Updated filtering of lat lon to use Series.between and added lat-lon bug fix from dashboard-prototype (Imageomics#50).

Author: egrace479

README.md

@@ -5,12 +5,12 @@ Prototype data dashboard for analyzing telemetry data.
 ## How it works
 
 For full dashboard functionality, upload a CSV or XLS file with the following columns: 
-- `lat`*: Latitude at which image was taken or specimen was collected.
-- `lon`*: Longitude at which image was taken or specimen was collected.
+- `lat`*: Latitude at which image was taken or specimen was collected: number in [-90,90].
+- `lon`*: Longitude at which image was taken or specimen was collected: number in [-180,180].
 
 
 ***Note:** 
-- `lat` and `lon` columns are not required to utilize the dashboard, but there will be no map view if they are not included.
+- Blank (or null) entries are recorded as `unknown`, and thus excluded from map view.
 
 ## Running Dashboard
 

components/divs.py

@@ -15,6 +15,10 @@
                 {'label': 'Descending', 'value': 'sum descending'}]
 
 DOCS_URL = "https://github.com/Imageomics/telemetry-dashboard/tree/dev#how-it-works"
+DOCS_LINK = html.A("documentation",
+                    href=DOCS_URL,
+                    target='_blank',
+                    style = ERROR_STYLE)
 
 def get_hist_div(cat_list):
     '''
@@ -212,20 +216,24 @@ def get_error_div(error_dict):
                             html.H3("Source data does not have '" + feature + "' column. ",
                                     style = ERROR_STYLE),
                             html.H4(["Please see the ",
-                                        html.A("documentation",
-                                                href=DOCS_URL,
-                                                target='_blank',
-                                                style = ERROR_STYLE),
+                                        DOCS_LINK,
                                         " for list of required columns."], 
                                         style = ERROR_STYLE)
                         ])
+    elif 'mapping' in error_dict.keys():
+        error_msg = error_dict['mapping']
+        error_div = html.Div([
+                            html.H4("Latitude or longitude columns have non-numeric values: " + error_msg + ".",
+                                     style = ERROR_STYLE),
+                            html.H4(["Please see the ",
+                                     DOCS_LINK,
+                                     "."],
+                            style = ERROR_STYLE)
+        ])
     elif 'type' in error_dict.keys():
         error_div = html.Div([
                             html.H4(["The source file is not a valid CSV format, please see the ",
-                                     html.A("documentation", 
-                                            href=DOCS_URL,
-                                            target='_blank',
-                                            style = ERROR_STYLE),
+                                     DOCS_LINK,
                                      "."],
                             style = ERROR_STYLE)
         ])

components/query.py

@@ -99,10 +99,9 @@ def get_points_in_radius(df, radius):
             min_lon = lon - rad_deg
             max_lon = lon + rad_deg
 
-            filtered_df = filtered_df.loc[filtered_df['lat'].astype(float) > min_lat]
-            filtered_df = filtered_df.loc[filtered_df['lat'].astype(float) < max_lat]
-            filtered_df = filtered_df.loc[filtered_df['lon'].astype(float) > min_lon]
-            filtered_df = filtered_df.loc[filtered_df['lon'].astype(float) < max_lon]
+            filtered_df = filtered_df.loc[filtered_df['lat'].astype(float).between(min_lat, max_lat)]
+            filtered_df = filtered_df.loc[filtered_df['lon'].astype(float).between(min_lon, max_lon)]
+
             num_samples = len(filtered_df)
 
         pts_per_lat_lon.append(num_samples)

dashboard.py

@@ -84,6 +84,17 @@ def parse_contents(contents, filename):
         else:
             included_features = list(df.columns)
 
+    # Check for lat/lon bounds & type 
+    try:
+        # Check lat and lon within appropriate ranges (lat: [-90, 90], lon: [-180, 180])
+        valid_lat = df['lat'].astype(float).between(-90, 90)
+        df.loc[~valid_lat, 'lat'] = 'unknown'
+        valid_lon = df['lon'].astype(float).between(-180, 180)
+        df.loc[~valid_lon, 'lon'] = 'unknown'
+    except ValueError as e:
+        print(e)
+        return json.dumps({'error': {'mapping': str(e)}})
+
     # get dataset-determined static data:
         # the dataframe and categorical features - processed for map view if mapping is True
         # all possible species, subspecies