rcgalbo

import matplotlib.pyplot as plt
import numpy as np

# set seed for reproduceable
np.random.seed(123)

ind = np.arange(1, 40_001)
x = np.random.random(40_000)
y = np.random.random(40_000)
radius = (x**2 + y**2)**.5 

rcgalbo

import plotly.plotly as py
import plotly.graph_objs as go
import plotly.figure_factory as ff
import plotly.offline as offline
import pandas as pd

import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output

rcgalbo

from pdf2image import convert_from_path

image = convert_from_path("path_to_pdf.pdf")

rcgalbo

def convert_to_datetime(df, columns):
    return df[columns].apply(pd.to_datetime, axis=1)

rcgalbo

from google.cloud import storage

def list_blobs(bucket, prefix):
    """
    Lists all the blobs in the bucket
    make sure prefix ends with '/'
    """
    storage_client = storage.Client()
    bucket = storage_client.get_bucket(bucket)
    

rcgalbo

import os
with open('../data/labels.csv','r') as labels:
    lines = [line.split(',') for line in labels.readlines()][1:]
    for line in lines:
        image = line[0]
        label = line[1].split('/')[-1].replace('\n','')
        if not os.path.exists('../data/train/'+label):
            os.mkdir('../data/train/'+label)
        os.rename('../data/train/'+image+'.jpg', '../data/train/'+label+'/'+image+'.jpg')

rcgalbo

import folium

def plot_heatmap(data):
    '''
    Args:
      takes a list of lists [[lat, long]]
    Returns:
      Folium map object with heatmap overlay
    '''
    heatmap = folium.Map(tiles="CartoDBpositron", prefer_canvas=True)

rcgalbo

import sys
!conda install --yes --prefix={sys.prefix} numpy

rcgalbo

# For Windows users# Note: <> denotes changes to be made

#Create a conda environment
conda create --name <environment-name> python=<version:2.7/3.5>

#To create a requirements.txt file:
conda list #Gives you list of packages used for the environment

conda list -e > requirements.txt #Save all the info about packages to your folder

rcgalbo

# This script is designed to work with ubuntu 16.04 LTS
# with keras 1.2.2 and the latest Pytorch with CUDA 8 support 
##########################################################################
#This is used to install CUDA 8 driver for Tesla K80 
##########################################################################

#!/bin/bash
echo "Checking for CUDA and installing."
# Check for CUDA and try to install.
if ! dpkg-query -W cuda-8-0; then