def retrieval(observations):
#~ wrapretrieval.wrapretrieval(
#~ observations['cfg_filename'],
#~ observations['additional_output_filename'],
#~ observations['measurements_filename'],
#~ )
#alternative
mydir = os.path.dirname(__file__)
cmd = mydir + "/wrapretrieval.o '{}' '{}' '{}'".format(
observations['cfg_filename'],
observations['additional_output_filename'],
observations['measurements_filename'])
print cmd
os.system(cmd)
#alternative with debugging
#~ mydir = os.path.dirname(__file__)
#~ #cmd = "valgrind --leak-check=yes "+mydir+"/wrapretrieval.o '{}' '{}' '{}'".format(observations['cfg_filename'], observations['additional_output_filename'], observations['measurements_filename'])
#~ cmd = "valgrind "+mydir+"/wrapretrieval.o '{}' '{}' '{}'".format(observations['cfg_filename'], observations['additional_output_filename'], observations['measurements_filename'])
#~ print cmd
#~ os.system(cmd)
ao_dct = ao.ao_dct(observations['additional_output_filename'])
return ao_dct
def retrieval(observations):
wrapretrieval.wrapretrieval(
observations['cfg_filename'],
observations['additional_output_filename'],
observations['measurements_filename'],
)
ao = additional_output.ao_dct(observations['additional_output_filename'])
return ao
def retrieval(observations):
wrapretrieval.wrapretrieval(
observations['cfg_filename'],
observations['additional_output_filename'],
observations['measurements_filename'],
)
ao = additional_output.ao_dct(observations['additional_output_filename'])
return ao
def plot_spectrum(ao_name, fname, opts = {}):
fontsize0 = 20
mpl.rc('xtick', labelsize=fontsize0)
mpl.rc('ytick', labelsize=fontsize0)
ao = additional_output.ao_dct(ao_name)
recalc = {}
recalc['spectrum_velocity_center'] = np.repeat( [np.linspace(-15.,15., 500)], len(ao['azel_r1_m']), axis=0)
d = recalc['spectrum_velocity_center'][0,1] - recalc['spectrum_velocity_center'][0,0]
recalc['spectrum_velocity_ubound'] = recalc['spectrum_velocity_center'] + d/2.
recalc['spectrum_velocity_lbound'] = recalc['spectrum_velocity_center'] - d/2.
recalc = calc_hr_spectrum.ao_to_recalc(ao, recalc)
calc_hr_spectrum.smooth_spectra(recalc)
for plot in [
'Doppler_spectrum_dBZ_hh',
'Doppler_spectrum_dBZ_hv',
'Doppler_spectrum_dBZ_vh',
'Doppler_spectrum_dBZ_vv',
'specific_dBZdr',
'specific_dBLdr',
'specific_rho_co',
'specific_rho_cxh',
'specific_rho_cxv',
]:
if plot in recalc.keys():
fig = plt.figure(figsize=(5,5))
ax=plt.subplot(111)
if ('title' in opts.keys()):
ax.set_title(opts['title'])
z = recalc[plot][0]
z = np.where(z == calc_hr_spectrum._FillValueminINF, np.nan, z)
ax.plot(recalc['spectrum_velocity_center'][0,:], z, linewidth=2)
ax.set_xlabel("Doppler velocity [m/s]")
ax.set_ylabel('[dB]')
ax.set_xbound(-5., 5.)
