', y='y', color='name',
color_discrete_sequence=px.colors.qualitative.Vivid).data:
fig.add_trace(trace)
return fig
def mk_montecarlo_plot(self, num_issues=5):
df = self.model.run_montecarlo(num_issues)
percent_80 = df['days_taken'].quantile(0.8)
percent_70 = df['days_taken'].quantile(0.7)
percent_60 = df['days_taken'].quantile(0.6)
percent_50 = df['days_taken'].quantile(0.5)
issue_min = df['simulation'].min(numeric_only=False)
issue_max = df['simulation'].max(numeric_only=False)
quantiles_df = pd.DataFrame({
'x': [
issue_min, issue_max,
issue_min, issue_max,
issue_min, issue_max,
issue_min, issue_max
],
'y': [
percent_50, percent_50,
percent_60, percent_60,
percent_70, percent_70,
percent_80, percent_80
],
'name': [
f"50% {round(percent_50)} days",
f"50% {round(percent_50)} days",
f"60% {round(percent_60)} days",
f"60% {round(percent_60)} days",
f"70% {round(percent_70)} days",
f"70% {round(percent_70)} days",
f"80% {round(percent_80)} days",
f"80% {round(percent_80)} days"]
})
fig = px.bar(df, x=df.index, y='days_taken')
for trace in px.line(
quantiles_df, x='x', y='y', color='name',
color_discrete_sequence=px.colors.qualitative.Vivid).data:
fig.add_trace(trace)
return fig
def render(self):
return [
html.P("Input the number of remaining issues to reach your goal."),
dbc.Input(
id="issues-input", type="number",
min=0, step=1,
value=self.remaining_issues),
dcc.Graph(
id='throuput',
figure=self.mk_throughput_line()
),
# dcc.Graph(
# id="story-points",
# figure=self.mk_story_point_scatter()),
# dcc.Graph(
# id="overview",
# figure=self.mk_time_per_issue_scatter())
]
@classmethod
def callback_elements(cls):
return [
dbc.Input(id='issues-input')
]
