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42 KiB
1.0 Basic Variable Extraction¶
In [ ]:
from __future__ import (absolute_import, division, print_function, unicode_literals)
from wrf import getvar
from netCDF4 import Dataset as nc
#ncfile = nc("/Users/ladwig/Documents/wrf_files/wrfout_d01_2016-02-25_18_00_00")
ncfile = nc("/Users/ladwig/Documents/wrf_files/wrfout_d01_2016-10-07_00_00_00")
In [ ]:
p = getvar(ncfile, "P")
print(p)
1.0.1 DataArray attributes: 'dims', 'coords', 'attrs'¶
In [ ]:
print("dims: ", p.dims)
print("coords: ", p.coords)
print("attrs: ", p.attrs)
del p
1.0.2 Removing implicit 'squeeze' behavior to preserve single sized dimensions¶
In [ ]:
p_nosqueeze = getvar(ncfile, "P", timeidx=0, squeeze=False)
print (p_nosqueeze)
1.0.3 Single element metadata¶
In [ ]:
print (p_nosqueeze[0,0,100,200])
del p_nosqueeze
1.0.4 Disabling/Enabling xarray¶
In [ ]:
from wrf import disable_xarray, enable_xarray
# Disable xarray completely
disable_xarray()
p_no_meta = getvar(ncfile, "P")
print(type(p_no_meta))
print (p_no_meta)
del p_no_meta
enable_xarray()
# Disable on extraction
p_no_meta = getvar(ncfile, "P", meta=False)
print("\n")
print(type(p_no_meta))
print(p_no_meta)
del p_no_meta
2.0 Sequences of Input Files¶
2.0.1 Combining via the 'cat' method¶
In [ ]:
from wrf import ALL_TIMES
import numpy as np
wrflist = [ncfile, ncfile, ncfile]
p_cat = getvar(wrflist, "P", timeidx=ALL_TIMES, method="cat")
print(p_cat)
del p_cat
2.0.2 Combining via the 'join' method¶
In [ ]:
p_join = getvar(wrflist, "P", timeidx=ALL_TIMES, method="join")
print(p_join)
Note how the Time dimension was replaced with the file dimension, due to the 'squeezing' of the Time dimension.
To maintain the Time dimension, set squeeze to False.
In [ ]:
from wrf import ALL_TIMES
p_join = getvar(wrflist, "P", timeidx=ALL_TIMES, method="join", squeeze=False)
print(p_join)
del p_join
2.0.3 Dictionary Sequences¶
In [ ]:
wrf_dict = {"label1" : [ncfile, ncfile],
"label2" : [ncfile, ncfile]}
p_dict = getvar(wrf_dict, "P", timeidx=ALL_TIMES)
print(p_dict)
del p_dict
2.0.4 Generator Sequences¶
In [ ]:
def gen_seq():
wrfseq = [ncfile, ncfile, ncfile]
for wrf in wrfseq:
yield wrf
p_gen = getvar(gen_seq(), "P", method="join")
print(p_gen)
del p_gen
2.0.5 Custom Iterable Classes¶
In [ ]:
class FileGen(object):
def __init__(self, ncfile, count=3):
self._total = count
self._i = 0
self.ncfile = [ncfile]*count
def __iter__(self):
return self
def next(self):
if self._i >= self._total:
raise StopIteration
else:
val = self.ncfile[self._i]
self._i += 1
return val
# Python 3
def __next__(self):
return self.next()
obj_gen = FileGen(ncfile, 3)
p_obj_gen = getvar(obj_gen, "P", method="join", squeeze=False)
print(p_obj_gen)
del p_obj_gen
In [ ]:
import tempfile
import glob
import shutil
import os
from netCDF4 import Dataset
from wrf import getvar, ll_to_xy, CoordPair, GeoBounds
class FileReduce(object):
def __init__(self, filenames, geobounds, tempdir=None, delete=True, reuse=True):
"""An iterable object for cutting out geographic domains.
Args:
filenames (sequence): A sequence of full paths to the WRF files
geobounds (GeoBounds): A GeoBounds object defining the region of interest
tempdir (str): The location to store the temporary cropped data files. If None, tempfile.mkdtemp is used.
delete (bool): Set to True to delete the cropped files when FileReduce is garbage collected.
reuse (bool): Set to True when you want to resuse the files that were previously converted. *tempdir*
must be set to a specific directory that contains the converted files.
