def test_multi_dot():
for dim in [4, 6]:
my_list = []
length = 1000 + np.random.randint(200)
for i in range(dim**2):
my_list.append(
pe.Obs([np.random.rand(length),
np.random.rand(length + 1)], ['t1', 't2']))
my_array = pe.cov_Obs(1.0, 0.002, 'cov') * np.array(my_list).reshape(
(dim, dim))
tt = pe.linalg.matmul(
my_array, my_array, my_array,
my_array) - my_array @ my_array @ my_array @ my_array
for t, e in np.ndenumerate(tt):
assert e.is_zero(), t
my_list = []
length = 1000 + np.random.randint(200)
for i in range(dim**2):
my_list.append(
pe.CObs(
pe.Obs(
[np.random.rand(length),
np.random.rand(length + 1)], ['t1', 't2']),
pe.Obs(
[np.random.rand(length),
np.random.rand(length + 1)], ['t1', 't2'])))
my_array = np.array(my_list).reshape(
(dim, dim)) * pe.cov_Obs(1.0, 0.002, 'cov')
tt = pe.linalg.matmul(
my_array, my_array, my_array,
my_array) - my_array @ my_array @ my_array @ my_array
for t, e in np.ndenumerate(tt):
assert e.is_zero(), t
def test_matmul_irregular_histories():
dim = 2
length = 500
standard_array = []
for i in range(dim**2):
standard_array.append(
pe.Obs([np.random.normal(1.1, 0.2, length)], ['ens1']))
standard_matrix = np.array(standard_array).reshape(
(dim, dim)) * pe.cov_Obs(1.0, 0.002, 'cov') * pe.pseudo_Obs(
0.1, 0.002, 'qr')
for idl in [range(1, 501, 2), range(250, 273), [2, 8, 19, 20, 78]]:
irregular_array = []
for i in range(dim**2):
irregular_array.append(
pe.Obs([np.random.normal(1.1, 0.2, len(idl))], ['ens1'],
idl=[idl]))
irregular_matrix = np.array(irregular_array).reshape(
(dim, dim)) * pe.cov_Obs(
[1.0, 1.0], [[0.001, 0.0001], [0.0001, 0.002]], 'norm')[0]
t1 = standard_matrix @ irregular_matrix
t2 = pe.linalg.matmul(standard_matrix, irregular_matrix)
assert np.all([o.is_zero() for o in (t1 - t2).ravel()])
assert np.all([o.is_merged for o in t1.ravel()])
assert np.all([o.is_merged for o in t2.ravel()])
def test_covobs_name_collision():
covobs = pe.cov_Obs(0.5, 0.002, 'test')
my_obs = pe.pseudo_Obs(2.3, 0.2, 'test')
with pytest.raises(Exception):
summed_obs = my_obs + covobs
covobs2 = pe.cov_Obs(0.3, 0.001, 'test')
with pytest.raises(Exception):
summed_obs = covobs + covobs2
def test_covobs_exceptions():
with pytest.raises(Exception):
covobs = pe.cov_Obs(0.1, [[0.1, 0.2], [0.1, 0.2]], 'test')
with pytest.raises(Exception):
covobs = pe.cov_Obs(0.1, np.array([[0.1, 0.2], [0.1, 0.2]]), 'test')
with pytest.raises(Exception):
covobs = pe.cov_Obs([0.5, 0.1], np.array([[2, 1, 3], [1, 2, 3]]), 'test')
with pytest.raises(Exception):
covobs = pe.cov_Obs([0.5, 0.1], np.random.random((2, 2, 2)), 'test')
def test_covobs_overloading():
covobs = pe.cov_Obs([0.5, 0.5], np.array([[0.02, 0.02], [0.02, 0.02]]), 'test')
assert (covobs[0] / covobs[1]) == 1
assert (covobs[0] - covobs[1]) == 0
my_obs = pe.pseudo_Obs(2.3, 0.2, 'obs')
assert (my_obs * covobs[0] / covobs[1]) == my_obs
covobs = pe.cov_Obs(0.0, 0.3, 'test')
assert not covobs.is_zero()
def test_correlate():
my_obs1 = pe.Obs([np.random.rand(100)], ['t'])
