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from loren_frank_data_processing import get_trial_time, get_interpolated_position_dataframe, make_neuron_dataframe
import pandas as pd
import numpy as np

epoch_key = ('HPa', 3, 2)

# Get time of epoch from first LFP
SAMPLING_FREQUENCY = 500  # samples per second
time = get_trial_time(epoch_key, ANIMALS)
time = (pd.Series(np.ones_like(time, dtype=np.float), index=time)

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from loren_frank_data_processing import get_interpolated_position_dataframe, get_spike_indicator_dataframe

from src.parameters import ANIMALS

epoch_key = ('HPa', 6, 2)

def time_function(epoch_key, animals):
  neuron_info = make_neuron_dataframe(ANIMALS).xs(epoch_key, drop_level=False)
  neuron_key = neuron_info.index[0]
  return get_spike_indicator_dataframe(neuron_key, ANIMALS).resample('1ms').index

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def jspth(spikes1, spikes2):
    '''Joint Peristimulus Time Histogram
    
    Parameters
    ----------
    spikes1, spikes2: ndarray, shape (n_time, n_trials)
    
    Returns
    -------
    joint_histogram : ndarray, shape (n_time, n_time)

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def occupancy_normalized_hexbin(x, y, all_x, all_y, ax=None,
                                gridsize=(3, 3), **kwargs):
    '''Bins (x, y) into hexagonal grid and normalizes the
    count by the binned count of (all_x, all_y).
    
    Useful when measuring the frequency of events over time 
    and space when the time spent in each bin is not equal.
    
    Parameters
    ----------

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def coherence_rate_adjustment(firing_rate_condition1,
                              firing_rate_condition2, spike_power_spectrum,
                              homogeneous_poisson_noise=0, dt=1):
    '''Correction for the spike-field or spike-spike coherence when the
    conditions have different firing rates.

    When comparing the coherence of two conditions, a change in firing rate
    results in a change in coherence without an increase in coupling.
    This adjustment modifies the coherence of one of the conditions, so
    that a difference in coherence between conditions indicates a change

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from subprocess import PIPE, run
print(run(['git', 'rev-parse', 'HEAD'],
      stdout=PIPE, universal_newlines=True).stdout)

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% Wrapper function for save for use in parfor loops
% Based on http://www.mathworks.com/matlabcentral/answers/135285#answer_149537
% Handles save options such as -append
%
% Test Example 1
% a = 10;
% b = 'blah';
% c.test = 1;
% d = {'a'};
% e = [100 100];