"""
self._filenames = filenames
self._i = 0
self._geobounds = geobounds
self._delete = delete
self._cache = set()
self._own_data = True
self._reuse = reuse
if tempdir is not None:
if not os.path.exists(tempdir):
os.makedirs(tempdir)
self._tempdir = tempdir
if self._reuse:
self._cache = set((os.path.join(self._tempdir, name)
for name in os.listdir(self._tempdir)))
else:
self._tempdir = tempfile.mkdtemp()
print ("temporary directory is: {}".format(self._tempdir))
self._prev = None
self._set_extents()
def _set_extents(self):
fname = list(self._filenames)[0]
with Dataset(fname) as ncfile:
lons = [self._geobounds.bottom_left.lon, self._geobounds.top_right.lon]
lats = [self._geobounds.bottom_left.lat, self._geobounds.top_right.lat]
orig_west_east = len(ncfile.dimensions["west_east"])
orig_south_north = len(ncfile.dimensions["south_north"])
# Note: Not handling the moving nest here
# Extra points included around the boundaries to ensure domain is fully included
x_y = ll_to_xy(ncfile, lats, lons, meta=False)
self._start_x = 0 if x_y[0,0] == 0 else x_y[0,0] - 1
self._end_x = orig_west_east - 1 if x_y[0,1] >= orig_west_east - 1 else x_y[0,1] + 1
self._start_y = 0 if x_y[1,0] == 0 else x_y[1,0] - 1
self._end_y = orig_south_north if x_y[1,1] >= orig_south_north - 1 else x_y[1,1] + 1
self._west_east = self._end_x - self._start_x + 1
self._west_east_stag = self._west_east + 1
self._south_north = self._end_y - self._start_y + 1
self._south_north_stag = self._south_north + 1
def __iter__(self):
return self
def __copy__(self):
cp = type(self).__new__(self.__class__)
cp.__dict__.update(self.__dict__)
cp._own_data = False
cp._delete = False
return cp
def __del__(self):
if self._delete:
shutil.rmtree(self._tempdir)
def reduce(self, fname):
outfilename = os.path.join(self._tempdir, "reduced_" + os.path.basename(fname))
# WRF-Python can iterate over sequences several times during a 'getvar', so a cache is used to
if outfilename in self._cache:
return Dataset(outfilename)
# New dimension sizes
dim_d = {"west_east" : self._west_east,
"west_east_stag" : self._west_east_stag,
"south_north" : self._south_north,
"south_north_stag" : self._south_north_stag
}
# Data slice sizes for the 2D dimensions
slice_d = {"west_east" : slice(self._start_x, self._end_x + 1),
"west_east_stag" : slice(self._start_x, self._end_x + 2),
"south_north" : slice(self._start_y, self._end_y + 1),
"south_north_stag" : slice(self._start_y, self._end_y + 2)
}
with Dataset(fname) as infile, Dataset(outfilename, mode="w") as outfile:
print ("reduce getting called!")
# Copy the global attributes
outfile.setncatts(infile.__dict__)
# Copy Dimensions, limiting south_north and west_east to desired domain
for name, dimension in infile.dimensions.items():
dimsize = dim_d.get(name, len(dimension))
outfile.createDimension(name, dimsize)
# Copy Variables
for name, variable in infile.variables.iteritems():
new_slices = tuple((slice_d.get(dimname, slice(None)) for dimname in variable.dimensions))
outvar = outfile.createVariable(name, variable.datatype, variable.dimensions)
outvar[:] = variable[new_slices]
outvar.setncatts(variable.__dict__)
result = Dataset(outfilename)
self._cache.add(outfilename)
return result
def next(self):
if self._i >= len(self._filenames):
if self._prev is not None:
self._prev.close()
raise StopIteration
else:
fname = self._filenames[self._i]
reduced_file = self.reduce(fname)
if self._prev is not None:
self._prev.close()
self._prev = reduced_file
self._i += 1
return reduced_file
# Python 3
def __next__(self):
return self.next()
ll = CoordPair(lat=24.0, lon=-87.)
