def test_logspace(self):
"""logspace should return know answer for known input"""
try:
from matplotlib.dates import date2num
except ImportError: # just pass if matplotlib is not installed
return
real_ans = array([ 1. , 3.16227766, 10. , 31.6227766 , 100. ])
numpy.testing.assert_almost_equal(real_ans, tb.logspace(1, 100, 5) , 4)
t1 = datetime.datetime(2000, 1, 1)
t2 = datetime.datetime(2000, 1, 2)
real_ans = [datetime.datetime(1999, 12, 31, 23, 59, 59, 999989),
datetime.datetime(2000, 1, 1, 2, 39, 59, 994207),
datetime.datetime(2000, 1, 1, 5, 19, 59, 989762),
datetime.datetime(2000, 1, 1, 7, 59, 59, 986897),
datetime.datetime(2000, 1, 1, 10, 39, 59, 985369),
datetime.datetime(2000, 1, 1, 13, 19, 59, 985431),
datetime.datetime(2000, 1, 1, 15, 59, 59, 986830),
datetime.datetime(2000, 1, 1, 18, 39, 59, 989808),
datetime.datetime(2000, 1, 1, 21, 19, 59, 994124),
datetime.datetime(2000, 1, 2, 0, 0, 0, 30)]
ans = tb.logspace(t1, t2, 10)
numpy.testing.assert_almost_equal(date2num(real_ans), date2num(ans) , 4)
def test_logspace(self):
"""logspace should return know answer for known input"""
try:
from matplotlib.dates import date2num
except ImportError: # just pass if matplotlib is not installed
return
real_ans = array([ 1. , 3.16227766, 10. , 31.6227766 , 100. ])
numpy.testing.assert_almost_equal(real_ans, tb.logspace(1, 100, 5) , 4)
t1 = datetime.datetime(2000, 1, 1)
t2 = datetime.datetime(2000, 1, 2)
real_ans = [datetime.datetime(1999, 12, 31, 23, 59, 59, 999989),
datetime.datetime(2000, 1, 1, 2, 39, 59, 994207),
datetime.datetime(2000, 1, 1, 5, 19, 59, 989762),
datetime.datetime(2000, 1, 1, 7, 59, 59, 986897),
datetime.datetime(2000, 1, 1, 10, 39, 59, 985369),
datetime.datetime(2000, 1, 1, 13, 19, 59, 985431),
datetime.datetime(2000, 1, 1, 15, 59, 59, 986830),
datetime.datetime(2000, 1, 1, 18, 39, 59, 989808),
datetime.datetime(2000, 1, 1, 21, 19, 59, 994124),
datetime.datetime(2000, 1, 2, 0, 0, 0, 30)]
ans = tb.logspace(t1, t2, 10)
numpy.testing.assert_almost_equal(date2num(real_ans), date2num(ans) , 4)
def getSolarProtonSpectra(norm=3.20e7,
gamma=-0.96,
E0=15.0,
Emin=.1,
Emax=600,
nsteps=100):
'''Returns a SpaceData with energy and fluence spectra of solar particle events
The formulation follows that of:
Ellison and Ramaty ApJ 298: 400-408, 1985
dJ/dE = K^{-\gamma}exp(-E/E0)
and the defualt values are the 10/16/2003 SEP event of:
Mewaldt, R. A., et al. (2005), J. Geophys. Res., 110, A09S18, doi:10.1029/2005JA011038.
