def test_run_options(input_data):
# Default options is all 1's, so make sure they are equivalent
assert_array_equal(msis.run(*input_data, options=None),
msis.run(*input_data, options=[1] * 25))
with pytest.raises(ValueError, match='options needs to be a list'):
msis.run(*input_data, options=[1] * 22)
def test_run_versions(input_data):
# Make sure we accept these version numbers and don't throw an error
for ver in [0, 2, '00', '2']:
msis.run(*input_data, version=ver)
# Test for something outside of that list for an error to be thrown
with pytest.raises(ValueError, match='The MSIS version selected'):
msis.run(*input_data, version=1)
def test_run_wrapped_lon(input_data, expected_output):
date, _, lat, alt, f107, f107a, ap = input_data
input_data = (date, -180, lat, alt, f107, f107a, ap)
output1 = msis.run(*input_data)
input_data = (date, 180, lat, alt, f107, f107a, ap)
output2 = msis.run(*input_data)
assert_allclose(output1, output2, rtol=1e-5)
input_data = (date, 0, lat, alt, f107, f107a, ap)
output1 = msis.run(*input_data)
input_data = (date, 360, lat, alt, f107, f107a, ap)
output2 = msis.run(*input_data)
assert_allclose(output1, output2, rtol=1e-5)
def run_msis(dates, lons, lats, alts, f107s, f107as, aps, options):
"""Call MSIS looping over all possible inputs.
Parameters
----------
dates : list of dates
Date and time to calculate the output at.
lons : list of floats
Longitudes to calculate the output at.
lats : list of floats
Latitudes to calculate the output at.
alts : list of floats
Altitudes to calculate the output at.
f107s : list of floats
F107 value for the given date(s).
f107as : list of floats
F107 running 100-day average for the given date(s).
aps : list of floats
Ap for the given date(s).
options : list of floats (length 25)
A list of options (switches) to the model
"""
# 11 == 10 densities + 1 temperature
# Output density: He, O, N2, O2, Ar, Total (gm/cm3), H, N, Anomalous O
# Output temp: exospheric, specific altitude
_, input_data = msis.create_input(dates, lons, lats, alts, f107s, f107as,
aps)
output = msis.run(dates, lons, lats, alts, f107s, f107as, aps, options)
# Force to float, JSON serializer in future calls does not work
# with float32 output
# Return the altitutdes, latitudes, longitudes with the data
return (input_data[:, 2:5], output.reshape(-1, 11).astype(np.float))
def test_run_poles(input_data):
# Test that moving in longitude around a pole
# returns the same values
# North pole
date, _, _, alt, f107, f107a, ap = input_data
input_data = (date, 0, 90, alt, f107, f107a, ap)
output1 = msis.run(*input_data)
input_data = (date, 20, 90, alt, f107, f107a, ap)
output2 = msis.run(*input_data)
assert_allclose(output1, output2, rtol=1e-5)
# South pole
input_data = (date, 0, -90, alt, f107, f107a, ap)
output1 = msis.run(*input_data)
input_data = (date, 20, -90, alt, f107, f107a, ap)
output2 = msis.run(*input_data)
assert_allclose(output1, output2, rtol=1e-5)
def test_run_multi_point00(input_data, expected_output00):
date, lon, lat, alt, f107, f107a, ap = input_data
# 5 x 5 surface
input_data = (date, [lon] * 5, [lat] * 5, alt, f107, f107a, ap)
output = msis.run(*input_data, version=0)
assert output.shape == (1, 5, 5, 1, 11)
expected = np.tile(expected_output00, (5, 5, 1))
assert_allclose(np.squeeze(output), expected, rtol=1e-5)
def test_run_multi_point(input_data, expected_output):
# test multi-point run, like a satellite fly-through
# and make sure we don't grid the input data
# 5 input points
date, lon, lat, alt, f107, f107a, ap = input_data
input_data = ([date] * 5, [lon] * 5, [lat] * 5, [alt] * 5, [f107] * 5,
[f107a] * 5, ap * 5)
output = msis.run(*input_data)
assert output.shape == (5, 11)
expected = np.tile(expected_output, (5, 1))
assert_allclose(np.squeeze(output), expected, rtol=1e-5)
def run_input_line(line):
items = line.split()
expected = np.array(items[9:], dtype=np.float32)
# Needs to be "-3:" due to strip() taking off leading spaces
doy = int(items[0][-3:]) - 1 # Python wants DOY to start with 0
sec, alt, lat, lon, _, f107a, f107, ap = [float(x) for x in items[1:9]]
ap = [[ap] * 7]
year = int(items[0][:-3])
# Two digit year
if year > 60:
year += 1900
else:
year += 2000
date = (np.datetime64(str(year)) + np.timedelta64(doy, 'D') +
np.timedelta64(int(sec), 's'))
test_inp = (date, lon, lat, alt, f107, f107a, ap)
test_output = np.squeeze(msis.run(*test_inp))
# Rearrange the run's output to match the expected
# ordering of elements
x = np.array([
test_output[4],
test_output[3],
test_output[1],
test_output[2],
test_output[6],
test_output[0],
test_output[5],
test_output[7],
test_output[8],
test_output[10],
])
