def test_thread_set(self):
omp_set_num_threads(4)
max_threads = omp_get_max_threads()
self.assertEqual(max_threads, 4)
num_threads = omp_get_num_threads()
self.assertEqual(num_threads, 1) # Always 1 outside of parallel region
thread_num = omp_get_thread_num()
self.assertEqual(thread_num, 0) # Always 0 outside of parallel region
num_procs = omp_get_num_procs()
in_parallel = omp_in_parallel()
self.assertFalse(in_parallel) # Always False outside of parallel region
limit = omp_get_thread_limit()
def main():
# Get files from command line or take hard-coded folder.
# Arguements are optional, but if one is specified, they all should be
# vertical_profile_plots.py [input_pattern] [output_directory] [num_threads]
# you can use a wildcard pattern:
# i.e., python vertical_profile_plots.py ../output_files/wrfout* ../plots/ 8
# or you can list the input files:
# i.e., python vertical_profile_plots.py ../output_files/wrfout_d01 ../output_files/wrfout_d02 ../plots/ 1
if len(sys.argv) > 1:
filenames = sys.argv[1:-2] #glob.glob(sys.argv[1])
print(filenames)
output_dir = sys.argv[-2]
wrf.omp_set_num_threads(int(sys.argv[-1]))
else:
filenames = glob.glob(
"/project/ssmith_uksr/WRF_ARW/cases/eclipse_2017/eclipse_model_on_5_dom_out/wrfout_d01*"
)
output_dir = ''
# Get data from published sensor data
ws_workbook = xlrd.open_workbook(
'/project/ssmith_uksr/WRF_ARW/DATA/2017_eclipse_observed/Weather_Station_data.xlsx'
)
ws_first_sheet = ws_workbook.sheet_by_index(0)
tower_workbook = xlrd.open_workbook(
'/project/ssmith_uksr/WRF_ARW/DATA/2017_eclipse_observed/Tower_data.xlsx'
)
tower_first_sheet = tower_workbook.sheet_by_index(0)
soil_workbook = xlrd.open_workbook(
'/project/ssmith_uksr/WRF_ARW/DATA/2017_eclipse_observed/Soil_data.xlsx'
)
soil_first_sheet = soil_workbook.sheet_by_index(0)
time_ws, temp_ws, rad_ws, wspd_ws, wdir_ws = get_observed_series(
ws_first_sheet)
time_tower, temp_tower, _, wspd_tower, wdir_tower = get_observed_series(
tower_first_sheet)
time_soil, temp_soil, _, _, _ = get_observed_series(soil_first_sheet)
# Coordinates to take sample from
center_lat = 36.797326
center_lon = -86.812341
for filename in sorted(filenames):
print(filename)
#Structure the WRF output file
ncfile = Dataset(filename)
#Extract data from WRF output files
tc = wrf.getvar(ncfile, "tc",
wrf.ALL_TIMES) # Atmospheric temperature in celsius
t2 = wrf.getvar(ncfile, "T2",
wrf.ALL_TIMES) # Temperature at 2 m, in Kelvin
# Convert T2 to degrees C
t2 = t2 - 273.15
t2.attrs["units"] = "degC"
theta = wrf.getvar(ncfile, "theta", wrf.ALL_TIMES, units="degC")
rh = wrf.getvar(ncfile, "rh", wrf.ALL_TIMES)
wspd_wdir = wrf.getvar(ncfile, "uvmet_wspd_wdir", wrf.ALL_TIMES)
# Split wind speed and direction
wspd = wspd_wdir[0, :, :, :, :]
wdir = wspd_wdir[1, :, :, :, :]
# These variables aren't included in getvar, so have to be extracted manually
swdown = wrf.extract_vars(ncfile, wrf.ALL_TIMES,
"SWDOWN").get('SWDOWN')
gnd_flx = wrf.extract_vars(ncfile, wrf.ALL_TIMES,
