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update.py
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update.py
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#!/usr/bin/python3
from datetime import datetime, timedelta
import matplotlib
matplotlib.use('Agg')
from matplotlib import dates as mdates
from matplotlib import ticker as ticker
from matplotlib import pyplot as plt
import pandas as pd
import boto3
import re
import os
import warnings
warnings.simplefilter("ignore", UserWarning)
# filters
instanceTypes=['g2.2xlarge', 'g2.8xlarge']
productDescriptions = ['Linux/UNIX (Amazon VPC)']
regions = ['us-east-1','us-west-1','us-west-2','eu-central-1','ap-northeast-1','ap-southeast-1']
# colors
colors = ['#009e73','#d55e08','#e69f00','#cc79a7','#0072b2','#56b4e9','#000000']
region_color = {}
for region in regions:
region_color[region] = colors.pop(0)
# size
width = 1920
height = 720
# range
now = datetime.utcnow().replace(microsecond=0)
start = now - timedelta(days=3)
end = now
print('time\t', now)
# load data from file
try:
fileName = 'data.csv'
data = pd.read_csv(fileName, encoding='utf-8')
data['Timestamp'] = pd.to_datetime(data['Timestamp'], utc=True)
last_update = datetime.utcfromtimestamp(os.path.getmtime(fileName)).replace(microsecond=0)
print('last\t', last_update)
# find existing range
az_timestamps = data.groupby(['AvailabilityZone'])['Timestamp']
min_timestamp = az_timestamps.min().max()
max_timestamp = az_timestamps.max().min()
print('loaded\t', min_timestamp, 'to', max_timestamp)
except:
data = None
last_update = datetime.min
min_timestamp = datetime.max
max_timestamp = datetime.min
# load data from aws
if now - last_update > timedelta(minutes=10):
start_request = max_timestamp if min_timestamp < start else start
print('pulling\t', start_request, 'to', end)
l = []
for region in regions:
print(region, end='', flush=True)
client = boto3.client('ec2',region_name=region)
next = 0
while next != '':
if next == 0:
prices=client.describe_spot_price_history(StartTime=start_request, EndTime=end, InstanceTypes=instanceTypes, ProductDescriptions=productDescriptions)
else:
print('.', end='', flush=True)
prices=client.describe_spot_price_history(StartTime=start_request, EndTime=end, InstanceTypes=instanceTypes, ProductDescriptions=productDescriptions, NextToken=next)
for price in prices['SpotPriceHistory']:
l.append({
'Region': region,
'AvailabilityZone': price['AvailabilityZone'],
'InstanceType': price['InstanceType'],
'SpotPrice': price['SpotPrice'],
'Timestamp': price['Timestamp']
})
next = prices['NextToken']
print('', flush=True)
# import merge and persist data
aws_data = pd.DataFrame(l)
aws_data['Timestamp'] = pd.to_datetime(aws_data['Timestamp'], utc=True)
try:
data = data.append(aws_data).drop_duplicates()
except:
data = aws_data
data.to_csv(fileName, encoding='utf-8', index=False, date_format="%Y-%m-%dT%H:%M:%SZ")
data.set_index(data['Timestamp'], inplace=True)
data['SpotPrice'] = data['SpotPrice'].astype(float)
for type in instanceTypes:
print(type)
# select dataframe
df = data
df = df[df.InstanceType == type]
df = df[df.Timestamp > start]
df = df[df.Timestamp < end]
# process dataframe
df['Timeminute'] = df['Timestamp'].apply(lambda dt: dt.strftime("%m-%d-%y %H:%M"))
df['SpotPrice'] = df.groupby(['Region','Timeminute'])['SpotPrice'].transform(min)
# set up plot figure
pd.set_option('display.mpl_style', 'default')
plt.figure(1, figsize=(width/100,height/100), tight_layout=True)
# plot dataframe
for region, region_data in df.groupby(['Region'], as_index=False):
region_data = region_data.resample('60s').interpolate()
region_data = pd.rolling_mean(region_data,15)
plt.plot(region_data.index, region_data['SpotPrice'],label=region,color=region_color[region])
# set up axes
ax = plt.gca()
ax.xaxis.set_major_locator(ticker.MultipleLocator(1))
ax.xaxis.set_minor_locator(ticker.AutoMinorLocator(8))
ax.xaxis.set_major_formatter(mdates.DateFormatter('%a'))
ax.xaxis.set_minor_formatter(mdates.DateFormatter('%H:00'))
ax.invert_xaxis()
ymin,ymax = df['SpotPrice'].quantile(.01),df['SpotPrice'].quantile(.8)
plt.ylim(ymin,ymax)
# set up labels
title = type+' - '+productDescriptions[0]
plt.title(title)
plt.ylabel('Lowest Price')
plt.xlabel('Zulu Time')
plt.figtext(x=0.005,y=0.005*(width/height),s=now.strftime("%Y-%m-%dT%H:%M:%SZ"))
# provide estimated bid price
bidstart = end - timedelta(hours=3)
bidprices = df[df.Timestamp > bidstart]['SpotPrice']
bidmin,bidmax = bidprices.quantile(.05),bidprices.quantile(.25)
plt.figtext(x=0.1,y=0.005*(width/height),s="Bid " + ('%.3f' % bidmin) + " to " + ('%.3f' % bidmax))
# sort legend
handles,labels = ax.get_legend_handles_labels()
sorted_handles=[]
sorted_regions=[]
for region in regions:
try:
sorted_handles.append(handles[labels.index(region)])
sorted_regions.append(region)
except:
pass
ax.legend(sorted_handles, sorted_regions, loc=2)
# save output
filename=re.sub('(/UNIX|Amazon|[ \(\)])','',title)+'.png'
plt.savefig(filename)
plt.close()
print('done')