# Different units in the test file (cgs)
x[np.isclose(x, 9.9e-38, atol=1e-38)] = np.nan
x[:-1] *= 1e-6
x[5] *= 1e3
x[np.isnan(x)] = 9.999e-38
# The output print statements are messy.
# They technically give 4 decimal places, but only truly 3 decimal places
# in scientific notation. Example: 0.8888e+23 -> 8.888e+24
# We will require relative comparisons of 2e-3 to account for the
# last digit errors.
assert_allclose(x, expected, rtol=2e-3)
def lambda_handler(event, context):
"""Handle the incoming API event and route properly."""
# Parse the incoming path and route the event properly
if 'surface' in event['path']:
return surface_handler(event, context)
if 'altitude' in event['path']:
return altitude_handler(event, context)
# Data is passed in the body, so pull it out of there
data = json.loads(event['body'])
# Validate the event
validate_event(data)
# Convert the dates
data['dates'] = [
datetime.strptime(x, "%Y-%m-%dT%H:%M") for x in data['dates']
]
# Initialize the model, default is all ones
options = data.get("options", [1] * 25)
if len(options) != 25:
raise ValueError("options requires a length 25 array")
# 1 Ap for each date, need to make it a n x 7 array
aps = [[ap] * 7 for ap in data['aps']]
# Call the main loop
output = msis.run(data['dates'], data['lons'], data['lats'], data['alts'],
data['f107s'], data['f107as'], aps, options)
return {
'statusCode': 200,
'body': json.dumps(output.tolist()).replace("NaN", 'null'),
"headers": {
"Access-Control-Allow-Origin": "*",
"content-type": "application/json"
}
}
import matplotlib.pyplot as plt
import numpy as np
from pymsis import msis
lon = 0
lat = 70
alts = np.linspace(0, 1000, 1000)
f107 = 150
f107a = 150
ap = 7
aps = [[ap]*7]
date = np.datetime64('2003-01-01T00:00')
output_midnight = msis.run(date, lon, lat, alts, f107, f107a, aps)
date = np.datetime64('2003-01-01T12:00')
output_noon = msis.run(date, lon, lat, alts, f107, f107a, aps)
# output is now of the shape (1, 1, 1, 1000, 11)
# Get rid of the single dimensions
output_midnight = np.squeeze(output_midnight)
output_noon = np.squeeze(output_noon)
variables = ['Total mass density', 'N2', 'O2', 'O', 'He',
'H', 'Ar', 'N', 'Anomalous O', 'NO', 'Temperature']
_, ax = plt.subplots()
for i, label in enumerate(variables):
if label in ('NO', 'Total mass density', 'Temperature'):
# There is currently no NO data, also ignore non-number densities
lons = range(-180, 185, 5)
lats = range(-90, 95, 5)
alt = 200
f107 = 150
f107a = 150
ap = 4
# Diurnal data
dates = np.arange('2003-01-01', '2003-01-02', dtype='datetime64[30m]')
ndates = len(dates)
# (F107, F107a, ap) all need to be specified at the same length as dates
f107s = [f107] * ndates
f107as = [f107a] * ndates
aps = [[ap] * 7] * ndates
output = msis.run(dates, lons, lats, alt, f107s, f107as, aps)
# output is now of the shape (ndates, nlons, nlats, 1, 11)
# Get rid of the single dimensions
output = np.squeeze(output)
i = 7 # N
fig, (ax_time, ax_mesh) = plt.subplots(nrows=2,
gridspec_kw={'height_ratios': [1, 4]},
constrained_layout=True)
xx, yy = np.meshgrid(lons, lats)
vmin, vmax = 1e13, 8e13
norm = matplotlib.colors.Normalize(vmin, vmax)
