Starting to implement global wave watch forecast and Codacy cleanup

.codacy.yaml

@@ -1,2 +1,7 @@
+engines:
+  prospector:
+    enabled: true
+    python_version: 3
+
 exclude_paths:
   - "app/deployments/migrations/**.py"

Readme.md

@@ -17,7 +17,7 @@ Therefore when you are done, `ctrl-c` out of the logs and run `make down` to shu
 
 ### Forecasts
 
-See [forecasts/Readme.md](forecasts/Readme.md) for details about how the forecasts API works and how to add new forecasts.
+See [forecasts/Readme.md](app/forecasts/Readme.md) for details about how the forecasts API works and how to add new forecasts.
 
 ### Adding / Editing Platforms
 
@@ -123,7 +123,7 @@ You can use Django fixtures to quickly save models from the database and reload
 - `app/`
   - `account/` Django user account app.
   - `buoy_barn/` Primary Django application.
-  - `deployments/` Database models and API.
+  - `deployments/` Database models and API
   - `forecasts/` Forecast models and API
   - `utils/`
     - `wait-for-it.sh` Shell script that can wait until specified services are avaliable before finishing. Helps `make up` launch Django more reliably.

app/forecasts/forecasts/__init__.py

@@ -9,11 +9,14 @@
     GFSWindDirection,
 )
 
+# from forecasts.forecasts.coastwatch_erddap.wave_watch import GlobalWaveWatchHeight
+
 forecast_list = [
     BedfordWaveDirection(),
     BedfordWaveHeight(),
     BedfordWavePeriod(),
     GFSAirTemp(),
     GFSWindSpeed(),
     GFSWindDirection(),
+    # GlobalWaveWatchHeight(),
 ]

app/forecasts/forecasts/base_erddap_forecast.py

@@ -6,23 +6,13 @@
 # from memoize import memoize
 import pandas as pd
 import requests
-from requests import HTTPError
+
+# from requests import HTTPError
 
 from forecasts.forecasts.base_forecast import BaseForecast
 from forecasts.utils import erddap as erddap_utils
 
 
-# http://www.neracoos.org/erddap/griddap/WW3_72_GulfOfMaine_latest.json?
-# hs
-# [(2019-01-10T12:00:00Z):1:(2019-01-10T12:00:00Z)]
-# [(38.0):1:(46.0)][(-76.0):1:(-60.0)]
-
-
-# https://coastwatch.pfeg.noaa.gov/erddap/griddap/NCEP_Global_Best.json?
-# ugrd10m[(2019-01-07):1:(2019-01-10T12:00:00Z)][(45):1:(45)][(256):1:(256)],
-# vgrd10m[(2019-01-07):1:(2019-01-10T12:00:00Z)][(45):1:(45)][(256):1:(256)]
-
-
 class BaseERDDAPForecast(BaseForecast):
     """ Extends BaseForecast with methods for accessing and retrieving forecasts from ERDDAP servers
 
@@ -67,7 +57,7 @@ def point_forecast(self, lat: float, lon: float) -> List[Tuple[datetime, float]]
             for row in rows
         ]
 
-    def offset_value(self, value: float) -> float:
+    def offset_value(self, value: float) -> float:  # pylint: disable=no-self-use
         """ Allows you to override a value to return something more helpful (say Celsius rather than Kelvin) """
         return value
 
@@ -95,7 +85,7 @@ def dataset_info_df(self) -> pd.DataFrame:
         """ Retrieve the most recent metadata for a dataset to find valid time and coordinates
         
         Returns:
-            Pandas DataFrame 
+            Pandas DataFrame
         """
         conn = self.connection()
 
@@ -122,7 +112,7 @@ def dataset_query_string(self, lat: float, lon: float) -> str:
             lat (float): Latitude in degrees North
             lon (float): Longitude in degrees East
         
-        Returns: 
+        Returns:
             Query string for dataset with variables, times, and coordinates
         """
         info_df = self.dataset_info_df()
@@ -132,8 +122,10 @@ def dataset_query_string(self, lat: float, lon: float) -> str:
             for variable in self.request_variables()
         )
 
-    def coverage_time_str(self, info_df=pd.DataFrame) -> str:
-        """ Formatted query string element for forecast coverage time range 
+    def coverage_time_str(
+        self, info_df=pd.DataFrame
+    ) -> str:  # pylint: disable=no-self-use
+        """ Formatted query string element for forecast coverage time range
         
