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Cython example of exposing C-computed arrays in Python without data copies

The goal of this example is to show how an existing C codebase for numerical computing (here c_code.c) can be wrapped in Cython to be exposed in Python.

The meat of the example is that the data is allocated in C, but exposed in Python without a copy using the PyArray_SimpleNewFromData numpy

phaustin

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

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

Each cell is submitted to the kernel, and checked for errors.
"""

import os,sys,time

phaustin

#!/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

phaustin

Cython example of exposing C-computed arrays in Python without data copies

The goal of this example is to show how an existing C codebase for numerical computing (here c_code.c) can be wrapped in Cython to be exposed in Python.

The meat of the example is that the data is allocated in C, but exposed in Python without a copy using the PyArray_SimpleNewFromData numpy

phaustin

git hints

phaustin

"""
  test docstring
  https://docs.python.org/3/library/argparse.html#nargs
"""

import argparse, textwrap

if __name__ == '__main__':
    linebreaks=argparse.RawTextHelpFormatter
    descrip=textwrap.dedent(globals()['__doc__'])

phaustin

1 18 c
2 19 c
3 20 a
4 4 e
5 1 a
6 9 b
7 15 a
8 2 a
9 3 e
10 5 c

phaustin

#[[file:/pip_raid/phil/gcm_e340/read_names.py]]
import xlrd
filename='standard_output.xls'
workbook = xlrd.open_workbook(filename)
worksheet = workbook.sheet_by_name('Amon')

twod_fields=[]
headers=worksheet.row(14)
colnames=[item.value for item in headers]
for rownum in np.arange(16,66):

phaustin

# -*- coding: utf-8 -*-
from __future__ import print_function
from builtins import next
from openpyxl import load_workbook
import glob,os
import pandas as pd
import numpy as np
import logging

logging.basicConfig(level=logging.DEBUG)

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fig, ax = plt.subplots(1, 1, figsize = (12, 12) )
height,bins=np.histogram(group,bins=marks,normed=True)
out = ax.bar(bins[:-1],height*len(group),width=np.diff(bins))
ax.set_title('group')
ax.set_xlim([60,100])