Other Parameters
================
norm : float
Normilization factor for the intensity of the SEP event
gamma : float
Power law index
E0 : float
Expoential scaling factor
Emin : float
Minimum energy for fit
Emax : float
Maximum energy for fit
nsteps : int
The number of log spaced energy steps to return
Returns
=======
data : dm.SpaceData
SpaceData with the energy and fluence values
'''
E = tb.logspace(Emin, Emax, nsteps)
fluence = norm * E**(gamma) * np.exp(-E / E0)
ans = dm.SpaceData()
ans['Energy'] = dm.dmarray(E)
ans['Energy'].attrs = {
'UNITS': 'MeV',
'DESCRIPTION': 'Particle energy per nucleon'
}
ans['Fluence'] = dm.dmarray(fluence)
ans['Fluence'].attrs = {
'UNITS': 'cm^{-2} sr^{-1} (MeV/nuc)^{-1}',
'DESCRIPTION': 'Fluence spectra fir to the model'
}
return ans
def getSolarProtonSpectra(norm=3.20e7, gamma=-0.96, E0=15.0, Emin=.1, Emax=600, nsteps=100):
'''Returns a SpaceData with energy and fluence spectra of solar particle events
The formulation follows that of:
Ellison and Ramaty ApJ 298: 400-408, 1985
dJ/dE = K^{-\gamma}exp(-E/E0)
and the defualt values are the 10/16/2003 SEP event of:
Mewaldt, R. A., et al. (2005), J. Geophys. Res., 110, A09S18, doi:10.1029/2005JA011038.
Other Parameters
==========
norm : float
Normilization factor for the intensity of the SEP event
gamma : float
Power law index
E0 : float
Expoential scaling factor
Emin : float
Minimum energy for fit
Emax : float
Maximum energy for fit
nsteps : int
The number of log spaced energy steps to return
Returns
=======
data : dm.SpaceData
SpaceData with the energy and fluence values
'''
E = tb.logspace(Emin, Emax, nsteps)
fluence = norm*E**(gamma)*np.exp(-E/E0)
ans = dm.SpaceData()
ans['Energy'] = dm.dmarray(E)
ans['Energy'].attrs = {'UNITS':'MeV',
'DESCRIPTION':'Particle energy per nucleon'}
ans['Fluence'] = dm.dmarray(fluence)
ans['Fluence'].attrs = {'UNITS' : 'cm^{-2} sr^{-1} (MeV/nuc)^{-1}',
'DESCRIPTION':'Fluence spectra fir to the model'}
return ans
# ans = np.sqrt(np.inner(data[0:n], data[0:n]))
ans = np.sqrt(np.inner(data, data))
def extension_t(n, data):
# ans = tb.hypot(data[0:n])
ans = tb.hypot(data)
ans = {}
ans['ctypes_t'] = []
ans['python_t'] = []
ans['numpy_t'] = []
ans['extension_t'] = []
for loop in tb.logspace(3, max_size, 20):
print "loop", loop
for bm in Benchmarker(width=25, cycle=5, extra=1):
data = np.arange(1, loop, dtype=ctypes.c_double)
bm.run(ctypes_t, loop, data)
bm.run(python_t, loop, data)
bm.run(numpy_t, loop, data)
bm.run(extension_t, loop, data)
for result in bm.results:
ans[result.label].append((loop, result.real))
#==============================================================================
# plot up the times
#==============================================================================
plt.figure()
for key in ans:
max_size = 1e6
date_data = np.arange(1, max_size , dtype=np.float)
date_data = np.asarray([datetime.datetime(2000, 1, 1) + datetime.timedelta(minutes=v) for v in date_data])
def date2num_mpl(data):
ans = mpl_stock_date2num(data)
def date2num_spt(data):
tt = spt.Ticktock(data)
ans = tt.JD - 1721424.5
ans = {}
ans['date2num_mpl'] = []
ans['date2num_spt'] = []
for loop in tb.logspace(1, max_size, 20):
print "loop", loop
for bm in Benchmarker(width=25, cycle=5, extra=1):
data = date_data[:loop]
bm.run(date2num_mpl, data)
bm.run(date2num_spt, data)
for result in bm.results:
ans[result.label].append((loop, result.real))
#==============================================================================
# plot up the times
#==============================================================================
plt.figure()
for key in ans:
plt.loglog(zip(*ans[key])[0], zip(*ans[key])[1],'o-', label=key[:-2], lw=2 )