"GRDFLX").get('GRDFLX')
#Create Dictionary to associate quanitity names with the corresponding data
two_dim_vars = {'swdown': swdown, 'gnd_flx': gnd_flx, 'T2': t2}
three_dim_vars = {'tc': tc, 'theta': theta, 'rh': rh, 'wspd': wspd}
#Get the grid coordinates from our earth lat/long coordinates
center_x, center_y = wrf.ll_to_xy(ncfile, center_lat, center_lon)
# Plot all 3D variables over time
for var_name, var_data in three_dim_vars.items():
# Convert to Local Time
var_data = to_local_time(var_data)
# Get data frequency
freq = pd.Timedelta(var_data["Time"][1].values -
var_data["Time"][0].values)
# Interpolate to height above ground level
try:
var_data_agl = wrf.vinterp(ncfile,
var_data,
'ght_agl',
np.linspace(0, 0.1, 100),
field_type=var_name,
timeidx=wrf.ALL_TIMES)
except ValueError:
var_data_agl = wrf.vinterp(ncfile,
var_data,
'ght_agl',
np.linspace(0, 0.1, 100),
field_type='none',
timeidx=wrf.ALL_TIMES)
# Convert height to meters
var_data_agl["interp_level"] = var_data_agl["interp_level"] * 1000
# Time ranges
plot_time_ranges = []
plot_time_range1 = pd.date_range(start="2017-08-21T10:24:00",
end="2017-08-21T14:33:00",
freq=freq)
plot_time_range1 = plot_time_range1.floor(freq)
plot_time_range2 = pd.date_range(start="2017-08-21T13:09:00",
end="2017-08-21T14:33:00",
freq=freq)
plot_time_range2 = plot_time_range2.floor(freq)
plot_time_range3 = pd.date_range(start="2017-08-21T09:45:00",
end="2017-08-21T15:00:00",
freq=freq)
plot_time_range3 = plot_time_range3.floor(freq)
plot_time_ranges = [
plot_time_range1, plot_time_range2, plot_time_range3
]
# Vertical Profile Plots
for plot_time_range in [
rng for rng in plot_time_ranges if len(rng) > 1
]:
fig, ax = plt.subplots()
var_data_agl.isel(
south_north=center_y,
west_east=center_x).sel(Time=plot_time_range,
method="nearest").plot(ax=ax,
x="Time")
save_plot(ax=ax,
title='',
y_label="z (m)",
x_label="Local Time (CDT)",
var_name=var_name,
plot_type_name="vertical_profile",
plot_time_range=plot_time_range,
filename_in=filename,
output_dir=output_dir)
plt.close(fig)
# Line plots
fig, ax = plt.subplots()
if (var_name == 'tc'):
ax.plot(time_ws,
temp_ws,
'^k-',
label='2.5m',
markevery=500)
ax.plot(time_soil,
temp_soil,
'vb-',
label='-0.02m',
markevery=500)
if (var_name == 'wspd'):
y_label = "wind speed" + " (" + var_data.attrs[
"units"] + ")"
wspd_ws_rolling = pd.DataFrame(wspd_ws).rolling(
120).mean().values
wspd_tower_rolling = pd.DataFrame(wspd_tower).rolling(
120).mean().values
ax.plot(time_ws,
wspd_ws_rolling,
'c-',
label='3 m, 2 min avg',
linewidth=0.5)
ax.plot(time_tower,
wspd_tower_rolling,
'k-',
label='7 m, 2 min avg',
linewidth=0.5,
zorder=0)
var_data.isel(bottom_top=0,
south_north=center_y,
west_east=center_x).sel(Time=plot_time_range,
method="nearest").plot(
ax=ax,
x="Time",
label="WRF-Eclipse",
color="orange")
y_label = var_data.name + " (" + var_data.attrs["units"] + ")"
save_plot(ax=ax,
title='',
y_label=y_label,
x_label="Local Time (CDT)",
var_name=var_name,
