tacaswell

class uneven_FFT_recon(object):
    """
    A class to wrap up dealing with the irregularly sampled periodic
    data
    """
    @classmethod
    def reconstruct_iterative(cls, phi, h, target_error,
                              start_N=3, bound_N=15, iters=25):
        """
        reconstruct a band-limited a curve from unevenly

tacaswell

import numpy as np
import matplotlib.pyplot as plt
from itertools import cycle
from six.moves import zip

def stack_bar(ax, list_of_vals, color_cyle=None, **kwargs):
    """
    Generalized stacked bar graph.

    kwargs are passed through to the call to `bar`

tacaswell

# this bit of code makes sure that the head stays a fixed size in terms of
# axes fractions.  To tune this size change `head_size`
head_size = 30
# to tun the ratio between the head size and the shaft, change `ascale`
ascale = .2

# tunes how 'pinched' the arrow head is, < 1 and the bottom will be indented
# > 1 the arrow will be a distorted diamond
pinched = .7

tacaswell

from collections import Mapping
import six


_XRAYLIB_MAP_MAP = {'edges': ({'Ka1': xraylib.KALPHA,..}, xraylib.CS_FlourLine),
                    'levels': ({'K': xraylib.K, }),
                    }
    

_OTHER_VALUES = {'H': {'Z': 1, 'rho': .524, },

tacaswell

from vistrails import api

csv = api.add_module(100, 100, 'org.vistrails.vistrails.tabledata', 'CSVFile', 'read')
fd = api.add_module(65, 50, 'org.vistrails.vistrails.url', 'DownloadFile', '')
api.add_connection(fd.id, 'file', csv.id, 'file')
api.change_parameter(fd.id, 'url',['http://www.vistrails.org/download/download.php?type=DATA&id=citibike_from_launch.csv'])

tacaswell

channels:
 - tacaswell
 - defaults

tacaswell

import pandas as pd
from datetime import datetime, timedelta

dt = timedelta(seconds=1)
base_time = datetime(2014, 9, 24, 10, 5, 30)

indx1 = [base_time + j * dt for j in range(0, 10)]
indx2 = [base_time + j * dt for j in range(0, 10, 2)]

ds1 = pd.Series(5, index=indx1)

tacaswell

tacaswell

tacaswell