ur = CoordPair(lat=27.0, lon=-84)
bounds = GeoBounds(ll, ur)
reduced_files = FileReduce(glob.glob("/Users/ladwig/Documents/wrf_files/wrf_vortex_multi/moving_nest/wrfout_d02*"),
bounds, tempdir="/Users/ladwig/mytemp", delete=False, reuse=True)
slp = getvar(reduced_files, "slp")
print(slp)
del (reduced_files)
3.0 WRF Variable Computational Routines¶
In [ ]:
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", "ctt", "wspd_wdir", "wspd_wdir10",
"uvmet_wspd_wdir", "uvmet10_wspd_wdir"]
#wrf_vars = ["slp"]
vard = {varname: getvar(ncfile, varname, method="cat", squeeze=True) for varname in wrf_vars}
for varname in wrf_vars:
print(vard[varname])
print ("\n")
(Note all of the NaNs in the above routines which produce missing values (e.g. cape_2d). xarray always converts all masked_array missing values to NaN in order to work with pandas. To get back the original missing values in a numpy masked_array, you need to use the 'to_np' method from wrf.)
In [ ]:
from wrf import to_np
masked_ndarray = to_np(vard["slp"])
print(type(masked_ndarray))
del masked_ndarray
In [ ]:
keys = [x for x in vard.keys()]
for key in keys:
del vard[key]
3.1 Interpolation Routines¶
3.1.1 Horizontal Level Interpolation¶
In [ ]:
# 500 MB Heights
from wrf import getvar, interplevel
z = getvar(ncfile, "z")
p = getvar(ncfile, "pressure")
ht_500mb = interplevel(z, p, 500)
print(ht_500mb)
del ht_500mb, z, p
3.1.2 Vertical Cross Section Interpolation¶
In [ ]:
# Pressure using pivot and angle
from wrf import getvar, vertcross, CoordPair
z = getvar(ncfile, "z")
p = getvar(ncfile, "pressure")
pivot_point = CoordPair((z.shape[-1]-1) // 2, (z.shape[-2] - 1) // 2)
angle = 90.0
p_vert = vertcross(p, z, pivot_point=pivot_point, angle=angle, latlon=True)
print(p_vert)
print ("\n")
del p_vert
# Pressure using start_point and end_point
start_point = CoordPair(0, (z.shape[-2]-1) // 2)
end_point = CoordPair(-1, (z.shape[-2]-1) // 2)
p_vert = vertcross(p, z, start_point=start_point, end_point=end_point, latlon=True)
print(p_vert)
del p_vert, p, z
In [ ]:
# Pressure using pivot and angle
from wrf import getvar, vertcross, CoordPair, xy_to_ll
z = getvar(ncfile, "z")
p = getvar(ncfile, "pressure")
lats = getvar(ncfile, "lat")
lons = getvar(ncfile, "lon")
#print ((lats.shape[-2]-1) / 2)
#print ((lats.shape[-1]-1) / 2)
#print (to_np(lats[529, 899]))
#print (to_np(lons[529, 899]))
#print (to_np(lats[529, 0]))
#print (to_np(lons[529, 0]))
#print (to_np(lats[529, -1]))
#print (to_np(lons[529, -1]))
pivot_point = CoordPair(lat=38.5, lon=-97.5)
angle = 90.0
p_vert = vertcross(p, z, wrfin=ncfile, pivot_point=pivot_point, angle=angle, latlon=True)
print (p_vert)
print ("\n")
start_lat = lats[(lats.shape[-2]-1)//2, 0]
end_lat = lats[(lats.shape[-2]-1)//2, -1]
start_lon = lons[(lats.shape[-2]-1)//2, 0]
end_lon = lons[(lats.shape[-2]-1)//2, -1]
print (start_lat)
print (end_lat)
print (start_lon)
print (end_lon)
# Pressure using start_point and end_point
start_point = CoordPair(lat=start_lat, lon=start_lon)
end_point = CoordPair(lat=end_lat, lon=end_lon)
p_vert = vertcross(p, z, wrfin=ncfile, start_point=start_point, end_point=end_point, latlon=True)
print(p_vert)
In [ ]:
# Pressure using pivot and angle
from wrf import getvar, vertcross, CoordPair, xy_to_ll
z = getvar(ncfile, "z")
p = getvar(ncfile, "pressure")
lats = getvar(ncfile, "lat")
lons = getvar(ncfile, "lon")
#print ((lats.shape[-2]-1) / 2)
#print ((lats.shape[-1]-1) / 2)
#print (to_np(lats[529, 899]))
#print (to_np(lons[529, 899]))
#print (to_np(lats[529, 0]))
#print (to_np(lons[529, 0]))
#print (to_np(lats[529, -1]))
#print (to_np(lons[529, -1]))
pivot_point = CoordPair(lat=38.5, lon=-97.5)
angle = 90.0
p_vert = vertcross(p, z, wrfin=ncfile, pivot_point=pivot_point, angle=angle, latlon=True)
print (p_vert)
print ("\n")
start_lat = lats[(lats.shape[-2]-1)//2, 0]
end_lat = lats[(lats.shape[-2]-1)//2, -1]
start_lon = lons[(lats.shape[-2]-1)//2, 0]
end_lon = lons[(lats.shape[-2]-1)//2, -1]
print (start_lat)
print (end_lat)
print (start_lon)
print (end_lon)
# Pressure using start_point and end_point
start_point = CoordPair(lat=start_lat, lon=start_lon)
end_point = CoordPair(lat=end_lat, lon=end_lon)
levels = [1000., 2000., 3000.]
