njsmith

import six
import tokenize
import numpy as np

def _filter_tokens(s):
    last_token_was_number = False
    for token in tokenize.generate_tokens(six.StringIO(s).read):
        token_type = token[0]
        token_string = token[1]
        if (last_token_was_number

njsmith

>>> np.__version__
'1.8.1'
>>> ar = np.ma.array([1, 1, 1, np.ma.masked, 1, 1, 1])
>>> ar
masked_array(data = [1.0 1.0 1.0 -- 1.0 1.0 1.0],
             mask = [False False False  True False False False],
       fill_value = 1e+20)

>>> np.gradient(ar)
masked_array(data = [0.0 0.0 -- 0.0 -- 0.0 0.0],

njsmith

# Attempt to implement in pure Python a Sequence class which behaves the same
# way as a CPython object that provides tp_as_sequence but not
# tp_as_number (like, for example, the built-in types list, tuple, ...).

def _proper_subinstance(a, b):
    ta = type(a)
    tb = type(b)
    return ta is not tb and issubclass(ta, tb)

class Sequence(object):

njsmith

N = 100
x = np.linspace(0, 1, N)
jet_samples_sRGB = jet(x)
jet_samples_Jpapbp = cspace_convert(jet_samples_sRGB, "sRGB1", "CAM02-UCS")

distances = np.cumsum(np.sqrt(np.sum((jet_samples_Jpapbp[:-1, ...] - jet_samples_Jpapbp[1:, ...]) ** 2), axis=-1))
distances = np.concatenate(([0], distances))

newjet_samples_Jpapbp = np.interp(x, distances, jet_samples_Jpapbp)
newjet_samples_sRGB = cspace_convert(newjet_samples_Jpapbp, "CAM02-UCS", "sRGB1")

njsmith

$ conda create -n anaconda-34 python=3.4 anaconda
<output elided -- I ended up with anaconda 2.3.0 npy19py34_0>
$ source activate anaconda-34

# This is a list of every package they link to outside of what they ship, but see below:

~$ find miniconda/envs/anaconda-34 -name '*.so' | xargs -n1 ldd | grep -v miniconda | cut -f1 -d' ' | sort -u
	/lib64/ld-linux-x86-64.so.2
	libcrypt.so.1
	libc.so.6

njsmith

njsmith

A mini-NEP for the where= argument to ufuncs

To try and make more progress on the whole missing values/masked arrays/... debate, it seems useful to have a more technical discussion of the pieces which we can agree on. This is the first, which attempts to nail down the details of the new where= argument to ufuncs.

Rationale

njsmith

####################################### miniNEP 2: NA support via special dtypes #######################################

To try and make more progress on the whole missing values/masked arrays/... debate, it seems useful to have a more technical discussion of the pieces which we can agree on. This is the second, which attempts to nail down the details of how NAs can be implemented using special dtype's.

Table of contents

njsmith

Metadata-Version: 2.0
Name: ipython
Version: 4.0.0
Summary: IPython: Productive Interactive Computing
Home-page: http://ipython.org
Author: The IPython Development Team
Author-email: ipython-dev@scipy.org
License: BSD
Download-URL: https://github.com/ipython/ipython/downloads
Keywords: Interactive,Interpreter,Shell,Parallel,Distributed,Web-based computing,Qt console,Embedding

njsmith

import sys
import os
import os.path
import subprocess
import json

# entry point for 'python -m pip'
def python_m_pip_main():
    do_pip(sys.argv)