= analysis =

[[PageOutline]]

== Data ==

'''!__name!__''' = !NeuroTools.analysis

== Functions ==

==== _dict_max(D) ====
For a dict containing numerical values, contain the key for the
highest value. If there is more than one item with the same highest
value, return one of them (arbitrary - depends on the order produced
by the iterator).
==== arrays_almost_equal(a, b, threshold) ====

==== ccf(x, y, axis=None) ====
Computes the cross-correlation function of two series x and y.
Note that the computations are performed on anomalies (deviations from
average).
Returns the values of the cross-correlation at different lags.

Inputs:
x    - 1D !MaskedArray of a Time series.
y    - 1D !MaskedArray of a Time series.
axis - integer *[None]* Axis along which to compute (0 for rows, 1 for cols).
If `None`, the array is flattened first.

Examples:
>> z= arange(1000)
>> ccf(z,z)
==== check_dependency(name) ====

==== simple_frequency_spectrum(x) ====
Very simple calculation of frequency spectrum with no detrending,
windowing, etc. Just the first half (positive frequency components) of
abs(fft(x))
== Classes ==

=== !TuningCurve ===
Class to facilitate working with tuning curves.
==== !__getitem!__(self, i) ====

==== !__init!__(self, D=None) ====
If `D` is a dict, it is used to give initial values to the tuning curve.
==== add(self, D) ====

==== max(self) ====
Return the key of the max value and the max value.
==== stats(self) ====
Return the mean tuning curve with stderrs.
----