plt.tight_layout()
plt.savefig(fname + '_'+plot+'.png')
plt.close(fig)
def calc_radar_meas(observations):
nrad = len(observations['azel_r1_m'])
txtfile = ""
for variable in [
'enu_radar_location_x',
'enu_radar_location_y',
'enu_radar_location_z',
'enu_radar_time_t',
'azel_r1_m',
'azel_r2_m',
'azel_alpha_rad',
'azel_gamma_rad',
'beam_FWHM0_rad',
'beam_FWHM1_rad',
'dt',
]:
if variable not in observations.keys():
print "variable '{0}' was not found in dictionary!".format(
variable)
exit(0)
txtfile += "!! {0} ".format(variable)
txtfile += "{0} {1} ".format(1, nrad)
for i in range(nrad):
txtfile += "{0:.5e} ".format(observations[variable][i])
txtfile += "\n"
f = open('./tmpmeasurements.z', 'w')
f.write(txtfile)
f.close()
wrapradarfilter.wrapradarfilter(
observations['cfg_filename'],
observations['additional_output_filename'],
'./tmpmeasurements.z',
)
#alternative
#mydir = os.path.dirname(__file__)
#cmd = mydir+"/radarfilter.o '{}' '{}' '{}'".format(observations['cfg_filename'], observations['additional_output_filename'], './tmpmeasurements.z',)
#os.system(cmd)
#alternative with debugging
#mydir = os.path.dirname(__file__)
#cmd = "valgrind --leak-check=yes "+mydir+"/radarfilter.o '{}' '{}' '{}'".format(observations['cfg_filename'], observations['additional_output_filename'], './tmpmeasurements.z',)
#print cmd
#os.system(cmd)
os.remove('./tmpmeasurements.z')
ao = additional_output.ao_dct(observations['additional_output_filename'])
return ao
def plot_spectrum(ao_name, fname, opts={}):
fontsize0 = 20
mpl.rc('xtick', labelsize=fontsize0)
mpl.rc('ytick', labelsize=fontsize0)
ao = additional_output.ao_dct(ao_name)
recalc = {}
recalc['spectrum_velocity_center'] = np.repeat(
[np.linspace(-15., 15., 500)], len(ao['azel_r1_m']), axis=0)
d = recalc['spectrum_velocity_center'][
0, 1] - recalc['spectrum_velocity_center'][0, 0]
recalc['spectrum_velocity_ubound'] = recalc[
'spectrum_velocity_center'] + d / 2.
recalc['spectrum_velocity_lbound'] = recalc[
'spectrum_velocity_center'] - d / 2.
recalc = calc_hr_spectrum.ao_to_recalc(ao, recalc)
calc_hr_spectrum.smooth_spectra(recalc)
for plot in [
'Doppler_spectrum_dBZ_hh',
'Doppler_spectrum_dBZ_hv',
'Doppler_spectrum_dBZ_vh',
'Doppler_spectrum_dBZ_vv',
'specific_dBZdr',
'specific_dBLdr',
'specific_rho_co',
'specific_rho_cxh',
'specific_rho_cxv',
]:
if plot in recalc.keys():
fig = plt.figure(figsize=(5, 5))
ax = plt.subplot(111)
if ('title' in opts.keys()):
ax.set_title(opts['title'])
z = recalc[plot][0]
z = np.where(z == calc_hr_spectrum._FillValueminINF, np.nan, z)
ax.plot(recalc['spectrum_velocity_center'][0, :], z, linewidth=2)
ax.set_xlabel("Doppler velocity [m/s]")
ax.set_ylabel('[dB]')
ax.set_xbound(-5., 5.)
plt.tight_layout()
plt.savefig(fname + '_' + plot + '.png')
plt.close(fig)
def calc_radar_meas(observations):
nrad = len(observations["azel_r1_m"])
txtfile = ""
for variable in [
"enu_radar_location_x",
"enu_radar_location_y",
"enu_radar_location_z",
"enu_radar_time_t",
"azel_r1_m",
"azel_r2_m",
"azel_alpha_rad",
"azel_gamma_rad",
"beam_FWHM0_rad",
"beam_FWHM1_rad",
"dt",
]:
if variable not in observations.keys():
print "variable '{0}' was not found in dictionary!".format(variable)
exit(0)
txtfile += "!! {0} ".format(variable)
txtfile += "{0} {1} ".format(1, nrad)
for i in range(nrad):
txtfile += "{0:.5e} ".format(observations[variable][i])
txtfile += "\n"
f = open("./tmpmeasurements.z", "w")
f.write(txtfile)
f.close()
wrapradarfilter.wrapradarfilter(
observations["cfg_filename"], observations["additional_output_filename"], "./tmpmeasurements.z"
