danieljfarrell

import numpy as np

def magnitude(vector):
   return np.sqrt(np.dot(np.array(vector),np.array(vector)))

def norm(vector):
   return np.array(vector)/magnitude(np.array(vector))

def lineRayIntersectionPoint(rayOrigin, rayDirection, point1, point2):
    """

danieljfarrell

from tables import *

# Isolated the copying logic
def append_column(table, group, name, column):
    """Returns a copy of `table` with an empty `column` appended named `name`."""
    
    description = table.description._v_colObjects.copy()
    description[name] = column
    copy = Table(group, table.name+"_copy", description)
    

danieljfarrell

import pandas as pd
from sklearn.preprocessing import MultiLabelBinarizer

# Feature-list data frame
df = pd.DataFrame(columns = ["features"], index=['Item 1', 'Item 2'])
df['features'] = [["A", "B"], ["C", "D"]]

# Use scikits-learn to create feature matrix and feature names
mlb = MultiLabelBinarizer()
feature_column_name = 'features'

danieljfarrell

def non_linear_parameters_95_percent_confidence_interval(fvec, jac):
    """Returns the 95% confidence interval on parameters from
    non-linear fit results."""
    # residual sum of squares
    rss = np.sum(fvec**2)
    # number of data points and parameters
    n, p = jac.shape
    # the statistical degrees of freedom
    nmp = n - p
    # mean residual error

danieljfarrell

// http://apple.stackexchange.com/questions/6707/how-to-stop-os-x-from-writing-spotlight-and-trash-files-to-memory-cards-and-usb
mdutil -i off /Volumes/yourUSBstick
cd /Volumes/yourUSBstick
rm -rf .{,_.}{fseventsd,Spotlight-V*,Trashes}
mkdir .fseventsd
touch .fseventsd/no_log .metadata_never_index .Trashes
cd -

danieljfarrell

import os, psutil, subprocess
# Usage:
# This script MUST be run from the root of the 
# teraview-test-robot directory for example using,
#     python run_test_robot_cmd.py


def make_test_robot_process():
    """Creates the test robot process."""
    REL_TEST_ROBOT_EXE = "bin\\Debug\\test-robot.exe"

danieljfarrell

    def make_frequency_information(self):
      """NB kUseNewFFTMethod = True for MATLAB algorithm."""
        reference = self.measurement.reference
        if len(reference) > 1:
            dt = self.measurement.optical_delay_spacing
            t_offset = self.measurement.optical_delay_offset
            f_max = 1./(2*dt)
            if kUseNewFFTMethod:
                freq = np.linspace(0.0, f_max, len(reference) + 1)
            else:

danieljfarrell

def remove_reference_signal_with_experimental_reference_method_with_extended_FFT(
                                                  signal,
                                                  reference,
                                                  optical_delay_spacing,
                                                  ref_calibration=None):
    """Removes reference signal using extended FFT method."""
    # transform reference signal to frequency domain
    t_points = reference.shape[0]
    ref_spec = np.fft.rfft(reference, reference.shape[0]*2)
    signal_spec = np.fft.rfft(signal, signal.shape[0]*2)

danieljfarrell

# tree_editor.py -- Example of a tree editor

from traits.api \
    import HasTraits, Str, Regex, List, Instance, Any, Property
from traitsui.api \
    import TreeEditor, TreeNode, View, Item, VSplit, \
           HGroup, Handler, Group, ModelView
from traitsui.menu \
    import Menu, Action, Separator
from traitsui.wx.tree_editor \

danieljfarrell

import datetime
import time

now = datetime.datetime.now()
now_iso = now.isoformat()

def parse_iso8601_badly(iso_str):
    date_str, time_str = iso_str.split('T')
    d = dict()
    d.update(zip(['hour', 'minute', 'second'],