shoyer

#!/usr/bin/env python
"""
simple example script for running and testing notebooks.

Usage: `ipnbdoctest.py foo.ipynb [bar.ipynb [...]]`

Each cell is submitted to the kernel, and the outputs are compared with those stored in the notebook.
"""

import os,sys,time

shoyer

import bottleneck
import iris
import numpy as np
import warnings


def _as_cube_coord(cube, name_or_coord):
    """
    Returns the cube coordinate corresponding to given coordinate name or cube
    """

shoyer

import numpy as np

def convert_label_to_indexer(index, label):
    """Given a pandas.Index and labels (e.g., from __getitem__) for one dimension,
    return an indexer suitable for indexing an ndarray along that dimension
    """
    if isinstance(label, slice):
        indexer = index.slice_indexer(label.start, label.stop, label.step)
    elif np.isscalar(label):
        indexer = indexer.get_loc(label)

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"City"	"City_Alternate"	"Country"	"Latitude"	"Longitude"	"Country_ISO3"	"pop1950"	"pop1955"	"pop1960"	"pop1965"	"pop1970"	"pop1975"	"pop1980"	"pop1985"	"pop1990"	"pop1995"	"pop2000"	"pop2005"	"pop2010"	"pop2015"	"pop2020"	"pop2025"	"pop2050"
"Sofia"	""	"Bulgaria"	42.70	23.33	"BGR"	520.00	620.00	710.00	810.00	890.00	980.00	1070.00	1180.00	1190.00	1170.00	1130.00	1170.00	1180.00	1210.00	1230.00	1240.00	1236
"Mandalay"	""	"Myanmar"	21.97	96.08	"MMR"	170.00	200.00	250.00	310.00	370.00	440.00	500.00	560.00	640.00	720.00	810.00	920.00	960.00	1030.00	1170.00	1310.00	1446
"Nay Pyi Taw"		"Myanmar"	19.75	96.10	"MMR"	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	60.00	930.00	1020.00	1180.00	1320.00	1461
"Yangon"	"Rangoon"	"Myanmar"	16.87	96.12	"MMR"	1300.00	1440.00	1590.00	1760.00	1950.00	2150.00	2380.00	2630.00	2910.00	3210.00	3550.00	3930.00	4090.00	4350.00	4840.00	5360.00	5869
"Minsk"	""	"Belarus"	53.89	27.57	"BLR"	280.00	410.00	550.00	720.00	930.00	1120.00	1320.00	1470.00	1610.00	1650.00	1700.00	1780.00	180

shoyer

shoyer

shoyer

"""Proof of concept for implementing bottleneck style aggregators with numba

These functions aggregate over any number of axes, just like the built-in
numpy functions, e.g.,
- nansum(x) # aggregates over all axes
- nansum(x, axis=1) # aggregates over axis 1
- nansum(x, axis=(0, 2)) # aggregtes over axes 0 and 2
"""
import functools