)
# alternative
# mydir = os.path.dirname(__file__)
# cmd = mydir+"/radarfilter.o '{}' '{}' '{}'".format(observations['cfg_filename'], observations['additional_output_filename'], './tmpmeasurements.z',)
# os.system(cmd)
# alternative with debugging
# mydir = os.path.dirname(__file__)
# cmd = "valgrind --leak-check=yes "+mydir+"/radarfilter.o '{}' '{}' '{}'".format(observations['cfg_filename'], observations['additional_output_filename'], './tmpmeasurements.z',)
# print cmd
# os.system(cmd)
os.remove("./tmpmeasurements.z")
ao = additional_output.ao_dct(observations["additional_output_filename"])
return ao
def plot_scanning(ao_name, fname, input_opts={}):
default_opts = {
'xmin': -15.,
'xmax': 15.,
'ymin': -15.,
'ymax': 15.,
'tmin': -1.e100,
'tmax': 1.e100,
'dBZmin': -40.,
'dBZmax': 40.,
'Doppler_velocity_ms_min': -15.,
'Doppler_velocity_ms_max': 15.,
}
opts = deepcopy(default_opts)
opts.update(input_opts)
fontsize0 = 12
mpl.rc('xtick', labelsize=fontsize0)
mpl.rc('ytick', labelsize=fontsize0)
ao = additional_output.ao_dct(ao_name)
okindices = np.arange(len(
ao['enu_radar_time_t']))[(opts['tmin'] < ao['enu_radar_time_t'])
& (ao['enu_radar_time_t'] < opts['tmax'])]
for plot in [
'dBZ_hh',
'Doppler_velocity_hh_ms',
'Doppler_spectral_width_hh_ms',
]:
if plot in ao.keys():
fig = plt.figure(figsize=(4, 4))
ax = plt.subplot(111)
if plot == 'dBZ_hh':
plt_cmap = plt.cm.get_cmap("jet", 100)
z = np.array(ao['dBZ_hh'][okindices])
plt.title(r"dBZ", fontsize=fontsize0)
#vmin, vmax = z.min(), z.max()
vmin, vmax = opts['dBZmin'], opts['dBZmax']
if plot == 'Doppler_velocity_hh_ms':
plt_cmap = plt.cm.RdBu_r
z = np.array(ao['Doppler_velocity_hh_ms'][okindices])
plt.title(r"Doppler mean velocity [m/s]", fontsize=fontsize0)
tmp = np.max(np.abs(((1, z.min(), z.max()))))
#vmin, vmax = -tmp, tmp
vmin, vmax = opts['Doppler_velocity_ms_min'], opts[
'Doppler_velocity_ms_max']
if plot == 'Doppler_spectral_width_hh_ms':
plt_cmap = plt.cm.get_cmap("jet", 100)
z = np.array(
np.sqrt(ao['Doppler_spectral_width_hh_ms'][okindices]**2.))
plt.title(r"spectral width [m/s]", fontsize=fontsize0)
vmin, vmax = 0., np.max((1., z.max()))
#vmin, vmax = 0., 5.
#plt_cmap.set_over('Purple')
plt_cmap.set_over('Magenta')
plt_cmap.set_under('Pink')
plt_cmap.set_bad('1.')
if ('title' in opts.keys()):
ax.set_title(opts['title'])
x = np.array(ao['center_x'][okindices]) * 1.e-3
y = np.array(ao['center_y'][okindices]) * 1.e-3
ang = np.rad2deg(np.arctan2(x, y)) % 360.
# Set up a regular grid of interpolation points
n = 50
dx = (opts['xmax'] - opts['xmin']) / n
xedges = np.linspace(opts['xmin'] - dx / 2.,
opts['xmax'] + dx / 2., n + 1)
dy = (opts['ymax'] - opts['ymin']) / n
yedges = np.linspace(opts['ymin'] - dy / 2.,
opts['ymax'] + dy / 2., n + 1)
xi, yi = np.linspace(opts['xmin'], opts['xmax'],
n), np.linspace(opts['ymin'], opts['ymax'], n)
xi, yi = np.meshgrid(xi, yi)
plt.xlabel('x [km]', fontsize=fontsize0)
plt.ylabel('y [km]', fontsize=fontsize0)
zi = scipy.interpolate.griddata((x, y),
z, (xi, yi),
method='linear',
fill_value=np.nan)
if 'retrieved_u' in ao.keys():
zi_u = scipy.interpolate.griddata((x, y),
ao['retrieved_u'][okindices],
(xi, yi),
method='linear',
fill_value=np.nan)
zi_v = scipy.interpolate.griddata((x, y),
ao['retrieved_v'][okindices],
(xi, yi),
method='linear',
fill_value=np.nan)
H, xedges, yedges = np.histogram2d(y, x, bins=(xedges, yedges))
okdata = H > 0
#remove badly interpolated points
#~ ri = np.sqrt((xi ** 2.) + (yi ** 2.))
