matplotlib.use('Agg')
import matplotlib.pyplot as plt
from matplotlib.dates import YearLocator, MonthLocator, DayLocator, DateFormatter, HourLocator
import numpy as np
from scipy import integrate, interpolate
from matplotlib.dates import YearLocator, MonthLocator, DayLocator, DateFormatter
from operator import itemgetter
import datetime
from methods import parse_data_file, interpolate_wind_power_table
data_fname = 'wind_data.csv'
cpath = os.path.dirname(os.path.realpath(__file__))
data_fpath = os.path.join(cpath, data_fname)
windtable_fname = 'power_wind_table.csv'
data, mindate = parse_data_file(data_fpath)
dates, temps, winds, gusts, deltas = zip(*data)
with open(windtable_fname, 'r') as f:
reader =csv.reader(f)
pw = [row for row in reader]
xpw, ypw = zip(*pw)
xnew = np.arange(0, 50, 0.1)
xnew, ynew = interpolate_wind_power_table(windtable_fname, xnew)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(xnew, ynew)
ax.plot(xpw, ypw, 'o')
import os
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from methods import parse_data_file, interpolate_wind_power_table
cpath = os.path.dirname(os.path.realpath(__file__))
filepath = os.path.join(cpath, 'wind_data.csv')
windtable_fname = 'power_wind_table.csv'
data, mindate = parse_data_file(filepath)
print(data[0])
xnew = np.arange(0, 50, 0.1)
xnew, ynew = interpolate_wind_power_table(windtable_fname, xnew)
def furling(_min, _max, data):
total_time = 0
for i, row in enumerate(data[:-1]):
ws = row[2] if row[2] is not None else 0
gs = row[3]
ws = ws if gs is None else (ws+gs)/2
td = row[-1] # timedelta
ntd = data[i+1][-1] # next timedelta
condition = _min <= ws <= _max