plot_type_name="line_plot",
plot_time_range=plot_time_ranges[2],
filename_in=filename,
output_dir=output_dir)
plt.close(fig)
# Plot 2D values
for var_name, var_data in two_dim_vars.items():
# Convert to Local Time
var_data = to_local_time(var_data)
# Line plots
fig, ax = plt.subplots()
y_label = var_data.name + " (" + var_data.attrs["units"] + ")"
if (var_name == 'swdown'):
ax.plot(time_ws,
rad_ws,
'or-',
label='measured',
linewidth=0.5,
markevery=500)
y_label = "solar radiation" + " (" + var_data.attrs[
"units"] + ")"
if (var_name == 'T2'):
ax.plot(time_ws, temp_ws, '^k-', label='2.5m', markevery=500)
ax.plot(time_soil,
temp_soil,
'vb-',
label='-0.02m',
markevery=500)
y_label = "temperature" + " (" + var_data.attrs["units"] + ")"
var_data.isel(south_north=center_y,
west_east=center_x).sel(Time=plot_time_range,
method="nearest").plot(
ax=ax,
x="Time",
label="WRF-Eclipse",
color="orange")
save_plot(ax=ax,
title='',
y_label=y_label,
x_label="Local Time (CDT)",
var_name=var_name,
plot_type_name="line_plot",
plot_time_range=plot_time_range,
filename_in=filename,
output_dir=output_dir)
class WRFVarsTest(ut.TestCase):
longMessage = True
class WRFInterpTest(ut.TestCase):
longMessage = True
class WRFLatLonTest(ut.TestCase):
longMessage = True
if __name__ == "__main__":
from wrf import (omp_set_num_threads, omp_set_schedule, omp_get_schedule,
omp_set_dynamic, omp_get_num_procs, OMP_SCHED_STATIC)
omp_set_num_threads(omp_get_num_procs() // 2)
omp_set_schedule(OMP_SCHED_STATIC, 0)
omp_set_dynamic(False)
ignore_vars = [] # Not testable yet
wrf_vars = [
"avo", "eth", "cape_2d", "cape_3d", "ctt", "dbz", "mdbz", "geopt",
"helicity", "lat", "lon", "omg", "p", "pressure", "pvo", "pw", "rh2",
"rh", "slp", "ter", "td2", "td", "tc", "theta", "tk", "tv", "twb",
"updraft_helicity", "ua", "va", "wa", "uvmet10", "uvmet", "z", "cfrac",
"zstag", "geopt_stag", "height_agl", "wspd_wdir", "wspd_wdir10",
"uvmet_wspd_wdir", "uvmet10_wspd_wdir"
]
interp_methods = ["interplevel", "vertcross", "interpline", "vinterp"]
latlon_tests = ["xy", "ll"]
class WRFVarsTest(ut.TestCase):
longMessage = True
class WRFInterpTest(ut.TestCase):
longMessage = True
class WRFLatLonTest(ut.TestCase):
longMessage = True
if __name__ == "__main__":
from wrf import (omp_set_num_threads, omp_set_schedule, omp_get_schedule,
omp_set_dynamic, omp_get_num_procs, OMP_SCHED_STATIC)
omp_set_num_threads(omp_get_num_procs() - 1)
omp_set_schedule(OMP_SCHED_STATIC, 0)
omp_set_dynamic(False)
ignore_vars = [] # Not testable yet
wrf_vars = [
"avo", "eth", "cape_2d", "cape_3d", "ctt", "dbz", "mdbz", "geopt",
"helicity", "lat", "lon", "omg", "p", "pressure", "pvo", "pw", "rh2",
"rh", "slp", "ter", "td2", "td", "tc", "theta", "tk", "tv", "twb",
"updraft_helicity", "ua", "va", "wa", "uvmet10", "uvmet", "z", "cfrac",
"zstag", "geopt_stag"
]
interp_methods = ["interplevel", "vertcross", "interpline", "vinterp"]
latlon_tests = ["xy_out", "ll_out"]
for nc_lib in ("netcdf4", "pynio", "scipy"):