#~ angi = np.rad2deg(np.arctan2(xi, yi)) % 360.
#~ mydang = 360./ n
#~ okdata = np.zeros(ri.shape)
#~ fct = 1.5
#~ for myang in np.arange(0, 360., mydang):
#~ sel = ((myang < ang) & (ang <= (myang + fct * mydang)))
#~ if np.sum(sel) > 1:
#~ myrmin = np.min(1.e-3 * ao['azel_r1_m'][sel])
#~ myrmax = np.max(1.e-3 * ao['azel_r2_m'][sel])
#~ sel2 = (((myang < angi) & (angi <= (myang + fct * mydang))) &
#~ ((myrmin < ri) & (ri <= myrmax)))
#~ okdata = np.where(sel2
#~ , 1., okdata)
zi = np.where(okdata, zi, np.nan)
if 'retrieved_u' in ao.keys():
zi_u = np.where(okdata, zi_u, np.nan)
zi_v = np.where(okdata, zi_v, np.nan)
iskip = 5
Q = ax.quiver(
np.ndarray.flatten(xi[::iskip, ::iskip]),
np.ndarray.flatten(yi[::iskip, ::iskip]),
np.ndarray.flatten(zi_u[::iskip, ::iskip]),
np.ndarray.flatten(zi_v[::iskip, ::iskip]),
)
plt.imshow(zi,
origin='lower',
extent=[
opts['xmin'], opts['xmax'], opts['ymin'],
opts['ymax']
],
cmap=plt_cmap,
interpolation='none',
vmin=vmin,
vmax=vmax)
plt.colorbar(shrink=0.6, extend='both')
plt.tight_layout()
plt.savefig(fname + "_" + plot + ".png")
plt.close(fig)
observations['enu_radar_time_t'] = np.zeros(observations['n'])
observations['azel_r2_m'] = observations['azel_r1_m'] + observations['dr']
observations['azel_alpha_rad'] = np.zeros(
observations['n']) + np.deg2rad(alpha)
observations['azel_gamma_rad'] = np.zeros(
observations['n']) + np.deg2rad(elevation_angle)
observations['beam_FWHM0_rad'] = np.zeros(
observations['n']) + myplotdct['beam_FWHM0_rad']
observations['beam_FWHM1_rad'] = np.zeros(
observations['n']) + myplotdct['beam_FWHM1_rad']
observations['dt'] = np.ones(observations['n'])
ao = radarfilter.calc_radar_meas(observations)
else:
ao = additional_output.ao_dct(observations['additional_output_filename'])
if True:
#~ #do a recalculation with nicer resolution
import scipy.interpolate
from matplotlib.mlab import griddata
import matplotlib.pyplot as plt
import matplotlib
fontsize0 = 20
matplotlib.rc('xtick', labelsize=fontsize0)
matplotlib.rc('ytick', labelsize=fontsize0)
recalc = ao_to_recalc(ao)
def plot_spectogram(ao_name, fname, opts={}):
fontsize0 = 20
mpl.rc('xtick', labelsize=fontsize0)
mpl.rc('ytick', labelsize=fontsize0)
ao = additional_output.ao_dct(ao_name)
recalc = {}
recalc['spectrum_velocity_center'] = np.repeat(
[np.linspace(-15., 15., 1000)], len(ao['azel_r1_m']), axis=0)
recalc = calc_hr_spectrum.ao_to_recalc(ao, recalc)
for plot in [
'Doppler_spectrum_dBZ_hh',
'Doppler_spectrum_dBZ_hv',
'Doppler_spectrum_dBZ_vh',
'Doppler_spectrum_dBZ_vv',
'specific_dBZdr',
'specific_dBLdr',
'specific_rho_co',
'specific_rho_cxh',
'specific_rho_cxv',
]:
if plot in recalc.keys():
fig = plt.figure(figsize=(5, 5))
ax = plt.subplot(111)
#plt_cmap = plt.cm.jet
plt_cmap = plt.cm.get_cmap("jet", 100)
plt_cmap.set_over('Black')
plt_cmap.set_under('LightGray')
plt_cmap.set_bad('0.')
