Welcome to ATMOS
The pyatmos package is an archive of scientific routines that can be used to handle atmospheric models. Currently, only nrlmsise00 is feasible.
How to Install
pip install pyatmos
How to Use
from pyatmos.msise import download_sw,read_sw
from pyatmos.atmosclasses import Coordinate
# Download or update the space weather file from www.celestrak.com
swfile = download_sw()
# Read the space weather data
sw_obs_pre = read_sw(swfile)
# Test 1
# Set a specific time and location
t = '2015-10-05 03:00:00' # time(UTC)
lat,lon = 25,102 # latitude and longitude [degree]
alt = 70 # altitude [km]
# Initialize a coordinate instance by a space-time point
st = Coordinate(t,lat,lon,alt)
para_input,para_output = st.nrlmsise00(sw_obs_pre)
print(para_input)
{'doy': 278, 'year': 2015, 'sec': 10800.0, 'alt': 70, 'g_lat': 25, 'g_long': 102, 'lst': 9.8, 'f107A': 150, 'f107': 150, 'ap': 4, 'ap_a': array([4, 4, 4, 4, 4, 4, 4])}
print(para_output)
{'d': {'He': 9100292488300570.0, 'O': 0, 'N2': 1.3439413974205876e+21, 'O2': 3.52551376755781e+20, 'AR': 1.6044163757370681e+19, 'RHO': 8.225931818480755e-05, 'H': 0, 'N': 0, 'ANM O': 0}, 't': {'TINF': 1027.3184649, 'TG': 219.9649472491653}}
# Test 2
t = '2004-07-08 10:30:50'
lat,lon,alt = -65,-120,100
st = Coordinate(t,lat,lon,alt)
para_input,para_output = st.nrlmsise00(sw_obs_pre)
print(para_input)
{'doy': 190, 'year': 2004, 'sec': 37850.0, 'alt': 100, 'g_lat': -65, 'g_long': -120, 'lst': 2.5138888888888893, 'f107A': 109.0, 'f107': 79.3, 'ap': 2, 'ap_a': array([2. , 2. , 2. , 2. , 2. , 3.125, 4.625])}
print(para_output)
{'d': {'He': 119477307274636.89, 'O': 4.1658304136233e+17, 'N2': 7.521248904485598e+18, 'O2': 1.7444969074975662e+18, 'AR': 7.739495767665198e+16, 'RHO': 4.584596293339505e-07, 'H': 22215754381448.5, 'N': 152814261016.3964, 'ANM O': 1.8278224834873257e-37}, 't': {'TINF': 1027.3184649, 'TG': 192.5868649143824}}
# Test 3
t = '2010-02-15 12:18:37'
lat,lon,alt = 85,210,500
st = Coordinate(t,lat,lon,alt)
para_input,para_output = st.nrlmsise00(sw_obs_pre,'NoOxygen','Aph')
print(para_input)
{'doy': 46, 'year': 2010, 'sec': 44317.0, 'alt': 500, 'g_lat': 85, 'g_long': 210, 'lst': 2.310277777777779, 'f107A': 83.4, 'f107': 89.4, 'ap': 14, 'ap_a': array([14. , 5. , 7. , 6. , 15. , 5.375, 4. ])}
print(para_output)
{'d': {'He': 3314507585382.5425, 'O': 3855595951659.0874, 'N2': 19285497858.028534, 'O2': 395599656.3119481, 'AR': 146073.85956102316, 'RHO': 1.2650700238089615e-13, 'H': 171775437382.8238, 'N': 38359828672.39737, 'ANM O': 5345258193.554493}, 't': {'TINF': 776.3155804924045, 'TG': 776.3139192714452}}
# Test 4
t = '2019-08-20 23:10:59'
lat,lon,alt = 3,5,900
st = Coordinate(t,lat,lon,alt)
para_input,para_output = st.nrlmsise00(sw_obs_pre,aphmode = 'Aph')
print(para_input)
{'doy': 232, 'year': 2019, 'sec': 83459.0, 'alt': 900, 'g_lat': 3, 'g_long': 5, 'lst': 23.51638888888889, 'f107A': 67.4, 'f107': 67.7, 'ap': 4, 'ap_a': array([4. , 4. , 3. , 3. , 5. , 3.625, 3.5 ])}
print(para_output)
{'d': {'He': 74934329990.0412, 'O': 71368139.39199762, 'N2': 104.72048033793158, 'O2': 0.09392848471935447, 'AR': 1.3231114543012155e-07, 'RHO': 8.914971667362366e-16, 'H': 207405192640.34592, 'N': 3785341.821909535, 'ANM O': 1794317839.638502}, 't': {'TINF': 646.8157488121493, 'TG': 646.8157488108872}}
Next release
- Complete the help documentation
- Improve the code structure to make it easier to read
- Add other atmospheric models