completing the remote tests

Author: ceb8

astroquery/esa/neocc/core.py

@@ -408,9 +408,8 @@ def query_object(name, tab, **kwargs):
         # Check the input of the method if tab is not in the list
         # print and error and show the valid names
         if tab not in tab_list:
-            raise KeyError('Please introduce a valid tab name. '
-                           'valid tabs names are: '
-                           ', '.join([str(elem) for elem in tab_list]))
+            raise KeyError(("Please introduce a valid table name. "
+                           f"Valid names are: {', '.join(tab_list)}"))
         # Depending on the tab selected the information will be requested
         # following different methods. Create "switch" for each case:
 
@@ -426,7 +425,7 @@ def query_object(name, tab, **kwargs):
                 data_obj = tabs.get_object_data(url)
             except ConnectionError:  # pragma: no cover
                 print('Initial attempt to obtain object data failed. '
-                    'Reattempting...')
+                      'Reattempting...')
                 # Wait 5 seconds
                 time.sleep(5)
                 # Get object data
@@ -500,10 +499,6 @@ def query_object(name, tab, **kwargs):
             resp_str = tabs.get_summary_data(name)
 
             neocc_obj = tabs.parse_summary(resp_str)
-            # Create empty object with class Summary
-            #neocc_obj = tabs.Summary()
-            # Parse the requested data using Summary parser
-            #neocc_obj._summary_parser(name)
 
         return neocc_obj
 

astroquery/esa/neocc/lists.py

@@ -94,9 +94,12 @@ def get_list_data(url, list_name):
 
     # Get data from URL
     response = requests.get(API_URL + url, timeout=TIMEOUT, verify=VERIFICATION)
-    data_string = response.content.decode('utf-8')
+
+    # Raising error based on HTTP status if necessary
+    response.raise_for_status()
 
     # Parse decoded data
+    data_string = response.content.decode('utf-8')
     neocc_list = parse_list(list_name, data_string)
 
     return neocc_list
@@ -162,7 +165,8 @@ def parse_nea(resp_str):
     """
 
     resp_str = resp_str.replace('#', '')
-    return Table.read(resp_str, data_start=0, format="ascii.fixed_width", names=["NEA"])
+    resp_str = re.sub(' +', ' ', resp_str)
+    return Table.read(resp_str, data_start=0, format="ascii.csv", names=["NEA"])
 
 
 def parse_risk(resp_str):
@@ -184,7 +188,8 @@ def parse_risk(resp_str):
     neocc_lst.rename_columns(("Num/des.       Name", "m",  "Vel km/s"),
                              ('Object Name', 'Diameter in m', 'Vel in km/s'))
 
-    neocc_lst['Date/Time'] = Time(neocc_lst['Date/Time'], scale="utc") 
+    neocc_lst['Date/Time'] = Time(neocc_lst['Date/Time'], scale="utc")
+    neocc_lst['*=Y'] = neocc_lst['*=Y'].astype("<U1")
 
     if "Years" in neocc_lst.colnames:
         first_year, last_year  = np.array([x.split("-") for x in neocc_lst["Years"]]).swapaxes(0,1).astype(int)
@@ -224,9 +229,13 @@ def parse_clo(resp_str):
     neocc_lst = Table.read(resp_str, header_start=2, data_start=4, format="ascii.fixed_width", 
                        names=('Object Name', 'Date', 'Miss Distance in km', 'Miss Distance in au',
                               'Miss Distance in LD', 'Diameter in m', '*=Yes', 'H', 'Max Bright',
-                              'Rel. vel in km/s'))
+                              'Rel. vel in km/s', "drop"))
+
+    # Remove last column
+    neocc_lst.remove_column("drop")
 
     neocc_lst['Date'] = Time(neocc_lst['Date'], scale="utc")
+    neocc_lst["Diameter in m"] = neocc_lst["Diameter in m"].astype(float)
 
     neocc_lst.meta = {'Object Name': 'name of the NEA',
                       'Date': 'close approach date in datetime format',

astroquery/esa/neocc/tabs.py

@@ -810,15 +810,27 @@ def parse_summary(resp_str):
     Uses BeautifulSoup, since it is html data.
     """
 
+    if "Object not found" in resp_str:
+        raise ValueError('Object not found: the name of the object is wrong or misspelt')
+    
     parsed_html = BeautifulSoup(resp_str, 'lxml')
 
     # Pull out the properties
     props = parsed_html.find_all("div", {"class": "simple-list__cell"})
     prop_list = [str(x.contents[0]).strip() for x in props]
 
-    # Seperate the ones that don't follow the same format (name followed by value followed by unit)
-    obs_props = prop_list[-4:]
-    prop_list = prop_list[:-4]
+    # Pulling out Discovery data/Observatory if they are present
+    # (These don't follow the name-value-unit format
+    try:
+        obs_ind = prop_list.index("Discovery Date")
+    except ValueError:
+        try:
+            obs_ind = prop_list.index("Observatory")
+        except:
+            obs_ind = len(prop_list)
+    
+    obs_props = prop_list[obs_ind:]
+    prop_list = prop_list[:obs_ind]
 
     # Building the table
     summary_tab = Table(names=["Physical Properties", "Value", "Units"],