if ('title' in opts.keys()):
ax.set_title(opts['title'])
z = recalc[plot]
z = np.where(z == calc_hr_spectrum._FillValueminINF, np.nan, z)
vmin = np.nanmin(z)
vmax = np.nanmax(z)
if plot in opts.keys():
if 'vmin' in opts[plot].keys():
vmin = opts[plot]['vmin']
#z = np.where(z < vmin, np.nan, z)
if 'vmax' in opts[plot].keys():
vmax = opts[plot]['vmax']
#z = np.where(z > vmax, np.nan, z)
bounds = np.linspace(vmin, vmax, 101)
#norm = mpl.colors.BoundaryNorm(bounds, plt_cmap.N)
#~ if plot in [
#~ 'Doppler_spectrum_dBZ_hh',
#~ 'Doppler_spectrum_dBZ_hv',
#~ 'Doppler_spectrum_dBZ_vh',
#~ 'Doppler_spectrum_dBZ_vv',
#~ ]:
#~ for i in range(z.shape[0]):
#~ z[i,:] = np.where(z[i,:] < (np.nanmax(z[i,:]) - 20.), np.nan, z[i,:])
nspectrum = z.shape[1]
ranges = np.repeat(ao['azel_r1_m'], nspectrum).reshape(z.shape)
heights = np.repeat(ao['center_z'], nspectrum).reshape(z.shape)
CF = ax.contourf(recalc['spectrum_velocity_center'],
1.e-3 * heights,
z,
bounds,
cmap=plt_cmap,
vmin=vmin,
vmax=vmax,
extend='both')
ax.set_xbound(-5., 5.)
ax.set_xlabel("Doppler velocity [m/s]")
ax.set_ylabel("height [km]")
cb = plt.colorbar(
CF,
shrink=0.7,
#ticks=bounds,
spacing='uniform',
)
#cb.set_clim(vmin-10.,vmax+10.)
plt.tight_layout()
plt.savefig(fname + '_' + plot + '.png')
plt.close(fig)
def plot_spectogram(ao_name, fname, opts = {}):
fontsize0 = 20
mpl.rc('xtick', labelsize=fontsize0)
mpl.rc('ytick', labelsize=fontsize0)
ao = additional_output.ao_dct(ao_name)
recalc = {}
recalc['spectrum_velocity_center'] = np.repeat( [np.linspace(-15.,15., 1000)], len(ao['azel_r1_m']), axis=0)
recalc = calc_hr_spectrum.ao_to_recalc(ao, recalc)
for plot in [
'Doppler_spectrum_dBZ_hh',
'Doppler_spectrum_dBZ_hv',
'Doppler_spectrum_dBZ_vh',
'Doppler_spectrum_dBZ_vv',
'specific_dBZdr',
'specific_dBLdr',
'specific_rho_co',
'specific_rho_cxh',
'specific_rho_cxv',
]:
if plot in recalc.keys():
fig = plt.figure(figsize=(5,5))
ax=plt.subplot(111)
#plt_cmap = plt.cm.jet
plt_cmap = plt.cm.get_cmap("jet", 100)
plt_cmap.set_over('Black')
plt_cmap.set_under('LightGray')
plt_cmap.set_bad('0.')
