Thiagobc23

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Patch
from pandas import Timestamp

##### DATA #####
data = {'Task': {0: 'TSK M',
                 1: 'TSK N',
                 2: 'TSK L',

kived

import kivy
kivy.require('1.8.0')

from kivy.app import App
from kivy.lang import Builder

from jnius import autoclass
PythonActivity = autoclass('org.renpy.android.PythonActivity')
View = autoclass('android.view.View')
Params = autoclass('android.view.WindowManager$LayoutParams')

dan-blanchard

For ETS's SKLL project, we found out the hard way that Travis-CI's support for numpy and scipy is pretty abysmal. There are pre-installed versions of numpy for some versions of Python, but those are seriously out of date, and scipy is not there are at all. The two most popular approaches for working around this are to (1) build everything from scratch, or (2) use apt-get to install more recent (but still out of date) versions of numpy and scipy. Both of these approaches lead to longer build times, and with the second approach, you still don't have the most recent versions of anything. To circumvent these issues, we've switched to using Miniconda (Anaconda's lightweight cousin) to install everything.

A template for installing a simple Python package that relies on numpy and scipy using Miniconda is provided below. Since it's a common s

kien-truong

logconfig = {
    "version": 1,
    "disable_existing_loggers": 0,
    "root": {
        "level": "DEBUG",
        "handlers": [
            "console",
            "file",
            "debugfile"
        ]