p_vert = vertcross(p, z, wrfin=ncfile, levels=levels, start_point=start_point, end_point=end_point, latlon=True)
print(p_vert)
3.1.3 Interpolate 2D Variable to a Line¶
In [ ]:
# T2 using pivot and angle
from wrf import interpline, getvar, CoordPair
t2 = getvar(ncfile, "T2")
pivot_point = CoordPair((t2.shape[-1]-1)//2, (t2.shape[-2]-1)//2)
angle = 0.0
t2_line = interpline(t2, pivot_point=pivot_point, angle=angle, latlon=True)
print(t2_line, "\n")
del t2_line
# T2 using start_point and end_point
start_point = CoordPair((t2.shape[-1]-1)//2, 0)
end_point = CoordPair((t2.shape[-1]-1)//2, -1)
t2_line = interpline(t2, start_point=start_point, end_point=end_point, latlon=True)
print(t2_line, "\n")
del t2_line
t2 = getvar(ncfile, "T2")
lats = getvar(ncfile, "lat")
lons = getvar(ncfile, "lon")
start_lat = lats[0, (lats.shape[-1]-1)//2]
end_lat = lats[-1, (lats.shape[-1]-1)//2]
start_lon = lons[0, (lons.shape[-1]-1)//2]
end_lon = lons[-1, (lons.shape[-1]-1)//2]
start_point = CoordPair(lat=start_lat, lon=start_lon)
end_point = CoordPair(lat=end_lat, lon=end_lon)
t2_line = interpline(t2, wrfin=ncfile, start_point=start_point, end_point=end_point, latlon=True)
print (t2_line)
del t2_line, t2
3.1.4 Vertical Coordinate Interpolation¶
In [ ]:
from wrf import vinterp, getvar
# Interpolate tk to theta levels
tk = getvar(ncfile, "tk")
interp_levels = [200, 300, 500, 1000]
interp_field = vinterp(ncfile,
field=tk,
vert_coord="theta",
interp_levels=interp_levels,
extrapolate=True,
field_type="tk",
log_p=True)
print(interp_field)
del interp_field
# Interpolate tk to theta-e levels
interp_levels = [200, 300, 500, 1000]
interp_field = vinterp(ncfile,
field=tk,
vert_coord="eth",
interp_levels=interp_levels,
extrapolate=True,
field_type="tk",
log_p=True)
print(interp_field)
del interp_field
# Interpolate tk to geopotential height (MSL) levels
interp_levels = [30, 60, 90]
interp_field = vinterp(ncfile,
field=tk,
vert_coord="ght_msl",
interp_levels=interp_levels,
extrapolate=True,
field_type="tk",
log_p=True)
print(interp_field)
del interp_field
# Interpolate tk to geopotential height (MSL) levels
interp_levels = [30, 60, 90]
interp_field = vinterp(ncfile,
field=tk,
vert_coord="ght_agl",
interp_levels=interp_levels,
extrapolate=True,
field_type="tk",
log_p=True)
print(interp_field)
del interp_field
# Interpolate tk to pressure levels
interp_levels = [850, 500]
interp_field = vinterp(ncfile,
field=tk,
vert_coord="pressure",
interp_levels=interp_levels,
extrapolate=True,
field_type="tk",
log_p=True)
print(interp_field)
del interp_field, tk
3.2 Lat/Lon to X/Y Routines¶
In [ ]:
from wrf import xy_to_ll, ll_to_xy
a = xy_to_ll(ncfile, 400, 200)
a1 = ll_to_xy(ncfile, a[0], a[1])
#print(a)
#print("\n")
#print(a1)
#print("\n")
a = xy_to_ll(ncfile, [400,105], [200,205])
a1 = ll_to_xy(ncfile, a[0,:], a[1,:])
b = ll_to_xy(ncfile, 45.5, -110.8, as_int=True)
