Get interpolated data from loren frank data

get_interpolated_data.py

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)
        .resample('2ms').mean()  # resample to 2 ms aka 500 samples / second
        .index)

# `get_interpolated_position_dataframe` takes a function for determining the timing
def _time_function(*args, **kwargs):
    return time

# Get the position, speed, velocity, etc
position_info = (
    get_interpolated_position_dataframe(
        epoch_key, ANIMALS, _time_function, spacing=30)
    .dropna(subset=['linear_distance', 'linear_speed']))

# Get the neurons based on the `neuron_info` dataframe
neuron_info = neuron_info.loc[
    (neuron_info.numspikes > 100)]

# Get the spikes from the neuron_info dataframe keys and reindex it to the same
# time as the position info to make sure they match.
spikes = get_all_spike_indicators(
    neuron_info.index, ANIMALS, _time_function).reindex(time)