def test_igrf11():
mag = igrf12.igrf(time, 65, 85, 0, model=11)
assert mag.north.item() == approx(9301.523160)
assert mag.east.item() == approx(2563.450424)
assert mag.down.item() == approx(59666.132881)
assert mag.total.item() == approx(60441.186489)
assert mag.incl.item() == approx(80.814513)
assert mag.decl.item() == approx(15.407924)
def test_igrf12():
mag = igrf12.igrf(time, 65, 85, 0, model=12)
assert mag.north.item() == approx(9295.100256)
assert mag.east.item() == approx(2560.199706)
assert mag.down.item() == approx(59670.251893)
assert mag.total.item() == approx(60444.126863)
assert mag.incl.item() == approx(80.821738)
assert mag.decl.item() == approx(15.399442)
def merge_custom_iss(file_name):
#TODO: Create custom equation to mimic altitude
altitude = 330
df = pd.read_csv(file_name)
for i,row in df.iterrows():
latitude = df.iloc[i][1]
longitude = df.iloc[i][2]
mag = igrf12.igrf('2010-07-12', glat=latitude, glon=longitude, alt_km=altitude)
df.at[i, 'mag_x'] = mag.north.values[0] / 100000
df.at[i, 'mag_y'] = mag.east.values[0] / 100000
df.at[i, 'mag_z'] = mag.down.values[0] / 100000
new_file_name = "{}_merged.csv".format(file_name.split('.')[0])
df.to_csv(new_file_name, index=False)
return True
def generate_m_data():
'''
1) Pull random datapoint from https://natronics.github.io/ISS-photo-locations/
2) Create igrf model at lat and lon specified by #1 and at alt_km = between 330-435km (https://en.wikipedia.org/wiki/International_Space_Station)
3) Convert igrf north, east, down -> x,y,z (nT to gauss)
According to http://geomag.nrcan.gc.ca/mag_fld/comp-en.php:
X is positive northward
Y is positive eastward
Z is positive downward
4) Return expected (x,y,z) + random noise
'''
# TODO: Instead of random selection from a single csv, could use the entire dataset.
lines = open('data/ISS001.csv').read().splitlines()
line = random.choice(lines)
columns = line.split(',')
latitude = float(columns[1])
longitude = float(columns[2])
altitude = random.randint(330, 435)
#TODO: Instead of the 2010 model, could use the 2015 model.
mag = igrf12.igrf('2010-07-12',
glat=latitude,
glon=longitude,
alt_km=altitude)
#Convert nT to Gauss
x = mag.north.values[0] / 100000
y = mag.east.values[0] / 100000
z = mag.down.values[0] / 100000
#Add random noise
x = x + random.uniform(-0.01, 0.01)
y = y + random.uniform(-0.01, 0.01)
z = z + random.uniform(-0.01, 0.01)
return [x, y, z]
def declination_tables(query):
lats = np.arange(query.min_lat, query.max_lat + query.res, query.res)
lons = np.arange(query.min_lon, query.max_lon + query.res, query.res)
num_lat = lats.size
num_lon = lons.size
intensity = np.empty((num_lat, num_lon))
inclination = np.empty((num_lat, num_lon))
declination = np.empty((num_lat, num_lon))
for i, lat in enumerate(lats):
for j, lon in enumerate(lons):
mag = igrf12.igrf(date, glat=lat, glon=lon, alt_km=0., isv=0, itype=1)
intensity[i][j] = mag.total / 1e5
inclination[i][j] = mag.incl
declination[i][j] = mag.decl
return Result(query=query, lats=lats, lons=lons,
declination=declination, inclination=inclination, intensity=intensity)
def add_mag(df):
'''
Calculates the magnetic field using the IGRF-12 coefficients.
Feel free to change altitude
'''
altitude = 330
for i, row in df.iterrows():
latitude = df.iloc[i][1]
longitude = df.iloc[i][2]
mag = igrf12.igrf('2010-07-12',
glat=latitude,
glon=longitude,
alt_km=altitude)
df.at[i, 'mag_x'] = mag.north.values[0] / 100000 - 0.2
df.at[i, 'mag_y'] = mag.east.values[0] / 100000
df.at[i, 'mag_z'] = mag.down.values[0] / 100000
return True
SAMPLING_MIN_LON = -180
SAMPLING_MAX_LON = 180
lats = np.arange(SAMPLING_MIN_LAT, SAMPLING_MAX_LAT+SAMPLING_RES, SAMPLING_RES)
lons = np.arange(SAMPLING_MIN_LON, SAMPLING_MAX_LON+SAMPLING_RES, SAMPLING_RES)
NUM_LAT = lats.size
NUM_LON = lons.size
intensity_table = np.empty((NUM_LAT, NUM_LON))
inclination_table = np.empty((NUM_LAT, NUM_LON))
declination_table = np.empty((NUM_LAT, NUM_LON))
for i,lat in enumerate(lats):
for j,lon in enumerate(lons):
mag = igrf.igrf(date, glat=lat, glon=lon, alt_km=0., isv=0, itype=1)
intensity_table[i][j] = mag.total/1e5
inclination_table[i][j] = mag.incl
declination_table[i][j] = mag.decl
with open("tables.cpp", 'w') as f:
f.write('''// this is an auto-generated file from the IGRF tables. Do not edit
// To re-generate run generate/generate.py
#include "AP_Declination.h"
''')
f.write('''const float AP_Declination::SAMPLING_RES = %u;
const float AP_Declination::SAMPLING_MIN_LAT = %u;
const float AP_Declination::SAMPLING_MAX_LAT = %u;
SAMPLING_RES)
lons = np.arange(SAMPLING_MIN_LON, SAMPLING_MAX_LON + SAMPLING_RES,
SAMPLING_RES)
NUM_LAT = lats.size
NUM_LON = lons.size
intensity_table = np.empty((NUM_LAT, NUM_LON))
inclination_table = np.empty((NUM_LAT, NUM_LON))
declination_table = np.empty((NUM_LAT, NUM_LON))
for i, lat in enumerate(lats):
for j, lon in enumerate(lons):
mag = igrf.igrf(date,
glat=lat,
glon=lon,
alt_km=0.,
isv=0,
itype=1)
intensity_table[i][j] = mag.total / 1e5
inclination_table[i][j] = mag.incl
declination_table[i][j] = mag.decl
with open("tables.cpp", 'w') as f:
f.write(
'''// this is an auto-generated file from the IGRF tables. Do not edit
// To re-generate run generate/generate.py
#include "AP_Declination.h"
''')