if ('title' in opts.keys()):
ax.set_title(opts['title'])
z = recalc[plot]
z = np.where(z == calc_hr_spectrum._FillValueminINF, np.nan, z)
vmin = np.nanmin(z); vmax = np.nanmax(z)
if plot in opts.keys():
if 'vmin' in opts[plot].keys():
vmin = opts[plot]['vmin']
#z = np.where(z < vmin, np.nan, z)
if 'vmax' in opts[plot].keys():
vmax = opts[plot]['vmax']
#z = np.where(z > vmax, np.nan, z)
bounds = np.linspace(vmin, vmax,101)
#norm = mpl.colors.BoundaryNorm(bounds, plt_cmap.N)
#~ if plot in [
#~ 'Doppler_spectrum_dBZ_hh',
#~ 'Doppler_spectrum_dBZ_hv',
#~ 'Doppler_spectrum_dBZ_vh',
#~ 'Doppler_spectrum_dBZ_vv',
#~ ]:
#~ for i in range(z.shape[0]):
#~ z[i,:] = np.where(z[i,:] < (np.nanmax(z[i,:]) - 20.), np.nan, z[i,:])
nspectrum = z.shape[1]
ranges = np.repeat(ao['azel_r1_m'] , nspectrum).reshape( z.shape)
heights = np.repeat(ao['center_z'] , nspectrum).reshape( z.shape)
CF = ax.contourf(
recalc['spectrum_velocity_center'],
1.e-3 * heights,
z,
bounds,
cmap=plt_cmap,
vmin=vmin,
vmax=vmax,
extend='both')
ax.set_xbound(-5., 5.)
ax.set_xlabel("Doppler velocity [m/s]")
ax.set_ylabel("height [km]")
cb = plt.colorbar( CF,
shrink=0.7,
#ticks=bounds,
spacing='uniform',
)
#cb.set_clim(vmin-10.,vmax+10.)
plt.tight_layout()
plt.savefig(fname + '_'+plot+'.png')
plt.close(fig)
observations["cfg_filename"] = "../../../input_files/general/standard_output.cfg;"
observations["cfg_filename"] += "../../../input_files/general/water_refractive_index_segelstein.cfg;"
observations["cfg_filename"] += "../../../input_files/general/white1999_integral.cfg;"
observations["cfg_filename"] += "../../../input_files/general/atmosphere/US1976.cfg;"
observations["cfg_filename"] += "../../../input_files/general/instruments/tara.cfg;"
observations["cfg_filename"] += "../../../input_files/retrieval/radarfilter/standard.cfg;"
# observations['cfg_filename'] += "../../../input_files/retrieval/scatterers/rain.cfg;"
observations["cfg_filename"] += "../../../input_files/retrieval/algorithm/windvectors_lwm.cfg;"
observations["additional_output_filename"] = additional_output_filename
observations["measurements_filename"] = measurements_filename
ao = retrieval.retrieval(observations)
else:
ao = additional_output.ao_dct(additional_output_filename)
# from mpl_toolkits.basemap import Basemap, cm
# from matplotlib import rc
# rc('text',usetex=True)
fontsize0 = 16
matplotlib.rc("xtick", labelsize=fontsize0)
matplotlib.rc("ytick", labelsize=fontsize0)
for plot in ["dBZ_hh", "Doppler_velocity_hh_ms", "Doppler_spectral_width_hh_ms"]:
fig = plt.figure(figsize=(8, 8))
ax = fig.add_subplot(1, 1, 1)
def calc_circulation_divergence(ao_name, input_opts={}):
default_opts = {
'rangeintervals': 20,
'rmin_km': 0.,
'rmax_km': 15.,
}
opts = deepcopy(default_opts)
opts.update(input_opts)
ao = additional_output.ao_dct(ao_name)
res = {}
if ('retrieved_u' in ao.keys()) and ('retrieved_v' in ao.keys()):
lst_ranges = np.linspace(opts['rmin_km'] * 1.e3,
opts['rmax_km'] * 1.e3,
opts['rangeintervals'] + 1)
ao_range = (ao['azel_r1_m'] + ao['azel_r2_m']) / 2.
res['range_min_km'] = lst_ranges[:-1] * 1.e-3
res['range_max_km'] = lst_ranges[1:] * 1.e-3
res['range_km'] = (res['range_min_km'] + res['range_max_km']) / 2.
