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import pandas as pd

data_frame = pd.io.parsers.read_csv('time-series.csv',index_col = 0, names=['index','heave'])
_max = [[5, 259.0]]
indexes = [x[0] for x in _max ]
index = data_frame.index
timestamps = [index[z] for z in indexes]
#['2011-12-30 00:00:04']
print data_frame.ix[timestamps]
#                     heave

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/usr/local/lib/python3.2/subprocess.py:389: RuntimeWarning: The _posixsubprocess module is not being used. Child process reliability may suffer if your program uses threads.
  "program uses threads.", RuntimeWarning)
basedirlist is: ['/usr/local', '/usr']
============================================================================
BUILDING MATPLOTLIB
            matplotlib: 1.2.x
                python: 3.2.3 (default, Sep  6 2012, 14:51:03)  [GCC 4.1.2
                        20080704 (Red Hat 4.1.2-46)]
              platform: linux2

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# Python modules for taking Cumulus ( sandaysoft.com ) logs into a Pandas
# ( pandas.pydata.org ) DataFrame, and using the date and time columns to
# create a DateTimeIndex for the DataFrame and sort upon it.

import pandas as pd
import os
import glob
from datetime import datetime

log_path = 'D:\\log_files_folder'

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import os
import glob
import pandas as pd
from datetime import datetime

# Example below assumes following folder hierarchy D:\Buoy_Data\Buoy_Name\Year\Month\
buoy_names = ['Buoy_Name']
buoy_path = 'D:\\Buoy_Data\\'

his_columns = ['date_time', 'Tp', 'dirp', 'sprp', 'Tz', 'Hm0', 'TI', 'T1', 'Tc', 

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{
 "metadata": {
  "name": "Basemap HebMarine"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {

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# Initial data obtained from hdr file produced with flt file when running 
# Raster to Float in ArcToolBox and numpy file ( npy ) from RasterToNumpyArray 
# in ArcPy
import numpy as np
np.load('numpy_bathy.npy')
cell_size = 3
no_of_columns = 18325
no_of_rows = 13303
lower_left_x = 607512
lower_left_y = 6455598

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# Code copied from Joe Kington's StackOverflow answer 
# http://stackoverflow.com/a/8964779/1135883
# For large files with regular records this is a much faster and memory 
# efficient solution

def iter_loadtxt(filename, delimiter=' ', skiprows=0, dtype=float):
    def iter_func():
        with open(filename, 'r') as infile:
            for _ in range(skiprows):
                next(infile)

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# Initial data obtained from hdr file produced with flt file when running 
# Raster to Float in ArcToolBox and numpy file ( npy ) from RasterToNumpyArray 
# in ArcPy

# Numpy uses top left as origin and geopgraphic coordinate systems treat the 
# origin as lower left, you can either flip the incoming bathymetry maxtrix
# vertically or create a descending range of y_coordinates

import numpy as np
bathy = np.load('D:\\numpy_bathy.npy')

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# -*- coding: utf-8 -*-
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.mlab import griddata
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import matplotlib.pyplot as plt
import numpy as np
import sys

def xyz_ret(file):

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#Example of searching for a string in IPython sqlite history
from IPython.core.history import HistoryAccessor
history_accessor = HistoryAccessor()
sqlite_cursor = history_accessor.search('*threshold*')
results = sqlite_cursor.fetchall()
for result in results:
    print result
#From the first number in the printed results you can see the relevant session 
#number, and then below retrieve the whole session using get_range
session_results = history_accessor.get_range(1321)