my_obs2 = pe.Obs([np.random.rand(100)], ['t'])
corr1 = pe.correlate(my_obs1, my_obs2)
corr2 = pe.correlate(my_obs2, my_obs1)
assert corr1 == corr2
my_obs3 = pe.Obs([np.random.rand(100)], ['t'], idl=[range(2, 102)])
with pytest.raises(Exception):
pe.correlate(my_obs1, my_obs3)
my_obs4 = pe.Obs([np.random.rand(99)], ['t'])
with pytest.raises(Exception):
pe.correlate(my_obs1, my_obs4)
my_obs5 = pe.Obs([np.random.rand(100)], ['t'], idl=[range(5, 505, 5)])
my_obs6 = pe.Obs([np.random.rand(100)], ['t'], idl=[range(5, 505, 5)])
corr3 = pe.correlate(my_obs5, my_obs6)
assert my_obs5.idl == corr3.idl
my_new_obs = pe.Obs([np.random.rand(100)], ['q3'])
with pytest.raises(Exception):
pe.correlate(my_obs1, my_new_obs)
my_covobs = pe.cov_Obs(1.0, 0.003, 'cov')
with pytest.raises(Exception):
pe.correlate(my_covobs, my_covobs)
r_obs = pe.reweight(my_obs1, [my_obs1])[0]
with pytest.warns(RuntimeWarning):
pe.correlate(r_obs, r_obs)
def test_reweighting():
my_obs = pe.Obs([np.random.rand(1000)], ['t'])
assert not my_obs.reweighted
r_obs = pe.reweight(my_obs, [my_obs])
assert r_obs[0].reweighted
r_obs2 = r_obs[0] * my_obs
assert r_obs2.reweighted
my_irregular_obs = pe.Obs([np.random.rand(500)], ['t'],
idl=[range(1, 1001, 2)])
assert not my_irregular_obs.reweighted
r_obs = pe.reweight(my_obs, [my_irregular_obs], all_configs=True)
r_obs = pe.reweight(my_obs, [my_irregular_obs], all_configs=False)
r_obs = pe.reweight(my_obs, [my_obs])
assert r_obs[0].reweighted
r_obs2 = r_obs[0] * my_obs
assert r_obs2.reweighted
my_covobs = pe.cov_Obs(1.0, 0.003, 'cov')
with pytest.raises(Exception):
pe.reweight(my_obs, [my_covobs])
my_obs2 = pe.Obs([np.random.rand(1000)], ['t2'])
with pytest.raises(Exception):
pe.reweight(my_obs, [my_obs + my_obs2])
with pytest.raises(Exception):
pe.reweight(my_irregular_obs, [my_obs])
def test_renorm_deriv_of_corr(tmp_path):
c = pe.Corr([pe.pseudo_Obs(i, .1, 'test') for i in range(10)])
c *= pe.cov_Obs(1., .1, '#ren')
c = c.deriv()
pe.input.json.dump_to_json(c, (tmp_path / 'test').as_posix())
recover = pe.input.json.load_json((tmp_path / 'test').as_posix())
assert np.all([o == 0 for o in (c - recover)[1:-1]])
def test_thin():
c = pe.Corr([pe.pseudo_Obs(i, .1, 'test') for i in range(10)])
c *= pe.cov_Obs(1., .1, '#ren')
thin = c.thin()
thin.gamma_method()
thin.fit(lambda a, x: a[0] * x)
c.thin(offset=1)
c.thin(3, offset=1)
def test_merge_obs():
my_obs1 = pe.Obs([np.random.rand(100)], ['t'])
my_obs2 = pe.Obs([np.random.rand(100)], ['q'], idl=[range(1, 200, 2)])
merged = pe.merge_obs([my_obs1, my_obs2])
diff = merged - my_obs2 - my_obs1
assert diff == -(my_obs1.value + my_obs2.value) / 2
with pytest.raises(Exception):
pe.merge_obs([my_obs1, my_obs1])
my_covobs = pe.cov_Obs(1.0, 0.003, 'cov')
with pytest.raises(Exception):
pe.merge_obs([my_obs1, my_covobs])
def test_covobs():
val = 1.123124
cov = .243423
name = 'Covariance'
co = pe.cov_Obs(val, cov, name)
co.gamma_method()
co.details()
assert (co.dvalue == np.sqrt(cov))