res['circulation_inproduct'] = np.zeros(res['range_km'].shape)
res['divergence_inproduct'] = np.zeros(res['range_km'].shape)
#walk through range intervals
for i_int in range(opts['rangeintervals']):
range0 = lst_ranges[i_int]
range1 = lst_ranges[i_int + 1]
sel = (range0 < ao_range) & (ao_range < range1)
#obtain results
thisdata = {}
thisdata['range'] = np.compress(sel, ao_range)
thisdata['x'] = np.compress(sel, ao['center_x'])
thisdata['y'] = np.compress(sel, ao['center_y'])
thisdata['azel_alpha_rad'] = np.compress(sel, ao['azel_alpha_rad'])
thisdata['u'] = np.compress(sel, ao['retrieved_u'])
thisdata['v'] = np.compress(sel, ao['retrieved_v'])
thisdata['norm_x'] = thisdata['x'] / np.sqrt((thisdata['x']**2.) +
(thisdata['y']**2.))
thisdata['norm_y'] = thisdata['y'] / np.sqrt((thisdata['x']**2.) +
(thisdata['y']**2.))
#calculate inproducts
thisdata['circulation_inproduct'] = (
thisdata['u'] * thisdata['norm_x']) + (thisdata['v'] *
thisdata['norm_y'])
thisdata['divergence_inproduct'] = (
thisdata['u'] * thisdata['norm_y']) + (thisdata['v'] *
thisdata['norm_x'])
#delete nans
thisdata['circulation_inproduct'] = np.compress(
np.isnan(thisdata['circulation_inproduct']) == False,
thisdata['circulation_inproduct'])
thisdata['divergence_inproduct'] = np.compress(
np.isnan(thisdata['divergence_inproduct']) == False,
thisdata['divergence_inproduct'])
#calculate weighting function
n_ang = 100
lst_ang = np.linspace(0, 2 * np.pi, n_ang + 1)
thisdata['weights'] = np.zeros(thisdata['range'].shape)
for i_ang in range(n_ang):
myindicies = np.argmin(
(lst_ang[i_ang] < thisdata['azel_alpha_rad'])
& (thisdata['azel_alpha_rad'] < lst_ang[i_ang + 1]))
thisdata['weights'][myindicies] = np.sum(myindicies)
thisdata['weights'] = thisdata['weights'] / np.sum(
thisdata['weights'])
res['circulation_inproduct'][i_int] = np.sum(
thisdata['weights'] * thisdata['circulation_inproduct'])
res['divergence_inproduct'][i_int] = np.sum(
thisdata['weights'] * thisdata['divergence_inproduct'])
del thisdata
return res
#if not os.path.exists(additional_output_filename):
if True:
observations = {}
#general configuration files
observations['cfg_filename'] = "../../../input_files/general/standard_output.cfg;"
observations['cfg_filename'] += "../../../input_files/general/water_refractive_index_segelstein.cfg;"
observations['cfg_filename'] += "../../../input_files/general/white1999_integral.cfg;"
observations['cfg_filename'] += "../../../input_files/general/atmosphere/US1976.cfg;"
observations['cfg_filename'] += "../../../input_files/general/instruments/tara.cfg;"
observations['cfg_filename'] += "../../../input_files/retrieval/algorithm/windvectors_fdvar_horizontal_hdir_solution.cfg;"
observations['additional_output_filename'] = additional_output_filename
observations['measurements_filename'] = measurements_filename
ao = retrieval.retrieval(observations)
else:
ao = additional_output.ao_dct(additional_output_filename)
opts = {
'Doppler_velocity_ms_min': -10.,
'Doppler_velocity_ms_max': 10.,
}
fun_plot_scanning.plot_scanning(additional_output_filename, 'fdvar_plots_scanning/scanning_'+myway+'_', opts)
elevation_angle = 0.
observations['dr'] = dr
observations['r1_slice'] = slice(0., 15.e3 + 1., dr * di)
observations['phi_slice'] = slice(0., 2. * np.pi, 2. * np.pi / 72.)
