series⚓︎
Time series management and processing.
DataSeries
⚓︎
Bases: StatPrint
Basically just an np.ndarray
. But adds:
- Possibility of adding attributes.
- The class (type) provides way to acertain if an attribute is a series.
Note: subclassing ndarray
is too dirty => We'll just use the
array
attribute, and provide {s,g}etitem
.
FAUSt
⚓︎
Bases: DataSeries
, StatPrint
Container for time series of a statistic from filtering.
Four attributes, each of which is an ndarray:
.f
for forecast ,(Ko+1,)+item_shape
.a
for analysis ,(Ko+1,)+item_shape
.s
for smoothed ,(Ko+1,)+item_shape
.u
for universial/all,(K +1,)+item_shape
If store_u=False
, then .u
series has shape (1,)+item_shape
,
wherein only the most-recently-written item is stored.
Series can also be indexed as in
self[ko,'a']
self[whatever,ko,'a']
# ... and likewise for 'f' and 's'. For 'u', can use:
self[k,'u']
self[k,whatever,'u']
Note
If a data series only pertains to analysis times, then you should use a plain np.array instead.
__init__(K, Ko, item_shape, store_u, store_s, **kwargs)
⚓︎
Construct object.
item_shape
: shape of an item in the series.store_u
: if False: only the current value is stored.kwargs
: passed on to ndarrays.
RollingArray
⚓︎
ND-Array that implements "leftward rolling" along axis 0.
Used for data that gets plotted in sliding graphs.
StatPrint
⚓︎
Bases: NicePrint
Set NicePrint
options suitable for stats.
auto_cov(xx, nlags=4, zero_mean=False, corr=False)
⚓︎
Auto covariance function, computed along axis 0.
nlags
: max lag (offset) for which to compute acf.corr
: normalize acf byacf[0]
so as to return auto-CORRELATION.
With corr=True
, this is identical to
statsmodels.tsa.stattools.acf(xx,True,nlags)
estimate_corr_length(xx)
⚓︎
Estimate the correlation length of a time series.
For explanation, see mods.LA.homogeneous_1D_cov
.
Also note that, for exponential corr function, as assumed here,
fit_acf_by_AR1(acf_empir, nlags=None)
⚓︎
Fit an empirical auto cov function (ACF) by that of an AR1 process.
acf_empir
: auto-corr/cov-function.nlags
: length of ACF to use in AR(1) fitting
mean_with_conf(xx)
⚓︎
Compute the mean of a 1d iterable xx
.
Also provide confidence of mean, as estimated from its correlation-corrected variance.
monitor_setitem(cls)
⚓︎
Modify cls to track of whether its __setitem__
has been called.
See sub.py for a sublcass solution (drawback: creates a new class).