assert (co.value == val)
do = 2 * co
assert (do.covobs[name].grad[0] == 2)
do = co * co
assert (do.covobs[name].grad[0] == 2 * val)
assert np.array_equal(do.covobs[name].cov, co.covobs[name].cov)
pi = [16.7457, -19.0475]
cov = [[3.49591, -6.07560], [-6.07560, 10.5834]]
cl = pe.cov_Obs(pi, cov, 'rAP')
pl = pe.misc.gen_correlated_data(pi, np.asarray(cov), 'rAPpseudo')
def rAP(p, g0sq):
return -0.0010666 * g0sq * (1 + np.exp(p[0] + p[1] / g0sq))
for g0sq in [1, 1.5, 1.8]:
oc = rAP(cl, g0sq)
oc.gamma_method()
op = rAP(pl, g0sq)
op.gamma_method()
assert(np.isclose(oc.value, op.value, rtol=1e-14, atol=1e-14))
[o.gamma_method() for o in cl]
assert(pe.covariance(cl[0], cl[1]) == cov[0][1])
assert(pe.covariance(cl[0], cl[1]) == cov[1][0])
do = cl[0] * cl[1]
assert(np.array_equal(do.covobs['rAP'].grad, np.transpose([pi[1], pi[0]]).reshape(2, 1)))
def test_matmul():
for dim in [4, 6]:
for const in [
1,
pe.cov_Obs([1.0, 1.0], [[0.001, 0.0001], [0.0001, 0.002]],
'norm')[1]
]:
my_list = []
length = 100 + np.random.randint(200)
for i in range(dim**2):
my_list.append(
pe.Obs(
[np.random.rand(length),
np.random.rand(length + 1)], ['t1', 't2']))
my_array = const * np.array(my_list).reshape((dim, dim))
tt = pe.linalg.matmul(my_array, my_array) - my_array @ my_array
for t, e in np.ndenumerate(tt):
assert e.is_zero(), t
my_list = []
length = 100 + np.random.randint(200)
for i in range(dim**2):
my_list.append(
pe.CObs(
pe.Obs([
np.random.rand(length),
np.random.rand(length + 1)
], ['t1', 't2']),
pe.Obs([
np.random.rand(length),
np.random.rand(length + 1)
], ['t1', 't2'])))
my_array = np.array(my_list).reshape((dim, dim)) * const
tt = pe.linalg.matmul(my_array, my_array) - my_array @ my_array
for t, e in np.ndenumerate(tt):
assert e.is_zero(), t
def test_irregular_matrix_inverse():
dim = 3
length = 500
for idl in [
range(8, 508, 10),
range(250, 273), [2, 8, 19, 20, 78, 99, 828, 10548979]
]:
irregular_array = []
for i in range(dim**2):
irregular_array.append(
pe.Obs([
np.random.normal(1.1, 0.2, len(idl)),
np.random.normal(0.25, 0.1, 10)
], ['ens1', 'ens2'],
idl=[idl, range(1, 11)]))
irregular_matrix = np.array(irregular_array).reshape(
(dim, dim)) * pe.cov_Obs(1.0, 0.002, 'cov') * pe.pseudo_Obs(
1.0, 0.002, 'ens2|r23')
invertible_irregular_matrix = np.identity(
dim) + irregular_matrix @ irregular_matrix.T
inverse = pe.linalg.inv(invertible_irregular_matrix)
assert np.allclose(
np.linalg.inv(
np.vectorize(lambda x: x.value)(invertible_irregular_matrix)) -
np.vectorize(lambda x: x.value)(inverse), 0.0)
check1 = pe.linalg.matmul(invertible_irregular_matrix, inverse)
assert np.all(
[o.is_zero() for o in (check1 - np.identity(dim)).ravel()])
check2 = invertible_irregular_matrix @ inverse
assert np.all(
[o.is_zero() for o in (check2 - np.identity(dim)).ravel()])
def test_jsonio():
o = pe.pseudo_Obs(1.0, .2, 'one')
o2 = pe.pseudo_Obs(0.5, .1, 'two|r1')
o3 = pe.pseudo_Obs(0.5, .1, 'two|r2')
o4 = pe.merge_obs([o2, o3])
otag = 'This has been merged!'
