kylerbrown

import numpy as np
import matplotlib.pyplot as plt


def raster(event_times_list, **kwargs):
    """
    Creates a raster plot

    Parameters
    ----------

kylerbrown

"""an alpha version of the plotter"""

import signal
import sys
import pyqtgraph as pg
import pyqtgraph.dockarea as pgd
import h5py
import numpy as np
from PyQt4 import QtGui, QtCore
from PyQt4.phonon import Phonon

kylerbrown

#!/bin/bash

# File originally created by Dan Meliza, modified by Kyler Brown

apt-get -y install make scons g++ gcc git libjack-dev jackd1 \
    libboost-dev libboost-program-options-dev libboost-date-time-dev libboost-system-dev libboost-filesystem-dev \
    libsndfile1-dev libsamplerate0-dev libhdf5-dev hdf5-tools \
    libx11-dev x11proto-core-dev libxft-dev libzmq-dev uuid-dev

# set some configuration for better RT performance

kylerbrown

kylerbrown

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns # improves plot aesthetics

def _invert(x, limits):
    """inverts a value x on a scale from
    limits[0] to limits[1]"""
    return limits[1] - (x - limits[0])

def _scale_data(data, ranges):

kylerbrown

#!/bin/bash


## Rational ##
# A lot of data can be generated quickly with the Intan system. To keep the experimental computer
# from running out of space, the data should be regularly copied to a server and removed locally.
# This script does that safely by:
# 1) recursively copying all *.rhd files and deleting them after safe copy.
# 2) recursively copying everything else, but not deleting (saving notes and settings on the experimenal computer.

kylerbrown

#!/bin/bash

## stim.sh ##
# a script to use jstim for overnight stimulation experiments

# move to source directory
cd "$(dirname "$0")" || exit

d=$(date +%Y%m%d_%H%M%S)

kylerbrown

# Looking for a decent spectrogram? Check out resin.
#
# https://github.com/kylerbrown/resin
#
# Resin creates multi-taper spectrograms and spectral derivative spectrograms
#
# old code below...

import matplotlib.pyplot as plt
import matplotlib.cm as cm

kylerbrown

import numpy as np
import pandas as pd
from scipy.signal import fftconvolve

def warp_path(fid, test, sampling_rate, insertgaps=True, 
              function=True, remove_fid_offset=True, invert=False):
    """
    fid : fiducial (true) labels. A pandas dataset with fields 'start', 'stop', and 'label'.
    test : like fid, labels to be warped
    sampling_rate : sampling rate of returned warp path

kylerbrown

def da_firing_rate(df, dt, duration, gaussian_std=None, center_time=True):
    '''computes Dayan and Abbot average firing rate or <r>
    df: pandas dataframe with a "start" column in seconds and a
        "trial" column. Starts are relative to the start of a trial
    dt: timestep for computing histogram
    duration: length of a trial in seconds
    gaussian_std: if None, no smoothing is applied. Units: seconds.
    center_time: if True, the returned time vector is the same length as
                the firing rate. Otherwise, the time vector is the bin edges,
                which are useful for histogram style plots.