if __name__ == '__main__':
# Save directory
SAVE = "./"
# Date range for data
start = datetime(2016, 6, 15, 6, 0)
end = datetime(2016, 6, 16, 6, 0)
# MesoWest station ID
stn = 'MTMET'
# Get MesoWest Data
a = MW.get_mesowest_ts(stn,
start,
end,
variables='wind_direction,PM_25_concentration')
# Get data we want from the dictionary
wd = a['wind_direction']
ws = a['PM_25_concentration']
rose_with_labels()
rose_no_labels()
clock_rose()
"""
SOME NOTES:
to look at the values used to create the plot look
at ax._info
ax._info['table'] contains the frequency for each bin in each direction
np.sum(ax._info['table'],axis=0) is the total frequency for each direction
#ax.set_rmax(5) # uncomment to unify all rmax
ax.set_rmax(np.max(np.sum(ax._info['table'],
axis=0))) # set rmax as the biggest arm
plt.savefig("wind_hi_clock.png", bbox_inches="tight", dpi=500)
#plt.show()
#print np.sum(ax._info['table'],axis=0)
if __name__ == '__main__':
##All in-situ stations
start = datetime(2014, 11, 1, 0, 0)
end = datetime(2016, 2, 28, 0, 0)
a = MW.get_mesowest_ts('ukbkb', start, end)
# The idea is we are creating an ozone rose from a wind rose
# wd is the wind direction
# ws is typically the wind speed, but in this case it is ozone
wd = a['wind direction']
ws = a['wind speed']
rose_with_labels()
rose_no_labels()
clock_rose()
"""
SOME NOTES:
to look at the values used to create the plot look
at ax._info
ax._info['table'] contains the frequency for each bin in each direction
plt.savefig("hi_clock.png", bbox_inches="tight", dpi=500)
#plt.show()
print np.sum(ax._info['table'], axis=0)
if __name__ == '__main__':
##All in-situ stations
stations = [
'bgrut', 'qbv', 'qbr', 'fwp', 'o3s08', 'gslm', 'qhv', 'qhw', 'qh3',
'lms', 'ql4', 'naa', 'qnp', 'qo2', 'qsa', 'qsf', 'mtmet'
]
start = datetime(2015, 6, 1, 0, 0)
end = datetime(2015, 6, 30, 0, 0)
a = MW.get_mesowest_ts(stations[-1], start, end)
# The idea is we are creating an ozone rose from a wind rose
# wd is the wind direction
# ws is typically the wind speed, but in this case it is ozone
wd = a['wind direction']
ws = a['ozone']
rose_with_labels()
rose_no_labels()
clock_rose()
"""
SOME NOTES:
to look at the values used to create the plot look
at ax._info
ax._info['table'] contains the frequency for each bin in each direction
dtype=None,
delimiter=',')
HRRR_dates = HRRR[stn]
HRRR_DATES = np.array([])
for j in HRRR_dates:
converted_time = datetime.strptime(j, '%Y-%m-%d %H:%M')
HRRR_DATES = np.append(HRRR_DATES, converted_time)
HRRR_temp = HRRR['temp']
HRRR_dwpt = HRRR['dwpt']
HRRR_u = HRRR['u']
HRRR_v = HRRR['v']
HRRR_speed = HRRR['speed']
# Get the MesoWest Observations for the same time period
a = MesoWest_timeseries.get_mesowest_ts(stn, HRRR_DATES[0], HRRR_DATES[-1])
MW_DATES = a['datetimes']
MW_temp = a['temperature']
MW_dwpt = a['dew point']
MW_u, MW_v = wind_calcs.wind_spddir_to_uv(a['wind speed'],
a['wind direction'])
MW_speed = a['wind speed']
MW_dir = a['wind direction']
# Plot the comparison
fig = plt.figure(1)
plt.clf()
plt.cla()
ax = fig.add_subplot(111)
ax.plot(HRRR_DATES, HRRR_temp, color='k', lw=1)
ax.plot(MW_DATES, MW_temp, lw=.5, color='r')
def plot_ts(stations):
for stn in stations:
# Get the HRRR file
HRRR = np.genfromtxt('./2016/Anal_' + stn + '.csv',
names=True,
dtype=None,
delimiter=',')
HRRR_dates = HRRR[stn]
HRRR_DATES = np.array([])
for j in HRRR_dates:
converted_time = datetime.strptime(j, '%Y-%m-%d %H:%M')
HRRR_DATES = np.append(HRRR_DATES, converted_time)
HRRR_temp = HRRR['temp']
HRRR_dwpt = HRRR['dwpt']
HRRR_u = HRRR['u']
HRRR_v = HRRR['v']
HRRR_speed = HRRR['speed']
# Get the MesoWest Observations for the same time period
a = MesoWest_timeseries.get_mesowest_ts(stn, HRRR_DATES[0],
HRRR_DATES[-1])
MW_DATES = a['datetimes']
MW_temp = a['temperature']
MW_dwpt = a['dew point']
MW_u, MW_v = wind_calcs.wind_spddir_to_uv(a['wind speed'],
a['wind direction'])
MW_speed = a['wind speed']
MW_dir = a['wind direction']
# Plot the comparison
fig = plt.figure(1)
plt.clf()
plt.cla()
ax = fig.add_subplot(111)
ax.plot(HRRR_DATES, HRRR_temp, color='k', lw=1)
ax.plot(MW_DATES, MW_temp, lw=.5, color='r')
plt.title(stn)
plt.ylabel('Temperature (c)')
##Format Ticks##
##----------------------------------
from matplotlib.dates import DateFormatter, YearLocator, MonthLocator, DayLocator, HourLocator
# Find months
months = MonthLocator()