o4.tag = otag
do = o - .2 * o4
co1 = pe.cov_Obs(1., .123, 'cov1')
co3 = pe.cov_Obs(4., .1 ** 2, 'cov3')
do *= co1 / co3
do.tag = {'A': 2}
o5 = pe.pseudo_Obs(0.8, .1, 'two|r2')
co2 = pe.cov_Obs([1, 2], [[.12, .004], [.004, .02]], 'cov2')
o5 /= co2[0]
o3 /= co2[1]
o5.tag = 2 * otag
testl = [o3, o5]
arr = np.array([o3, o5])
mat = np.array([[pe.pseudo_Obs(1.0, .1, 'mat'), pe.pseudo_Obs(0.3, .1, 'mat')], [pe.pseudo_Obs(0.2, .1, 'mat'), pe.pseudo_Obs(2.0, .4, 'mat')]])
mat[0][1].tag = ['This', 'is', 2, None]
mat[1][0].tag = '{testt}'
mat[1][1].tag = '[tag]'
tt1 = pe.Obs([np.random.rand(100)], ['t|r1'], idl=[range(2, 202, 2)])
tt2 = pe.Obs([np.random.rand(100)], ['t|r2'], idl=[range(2, 202, 2)])
tt3 = pe.Obs([np.random.rand(102)], ['qe'])
tt = tt1 + tt2 + tt3
tt.tag = 'Test Obs: Ä'
ol = [o4, do, testl, mat, arr, np.array([o]), np.array([tt, tt]), [tt, tt], co1, co2, np.array(co2), co1 / co2[0]]
fname = 'test_rw'
jsonio.dump_to_json(ol, fname, indent=1, description='[I am a tricky description]')
rl = jsonio.load_json(fname)
os.remove(fname + '.json.gz')
for o, r in zip(ol, rl):
assert np.all(o == r)
for i in range(len(ol)):
if isinstance(ol[i], pe.Obs):
o = ol[i] - rl[i]
assert(o.is_zero())
assert(ol[i].tag == rl[i].tag)
or1 = np.ravel(ol[i])
or2 = np.ravel(rl[i])
for j in range(len(or1)):
o = or1[j] - or2[j]
assert(o.is_zero())
description = {'I': {'Am': {'a': 'nested dictionary!'}}}
jsonio.dump_to_json(ol, fname, indent=0, gz=False, description=description)
rl = jsonio.load_json(fname, gz=False, full_output=True)
os.remove(fname + '.json')
for o, r in zip(ol, rl['obsdata']):
assert np.all(o == r)
assert(description == rl['description'])
def test_least_squares():
dim = 10 + int(30 * np.random.rand())
x = np.arange(dim)
y = 2 * np.exp(-0.06 * x) + np.random.normal(0.0, 0.15, dim)
yerr = 0.1 + 0.1 * np.random.rand(dim)
oy = []
for i, item in enumerate(x):
oy.append(pe.pseudo_Obs(y[i], yerr[i], str(i)))
def f(x, a, b):
return a * np.exp(-b * x)
popt, pcov = scipy.optimize.curve_fit(f,
x,
y,
sigma=[o.dvalue for o in oy],
absolute_sigma=True)
def func(a, x):
y = a[0] * np.exp(-a[1] * x)
return y
out = pe.least_squares(x,
oy,
func,
expected_chisquare=True,
resplot=True,
qqplot=True)
beta = out.fit_parameters
str(out)
repr(out)
len(out)
for i in range(2):
beta[i].gamma_method(S=1.0)
assert math.isclose(beta[i].value, popt[i], abs_tol=1e-5)
assert math.isclose(pcov[i, i], beta[i].dvalue**2, abs_tol=1e-3)
assert math.isclose(pe.covariance(beta[0], beta[1]),
pcov[0, 1],
abs_tol=1e-3)
chi2_pyerrors = np.sum(
((f(x, *[o.value for o in beta]) - y) / yerr)**2) / (len(x) - 2)
chi2_scipy = np.sum(((f(x, *popt) - y) / yerr)**2) / (len(x) - 2)
assert math.isclose(chi2_pyerrors, chi2_scipy, abs_tol=1e-10)
out = pe.least_squares(x, oy, func, const_par=[beta[1]])
assert ((out.fit_parameters[0] - beta[0]).is_zero())
assert ((out.fit_parameters[1] - beta[1]).is_zero())
oyc = []
for i, item in enumerate(x):
oyc.append(pe.cov_Obs(y[i], yerr[i]**2, 'cov' + str(i)))
outc = pe.least_squares(x, oyc, func)
betac = outc.fit_parameters
for i in range(2):
betac[i].gamma_method(S=1.0)
assert math.isclose(betac[i].value, popt[i], abs_tol=1e-5)
assert math.isclose(pcov[i, i], betac[i].dvalue**2, abs_tol=1e-3)
assert math.isclose(pe.covariance(betac[0], betac[1]),
