import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from timeseries import read_data
# Load input data
index = 2
data = read_data('data_2D.txt', index)
# Plot data with year-level granularity
start = '2003'
end = '2011'
plt.figure()
data[start:end].plot()
plt.title('Input data from ' + start + ' to ' + end)
# Plot data with month-level granularity
start = '1998-2'
end = '2006-7'
plt.figure()
data[start:end].plot()
plt.title('Input data from ' + start + ' to ' + end)
plt.show()
#!/usr/bin/python
from flask import Flask, jsonify, request, abort
from timeseries import read_data
import json
app = Flask(__name__)
data, equipments, sensors_per_equipment = read_data('dataset/data-large.csv')
@app.route('/timeseries')
def series():
device_id = request.args.get('deviceid')
sensor = request.args.get('sensor')
for d in data:
if d.equipment == device_id and d.sensor == sensor:
return d.serialize
abort(404)
@app.route('/sensors')
def sensors():
device_id = request.args.get('deviceid')
if (device_id in sensors_per_equipment):
return json.dumps(sensors_per_equipment[device_id])
abort(404)
@app.route('/devices')
def devices():
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from timeseries import read_data
# Input filename
input_file = 'data_2D.txt'
# Load data
x1 = read_data(input_file, 2)
x2 = read_data(input_file, 3)
# Create pandas dataframe for slicing
data = pd.DataFrame({'dim1': x1, 'dim2': x2})
# Plot data
start = '1968'
end = '1975'
data[start:end].plot()
plt.title('Data overlapped on top of each other')
# Filtering using conditions
# - 'dim1' is smaller than a certain threshold
# - 'dim2' is greater than a certain threshold
data[(data['dim1'] < 45) & (data['dim2'] > 30)].plot()
plt.title('dim1 < 45 and dim2 > 30')
# Adding two dataframes
plt.figure()
diff = data[start:end]['dim1'] + data[start:end]['dim2']
