Python scipy.stats.circstd() Examples
The following are 16
code examples of scipy.stats.circstd().
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Example #1
| Source File: test_morestats.py From Computable with MIT License | 6 votes |
|
def test_circstd_axis(self):
x = np.array([[355,5,2,359,10,350],
[351,7,4,352,9,349],
[357,9,8,358,4,356]])
S1 = stats.circstd(x, high=360)
S2 = stats.circstd(x.ravel(), high=360)
assert_allclose(S1, S2, rtol=1e-11)
S1 = stats.circstd(x, high=360, axis=1)
S2 = [stats.circstd(x[i], high=360) for i in range(x.shape[0])]
assert_allclose(S1, S2, rtol=1e-11)
S1 = stats.circstd(x, high=360, axis=0)
S2 = [stats.circstd(x[:,i], high=360) for i in range(x.shape[1])]
assert_allclose(S1, S2, rtol=1e-11)
Example #2
| Source File: test_morestats.py From GraphicDesignPatternByPython with MIT License | 6 votes |
|
def test_circstd_axis(self):
x = np.array([[355, 5, 2, 359, 10, 350],
[351, 7, 4, 352, 9, 349],
[357, 9, 8, 358, 4, 356]])
S1 = stats.circstd(x, high=360)
S2 = stats.circstd(x.ravel(), high=360)
assert_allclose(S1, S2, rtol=1e-11)
S1 = stats.circstd(x, high=360, axis=1)
S2 = [stats.circstd(x[i], high=360) for i in range(x.shape[0])]
assert_allclose(S1, S2, rtol=1e-11)
S1 = stats.circstd(x, high=360, axis=0)
S2 = [stats.circstd(x[:, i], high=360) for i in range(x.shape[1])]
assert_allclose(S1, S2, rtol=1e-11)
Example #3
| Source File: test_utils_stats.py From pysat with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def test_circstd(self):
""" Test custom circular std."""
ref_std = scistats.circstd(self.test_angles, **self.circ_kwargs)
test_std = pystats.nan_circstd(self.test_angles, **self.circ_kwargs)
assert ref_std == test_std
Example #4
| Source File: test_utils_stats.py From pysat with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def test_circstd_nan(self):
""" Test custom circular std with NaN."""
ref_std = scistats.circstd(self.test_angles, **self.circ_kwargs)
ref_nan = scistats.circstd(self.test_nan, **self.circ_kwargs)
test_nan = pystats.nan_circstd(self.test_nan, **self.circ_kwargs)
assert np.isnan(ref_nan)
assert ref_std == test_nan
Example #5
| Source File: test_utils_stats.py From pysat with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def test_deprecation_warning_circstd(self):
"""Test if circstd in stats is deprecated"""
with warnings.catch_warnings(record=True) as war:
try:
pystats.nan_circstd(None)
except TypeError:
# Setting input to None should produce a TypeError after
# warning is generated
pass
assert len(war) >= 1
assert war[0].category == DeprecationWarning
Example #6
| Source File: test_morestats.py From Computable with MIT License | 5 votes |
|
def test_circfuncs(self):
x = np.array([355,5,2,359,10,350])
M = stats.circmean(x, high=360)
Mval = 0.167690146
assert_allclose(M, Mval, rtol=1e-7)
V = stats.circvar(x, high=360)
Vval = 42.51955609
assert_allclose(V, Vval, rtol=1e-7)
S = stats.circstd(x, high=360)
Sval = 6.520702116
assert_allclose(S, Sval, rtol=1e-7)
Example #7
| Source File: test_morestats.py From Computable with MIT License | 5 votes |
|
def test_circfuncs_small(self):
x = np.array([20,21,22,18,19,20.5,19.2])
M1 = x.mean()
M2 = stats.circmean(x, high=360)
assert_allclose(M2, M1, rtol=1e-5)
V1 = x.var()
V2 = stats.circvar(x, high=360)
assert_allclose(V2, V1, rtol=1e-4)
S1 = x.std()
S2 = stats.circstd(x, high=360)
assert_allclose(S2, S1, rtol=1e-4)
Example #8
| Source File: test_morestats.py From Computable with MIT License | 5 votes |
|
def test_circfuncs_array_like(self):
x = [355,5,2,359,10,350]
assert_allclose(stats.circmean(x, high=360), 0.167690146, rtol=1e-7)
assert_allclose(stats.circvar(x, high=360), 42.51955609, rtol=1e-7)
assert_allclose(stats.circstd(x, high=360), 6.520702116, rtol=1e-7)
