rebeccabilbro

func GetCurrentWeather() (ApiWeatherInfo, error) {
	req, err := http.NewRequest("GET", "http://api.weatherapi.com/v1/current.json?", nil)
	if err != nil {
		fmt.Println(err)
		return ApiWeatherInfo{}, err
	}

	// define the query parameters and their respective values
	q := req.URL.Query()
	q.Add("key", os.Getenv("WAPIKEY"))

rebeccabilbro

from elasticsearch.helpers import bulk
from elasticsearch import Elasticsearch

class ElasticIndexer(object):
    """
    Create an ElasticSearch instance, and given a list of documents, 
    index the documents into ElasticSearch.
    """
    def __init__(self):
        self.elastic_search = Elasticsearch()

rebeccabilbro

rebeccabilbro

# kimchi.py
# For converting Python 2 pickles to Python 3

import os
import dill
import pickle
import argparse


def convert(old_pkl):

rebeccabilbro

import matplotlib.pyplot as plt

from yellowbrick.datasets import load_occupancy
from yellowbrick.model_selection import CVScores
from yellowbrick.classifier import ConfusionMatrix

from sklearn.svm import SVC
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import train_test_split as tts

rebeccabilbro

#!/usr/bin/env python3
# barnacle.py
# Adds the ID line to directory files using git metadata.
#
# Author:   Rebecca Bilbro
# Created:  Sat Sep 14 13:26:53 EDT 2019
#

"""
Scans local directory for git repository metadata and adds ID line to file footers.

rebeccabilbro

#!/usr/bin/env python3
# toto.py
# Adds the ID line to directory files using git metadata.
#
# Author:   Rebecca Bilbro
# Created:  Sun Aug 11 11:27:57 EDT 2019
#

"""
Scans local directory for git repository metadata and adds ID line to file headers.

rebeccabilbro

#!/usr/bin/python
# -*- coding: utf-8 -*-
# plot_classifier_comparison.py
"""
A comparison of a several classifiers in scikit-learn on synthetic datasets.
The point of this example is to illustrate the nature of decision boundaries
of different classifiers.

Particularly in high-dimensional spaces, data can more easily be separated
linearly and the simplicity of classifiers such as naive Bayes and linear SVMs

rebeccabilbro

import os 
import zipfile
import requests 
import pandas as pd 

WALKING_DATASET = (
    "https://archive.ics.uci.edu/ml/machine-learning-databases/00286/User%20Identification%20From%20Walking%20Activity.zip", 
)

def download_data(path='data', urls=WALKING_DATASET):

rebeccabilbro

package main

import (
    "fmt"
    "log"

    "github.com/shirou/gopsutil/mem"
    "github.com/shirou/gopsutil/cpu"
    "github.com/shirou/gopsutil/disk"
    "github.com/shirou/gopsutil/host"