def plotObservationsComposite(obs, map, title, file_name):
pylab.clf()
colors = [ 'r', 'g', 'b', 'c', 'm', '#660099', '#ff9900', '#006666' ]
ob_ids = np.unique1d(obs['id'])
ttu_label = False
for ob_id in ob_ids:
ob_idxs = np.where(obs['id'] == ob_id)[0]
these_obs = obs[ob_idxs]
ob_xs, ob_ys = map(these_obs['longitude'], these_obs['latitude'])
if ob_id[0] == "P":
ob_num = int(ob_id[1]) - 1
pylab.plot(ob_xs, ob_ys, 'o', mfc=colors[ob_num], mec=colors[ob_num], ms=3, label="NSSL MM (%s)" % ob_id)
else:
if not ttu_label:
label = "TTU Sticknet"
ttu_label = True
else:
label = None
pylab.plot(ob_xs[0], ob_ys[0], 'ko', ms=3, label=label)
drawPolitical(map, scale_len=5)
pylab.legend(loc=3, numpoints=1, prop={'size':'medium'})
pylab.title(title)
pylab.savefig(file_name)
return
def plotObservations(obs, map, title, file_name, refl=None):
pylab.clf()
if refl is not None:
nx, ny = refl.shape
xs, ys = np.meshgrid(np.arange(nx) * 1000, np.arange(ny) * 1000)
map.contour(xs, ys, refl, levels=np.arange(20, 80, 20), colors='k')
obs_x, obs_y = map(obs['longitude'], obs['latitude'])
for ob, x, y in zip(obs, obs_x, obs_y):
pylab.plot(x, y, 'ko', ms=3.)
pylab.text(x - 500, y + 500, "%4.1f" % ob['temp'], size='small', va='bottom', ha='right', color='r')
pylab.text(x - 500, y - 500, "%4.1f" % ob['dewp'], size='small', va='top', ha='right', color='g')
pylab.text(x + 500, y - 500, ob['id'], size='small', va='top', ha='left', color='#808080')
good_idxs = np.where((obs['wind_spd'] >= 0.) & (obs['wind_dir'] >= 0.))
u = -obs['wind_spd'] * np.sin(obs['wind_dir'] * np.pi / 180) * 1.94
v = -obs['wind_spd'] * np.cos(obs['wind_dir'] * np.pi / 180) * 1.94
u_rot, v_rot = map.rotate_vector(u, v, obs['longitude'], obs['latitude'])
pylab.barbs(obs_x[good_idxs], obs_y[good_idxs], u_rot[good_idxs], v_rot[good_idxs])
drawPolitical(map, scale_len=5)
pylab.title(title)
pylab.savefig(file_name)
return
def plotTiming(vortex_prob, vortex_times, times, map, grid_spacing, tornado_track, title, file_name, obs=None, centers=None, min_prob=0.1):
nx, ny = vortex_prob.shape
gs_x, gs_y = grid_spacing
xs, ys = np.meshgrid(gs_x * np.arange(nx), gs_y * np.arange(ny))
time_color_map = matplotlib.cm.Accent
time_color_map.set_under('#ffffff')
vortex_times = np.where(vortex_prob >= min_prob, vortex_times, -1)
track_xs, track_ys = map(*reversed(tornado_track))
pylab.figure(figsize=(10, 8))
pylab.axes((0.025, 0.025, 0.95, 0.925))
pylab.pcolormesh(xs, ys, vortex_times, cmap=time_color_map, vmin=times.min(), vmax=times.max())
tick_labels = [ (datetime(2009, 6, 5, 18, 0, 0) + timedelta(seconds=int(t))).strftime("%H%M") for t in times ]
bar = pylab.colorbar()#orientation='horizontal', aspect=40)
bar.locator = FixedLocator(times)
bar.formatter = FixedFormatter(tick_labels)
bar.update_ticks()
pylab.plot(track_xs, track_ys, 'mv-', lw=2.5, mfc='k', ms=8)
drawPolitical(map, scale_len=(xs[-1, -1] - xs[0, 0]) / 10.)
