good_loc = rbn_df.dropna(
subset=['dx_lat', 'dx_lon', 'de_lat', 'de_lon'])
good_count_map = good_loc['callsign'].count()
total_count_map = len(rbn_df)
good_pct_map = float(good_count_map) / total_count_map * 100.
good_count += good_count_map
total_count += total_count_map
print 'Geolocation success: {0:d}/{1:d} ({2:.1f}%)'.format(
good_count_map, total_count_map, good_pct_map)
# Go plot!!
rbn_lib.rbn_map_plot(rbn_df,
legend=False,
ax=ax0,
tick_font_size=9,
ncdxf=True)
title = map_sTime.strftime('%H%M - ') + map_eTime.strftime(
'%H%M UT')
ax0.set_title(title, loc='center')
ax0.set_title(map_sTime.strftime('%d %b %Y'), loc='right')
if kk == 0:
ax0.set_title('Preflare', loc='left')
else:
ax0.set_title('Flare Peak', loc='left')
letter_prop = {'weight': 'bold', 'size': 20}
# ax0.text(.015,.90,'({0})'.format(letters[kk]),transform=ax0.transAxes,**letter_prop)
# for item in (ax0.get_xticklabels() + ax0.get_yticklabels()):
rbn_df = rbn_lib.read_rbn(map_sTime,map_eTime,data_dir='data/rbn')
# Figure out how many records properly geolocated.
good_loc = rbn_df.dropna(subset=['dx_lat','dx_lon','de_lat','de_lon'])
good_count_map = good_loc['callsign'].count()
total_count_map = len(rbn_df)
good_pct_map = float(good_count_map) / total_count_map * 100.
good_count += good_count_map
total_count += total_count_map
print 'Geolocation success: {0:d}/{1:d} ({2:.1f}%)'.format(good_count_map,total_count_map,good_pct_map)
# Go plot!!
rbn_lib.rbn_map_plot(rbn_df,legend=False,ax=ax0,tick_font_size=9,ncdxf=False,plot_paths=False)
title = map_sTime.strftime('%d %b %Y %H%M UT - ')+map_eTime.strftime('%d %b %Y %H%M UT')
ax0.set_title(title)
letter_prop = {'weight':'bold','size':16}
ax0.text(.015,.90,'({0})'.format(letters[kk]),transform=ax0.transAxes,**letter_prop)
print map_sTime
# leg = rbn_lib.band_legend(fig,loc='center',bbox_to_anchor=[0.48,0.180],ncdxf=False)
leg = rbn_lib.band_legend(fig,loc='center',bbox_to_anchor=[0.45,0.180],ncdxf=False)
title_prop = {'weight':'bold','size':22}
fig.text(0.525,0.925,'Reverse Beacon Network',ha='center',**title_prop)
fig.savefig(filepath,bbox_inches='tight')
# fig.savefig(filepath[:-3]+'pdf',bbox_inches='tight')
#!/usr/bin/env python
import os
import datetime
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot as plt
import rbn_lib
sTime = datetime.datetime(2010, 11, 19)
eTime = datetime.datetime(2010, 11, 19)
data_dir = os.path.join('data', 'rbn')
rbn_df = rbn_lib.read_rbn(sTime, eTime, data_dir=data_dir)
import ipdb
ipdb.set_trace()
fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111)
rbn_lib.rbn_map_plot(rbn_df, legend=False, ax=ax)
outfile = os.path.join('output', 'rbn', 'rbn_test.png')
fig.savefig(outfile, bbox_inches='tight')
rbn_df = rbn_lib.read_rbn(map_sTime,map_eTime,data_dir='data/rbn')
# Figure out how many records properly geolocated.
good_loc = rbn_df.dropna(subset=['dx_lat','dx_lon','de_lat','de_lon'])
good_count_map = good_loc['callsign'].count()
total_count_map = len(rbn_df)
good_pct_map = float(good_count_map) / total_count_map * 100.
good_count += good_count_map
total_count += total_count_map
print 'Geolocation success: {0:d}/{1:d} ({2:.1f}%)'.format(good_count_map,total_count_map,good_pct_map)
# Go plot!!
bounds = dict(llcrnrlon=-135.,llcrnrlat=20,urcrnrlon=-60.,urcrnrlat=60.)
rbn_lib.rbn_map_plot(rbn_df,legend=True,ax=ax0,tick_font_size=9,ncdxf=True,**bounds)
title = map_sTime.strftime('%H%M - ')+map_eTime.strftime('%H%M UT')
ax0.set_title(title)
#leg = rbn_lib.band_legend(fig,loc='center',bbox_to_anchor=[0.48,0.360],ncdxf=True)
title_prop = {'weight':'bold','size':22}
fig.text(0.525,1.025,'Reverse Beacon Network',ha='center',**title_prop)
fig.tight_layout(h_pad=2.5,w_pad=3.5)
fig.savefig(filepath,bbox_inches='tight')
good_pct = float(good_count)/total_count * 100.
print ''
print 'Final stats for: {0}'.format(filepath)
print 'Geolocation success: {0:d}/{1:d} ({2:.1f}%)'.format(good_count,total_count,good_pct)