def test_progress(self, capsys):
for i in Progress.range(4, has_output=True, timer=False):
print('hi')
stdout, stderr = capsys.readouterr()
output = """\
0% complete
0% complete
hi
25% complete
hi
50% complete
hi
75% complete
hi
100% complete
"""
assert stdout == output
def test_progress(self, capsys):
for i in Progress.range(4, has_output=True, timer=False):
print("hi")
stdout, stderr = capsys.readouterr()
output = """\
0% complete
0% complete
hi
25% complete
hi
50% complete
hi
75% complete
hi
100% complete
"""
assert stdout == output
def test_progress(self):
with captured_stdout() as stream:
for i in Progress.range(4, has_output=True, timer=False):
print('hi')
time.sleep(.5)
stream.seek(0)
output = """\
0% complete
0% complete
hi
25% complete
hi
50% complete
hi
75% complete
hi
100% complete
"""
self.assertEqual(stream.read(), output)
def calculate_lyapunov(unperturbed_file, perturbed_file): #TODO: remove references to N_calc
"""This function calculates the lyapunov exponent at each integration step between two solutions.
PARAMETERS:
unperturbed_file: (string) String object with the file name in which the unperturbed solution is stored
perturbed_file: (string) String object with the file name in which the perturbed solution is stored
RETURNS:
lyaps : (float[N_steps]) N_steps-dimension array-like containing the values of the lyapunov exponent calcuated between solution1 and solution2 at each integration step for."""
data_u, mu_u, k_u=read_data.get_data(unperturbed_file,form='lyapunov')
data_p, mu_p, k_p=read_data.get_data(perturbed_file,form='lyapunov')
lenght=len(data_u)
try:
assert lenght==len(data_p)
assert mu_u==mu_p
assert k_u==k_p
except AssertionError:
print(colors.red|"The integration results are not written as expexted", file=sys.stderr)
sys.exit([6]) #Integration results are not written as expected
""" Actual calculation of the Lyapunov exponents"""
"""Difference between the two simulations"""
difference=data_u-data_p
"""Norms of the difference vectors"""
norms=np.linalg.norm(difference, axis=1)
"""Norm of the initial difference"""
norm_0=norms[0]
"""Calculating the ln of the ratio between evolved state and initial state"""
log_diff_ratio=np.log(norms/norm_0)
lyaps=np.empty(lenght)
cumulative_sum=0
print("Lyapunov exponents calculation:")
for i in Progress.range(1, lenght):
"""calculating the mean of the ratios from initial state to state i"""
cumulative_sum+=log_diff_ratio[i]
lyaps[i]=cumulative_sum/i
return lyaps
def test_progress_empty(self, capsys):
for i in Progress.range(0, has_output=True, timer=False):
print('hi')
stdout, stderr = capsys.readouterr()
output = '0/0 complete'
assert output in stdout
def test_progress_empty(self, capsys):
for i in Progress.range(0, has_output=True, timer=False):
print("hi")
stdout, stderr = capsys.readouterr()
output = "0/0 complete"
assert output in stdout