# Note: Lists/Dictionaries of files will add a new dimension ('domain') only if the domain is moving
c = xy_to_ll([ncfile, ncfile, ncfile], [400,105], [200,205])
d = xy_to_ll({"label1" : [ncfile, ncfile],
"label2" : [ncfile, ncfile]},
[400,105], [200,205])
print(a)
print("\n")
print(a1)
print("\n")
print(b)
print("\n")
print(c)
print("\n")
print(d)
4.0 Plotting with Cartopy¶
In [ ]:
%matplotlib inline
In [ ]:
# SLP
import matplotlib.pyplot as plt
from matplotlib.cm import get_cmap
import cartopy.crs as crs
from cartopy.feature import NaturalEarthFeature
from wrf import to_np, getvar, smooth2d, get_cartopy, cartopy_xlim, cartopy_ylim, latlon_coords
slp = getvar(ncfile, "slp")
smooth_slp = smooth2d(slp, 3)
lats, lons = latlon_coords(slp)
cart_proj = get_cartopy(slp)
fig = plt.figure(figsize=(10,10))
ax = plt.axes(projection=cart_proj)
states = NaturalEarthFeature(category='cultural', scale='50m', facecolor='none',
name='admin_1_states_provinces_shp')
ax.add_feature(states, linewidth=.5)
ax.coastlines('50m', linewidth=0.8)
# Can only get this to work if I manually transform the lat/lon points to projected space.
xform_coords = cart_proj.transform_points(crs.PlateCarree(), to_np(lons), to_np(lats))
x = xform_coords[:,:,0]
y = xform_coords[:,:,1]
plt.contour(x, y, to_np(smooth_slp), 10, colors="black")
plt.contourf(x, y, to_np(smooth_slp), 10)
plt.colorbar(ax=ax, shrink=.47)
ax.set_xlim(cartopy_xlim(slp))
ax.set_ylim(cartopy_ylim(slp))
ax.gridlines()
In [ ]:
# SLP
from __future__ import (absolute_import, division, print_function, unicode_literals)
from netCDF4 import Dataset
import matplotlib.pyplot as plt
from matplotlib.cm import get_cmap
import cartopy.crs as crs
from cartopy.feature import NaturalEarthFeature
from wrf import to_np, getvar, smooth2d, get_cartopy, cartopy_xlim, cartopy_ylim, latlon_coords, geo_bounds
ncfile = Dataset("/Users/ladwig/Documents/wrf_files/wrfout_d01_2016-10-07_00_00_00")
# Get the sea level pressure
slp = getvar(ncfile, "slp")
# Smooth the sea level pressure since it tends to be noisey near the mountains
smooth_slp = smooth2d(slp, 3)
# Get the numpy array from the XLAT and XLONG coordinates
lats, lons = latlon_coords(slp, as_np=True)
# The cartopy() method returns a cartopy.crs projection object
cart_proj = get_cartopy(slp)
print (cart_proj)
print (get_cartopy(wrfin=ncfile))
bounds = geo_bounds(slp)
print (bounds)
subset = slp[150:250, 150:250]
subset_bounds = geo_bounds(subset)
print (subset_bounds)
file_bounds = geo_bounds(wrfin=ncfile)
print (file_bounds)
# Create a figure that's 10x10
fig = plt.figure(figsize=(10,10))
# Get the GeoAxes set to the projection used by WRF
ax = plt.axes(projection=cart_proj)
# Download and add the states and coastlines
states = NaturalEarthFeature(category='cultural', scale='50m', facecolor='none',
name='admin_1_states_provinces_shp')
ax.add_feature(states, linewidth=.5)
ax.coastlines('50m', linewidth=0.8)
