Update NOAA Request function (#332)

Fixes #223

mhkit/river/graphics.py

@@ -118,7 +118,7 @@ def plot_velocity_duration_curve(V, F, label=None, ax=None):
     """
     # Sort by F
     temp = xr.Dataset(data_vars={"V": V, "F": F})
-    temp.sortby("F", ascending=False)
+    temp = temp.sortby("F", ascending=False)
 
     ax = _xy_plot(
         temp["V"],

mhkit/river/io/usgs.py

@@ -82,7 +82,7 @@ def request_usgs_data(
     station : str
         USGS station number (e.g. '08313000')
     parameter : str
-        USGS paramter ID (e.g. '00060' for Discharge, cubic feet per second)
+        USGS parameter ID (e.g. '00060' for Discharge, cubic feet per second)
     start_date : str
         Start date in the format 'YYYY-MM-DD' (e.g. '2018-01-01')
     end_date : str
@@ -91,8 +91,8 @@ def request_usgs_data(
         Data type, options include 'Daily' (return the mean daily value) and
         'Instantaneous'.
     proxy : dict or None
-         To request data from behind a firewall, define a dictionary of proxy settings,
-         for example {"http": 'localhost:8080'}
+        To request data from behind a firewall, define a dictionary of proxy settings,
+        for example {"http": 'localhost:8080'}
     write_json : str or None
         Name of json file to write data
     clear_cache : bool
@@ -106,7 +106,7 @@ def request_usgs_data(
         Data indexed by datetime with columns named according to the parameter's
         variable description
     """
-    if not data_type in ["Daily", "Instantaneous"]:
+    if data_type not in ["Daily", "Instantaneous"]:
         raise ValueError(f"data_type must be Daily or Instantaneous. Got: {data_type}")
 
     if not isinstance(to_pandas, bool):

mhkit/tidal/io/noaa.py

@@ -4,14 +4,14 @@
 This module provides functions to fetch, process, and read NOAA (National
 Oceanic and Atmospheric Administration) current data directly from the
 NOAA Tides and Currents API (https://api.tidesandcurrents.noaa.gov/api/prod/). It
-supports loading data into a pandas DataFrame, handling data in XML and 
+supports loading data into a pandas DataFrame, handling data in XML and
 JSON formats, and writing data to a JSON file.
 
 Functions:
 ----------
-request_noaa_data(station, parameter, start_date, end_date, proxy=None, 
+request_noaa_data(station, parameter, start_date, end_date, proxy=None,
   write_json=None):
-    Loads NOAA current data from the API into a pandas DataFrame, 
+    Loads NOAA current data from the API into a pandas DataFrame,
     with optional support for proxy settings and writing data to a JSON
     file.
 
@@ -56,16 +56,18 @@ def request_noaa_data(
     Parameters
     ----------
     station : str
-        NOAA current station number (e.g. 'cp0101')
+        NOAA current station number (e.g. 'cp0101', "s08010", "9446484")
     parameter : str
-        NOAA paramter (e.g. '' for Discharge, cubic feet per second)
+        NOAA parameter (e.g. "currents", "salinity", "water_level", "water_temperature",
+        "air_temperature", "wind", "air_pressure")
+        https://api.tidesandcurrents.noaa.gov/api/prod/
     start_date : str
         Start date in the format yyyyMMdd
     end_date : str
         End date in the format yyyyMMdd
     proxy : dict or None
-         To request data from behind a firewall, define a dictionary of proxy
-         settings, for example {"http": 'localhost:8080'}
+        To request data from behind a firewall, define a dictionary of proxy
+        settings, for example {"http": 'localhost:8080'}
     write_json : str or None
         Name of json file to write data
     clear_cache : bool
@@ -158,26 +160,42 @@ def request_noaa_data(
             end_date = date_list[i + 1].strftime("%Y%m%d")
 
             api_query = f"begin_date={start_date}&end_date={end_date}&station={station}&product={parameter}&units=metric&time_zone=gmt&application=web_services&format=xml"
+            # Add datum to water level inquiries
+            if parameter == "water_level":
+                api_query += "&datum=MLLW"
             data_url = f"https://tidesandcurrents.noaa.gov/api/datagetter?{api_query}"
 
-            print("Data request URL: ", data_url)
+            print(f"Data request URL: {data_url}\n")
 
             # Get response
             try:
                 response = requests.get(url=data_url, proxies=proxy)
                 response.raise_for_status()
-            except requests.exceptions.HTTPError as err:
-                print(f"HTTP error occurred: {err}")
-                continue
-            except requests.exceptions.RequestException as err:
-                print(f"Error occurred: {err}")
-                continue
+                # Catch non-exception errors
+                if "error" in response.content.decode():
+                    raise Exception(response.content.decode())
+            except Exception as err:
+                if err.__class__ == requests.exceptions.HTTPError:
+                    print(f"HTTP error occurred: {err}")
+                    print(f"Error message: {response.content.decode()}\n")
+                    continue
+                elif err.__class__ == requests.exceptions.RequestException:
+                    print(f"Requests error occurred: {err}")
+                    print(f"Error message: {response.content.decode()}\n")
+                    continue
+                else:
+                    print(f"Requests error occurred: {err}\n")
+                    continue
+
             # Convert to DataFrame and save in data_frames list
             df, metadata = _xml_to_dataframe(response)
             data_frames.append(df)
 
         # Concatenate all DataFrames
-        data = pd.concat(data_frames, ignore_index=False)
+        if data_frames:
+            data = pd.concat(data_frames, ignore_index=False)
+        else:
+            raise ValueError("No data retrieved.")
 
         # Remove duplicated date values
         data = data.loc[~data.index.duplicated()]
@@ -236,7 +254,12 @@ def _xml_to_dataframe(response):
     df.drop_duplicates(inplace=True)
 
     # Convert data to float
-    df[["d", "s"]] = df[["d", "s"]].apply(pd.to_numeric)
+    cols = list(df.columns)
+    for var in cols:
+        try:
+            df[var] = df[var].apply(pd.to_numeric)
+        except ValueError:
+            pass
 
     return df, metadata
 

mhkit/wave/io/ndbc.py

@@ -329,10 +329,10 @@ def request_data(parameter, filenames, proxy=None, clear_cache=False, to_pandas=
         'cwind' :   'Continuous Winds Current Year Historical Data'
 
     filenames: pandas Series, pandas DataFrame, xarray DataArray, or xarray Dataset
-            Data filenames on https://www.ndbc.noaa.gov/data/historical/{parameter}/
+        Data filenames on https://www.ndbc.noaa.gov/data/historical/{parameter}/
 
     proxy: dict
-            Proxy dict passed to python requests,
+        Proxy dict passed to python requests,
         (e.g. proxy_dict= {"http": 'http:wwwproxy.yourProxy:80/'})
 
     to_pandas: bool (optional)
@@ -631,7 +631,7 @@ def parameter_units(parameter=""):
     If no parameter is passed then an ordered dictionary of all NDBC
     parameterz specified unites is returned. If a parameter is specified
     then only the units associated with that parameter are returned.
-    Note that many NDBC paramters report multiple measurements and in
+    Note that many NDBC parameters report multiple measurements and in
     that case the returned dictionary will contain the NDBC measurement
     name and associated unit for all the measurements associated with
     the specified parameter. Optional parameter values are given below.