# Find days
days = DayLocator(bymonthday=[1, 15])
# Find each 0 and 12 hours
hours = HourLocator(byhour=[0, 6, 12, 18])
# Find all hours
hours_each = HourLocator()
# Tick label format style
#dateFmt = DateFormatter('%b %d, %Y\n%H:%M')
dateFmt = DateFormatter('%b %d\n%Y')
# Set the x-axis major tick marks
ax.xaxis.set_major_locator(days)
# Set the x-axis labels
ax.xaxis.set_major_formatter(dateFmt)
# For additional, unlabeled ticks, set x-axis minor axis
#ax.xaxis.set_minor_locator(hours)
plt.savefig('./fig/' + stn + '_temp.png')
# Plot the comparison
fig = plt.figure(2)
plt.clf()
plt.cla()
ax = fig.add_subplot(111)
ax.plot(HRRR_DATES, HRRR_dwpt, color='k', lw=1)
ax.plot(MW_DATES, MW_dwpt, lw=.5, color='g')
plt.title(stn)
plt.ylabel('Dew Point (c)')
##Format Ticks##
##----------------------------------
from matplotlib.dates import DateFormatter, YearLocator, MonthLocator, DayLocator, HourLocator
# Find months
months = MonthLocator()
# Find days
days = DayLocator(bymonthday=[1, 15])
# Find each 0 and 12 hours
hours = HourLocator(byhour=[0, 6, 12, 18])
# Find all hours
hours_each = HourLocator()
# Tick label format style
#dateFmt = DateFormatter('%b %d, %Y\n%H:%M')
dateFmt = DateFormatter('%b %d\n%Y')
# Set the x-axis major tick marks
ax.xaxis.set_major_locator(days)
# Set the x-axis labels
ax.xaxis.set_major_formatter(dateFmt)
# For additional, unlabeled ticks, set x-axis minor axis
#ax.xaxis.set_minor_locator(hours)
plt.savefig('./fig/' + stn + '_dwpt.png')
# Plot the comparison
fig = plt.figure(3)
plt.clf()
plt.cla()
ax = fig.add_subplot(111)
ax.plot(HRRR_DATES, HRRR_speed, color='k', lw=1)
ax.plot(MW_DATES, MW_speed, lw=.5, color='b')
plt.title(stn)
plt.ylabel('Wind Speed (m/s)')
##Format Ticks##
##----------------------------------
from matplotlib.dates import DateFormatter, YearLocator, MonthLocator, DayLocator, HourLocator
# Find months
months = MonthLocator()
# Find days
days = DayLocator(bymonthday=[1, 15])
# Find each 0 and 12 hours
hours = HourLocator(byhour=[0, 6, 12, 18])
# Find all hours
hours_each = HourLocator()
# Tick label format style
#dateFmt = DateFormatter('%b %d, %Y\n%H:%M')
dateFmt = DateFormatter('%b %d\n%Y')
# Set the x-axis major tick marks
ax.xaxis.set_major_locator(days)
# Set the x-axis labels
ax.xaxis.set_major_formatter(dateFmt)
# For additional, unlabeled ticks, set x-axis minor axis
#ax.xaxis.set_minor_locator(hours)
plt.savefig('./fig/' + stn + '_wspeed.png')
ax.set_rmax(np.max(np.sum(ax._info['table'],axis=0))) # set rmax as the biggest arm
plt.savefig("wind_hi_clock.png",bbox_inches="tight",dpi=500)
#plt.show()
#print np.sum(ax._info['table'],axis=0)
if __name__ == '__main__':
##All in-situ stations
start = datetime(2014,11,1,0,0)
end = datetime(2016,2,28,0,0)
a = MW.get_mesowest_ts('ukbkb',start,end)
# The idea is we are creating an ozone rose from a wind rose
# wd is the wind direction
# ws is typically the wind speed, but in this case it is ozone
wd = a['wind direction']
ws = a['wind speed']
rose_with_labels()
rose_no_labels()
clock_rose()
"""
plt.savefig("hi_clock.png",bbox_inches="tight",dpi=500)
#plt.show()
print np.sum(ax._info['table'],axis=0)
if __name__ == '__main__':
##All in-situ stations
stations = ['bgrut','qbv','qbr','fwp','o3s08','gslm','qhv','qhw','qh3',
'lms','ql4','naa','qnp','qo2','qsa','qsf','mtmet']
start = datetime(2015,6,1,0,0)
end = datetime(2015,6,30,0,0)
a = MW.get_mesowest_ts(stations[-1],start,end)
# The idea is we are creating an ozone rose from a wind rose
# wd is the wind direction
# ws is typically the wind speed, but in this case it is ozone
wd = a['wind direction']
ws = a['ozone']
rose_with_labels()
rose_no_labels()
clock_rose()
"""
if __name__ == '__main__':
# Save directory
SAVE = './'
# Date range for data
start = datetime(2016, 6, 15, 6, 0)
end = datetime(2016, 6, 16, 6, 0)
# MesoWest station ID
stn = 'MTMET'
# Get MesoWest Data
a = MW.get_mesowest_ts(stn, start, end)
# Get data we want from the dictionary
wd = a['wind_direction']
ws = a['wind_speed']
rose_with_labels()
rose_no_labels()
clock_rose()
"""
SOME NOTES:
to look at the values used to create the plot look
at ax._info
ax._info['table'] contains the frequency for each bin in each direction
np.sum(ax._info['table'],axis=0) is the total frequency for each direction
np.max(np.sum(ax._info['table'],axis=0)) is the maximum frequency I like