# Make the contour outlines and filled contours for the smoothed sea level pressure.
# The transform keyword indicates that the lats and lons arrays are lat/lon coordinates and tells
# cartopy to transform the points in to grid space.
plt.contour(lons, lats, to_np(smooth_slp), 10, colors="black", transform=crs.PlateCarree())
plt.contourf(lons, lats, to_np(smooth_slp), 10, transform=crs.PlateCarree())
# Add a color bar
plt.colorbar(ax=ax, shrink=.47)
# Set the map limits
ax.set_xlim(cartopy_xlim(slp))
ax.set_ylim(cartopy_ylim(slp))
# Add the gridlines
ax.gridlines()
In [ ]:
# 500 MB Heights and Winds
import matplotlib.pyplot as plt
from matplotlib.cm import get_cmap
import cartopy.crs as crs
from cartopy.feature import NaturalEarthFeature
from wrf import getvar, interplevel, to_np, get_cartopy, cartopy_xlim, cartopy_ylim, latlon_coords
p = getvar(ncfile, "pressure")
z = getvar(ncfile, "z", units="dm")
ua = getvar(ncfile, "ua", units="kts")
va = getvar(ncfile, "va", units="kts")
ht_500 = interplevel(z, p, 500)
u_500 = interplevel(ua, p, 500)
v_500 = interplevel(va, p, 500)
lats, lons = latlon_coords(ht_500)
cart_proj = get_cartopy(slp)
fig = plt.figure(figsize=(20,20))
ax = plt.axes([0.1,0.1,0.8,0.8], projection=cart_proj)
states = NaturalEarthFeature(category='cultural', scale='50m', facecolor='none',
name='admin_1_states_provinces_shp')
ax.add_feature(states, linewidth=0.5)
ax.coastlines('50m', linewidth=0.8)
# Can only get this to work if I manually transform the lat/lon points to projected space.
xform_coords = cart_proj.transform_points(crs.PlateCarree(), to_np(lons), to_np(lats))
x = xform_coords[:,:,0]
y = xform_coords[:,:,1]
plt.contour(x, y, to_np(ht_500), 20, cmap=get_cmap("plasma"))
plt.barbs(x[::50,::50], y[::50,::50], to_np(u_500[::50, ::50]), to_np(v_500[::50, ::50]))
plt.colorbar(ax=ax, shrink=.7)
ax.set_xlim(cartopy_xlim(slp))
ax.set_ylim(cartopy_ylim(slp))
ax.gridlines()
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# Cross-section of pressure using xarray's builtin plotting
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.cm import get_cmap
from wrf import getvar, vertcross, to_np, CoordPair
p = getvar(ncfile, "pressure")
z = getvar(ncfile, "z", units="dm")
pivot_point = CoordPair(z.shape[-1] // 2, z.shape[-2] // 2)
angle = 90.0
p_vert = vertcross(p, z, pivot_point=pivot_point, angle=angle)#, levels=[1000,850,500])
fig = plt.figure(figsize=(20,8))
ax = plt.axes([0.1,0.1,0.8,0.8])
p_vert.plot.contour(ax=ax, levels=[0 + 50*n for n in range(20)], cmap=get_cmap("viridis"))
Multi-time Moving Domain Files¶
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import os
from wrf import getvar, ALL_TIMES
from netCDF4 import Dataset as nc
dir = "/Users/ladwig/Documents/wrf_files/wrf_vortex_multi/moving_nest"
ncfilenames = [os.path.join(dir, x) for x in os.listdir(dir) if x.find("_d02_") > 0]
ncfiles = [nc(x) for x in ncfilenames]
#print (ncfiles[0].variables["XLONG"][0,0,-1], ncfiles[0].variables["XLONG"][-1,0,-1])
#print (ncfiles[1].variables["XLONG"][0,0,-1], ncfiles[1].variables["XLONG"][-1,0,-1])
#print (ncfiles[-1].variables["XLONG"][0,0,-1], ncfiles[-1].variables["XLONG"][-1,0,-1])
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p = getvar(ncfiles, "ctt", timeidx=ALL_TIMES)
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print (p)
#print (p.attrs["projection"].shape)
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print (p.attrs["projection"])
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ncfiles[2].variables["XTIME"][:]
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p = getvar(ncfiles, "P", timeidx=None, method="cat", meta=True, squeeze=True)
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print (p)
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print (type(p.coords["Time"]))
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import datetime
import pandas
print (type(p.coords["Time"].values.astype(datetime.datetime)))
print (repr(datetime.datetime.utcfromtimestamp(p.coords["Time"][0].values.astype(int) * 1E-9)))
print (pandas.to_datetime(p.coords["Time"].values))
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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", "ctt", "cfrac", "uvmet_wspd_wdir",
"uvmet10_wspd_wdir", "wspd_wdir", "wspd_wdir10"]
#wrf_vars = ["cape_2d"]
vard = {}
for varname in wrf_vars:
print (varname)
vard[varname] = getvar(ncfiles, varname, timeidx=None, method="join", squeeze=False)
#vard = {varname: getvar(ncfiles, varname, method="join", squeeze=False) for varname in wrf_vars}
for varname in wrf_vars:
print(vard[varname])
