from evaluation import datawash
from evaluation import directions as di
from evaluation import percents
from evaluation import hour
from evaluation import gp
import termcolor
from plotcheck import pl
from evaluation import baynetwork
from evaluation import bino
path = "/home/bwei/PycharmProjects/data lib/pvtotal.csv"
#windset = rd(path)
#name = raw_input('the name of data set?')
#realwindset = windset[name]
pointsperday = 288 # CHANGE HERE FOR DIFFERENT RESOLUTION
realwindset = readcsv.rd(path)
#realwindset=hour(realwindset)#############
windsetoriginal = realwindset
realwindset.shape = (len(realwindset), )
#data reading is done
succ = []
action = []
ranges = np.arange(9000, 26100)
for iters in ranges:
def ma(data, days_to_keep, points_per_day, alpha=0.2):
days_covered = int(np.floor(len(data) / points_per_day))
points_covered = days_covered * points_per_day
daysdata = []
onedaydata = data[len(data) - points_covered:len(data) -
points_covered + points_per_day]
import numpy as np
import readcsv
import matplotlib.pyplot as plt
from plotcheck import pl
#read 2016:
path_2016 = "/home/bwei/PycharmProjects/data lib/pv_2016.csv"
dataset_2016 = readcsv.rd(path_2016)
dataset_2016[np.where(dataset_2016 > 50)] = 0
#read_2017:
path_2017 = "/home/bwei/PycharmProjects/data lib/pv_2017.csv"
dataset_2017 = readcsv.rd(path_2017)
dataset_2017[np.where(dataset_2017 > 50)] = 0
#read_2018:
path_2018 = "/home/bwei/PycharmProjects/data lib/pv_2018.csv"
dataset_2018 = readcsv.rd(path_2018)
points_per_day = 288
rounds = len(dataset_2016) / points_per_day
# sum up func:
def sumup(data, rounds, point_per_day=288):
sum_vector = []
for n in np.arange(rounds):
sum_vector_one = np.sum(data[n * point_per_day:(n + 1) *
point_per_day])
sum_vector.append(sum_vector_one)
return np.array(sum_vector)
from plotcheck import pl
import GPy
from matplotlib import pyplot as plt
from pydmd import HODMD
aaa = np.array([1, 2, 3, 4, 5, 6, 8, 19, 30])
bbb = np.array([2, 10, 4, 5, 12, 7])
ccc = np.array([3, 4, 5, 6, 7, 8])
imfs = np.array([])
kkkk = np.array([aaa, bbb, ccc])
Percentile = np.percentile(aaa, [0, 25, 50, 75, 100])
IQR = Percentile[3] - Percentile[1]
UpLimit = Percentile[3] + IQR * 1.5
DownLimit = Percentile[1] - IQR * 1.5
aaa[np.where(aaa > UpLimit)] = UpLimit
avvv = [{}] * 3
path = "/home/bwei/PycharmProjects/data lib/PVhourly6months.csv"
realwindset = readcsv.rd(path, datawashflag=1)
pointsperday = 24
days = 2
hodmd = HODMD(svd_rank=0, exact=True, opt=True,
d=pointsperday).fit(realwindset[-48:])
hodmd.reconstructed_data.shape
hodmd.plot_eigs()
hodmd.dmd_time['tend'] = (days +
1) * pointsperday - 1 # since it starts from zero