phi, r1 = np.mgrid[observations['phi_slice'], observations['r1_slice']]
observations['azel_r1_m'] = deepcopy(np.ndarray.flatten(r1))
observations['azel_alpha_rad'] = deepcopy(np.ndarray.flatten(phi))
del r1, phi
observations['n'] = len(observations['azel_r1_m'])
observations['enu_radar_location_x'] = np.zeros(observations['n'])
observations['enu_radar_location_y'] = np.zeros(observations['n'])
observations['enu_radar_location_z'] = np.zeros(observations['n']) + 500.
observations['enu_radar_time_t'] = np.zeros(observations['n'])
observations['azel_r2_m'] = observations['azel_r1_m'] + observations['dr']
observations['azel_gamma_rad'] = np.zeros(observations['n']) + np.deg2rad(elevation_angle)
observations['beam_FWHM0_rad'] = np.zeros(observations['n']) + np.deg2rad(2.1)
observations['beam_FWHM1_rad'] = np.zeros(observations['n']) + np.deg2rad(2.1)
observations['dt'] = np.ones(observations['n'])
ao = radarfilter.calc_radar_meas(observations)
else:
ao = additional_output.ao_dct(observations['additional_output_filename'])
fun_plot_scanning.plot_scanning(observations['additional_output_filename'], 'scanning_plots/scanning_'+myway+'_')
def calc_curl_and_divergence(ao_name, input_opts={}):
default_opts = {
'rangeintervals': 20,
'rmin_km': 0.,
'rmax_km': 15.,
}
opts = deepcopy(default_opts)
opts.update(input_opts)
ao = additional_output.ao_dct(ao_name)
res = {}
if ('retrieved_u' in ao.keys()) and ('retrieved_v' in ao.keys()):
lst_ranges_m = np.linspace(opts['rmin_km'] * 1.e3,
opts['rmax_km'] * 1.e3,
opts['rangeintervals'] + 1)
ao_range_m = (ao['azel_r1_m'] + ao['azel_r2_m']) / 2.
res['range_min_km'] = lst_ranges_m[:-1] * 1.e-3
res['range_max_km'] = lst_ranges_m[1:] * 1.e-3
res['range_km'] = (res['range_min_km'] + res['range_max_km']) / 2.
res['curl'] = np.zeros(res['range_km'].shape)
res['divergence'] = np.zeros(res['range_km'].shape)
#walk through range intervals
for i_int in range(opts['rangeintervals']):
range0_m = lst_ranges_m[i_int]
range1_m = lst_ranges_m[i_int + 1]
sel_range = (range0_m < ao_range_m) & (ao_range_m < range1_m)
sel_nonans = (np.isnan(ao['retrieved_u']) == False) & (np.isnan(
ao['retrieved_v']) == False)
n_ang = 100
lst_ang = np.linspace(0, 2 * np.pi, n_ang + 1, True)
for i_ang in range(n_ang):
ang0 = lst_ang[i_ang]
ang1 = lst_ang[i_ang + 1]
myindices = (sel_range & sel_nonans &
(ang0 < ao['azel_alpha_rad']) &
(ao['azel_alpha_rad'] < ang1))
avg_u = np.average(ao['retrieved_u'][myindices])
avg_v = np.average(ao['retrieved_v'][myindices])
if np.isnan(avg_u) or np.isnan(avg_v):
myindices = (sel_range & sel_nonans)
avg_u = np.average(ao['retrieved_u'][myindices])
avg_v = np.average(ao['retrieved_v'][myindices])
if np.isnan(avg_u) or np.isnan(avg_v):
myindices = (sel_nonans)
avg_u = np.average(ao['retrieved_u'][myindices])
avg_v = np.average(ao['retrieved_v'][myindices])
curl_tmp_val = (avg_u * (np.cos(ang1) - np.cos(ang0))) - (
avg_v * (np.sin(ang1) - np.sin(ang0)))
div_tmp_val = (-1. * avg_u * (np.sin(ang1) - np.sin(ang0))) + (
avg_v * (np.cos(ang1) - np.cos(ang0)))
res['curl'][i_int] += curl_tmp_val
res['divergence'][i_int] += div_tmp_val
return res
