Documentation update

demo/cnofs_vefi_dc_b_orbit_plots.py

@@ -1,12 +1,11 @@
-'''
+"""
 Demonstrates iterating over an instrument data set by orbit and plotting the
 results.
-'''
+"""
 
 import os
 import pysat
 import matplotlib.pyplot as plt
-import pandas as pds
 
 # set the directory to save plots to
 results_dir = ''
@@ -18,8 +17,8 @@
                         clean_level=None, orbit_info=orbit_info)
 
 # set limits on dates analysis will cover, inclusive
-start = pds.datetime(2010, 5, 9)
-stop = pds.datetime(2010, 5, 12)
+start = pysat.datetime(2010, 5, 9)
+stop = pysat.datetime(2010, 5, 12)
 
 # if there is no vefi dc magnetometer data on your system, then run command
 # below where start and stop are pandas datetimes (from above)
@@ -79,5 +78,6 @@
     ax[6].set_xlim((0, 360))
 
     f.tight_layout()
-    plt.savefig(os.path.join(results_dir, 'orbit_%05i.png' % orbit_count))
+    plt.savefig(os.path.join(results_dir,
+                             'orbit_{num:05}.png').format(num=orbit_count))
     plt.close()

demo/cosmic_and_ivm_demo.py

@@ -64,7 +64,7 @@ def geo2mag(incoord):
     r = 1.0
 
     # convert first to radians
-    lon, lat = [x*pi/180 for x in lon, lat]
+    lon, lat = [x*pi/180 for x in [lon, lat]]
 
     glat = incoord[0] * pi / 180.0
     glon = incoord[1] * pi / 180.0
@@ -273,15 +273,15 @@ def addTopsideScaleHeight(cosmic):
 cbar.set_label('Scale Height (km)')
 axarr[3].set_xlabel('Apex Longitude')
 f.tight_layout()
-f.savefig('1D_params.png')
+f.savefig('ssnl_median_ivm_cosmic_1d.png')
 
 
 # make COSMIC profile plots
 # 6 pages of plots, 4 plots per page
 for k in np.arange(6):
     f, axarr = plt.subplots(4, sharex=True, figsize=(8.5, 11))
     # iterate over a group of four sectors at a time (4 plots per page)
-    for (j, sector) in enumerate(zip(*cosmicResults['profiles']['median'])
+    for (j, sector) in enumerate(list(zip(*cosmicResults['profiles']['median']))
                                  [k * 4:(k + 1) * 4]):
         # iterate over all local times within longitude sector
         # data is returned from the median routine in plot order, [y, x]
@@ -309,4 +309,4 @@ def addTopsideScaleHeight(cosmic):
     axarr[-1].set_xticks([0., 6., 12., 18., 24.])
     axarr[-1].set_xlabel('Solar Local Time of Profile Maximum Density')
     f.tight_layout()
-    f.savefig('cosmic_part%i' % (k))
+    f.savefig('cosmic_part{}.png'.format(k))

demo/ssnl_occurrence_by_orbit.py

@@ -1,12 +1,11 @@
-'''
+"""
 Demonstrates iterating over an instrument data set by orbit and determining
 the occurrent probability of an event occurring.
-'''
+"""
 
 import os
 import pysat
 import matplotlib.pyplot as plt
-import pandas as pds
 import numpy as np
 
 # set the directory to save plots to
@@ -19,7 +18,7 @@
                         clean_level=None, orbit_info=orbit_info)
 
 
-# define functino to remove flagged values
+# define function to remove flagged values
 def filter_vefi(inst):
     idx, = np.where(vefi['B_flag'] == 0)
     vefi.data = vefi.data.iloc[idx]
@@ -28,8 +27,8 @@ def filter_vefi(inst):
 
 vefi.custom.add(filter_vefi, 'modify')
 # set limits on dates analysis will cover, inclusive
-start = pds.datetime(2010, 5, 9)
-stop = pds.datetime(2010, 5, 15)
+start = pysat.datetime(2010, 5, 9)
+stop = pysat.datetime(2010, 5, 15)
 
 # if there is no vefi dc magnetometer data on your system, then run command
 # below where start and stop are pandas datetimes (from above)
@@ -60,13 +59,13 @@ def filter_vefi(inst):
 plt.colorbar(im, ax=axarr[0], label='Occurrence Probability')
 
 im = axarr[1].pcolor(ans['bin_x'], ans['bin_y'], ans['count'])
+axarr[1].set_title('Number of Orbits in Bin')
 axarr[1].set_xlabel('Longitude')
 axarr[1].set_xticks((0, 60, 120, 180, 240, 300, 360))
 axarr[1].set_xlim((ans['bin_x'][0], ans['bin_x'][-1]))
 axarr[1].set_ylabel('Latitude')
-axarr[1].set_title('Number of Orbits in Bin')
-
 plt.colorbar(im, ax=axarr[1], label='Counts')
+
 f.tight_layout()
-plt.show()
 plt.savefig(os.path.join(results_dir, 'ssnl_occurrence_by_orbit_demo'))
+plt.close()