Example #9
| Source File: test_morestats.py From Computable with MIT License | 5 votes |
|
def test_empty(self):
assert_(np.isnan(stats.circmean([])))
assert_(np.isnan(stats.circstd([])))
assert_(np.isnan(stats.circvar([])))
Example #10
| Source File: test_morestats.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def test_circfuncs(self):
x = np.array([355, 5, 2, 359, 10, 350])
M = stats.circmean(x, high=360)
Mval = 0.167690146
assert_allclose(M, Mval, rtol=1e-7)
V = stats.circvar(x, high=360)
Vval = 42.51955609
assert_allclose(V, Vval, rtol=1e-7)
S = stats.circstd(x, high=360)
Sval = 6.520702116
assert_allclose(S, Sval, rtol=1e-7)
Example #11
| Source File: test_morestats.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def test_circfuncs_small(self):
x = np.array([20, 21, 22, 18, 19, 20.5, 19.2])
M1 = x.mean()
M2 = stats.circmean(x, high=360)
assert_allclose(M2, M1, rtol=1e-5)
V1 = x.var()
V2 = stats.circvar(x, high=360)
assert_allclose(V2, V1, rtol=1e-4)
S1 = x.std()
S2 = stats.circstd(x, high=360)
assert_allclose(S2, S1, rtol=1e-4)
Example #12
| Source File: test_morestats.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def test_circfuncs_array_like(self):
x = [355, 5, 2, 359, 10, 350]
assert_allclose(stats.circmean(x, high=360), 0.167690146, rtol=1e-7)
assert_allclose(stats.circvar(x, high=360), 42.51955609, rtol=1e-7)
assert_allclose(stats.circstd(x, high=360), 6.520702116, rtol=1e-7)
Example #13
| Source File: test_morestats.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def test_empty(self):
assert_(np.isnan(stats.circmean([])))
assert_(np.isnan(stats.circstd([])))
assert_(np.isnan(stats.circvar([])))
Example #14
| Source File: test_stats_utils.py From arviz with Apache License 2.0 | 5 votes |
|
def test_circular_standard_deviation_1d(data):
high = 8
low = 4
assert np.allclose(
_circular_standard_deviation(data, high=high, low=low), circstd(data, high=high, low=low),
)
Example #15
| Source File: models.py From protwis with Apache License 2.0 | 5 votes |
|
def radial_stddev(L):
scipy = circstd(L, 360,0)
return scipy
Example #16
| Source File: diagnostics.py From arviz with Apache License 2.0 | 4 votes |
|
def _mc_error(ary, batches=5, circular=False):
"""Calculate the simulation standard error, accounting for non-independent samples.
The trace is divided into batches, and the standard deviation of the batch
means is calculated.
Parameters
----------
ary : Numpy array
An array containing MCMC samples
batches : integer
Number of batches
circular : bool
Whether to compute the error taking into account `ary` is a circular variable
(in the range [-np.pi, np.pi]) or not. Defaults to False (i.e non-circular variables).
Returns
-------
mc_error : float
Simulation standard error
"""
_numba_flag = Numba.numba_flag
if ary.ndim > 1:
dims = np.shape(ary)
trace = np.transpose([t.ravel() for t in ary])
return np.reshape([_mc_error(t, batches) for t in trace], dims[1:])
else:
if _not_valid(ary, check_shape=False):
return np.nan
if batches == 1:
if circular:
if _numba_flag:
std = _circular_standard_deviation(ary, high=np.pi, low=-np.pi)
else:
std = stats.circstd(ary, high=np.pi, low=-np.pi)
else:
if _numba_flag:
std = np.float(_sqrt(svar(ary), np.zeros(1)))
else:
std = np.std(ary)
return std / np.sqrt(len(ary))
batched_traces = np.resize(ary, (batches, int(len(ary) / batches)))
if circular:
means = stats.circmean(batched_traces, high=np.pi, low=-np.pi, axis=1)
if _numba_flag:
std = _circular_standard_deviation(means, high=np.pi, low=-np.pi)
else:
std = stats.circstd(means, high=np.pi, low=-np.pi)
else:
means = np.mean(batched_traces, 1)
if _numba_flag:
std = _sqrt(svar(means), np.zeros(1))
else:
std = np.std(means)
return std / np.sqrt(batches)