mesh = ax_mesh.pcolormesh(xx,
yy,
output[0, :, :, i].T,
"""
import matplotlib.pyplot as plt
import numpy as np
from pymsis import msis
lon = 0
lat = 70
alts = np.linspace(0, 1000, 1000)
f107 = 150
f107a = 150
ap = 7
aps = [[ap] * 7]
date = np.datetime64('2003-01-01T00:00')
output_midnight2 = msis.run(date, lon, lat, alts, f107, f107a, aps)
output_midnight0 = msis.run(date, lon, lat, alts, f107, f107a, aps, version=0)
diff_midnight = (output_midnight2 - output_midnight0) / output_midnight0 * 100
date = np.datetime64('2003-01-01T12:00')
output_noon2 = msis.run(date, lon, lat, alts, f107, f107a, aps)
output_noon0 = msis.run(date, lon, lat, alts, f107, f107a, aps, version=0)
diff_noon = (output_noon2 - output_noon0) / output_noon0 * 100
# output is now of the shape (1, 1, 1, 1000, 11)
# Get rid of the single dimensions
diff_midnight = np.squeeze(diff_midnight)
diff_noon = np.squeeze(diff_noon)
variables = [
'Total mass density', 'N2', 'O2', 'O', 'He', 'H', 'Ar', 'N', 'Anomalous O',
import matplotlib.pyplot as plt
import numpy as np
from pymsis import msis
lons = range(-180, 185, 5)
lats = range(-90, 95, 5)
alt = 200
f107 = 150
f107a = 150
ap = 7
# One years worth of data at the 12th hour every day
date = np.datetime64('2003-01-01T12:00')
aps = [[ap] * 7]
output = msis.run(date, lons, lats, alt, f107, f107a, aps)
# output is now of the shape (1, nlons, nlats, 1, 11)
# Get rid of the single dimensions
output = np.squeeze(output)
i = 2 # O2
_, ax = plt.subplots()
xx, yy = np.meshgrid(lons, lats)
mesh = ax.pcolormesh(xx, yy, output[:, :, i].T, shading='auto')
plt.colorbar(mesh, label='Number density (/m$^3$)')
ax.set_title(f"O$_2$ number density at {alt} km")
ax.set_xlabel("Longitude")
ax.set_ylabel("Latitude")
def test_run_version00_low_altitude(input_data):
# There is no O, H, N below 72.5 km, should be NaN
date, lon, lat, _, f107, f107a, ap = input_data
input_data = (date, lon, lat, 71, f107, f107a, ap)
output = msis.run(*input_data, version=0)
assert np.all(np.isnan(np.squeeze(output)[[3, 5, 7]]))
def test_run_version00(input_data, expected_output00):
output = msis.run(*input_data, version=0)
assert output.shape == (1, 11)
assert_allclose(np.squeeze(output), expected_output00, rtol=1e-5)
import matplotlib.pyplot as plt
import numpy as np
from pymsis import msis
lons = range(-180, 185, 5)
lats = range(-90, 95, 5)
alt = 200
f107 = 150
f107a = 150
ap = 7
# One years worth of data at the 12th hour every day
date = np.datetime64('2003-01-01T12:00')
aps = [[ap] * 7]
output2 = msis.run(date, lons, lats, alt, f107, f107a, aps)
output0 = msis.run(date, lons, lats, alt, f107, f107a, aps, version=0)
diff = (output2 - output0) / output0 * 100
# diff is now of the shape (1, nlons, nlats, 1, 11)
# Get rid of the single dimensions
diff = np.squeeze(diff)
variables = [
'Total mass density', 'N2', 'O2', 'O', 'He', 'H', 'Ar', 'N', 'Anomalous O',
'NO', 'Temperature'
]
fig, axarr = plt.subplots(nrows=3,
ncols=3,
constrained_layout=True,
figsize=(8, 6))
def test_run_single_point(input_data, expected_output):
output = msis.run(*input_data)
assert output.shape == (1, 11)
assert_allclose(np.squeeze(output), expected_output, rtol=1e-5)