pcov[0, 1],
abs_tol=1e-3)
def test_covobs_init():
covobs = pe.cov_Obs(0.5, 0.002, 'test')
covobs = pe.cov_Obs([1, 2], [0.1, 0.2], 'test')
covobs = pe.cov_Obs([1, 2], np.array([0.1, 0.2]), 'test')
covobs = pe.cov_Obs([1, 2], [[0.1, 0.2], [0.1, 0.2]], 'test')
covobs = pe.cov_Obs([1, 2], np.array([[0.1, 0.2], [0.1, 0.2]]), 'test')
def test_covobs_replica_separator():
with pytest.raises(Exception):
covobs = pe.cov_Obs(0.5, 0.002, 'test|r2')
def test_total_least_squares():
dim = 10 + int(30 * np.random.rand())
x = np.arange(dim) + np.random.normal(0.0, 0.15, dim)
xerr = 0.1 + 0.1 * np.random.rand(dim)
y = 2 * np.exp(-0.06 * x) + np.random.normal(0.0, 0.15, dim)
yerr = 0.1 + 0.1 * np.random.rand(dim)
ox = []
for i, item in enumerate(x):
ox.append(pe.pseudo_Obs(x[i], xerr[i], str(i)))
oy = []
for i, item in enumerate(x):
oy.append(pe.pseudo_Obs(y[i], yerr[i], str(i)))
def f(x, a, b):
return a * np.exp(-b * x)
def func(a, x):
y = a[0] * np.exp(-a[1] * x)
return y
data = RealData([o.value for o in ox], [o.value for o in oy],
sx=[o.dvalue for o in ox],
sy=[o.dvalue for o in oy])
model = Model(func)
odr = ODR(data, model, [0, 0], partol=np.finfo(np.float64).eps)
odr.set_job(fit_type=0, deriv=1)
output = odr.run()
out = pe.total_least_squares(ox, oy, func, expected_chisquare=True)
beta = out.fit_parameters
str(out)
repr(out)
len(out)
for i in range(2):
beta[i].gamma_method(S=1.0)
assert math.isclose(beta[i].value, output.beta[i], rel_tol=1e-5)
assert math.isclose(
output.cov_beta[i, i], beta[i].dvalue**2, rel_tol=2.5e-1), str(
output.cov_beta[i, i]) + ' ' + str(beta[i].dvalue**2)
assert math.isclose(pe.covariance(beta[0], beta[1]),
output.cov_beta[0, 1],
rel_tol=2.5e-1)
out = pe.total_least_squares(ox, oy, func, const_par=[beta[1]])
diff = out.fit_parameters[0] - beta[0]
assert (diff / beta[0] < 1e-3 * beta[0].dvalue)
assert ((out.fit_parameters[1] - beta[1]).is_zero())
oxc = []
for i, item in enumerate(x):
oxc.append(pe.cov_Obs(x[i], xerr[i]**2, 'covx' + str(i)))
oyc = []
for i, item in enumerate(x):
oyc.append(pe.cov_Obs(y[i], yerr[i]**2, 'covy' + str(i)))
outc = pe.total_least_squares(oxc, oyc, func)
betac = outc.fit_parameters
for i in range(2):
betac[i].gamma_method(S=1.0)
assert math.isclose(betac[i].value, output.beta[i], rel_tol=1e-3)
assert math.isclose(
output.cov_beta[i, i], betac[i].dvalue**2, rel_tol=2.5e-1), str(
output.cov_beta[i, i]) + ' ' + str(betac[i].dvalue**2)
assert math.isclose(pe.covariance(betac[0], betac[1]),
output.cov_beta[0, 1],
rel_tol=2.5e-1)
outc = pe.total_least_squares(oxc, oyc, func, const_par=[betac[1]])
diffc = outc.fit_parameters[0] - betac[0]
assert (diffc / betac[0] < 1e-3 * betac[0].dvalue)
assert ((outc.fit_parameters[1] - betac[1]).is_zero())
outc = pe.total_least_squares(oxc, oy, func)
betac = outc.fit_parameters
for i in range(2):
betac[i].gamma_method(S=1.0)
assert math.isclose(betac[i].value, output.beta[i], rel_tol=1e-3)
assert math.isclose(
output.cov_beta[i, i], betac[i].dvalue**2, rel_tol=2.5e-1), str(
output.cov_beta[i, i]) + ' ' + str(betac[i].dvalue**2)
assert math.isclose(pe.covariance(betac[0], betac[1]),
output.cov_beta[0, 1],
rel_tol=2.5e-1)
outc = pe.total_least_squares(oxc, oy, func, const_par=[betac[1]])
diffc = outc.fit_parameters[0] - betac[0]
