robdmc

#! /usr/bin/env python
import logging

"""
I never remember all the steps for setting up logging in python.
This gist goes through the steps so I can copy and paste what I need.
"""

# create logger on the current module and set its level
logger = logging.getLogger(__file__)

robdmc

import numpy as np
from scipy import stats
import pandas as pd

def score_for_confidence(alpha, beta, confidence=.95):
    """
    alpha: 1 + num_positive
    beta: 1 + num_negative
    confidence: confidence you can have that positive fraction is greater than this
    """

robdmc

#! /usr/bin/env bash
echo "DROP DATABASE IF EXISTS ambition_dev_stash; CREATE DATABASE ambition_dev_stash WITH TEMPLATE ambition_dev OWNER ambition_dev;" | psql

robdmc

#! /usr/bin/env bash
echo "DROP DATABASE IF EXISTS ambition_dev; CREATE DATABASE ambition_dev WITH TEMPLATE ambition_dev_stash OWNER ambition_dev;" | psql

robdmc

mcqs = MetricConfig.objects.filter(name__in=['lme_order_volume_profit', 'broadsoft_call_incoming_calls'])
qs = Animal(mcqs, 'day').all()
#qs = qs.filter(time__lte=datetime.datetime(2020, 1, 1,1).date()) #, time__gte=datetime.datetime(1970, 1, 2,1))

print str(qs.query)
print

cursor = connections[qs.db].cursor()
cursor.execute('explain %s' % str(qs.query))
for s in cursor.fetchall():

robdmc

#! /usr/bin/env bash

#  RUN THIS SCRIPT WITH SUDO !

# You should add the following the the `dependencies` section of your circle file
#  cache_directories:
#    - ./Python-2.7.9.tgz
#    - ./Python-2.7.9

set -x

robdmc

# from
# http://pandas.pydata.org/pandas-docs/stable/style.html

df.style.background_gradient(cmap='viridis', low=1, high=0).set_precision(1)

robdmc

import numbers
import re

def formatter(val, precision=2):
    """
    Converts numbers to a have a given precision in a way that nicely displays in UI
    elements.

    Inputs:
        val:  input value (either numeric or string)

robdmc

import pylab as pl
from statsmodels.distributions.empirical_distribution import ECDF
def ecdf(x, n_out=100, label=None, xlabel=None, ylabel=None):
    x_out = np.linspace(min(x), max(x), n_out)
    y_out = ECDF(x)(x_out)
    # pl.plot(x_out, 1 - y_out, '-', label=label)
    pl.fill_between(x_out, 100*(1 - y_out), label=label, alpha=.3)
    if label:
        pl.legend(loc='best')
    if xlabel:

robdmc

#! /usr/bin/env python
import logging
import datetime
import fleming
import pytz
import os
from itertools import islice
import sys