analysis

  1. analysis
    1. Data
    2. Functions
        1. _dict_max(D)
        2. arrays_almost_equal(a, b, threshold)
        3. ccf(x, y, axis=None)
        4. check_dependency(name)
        5. simple_frequency_spectrum(x)
    3. Classes
      1. TuningCurve
        1. __getitem__(self, i)
        2. __init__(self, D=None)
        3. add(self, D)
        4. max(self)
        5. stats(self)

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.