assert (diffc / betac[0] < 1e-3 * betac[0].dvalue)
assert ((outc.fit_parameters[1] - betac[1]).is_zero())
def test_json_dict_io():
def check_dict_equality(d1, d2):
def dict_check_obs(d1, d2):
for k, v in d1.items():
if isinstance(v, dict):
v = dict_check_obs(v, d2[k])
elif isinstance(v, list) and all(
[isinstance(o, pe.Obs) for o in v]):
for i in range(len(v)):
assert ((v[i] - d2[k][i]).is_zero())
elif isinstance(v, list):
v = list_check_obs(v, d2[k])
elif isinstance(v, pe.Obs):
assert ((v - d2[k]).is_zero())
elif isinstance(v, pe.Corr):
for i in range(v.T):
assert ((v[i] - d2[k][i]).is_zero())
elif isinstance(v, np.ndarray):
a1 = np.ravel(v)
a2 = np.ravel(d2[k])
for i in range(len(a1)):
assert ((a1[i] - a2[i]).is_zero())
def list_check_obs(l1, l2):
for ei in range(len(l1)):
e = l1[ei]
if isinstance(e, list):
e = list_check_obs(e, l2[ei])
elif isinstance(e, list) and all(
[isinstance(o, pe.Obs) for o in e]):
for i in range(len(e)):
assert ((e[i] - l2[ei][i]).is_zero())
elif isinstance(e, dict):
e = dict_check_obs(e, l2[ei])
elif isinstance(e, pe.Obs):
assert ((e - l2[ei]).is_zero())
elif isinstance(e, pe.Corr):
for i in range(e.T):
assert ((e[i] - l2[ei][i]).is_zero())
elif isinstance(e, np.ndarray):
a1 = np.ravel(e)
a2 = np.ravel(l2[ei])
for i in range(len(a1)):
assert ((a1[i] - a2[i]).is_zero())
dict_check_obs(d1, d2)
return True
od = {
'l': {
'a':
pe.pseudo_Obs(1, .2, 'testa', samples=10),
'b': [
pe.pseudo_Obs(1.1, .1, 'test', samples=10),
pe.pseudo_Obs(1.2, .1, 'test', samples=10),
pe.pseudo_Obs(1.3, .1, 'test', samples=10)
],
'c': {
'd':
1,
'e':
pe.pseudo_Obs(.2, .01, 'teste', samples=10),
'f':
pe.Corr([
pe.pseudo_Obs(.1, .01, 'a', samples=10),
pe.pseudo_Obs(.1, .01, 'a', samples=10),
pe.pseudo_Obs(.1, .01, 'a', samples=10),
pe.pseudo_Obs(.1, .01, 'a', samples=10)
]),
'g':
np.reshape(
np.asarray([
pe.pseudo_Obs(.1, .01, 'a', samples=10),
pe.pseudo_Obs(.1, .01, 'a', samples=10),
pe.pseudo_Obs(.1, .01, 'a', samples=10),
pe.pseudo_Obs(.1, .01, 'a', samples=10)
]), (2, 2)),
}
},
's': {
'a':
'Infor123',
'b': ['Some', 'list'],
'd':
pe.pseudo_Obs(.01, .001, 'testd', samples=10) *
pe.cov_Obs(1, .01, 'cov1'),
'se':
None,
'sf':
1.2,
}
}
fname = 'test_rw'
desc = 'This is a random description'
with pytest.raises(Exception):
jsonio.dump_dict_to_json(od, fname, description=desc, reps='|Test')
jsonio.dump_dict_to_json(od, fname, description=desc, reps='TEST')
nd = jsonio.load_json_dict(fname, full_output=True, reps='TEST')
with pytest.raises(Exception):
nd = jsonio.load_json_dict(fname, full_output=True)
jsonio.dump_dict_to_json(od, fname, description=desc)
nd = jsonio.load_json_dict(fname, full_output=True)
assert (desc == nd['description'])
assert (check_dict_equality(od, nd['obsdata']))
nd = jsonio.load_json_dict(fname, full_output=False)
assert (check_dict_equality(od, nd))
nl = jsonio.load_json(fname, full_output=True)
nl = jsonio.load_json(fname, full_output=False)
with pytest.raises(Exception):
jsonio.dump_dict_to_json(nl, fname, description=desc)
od['k'] = 'DICTOBS2'
with pytest.raises(Exception):
jsonio.dump_dict_to_json(od, fname, description=desc)
od['k'] = ['DICTOBS2']
with pytest.raises(Exception):
jsonio.dump_dict_to_json(od, fname, description=desc)
os.remove(fname + '.json.gz')
