Hurricane Tracker, using nhc from siphon.simplewebservice

Author: Aodhan Sweeney

examples/HurricaneTracker.py

@@ -4,20 +4,17 @@
 By: Aodhan Sweeney
 
 This GUI takes data pulled from the National
-Hurricane Center and efficiently sifts through
-the data to plot specific storms and their
-tracks. The GUI runs using ipwidgets, and
-is intended to be used as an ipython
-notebook.
+Hurricane Center and plots specific storms and
+their tracks. The GUI runs using ipwidgets, and
+is intended to be used as an ipython notebook.
 """
-import os
 
 import cartopy.crs as ccrs
 import ipywidgets as widgets
 import matplotlib.pyplot as plt
 import numpy as np
 from pandas import DataFrame
-from siphon.simplewebservice.nhc import nhc
+from siphon.simplewebservice import nhc
 
 class NHC_GUI:
     """
@@ -36,7 +33,7 @@ def __init__(self):
         and 2.) indicate when they have chosen the year and when to continue with parsing
         the storm_table.
         """
-        self.NHCD = NHC.NHCD()
+        self.NHCD = nhc.NHCD()
         self.storm_table = self.NHCD.storm_table
         # Year Slider Widget to select year for which to retrieve storm data.
         self.year_slider = widgets.IntSlider(min=1851, max=2019, value=2019,
@@ -59,7 +56,7 @@ def get_storms_slider(self, year_slider):
             tells value of year chosen by the user
         """
         self.year = str(year_slider)
-        self.one_year_table = self.storm_table[self.storm_table.Year == str(year_slider)]
+        self.one_year_table = self.storm_table[self.storm_table.Year == year_slider]
         self.storm_names = widgets.Dropdown(options=self.one_year_table['Name'],
                                             description='Storm Names: ')
         widgets.interact(self.get_name_dropdown, storm_names=self.storm_names)
@@ -110,7 +107,7 @@ def get_models(self, models):
             list of ran for a given storm
         """
         self.NHCD.model_selection_latlon(models)
-        self.date_times = self.NHCD.date_times
+        self.date_times = self.NHCD.date_times.tolist()
         self.plot_slider = widgets.IntSlider(min=0, max=(len(self.date_times)-1),
                                                  value=0, description='Tracks Time',
                                                  disabled=False)
@@ -137,7 +134,7 @@ def plotting(self, plot_slider):
             month = self.date_times[plot_slider][4: 6]
             day = self.date_times[plot_slider][6: 8]
             hour = self.date_times[plot_slider][8: 10]
-            time_string = 'Date: {0}/{1}/{2} \n Hour: {3}'.format(month, day, year, hour)
+            time_string = 'Date: {0}/{1}/{2} \nHour: {3}'.format(month, day, year, hour)
 
             # Finding data for best track, and extremes for which to base axis extent on
             self.best_lats = np.array(self.NHCD.best_track_coordinates[0])
@@ -147,16 +144,20 @@ def plotting(self, plot_slider):
             min_best_lon = min(self.best_lons)
             max_best_lon = max(self.best_lons)
 
+
             #Plotting the tracks on top of a cartopy stock image projection
             self.fig = plt.figure(figsize=(14, 11))
             self.ax = self.fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())
             self.ax.stock_img()
             self.data_projection = ccrs.PlateCarree()
             self.ax.plot(self.best_lons, self.best_lats, marker='o', color='white',
                          label='Best Track', transform=self.data_projection)
+
             self.ax.set_extent([(min_best_lon - 40), (max_best_lon + 40),
                                 (min_best_lat - 40), (max_best_lat + 40)])
 
+
+
             jet = plt.get_cmap('jet')
             colors = iter(jet(np.linspace(0.2, 1, (len(self.model_select.value)+1))))
             left = .1
@@ -165,17 +166,15 @@ def plotting(self, plot_slider):
                          fontsize=14, color='black')
 
             for model_type in self.NHCD.model_table:
-                one_model_time = model_type[model_type['WarnDT'] ==
-                                            self.date_times[plot_slider]]
+                one_model_time = model_type[model_type['WarnDT'] == self.date_times[plot_slider]]
                 lats = one_model_time['Lat'].tolist()
                 lons = one_model_time['Lon'].tolist()
                 if len(lats) != 0:
                     model_list = model_type['Model'].tolist()
                     self.ax.plot(lons, lats, marker='o', color=next(colors),
                                  label=model_list[0])
-
             plt.title('Storm Name: {0} Year: {1}'.format(self.storm_names.value,
-                      str(self.year_slider.value)))
+                                                        self.year))
             plt.legend()
 
 ######################################################################