         Args:
             info_df (pd.DataFrame): Pandas DataFrame
@@ -151,7 +143,7 @@ def request_variables(self) -> List[str]:
 
     def coordinates_str(self, info_df: pd.DataFrame, lat: float, lon: float) -> str:
         """ Create coordinates query string element
-        
+
         Arguments:
             info_df(pd.DataFrame): Dataset metadata DataFrame
             lat (float): Latitude in degrees North

app/forecasts/forecasts/coastwatch_erddap/gfs.py

@@ -1,13 +1,12 @@
 from dataclasses import dataclass
 from datetime import datetime
-from math import degrees, hypot, sin, sqrt
+from math import degrees, hypot, sin
 from typing import List, Tuple
 
 from forecasts.forecasts.base_forecast import ForecastTypes
 from forecasts.forecasts.coastwatch_erddap.base_coastwatch import (
     BaseCoastWatchRDDAPForecast,
 )
-from forecasts.utils import erddap as erddap_utils
 
 
 class BaseGFSForecast(BaseCoastWatchRDDAPForecast):
@@ -32,7 +31,7 @@ def offset_value(self, value: float) -> float:
 
 @dataclass
 class WindReading:
-    """ Class for a wind measurement at a given time 
+    """ Class for a wind measurement at a given time
     
     Attributes:
         time (datetime): Time of forecasted value
@@ -70,7 +69,7 @@ def request_variables(self) -> List[str]:
         return [self.east_wind_field, self.north_wind_field]
 
     def time_series(self, lat: float, lon: float) -> List[WindReading]:
-        """ Return a list of WindReadings for a given lat, lon forecast point 
+        """ Return a list of WindReadings for a given lat, lon forecast point
         
         Args:
             lat (float): Latitude in degrees North
@@ -102,7 +101,7 @@ class GFSWindSpeed(BaseGFSWindForecast):
     forecast_type = ForecastTypes.WIND_SPEED
 
     def point_forecast(self, lat: float, lon: float) -> List[Tuple[datetime, float]]:
-        """ Return a list of tuples for the wind speed 
+        """ Return a list of tuples for the wind speed
         
         Args:
             lat (float): Latitude in degrees North
@@ -121,7 +120,7 @@ class GFSWindDirection(BaseGFSWindForecast):
 
     def point_forecast(self, lat: float, lon: float) -> List[Tuple[datetime, float]]:
         """ Return a list of tuples for the wind direction 
-        
+
         Args:
             lat (float): Latitude in degrees North
             lon (float): Longitude in degrees East

app/forecasts/forecasts/coastwatch_erddap/wave_watch.py

@@ -0,0 +1,22 @@
+from forecasts.forecasts.base_forecast import ForecastTypes
+from forecasts.forecasts.coastwatch_erddap.base_coastwatch import (
+    BaseCoastWatchRDDAPForecast,
+)
+
+
+class BaseWaveWatch(BaseCoastWatchRDDAPForecast):
+    dataset = "NWW3_Global_Best"
+    source_url = "https://coastwatch.pfeg.noaa.gov/erddap/griddap/NWW3_Global_Best.html"
+
+    to_360 = True
+
+
+class GlobalWaveWatchHeight(BaseWaveWatch):
+    slug = "global_wave_watch_height"
+    name = "Global Wave Watch III - Significant Wave Height"
+    description = (
+        "Wave Height from the Global Wave Watch III model by the University of Hawaii"
+    )
+    forecast_type = ForecastTypes.WAVE_HEIGHT
+
+    field = "Thgt"

app/forecasts/serializers.py

@@ -3,7 +3,7 @@
 
 
 class ForecastSerializer(serializers.BaseSerializer):
-    def to_representation(self, obj):
+    def to_representation(self, obj):  # pylint: disable=no-self-use
 
         return {
             "slug": obj.slug,

app/forecasts/views.py

@@ -1,5 +1,5 @@
 from rest_framework import viewsets
-from rest_framework.decorators import action
+from rest_framework.exceptions import NotFound
 from rest_framework.response import Response
 
 from forecasts.forecasts import forecast_list
@@ -9,13 +9,16 @@
 class ForecastViewSet(viewsets.ViewSet):
     """ A viewset for forecasts """
 
-    def list(self, request):
+    def list(self, request):  # pylint: disable=no-self-use
         serializer = ForecastSerializer(forecast_list, many=True)
         return Response(serializer.data)
 
-    def retrieve(self, request, pk=None):
+    def retrieve(self, request, pk=None):  # pylint: disable=no-self-use
         filtered = [forecast for forecast in forecast_list if forecast.slug == pk]
-        forecast = filtered[0]
+        try:
+            forecast = filtered[0]
+        except IndexError:
+            raise NotFound(detail=f"Unknown forecast slug: {pk}")
         seralizer = ForecastSerializer(forecast)
         data = seralizer.data