def get_time_ticks_zoomed(iday):
# compute ticks
t0 = datetime.datetime(2016, 9, 2 + iday, 0)
t1 = datetime.datetime(2016, 9, 3 + iday, 0)
# major tick
tck = []
tck_label = []
t = t0
while t < t1 + datetime.timedelta(hours=1):
tck.append(t)
if np.mod(t.hour, 3) == 0:
tck_label.append(t.strftime('%H'))
else:
tck_label.append('')
t += datetime.timedelta(hours=1)
# minor tick
tck_minor = []
t = t0
while t < t1 + datetime.timedelta(hours=1):
tck_minor.append(t)
t += datetime.timedelta(minutes=15)
# string of day
day_str = t0.strftime('%Y-%m-%d')
return t0, t1, tck, tck_label, day_str, tck_minor
def __init__(self, label=None, logline=None, coord_min=True):
# split string
l = logline.split()
# label
self.label = label
# time information
# assumes: 02/09/2016 05:35:00 7 17.124 43 19.866
self.time = datetime.datetime(int(l[0].split('/')[2]),
int(l[0].split('/')[1]),
int(l[0].split('/')[0]),
int(l[1].split(':')[0]),
int(l[1].split(':')[1]),
int(l[1].split(':')[2]))
if len(l) > 2:
# lon, lat data
if coord_min:
self.lon = float(l[2]) + float(l[3]) / 60.
self.lat = float(l[4]) + float(l[5]) / 60.
else:
self.lon = float(l[2])
self.lat = float(l[3])
def get_time_ticks():
# for time plotting purposes
t0 = datetime.datetime(2016, 9, 2, 0)
t1 = datetime.datetime(2016, 9, 4, 12)
### plot time line
# assign date locator / formatter to the x-axis to get proper labels
# dloc = mdates.DayLocator()
# dform = mdates.DateFormatter('%Y-%m-%d')
# hloc = mdates.HourLocator(interval=6)
# hform = mdates.DateFormatter('%H:%M')
# locator = AutoDateLocator(minticks=3)
# formatter = AutoDateFormatter(locator)
# formatter = DateFormatter('%Y-%m-%d %H:%M:%S')
# plt.gcf().axes[0].xaxis.set_major_formatter(formatter)
# ax.xaxis.set_minor_locator(hloc)
# ax.xaxis.set_minor_formatter(hform)
# ax.xaxis.set_major_locator(hloc)
# ax.xaxis.set_major_formatter(dform)
# fig.autofmt_xdate()
tck = []
tck_label = []
tdays = []
t = t0
while t < t1 + datetime.timedelta(hours=6):
tck.append(t)
if t.hour == 12:
tck_label.append(t.strftime('%Y-%m-%d'))
else:
tck_label.append('')
t += datetime.timedelta(hours=6)
if t.hour == 0:
tdays.append(date2num(t))
return t0, t1, tck, tck_label, tdays
def parse_nmea_sentence(s, t=None):
if 'GPRMC' in s.identifier():
if len(s.fields)>0 and all([s.datestamp, s.timestamp]):
time = datetime.datetime.combine(s.datestamp, s.timestamp)
elif 'GPGGA' in s.identifier():
if t:
time = datetime.datetime(t.year,
t.month,
t.day,
int(s.data[0][:2]),
int(s.data[0][2:4]),
int(s.data[0][4:6]),
)
if time<t:
time+=datetime.timedelta(days=1)
else:
time = t
return dict(lon=s.longitude,
lat=s.latitude,
time=time
)
ssh_clim_pop = np.concatenate([ssh_clim_pop, ssh_clim_pop, ssh_clim_pop, ssh_clim_pop])
# load wind
file = np.loadtxt(fname='../../winds/wind_mahe_2018_2019.csv', delimiter=',')
year = file[:, 0]
month = file[:, 1]
day = file[:,2]
hour = file[:,3]
dir = file[:, 4]
speed = file[:,5] / 1.94384 # Convert m/s to knots
u_wind1 = speed * np.cos(np.deg2rad(dir))
v_wind1 = speed * np.sin(np.deg2rad(dir))
time1 = np.zeros(year.shape[0])
for ii in range(year.shape[0]):
time1[ii] = date2num(datetime.datetime(np.int(year[ii]), np.int(month[ii]),
np.int(day[ii]), np.int(hour[ii])))
mat = sci.loadmat('../../winds/winds.mat')
wind = mat['wind_MO']
u_wind = wind['u'][0,0]
v_wind = wind['v'][0,0]
time = wind['time'][0,0] - 366 # we need this to change to python time
fi1 = date2num(datetime.datetime.strptime('2016-01', '%Y-%m'))
fi2 = date2num(datetime.datetime.strptime('2018-06-30', '%Y-%m-%d'))
idxa = np.where(time > fi1)[0][0]
idxb = np.where(time > fi2)[0][0]
ui = u_wind[idxa:398789+1].squeeze()
vi = v_wind[idxa:398789+1].squeeze()
ti = time[idxa:398789+1].squeeze()
ui = np.concatenate((ui, u_wind1))
vi = np.concatenate((vi, v_wind1))
ti = np.concatenate((ti, time1))