The MIT License (MIT)
Copyright (c) 2017 Massachusetts Institute of Technology
Authors: Cody Rude and Victor Pankratius
This software has been created in projects supported by the US National
Science Foundation and NASA (PI: Pankratius)
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Mobile Crowd Sensing in Space Weather Monitoring: The Mahali Project
Victor Pankratius, Frank Lind, Anthea Coster, Philip Erickson, Joshua Semeter. IEEE Communications Magazine, Special Issue on Mobile Crowd Sensing, 52(8), pp. 22-28, August 2014
The Mahali Project: Deployment Experiences from a Field Campaign in Alaska
Anthea Coster, Victor Pankratius, Timothy Morin, Will Rogers, Frank Lind, Philip Erickson, David Mascharka, Don Hampton, Joshua Semeter.
Institute of Navigation: International Technical Meeting- GNSS in Environmentally Challenged Environments, Monterey, California, Jan 25-28, 2016
GPS Signal Corruption by the Discrete Aurora: Precise Measurements From the Mahali Experiment)
Joshua Semeter, Sebastijan Mrak, Michael Hirsch, John Swoboda, Gregory Starr, Donald Hampton, Philip Erickson, Frank Lind, Anthea Coster, Hassanali Akbari, Victor Pankratius, Geophysical Research Letters, Wiley, accepted Sep, 2017
GNSS-ISR data fusion: General framework with application to the high-latitude ionosphere)
Joshua Semeter, Michael Hirsch, Frank Lind, Anthea Coster, Philip Erickson, Victor Pankratius, Radio Science, pp. 118-129, March 2016
Mahali: Space Weather Monitoring Using Multicore Mobile Devices)
Victor Pankratius, Frank Lind, Anthea Coster, Philip Erickson, Joshua Semeter. American Geophysical Union, 46th Annual Fall Meeting, December 2013
Mahali: Mobile Phones and Cloud Computing in Space Weather and Beyond)
Victor Pankratius, David Mascharka, Phil Erickson, Frank Lind, Anthea Coster, Michael Hirsch, John Swoboda, Joshua Semeter. 97th American Meteorological Society Annual Conference, 14th Conference on Space Weather, Seattle, January 22-27, 2017
In [1]:
%matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
import matplotlib.dates as mdates
from mpl_toolkits.basemap import Basemap
import numpy as np
Total Electron Content (TEC) data from Mahali Alaska 2015 Experiment
MIT led NSF project studying the Earth’s ionosphere with GPS
http://mahali.mit.edu/
In [2]:
station_list = [ 'mh02', 'mh03', 'mh04', 'mh05', 'mh06', 'mh07', 'mh08', 'mh09', 'mh13']
lon_list = [-148.2572, -149.0948, -148.7776, -148.5478, -148.1655, -146.7602, -147.4329, -146.0895, -145.5928]
lat_list = [65.3824, 65.6201, 65.4988, 65.5241, 65.2884, 65.2582, 65.1179, 65.3637, 65.4148]
Function to plot receivers on map
In [3]:
def plotGPSReceiversOnMap():
# Create map for coordinates
llat = 55.8
ulat = 70.9
llon = -168.9
rlon = -137.7
plt.gcf().set_size_inches(10,10);
bmap = Basemap(llcrnrlat=llat, urcrnrlat=ulat, llcrnrlon=llon,
urcrnrlon=rlon,projection='merc', lon_0=np.mean([llon,rlon]),
lat_0=np.mean([llat,ulat]), resolution='i', area_thresh=10000);
bmap.shadedrelief();
# bmap.scatter(lon_list, lat_list, latlon=True, color='red');
plt.title("Locations of Mahali GPS Receivers in Alaska", fontsize=14)
scatter_list = []
for label, lon, lat in zip(station_list, lon_list, lat_list):
x, y = bmap(lon,lat)
# plt.gca().annotate(label[2:], xy=(x,y), xytext=(5,-16), ha='right', textcoords='offset points', fontsize=11);
# legend colors mahal
scatter_list.append(bmap.scatter(lon, lat, latlon=True, label=label, s=62))
bmap.drawmapscale(-144,58,np.mean([llon,rlon]), np.mean([ulat, llat]), 500, barstyle='fancy', fontsize=14)
leg = plt.legend(scatter_list, station_list, fontsize=14)
In [4]:
plotGPSReceiversOnMap();
In [5]:
from skdaccess.framework.param_class import *
from skdaccess.geo.mahali.tec.data_fetcher import DataFetcher
In [6]:
mahalidf = DataFetcher([AutoList(station_list)])
In [7]:
dw = mahalidf.output()
In [39]:
# Get station mh03
it = dw.getIterator();
next(it);
label, data = next(it);
In [40]:
plt.plot(data.vertical_tec[data.elevation > 20.0],'.', markersize=0.5);
plt.gcf().set_size_inches(12,9)
plt.title(label);
plt.ylabel('TEC');
plt.xticks(rotation=25);
datefmt = mdates.DateFormatter('%Y-%m-%d: %H:%M');
plt.xlim(pd.to_datetime('2015-10-07 05:30'), pd.to_datetime('2015-10-07 13:30'))
plt.gca().xaxis.set_major_formatter(datefmt);
In [41]:
def plotAllStations(start_date, end_date):
plt.gcf().set_size_inches(12,9)
for index, (label, data) in enumerate(dw.getIterator()):
plt.subplot(3,3, index+1)
plt.title(label);
plt.ylabel('TEC');
plt.xticks(rotation=25);
plt.plot(data.vertical_tec[data.elevation > 20.0],'.', markersize=0.5);
datefmt = mdates.DateFormatter('%Y-%m-%d: %H:%M');
plt.gca().xaxis.set_major_formatter(datefmt);
plt.xlim(start_date, end_date)
plt.tight_layout()
Plot data for all days
In [42]:
plotAllStations(pd.to_datetime('2015-10-02 00:00:00'), pd.to_datetime('2015-11-04 00:00:00'))
Plot data for 2015-10-07 (day with enhanced ionospheric activity)
In [43]:
plotAllStations(pd.to_datetime('2015-10-07 05:45:00'), pd.to_datetime('2015-10-07 013:45:00'))