# NOTE: Warnings below are due to "join" and the fill values used since the last file only contains 1 time step.
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import os
from wrf import getvar
from netCDF4 import Dataset as nc
dir = "/Users/ladwig/Documents/wrf_files/wrf_vortex_multi/moving_nest"
ncfilenames = [os.path.join(dir, x) for x in os.listdir(dir) if x.find("_d02_") > 0]
ncfiles = [nc(x) for x in ncfilenames]
# Pressure using pivot and angle
from wrf import getvar, vertcross, CoordPair
timeidx = 0
z = getvar(ncfiles, "z", timeidx, method="join")
p = getvar(ncfiles, "pressure", timeidx, method="join")
pivot_point = CoordPair(z.shape[-1] / 2, z.shape[-2] / 2)
angle = 40.0
p_vert = vertcross(p, z, pivot_point=pivot_point, angle=angle)
print(p_vert)
print ("\n")
del p_vert
# Pressure using start_point and end_point
start_point = CoordPair(0, z.shape[-2]/2)
end_point = CoordPair(-1, z.shape[-2]/2)
p_vert = vertcross(p, z, start_point=start_point, end_point=end_point)
print(p_vert)
del p_vert, p, z
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import os
from wrf import getvar
from netCDF4 import Dataset as nc
dir = "/Users/ladwig/Documents/wrf_files/wrf_vortex_multi/moving_nest"
ncfilenames = [os.path.join(dir, x) for x in os.listdir(dir) if x.find("_d02_") > 0]
ncfiles = [nc(x) for x in ncfilenames]
timeidx = None
# T2 using pivot and angle
from wrf import interpline, getvar, to_np, CoordPair
t2 = getvar(ncfiles, "T2", timeidx)
pivot_point = CoordPair(t2.shape[-2] / 2, t2.shape[-1] / 2)
angle = 90.0
t2_line = interpline(t2, pivot_point=pivot_point, angle=angle, latlon=True)
print(t2_line)
print("\n")
del t2_line
# T2 using start_point and end_point
start_point = CoordPair(t2.shape[-2]/2, 0)
end_point = CoordPair(t2.shape[-2]/2, -1)
t2_line = interpline(t2, start_point=start_point, end_point=end_point, latlon=True)
print(t2_line)
print("\n")
del t2_line, t2
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from wrf import getvar
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from wrf import xy_to_ll, ll_to_xy
a = xy_to_ll(ncfiles, 400, 200)
a = xy_to_ll(ncfiles, [400,105], [200,205])
b = ll_to_xy(ncfiles, 45.5, -110.8, as_int=True)
# Note: Lists/Dictionaries of files will add a new dimension ('domain') only if the domain is moving
c = xy_to_ll(ncfiles, [400,105], [200,205])
d = xy_to_ll({"label1" : ncfiles,
"label2" : ncfiles},
[400,105], [200,205])
print(a)
print("\n")
print(b)
print("\n")
print(c)
print("\n")
print(d)
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from glob import glob
from wrf import getvar, ALL_TIMES, geo_bounds, get_cartopy, get_basemap, get_pyngl, cartopy_xlim, cartopy_ylim
from netCDF4 import Dataset as nc
wrf_filenames = glob("/Users/ladwig/Documents/wrf_files/wrf_vortex_multi/moving_nest/wrfout_d02_*")
ncfiles = [nc(x) for x in wrf_filenames]
slp = getvar(ncfiles, "slp", timeidx=ALL_TIMES)
bounds = geo_bounds(slp)
print (bounds)
print ()
cart_proj = get_cartopy(slp)
print (cart_proj)
print ("\n")
xlims = cartopy_xlim(slp)
print (xlims)
print ("\n")
ylims = cartopy_ylim(slp)
print (ylims)
print ("\n")
bm = get_basemap(slp)
print (bm)
print ("\n")
pyngl_res = get_pyngl(slp)
print (pyngl_res)
print ("\n")
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