def add_sensor_data(self, name, amplitude, noise_amp, delta_t, max_t,
run_ave, trigger_value):
"""
Simulate a sensor and collect data with various parameters
amplitude, noise_amp, delta_t, max_t, run_ave,
trigger_value sets the trigger level for time, data not saved untill trigger value is reached
"""
# make a simulated sensor
sensor = ss.SensorSim(amplitude, noise_amp, delta_t=delta_t)
data0 = []
data1 = []
x0 = 0.
if trigger_value > 0:
trigger = dt.DataTrigger(trigger_value)
if run_ave > 0:
averager = moving_average.MovingAverage(run_ave)
while x0 < max_t:
x0, x1 = sensor.get_next_sample()
if run_ave > 0:
x1 = averager.nextVal(x1)
add_flag = True
if trigger_value > 0:
add_flag = trigger.need_save(x0)
if add_flag:
data0.append(x0)
data1.append(x1)
self.add_line(name, data0, data1)
def test_validation(self):
# check illegal characters in list
with self.assertRaises(TypeError):
data = [1, 2, 3, 'a']
ma.MovingAverage(data)
# check negative values
with self.assertRaises(ValueError):
data = [1, 2, -3, 0]
ma.MovingAverage(data, allow_negative_values=False)
try:
data = [1, 2, -3, 0]
ma.MovingAverage(data, allow_negative_values=True)
except ValueError:
self.fail()
# check empty values in list
with self.assertRaises(Exception):
data = [1, 2, 3, '']
ma.MovingAverage(data)
data = [1, 2, 3, None]
ma.MovingAverage(data)
# check enough elements to calculate moving average
with self.assertRaises(Exception):
data = []
ma.MovingAverage(data)
data = [1, 2, 3, 4]
ma.MovingAverage(data, window=5)
def test_calculation(self):
test_data = [10, 10, 10, 10]
moving_average = ma.MovingAverage(test_data).get_data()
ma_list = moving_average.replace(np.nan, '').tolist()
result = ['', 10.0, 10.0, 10.0]
self.assertListEqual(ma_list, result)
test_data = [0, 10, 5, -4, 8, 16]
moving_average = ma.MovingAverage(
test_data, window=3, allow_negative_values=True).get_data()
ma_list = moving_average.replace(np.nan, '').tolist()
result = ['', '', 5.0, 11 / 3, 3.0, 20 / 3]
self.assertListEqual(ma_list, result)
test_data = [0, 12, 40, 671, 4, -531]
moving_average = ma.MovingAverage(
test_data, window=6, allow_negative_values=True).get_data()
ma_list = moving_average.replace(np.nan, '').tolist()
result = ['', '', '', '', '', sum(test_data) / len(test_data)]
self.assertListEqual(ma_list, result)
import csv
import japanize_matplotlib
import pandas as pd
import matplotlib.pyplot as plt
import moving_average as ma
#Analitics Weather data
#Tokyo
titile = 'TOKYO'
data1990 = pd.read_csv('data/Tokyo_1990 - Tokyo1990.csv')
temp_rolling1 = ma.MovingAverage(data1990['temp'])
vap_pressure_rolling1 = ma.MovingAverage(data1990['vap_pressure'])
humidity_rolling1 = ma.MovingAverage(data1990['humidity'])
data1995 = pd.read_csv('data/Tokyo1995.csv')
temp_rolling1 = ma.MovingAverage(data1995['temp'])
vap_pressure_rolling1 = ma.MovingAverage(data1995['vap_pressure'])
humidity_rolling1 = ma.MovingAverage(data1995['humidity'])
data2000 = pd.read_csv('data/Tokyo_2000 - Tokyo2000.csv')
temp_rolling2 = ma.MovingAverage(data2000['temp'])
vap_pressure_rolling2 = ma.MovingAverage(data2000['vap_pressure'])
humidity_rolling2 = ma.MovingAverage(data2000['humidity'])
data2005 = pd.read_csv('data/Tokyo_2005.csv')
temp_rolling2 = ma.MovingAverage(data2005['temp'])
vap_pressure_rolling2 = ma.MovingAverage(data2005['vap_pressure'])
humidity_rolling2 = ma.MovingAverage(data2005['humidity'])