def main():
"""In the main function we basically read the files and prepare the variables to be plotted.
This is not included in utils.py as it can change from case to case."""
dset = read_dataset(variables=['T', 'FI'], level=[5000],
projection=projection, remapped=True)
levels_temp = np.arange(-86, -18, 1)
levels_gph = np.arange(19700., 20500., 250.)
cmap = get_colormap('temp_meteociel')
_ = plt.figure(figsize=(figsize_x, figsize_y))
ax = plt.gca()
_, x, y, mask = get_projection(dset, projection, remapped=True)
# Subset dataset only on the area
dset = dset.where(mask, drop=True)
# and then compute what we need
dset = compute_geopot_height(dset, zvar='z')
dset = dset.drop(['z', 'lon', 'lat']).squeeze().load()
# All the arguments that need to be passed to the plotting function
args=dict(x=x, y=y, ax=ax,
levels_temp=levels_temp, cmap=cmap,
levels_gph=levels_gph)
print_message('Pre-processing finished, launching plotting scripts')
if debug:
plot_files(dset.isel(time=slice(-2, -1)), **args)
else:
# Parallelize the plotting by dividing into chunks and processes
dss = chunks_dataset(dset, chunks_size)
plot_files_param = partial(plot_files, **args)
p = Pool(processes)
p.map(plot_files_param, dss)
def main():
"""In the main function we basically read the files and prepare the variables to be plotted.
This is not included in utils.py as it can change from case to case."""
dset = read_dataset(variables=['t', 'fi'],
level=[l * 100 for l in levels],
projection=projection)
dset = compute_geopot_height(dset)
cmap = get_colormap('temp')
for level in levels:
dset_level = dset.sel(plev=level * 100., method='nearest')
dset_level.t.metpy.convert_units('degC')
levels_gph = np.arange(
np.nanmin(dset_level.geop).astype("int"),
np.nanmax(dset_level.geop).astype("int"), 25.)
levels_temp = np.arange(
np.nanmin(dset_level.t).astype("int"),
np.nanmax(dset_level.t).astype("int"), 1.)
_ = plt.figure(figsize=(figsize_x, figsize_y))
ax = plt.gca()
# Get coordinates from dataset
m, x, y = get_projection(dset_level, projection, labels=True)
dset_level = dset_level.drop(['lon', 'lat', 'z']).load()
# All the arguments that need to be passed to the plotting function
args = dict(x=x,
y=y,
ax=ax,
cmap=cmap,
level=level,
levels_temp=levels_temp,
levels_gph=levels_gph,
time=dset_level.time,
projection=projection)
print_message('Pre-processing finished, launching plotting scripts')
if debug:
plot_files(dset_level.isel(time=slice(0, 2)), **args)
else:
# Parallelize the plotting by dividing into chunks and processes
dss = chunks_dataset(dset_level, chunks_size)
plot_files_param = partial(plot_files, **args)
p = Pool(processes)
p.map(plot_files_param, dss)
def main():
"""In the main function we basically read the files and prepare the variables to be plotted.
This is not included in utils.py as it can change from case to case."""
dset = read_dataset(variables=['U', 'V', 'FI'],
level=30000,
projection=projection,
remapped=True)
# Select 850 hPa level using metpy
levels_wind = np.arange(80., 300., 10.)
levels_gph = np.arange(8200., 9700., 100.)
cmap = truncate_colormap(plt.get_cmap('CMRmap_r'), 0., 0.9)
_ = plt.figure(figsize=(figsize_x, figsize_y))
ax = plt.gca()
m, x, y, mask = get_projection(dset, projection, remapped=True)
m.fillcontinents(color='lightgray', lake_color='whitesmoke', zorder=0)
# Subset dataset only on the area
dset = dset.where(mask, drop=True)
dset = compute_wind_speed(dset)
dset = compute_geopot_height(dset)
dset = dset.drop(['z', 'u', 'v']).load()
# All the arguments that need to be passed to the plotting function
args = dict(x=x,
y=y,
ax=ax,
levels_wind=levels_wind,
levels_gph=levels_gph,
time=dset.time,
cmap=cmap)
print_message(sys.argv[0] +
': Pre-processing finished, launching plotting scripts')
if debug:
plot_files(dset.isel(time=slice(0, 2)), **args)
else:
# Parallelize the plotting by dividing into chunks and processes
dss = chunks_dataset(dset, chunks_size)
plot_files_param = partial(plot_files, **args)
p = Pool(processes)
p.map(plot_files_param, dss)
def main():
"""In the main function we basically read the files and prepare the variables to be plotted.
This is not included in utils.py as it can change from case to case."""
dset = read_dataset(variables=['t', 'fi'],
level=[50000, 85000],
projection=projection)
dset = compute_geopot_height(dset, zvar='z', level=50000)
dset = dset.sel(plev=50000, method='nearest')
levels_temp = np.arange(-58, 12, 2)
levels_gph = np.arange(4700., 6000., 70.)
cmap = get_colormap('temp_meteociel')
_ = plt.figure(figsize=(figsize_x, figsize_y))
ax = plt.gca()
# Get coordinates from dataset
m, x, y = get_projection(dset, projection, labels=True)
dset = dset.drop(['lon', 'lat', 'z']).load()
# All the arguments that need to be passed to the plotting function
args = dict(x=x,
y=y,
ax=ax,
cmap=cmap,
levels_temp=levels_temp,
levels_gph=levels_gph,
time=dset.time)
print_message('Pre-processing finished, launching plotting scripts')
if debug:
plot_files(dset.isel(time=slice(0, 2)), **args)
else:
# Parallelize the plotting by dividing into chunks and processes
dss = chunks_dataset(dset, chunks_size)
plot_files_param = partial(plot_files, **args)
p = Pool(processes)
p.map(plot_files_param, dss)
def main():
"""In the main function we basically read the files and prepare the variables to be plotted.
This is not included in utils.py as it can change from case to case."""
dset = read_dataset(variables=['fi', 'pmsl'], level=[50000],
projection=projection)
dset = compute_geopot_height(dset, zvar='z', level=50000)
dset['prmsl'].metpy.convert_units('hPa')
levels_gph = np.arange(5000., 6000., 40.)
cmap = get_colormap('gph')
#cmap = truncate_colormap(cmap, 0.05, 0.9)
_ = plt.figure(figsize=(figsize_x, figsize_y))
ax = plt.gca()
# Get coordinates from dataset
m, x, y = get_projection(dset, projection, labels=True)
dset = dset.drop(['lon', 'lat', 'z']).load()
levels_mslp = np.arange(dset['prmsl'].min().astype("int"),
dset['prmsl'].max().astype("int"), 4.)
# All the arguments that need to be passed to the plotting function
args = dict(x=x, y=y, ax=ax, cmap=cmap,
levels_gph=levels_gph,
levels_mslp=levels_mslp)
print_message('Pre-processing finished, launching plotting scripts')
if debug:
plot_files(dset.isel(time=slice(0, 2)), **args)
else:
# Parallelize the plotting by dividing into chunks and processes
dss = chunks_dataset(dset, chunks_size)
plot_files_param = partial(plot_files, **args)
p = Pool(processes)
p.map(plot_files_param, dss)