pylab.title(title)
pylab.savefig(file_name)
pylab.close()
def plotRadTilt(plot_data, plot_cmaps, plot_titles, grid, file_name, base_ref=None):
subplot_base = 220
n_plots = 4
xs, ys, gs_x, gs_y, map = grid
pylab.figure(figsize=(12,12))
pylab.subplots_adjust(left=0.02, right=0.98, top=0.98, bottom=0.02, wspace=0.04)
for plot in range(n_plots):
pylab.subplot(subplot_base + plot + 1)
cmap, vmin, vmax = plot_cmaps[plot]
cmap.set_under("#ffffff", alpha=0.0)
pylab.pcolormesh(xs - gs_x / 2, ys - gs_y / 2, plot_data[plot] >= -90, vmin=0, vmax=1, cmap=mask_cmap)
pylab.pcolormesh(xs - gs_x / 2, ys - gs_y / 2, plot_data[plot], vmin=vmin, vmax=vmax, cmap=cmap)
pylab.colorbar()
if base_ref is not None:
pylab.contour(xs, ys, base_ref, levels=[10.], colors='k', lw=0.5)
# if plot == 0:
# pylab.contour(xs, ys, refl_88D[:, :, 0], levels=np.arange(10, 80, 10), colors='#808080', lw=0.5)
# pylab.plot(radar_x, radar_y, 'ko')
pylab.title(plot_titles[plot])
drawPolitical(map)
pylab.savefig(file_name)
pylab.close()
return
def plotObservationsComposite(obs, map, scale_len, title, file_name):
pylab.figure(figsize=(10,8))
pylab.axes((0, 0, 1, 0.95))
colors = [ 'r', 'g', 'b', 'c', 'm', '#660099', '#ff9900', '#006666' ]
ob_ids = np.unique1d(obs['id'])
ttu_label = False
asos_label = False
sndg_label = False
for ob_id in ob_ids:
ob_idxs = np.where(obs['id'] == ob_id)[0]
these_obs = obs[ob_idxs]
ob_xs, ob_ys = map(these_obs['longitude'], these_obs['latitude'])
if ob_id[0] == "P":
ob_num = int(ob_id[1]) - 1
pylab.plot(ob_xs, ob_ys, 'o', mfc=colors[ob_num], mec=colors[ob_num], ms=3, label="NSSL MM (%s)" % ob_id)
elif ob_id[0] == "K":
if not asos_label:
label = "ASOS"
asos_label = True
else:
label = None
pylab.plot(ob_xs[0], ob_ys[0], 'k*', ms=5, label=label)
elif ob_id[0] == "1" or ob_id[0] == "2":
if not ttu_label:
label = "TTU Sticknet"
ttu_label = True
else:
label = None
pylab.plot(ob_xs[0], ob_ys[0], 'o', mfc="#999999", mec="#999999", ms=3, label=label)
elif these_obs[0]['obtype'] == "SNDG":
if not sndg_label:
label = "Sounding"
sndg_label = True
else:
label = None
pylab.plot(ob_xs[0], ob_ys[0], 'k^', ms=4, label=label)
drawPolitical(map, scale_len=scale_len)
line_objects = [ l for l in sorted(pylab.gca().lines, key=lambda x: x.get_label()) if l.get_label()[0] != "_" ]
pylab.legend(line_objects, [ l.get_label() for l in line_objects], loc=2, numpoints=1, prop={'size':'medium'})
pylab.suptitle(title)
pylab.savefig(file_name)
pylab.close()
return
def makeplot(refl, winds, w, stride, map, gs, title, file_name, box=None):
pylab.figure()
axmain = pylab.axes((0, 0.025, 1, 0.9))
gs_x, gs_y = gs
nx, ny = refl.shape
xs, ys = np.meshgrid(gs_x * np.arange(nx), gs_y * np.arange(ny))
pylab.contourf(xs, ys, refl, levels=np.arange(10, 80, 10))
pylab.colorbar()
pylab.contour(xs, ys, w, levels=np.arange(-10, 0, 2), colors='#666666', style='--')
pylab.contour(xs, ys, w, levels=np.arange(2, 12, 2), colors='#666666', style='-')
u, v = winds
wind_slice = tuple([ slice(None, None, stride) ] * 2)
pylab.quiver(xs[wind_slice], ys[wind_slice], u[wind_slice], v[wind_slice])
if box:
lb_y, lb_x = [ b.start for b in box ]
ub_y, ub_x = [ b.stop for b in box ]
box_xs = gs_x * np.array([ lb_x, lb_x, ub_x, ub_x, lb_x])
box_ys = gs_y * np.array([ lb_y, ub_y, ub_y, lb_y, lb_y])
map.plot(box_xs, box_ys, '#660099')
axins = zoomed_inset_axes(pylab.gca(), 4, loc=4)
pylab.sca(axins)
pylab.contourf(xs[box], ys[box], refl[box], levels=np.arange(10, 80, 10))
pylab.contour(xs[box], ys[box], w[box], levels=np.arange(-10, 0, 2), colors='#666666', style='--')
pylab.contour(xs[box], ys[box], w[box], levels=np.arange(2, 12, 2), colors='#666666', style='-')
pylab.quiver(xs[box], ys[box], u[box], v[box])
drawPolitical(map)
pylab.xlim([lb_x * gs_x, ub_x * gs_x - 1])
pylab.ylim([lb_y * gs_y, ub_y * gs_y - 1])
mark_inset(axmain, axins, loc1=1, loc2=3, fc='none', ec='k')
pylab.sca(axmain)
drawPolitical(map)
pylab.suptitle(title)
pylab.savefig(file_name)
pylab.close()
return
def plotObservations(obs, map, title, file_name, refl=None):
pylab.clf()
if refl is not None:
nx, ny = refl.shape
xs, ys = np.meshgrid(np.arange(nx) * 1000, np.arange(ny) * 1000)
map.contour(xs, ys, refl, levels=np.arange(20, 80, 20), colors='k')
obs_x, obs_y = map(obs['longitude'], obs['latitude'])
for ob, x, y in zip(obs, obs_x, obs_y):
pylab.plot(x, y, 'ko', ms=3.)
pylab.text(x - 500,
y + 500,
"%4.1f" % ob['temp'],
size='small',
va='bottom',
ha='right',
color='r')
pylab.text(x - 500,
y - 500,
"%4.1f" % ob['dewp'],
size='small',
va='top',
ha='right',
color='g')
pylab.text(x + 500,
y - 500,
ob['id'],
size='small',
va='top',
ha='left',
color='#808080')
good_idxs = np.where((obs['wind_spd'] >= 0.) & (obs['wind_dir'] >= 0.))
u = -obs['wind_spd'] * np.sin(obs['wind_dir'] * np.pi / 180) * 1.94
v = -obs['wind_spd'] * np.cos(obs['wind_dir'] * np.pi / 180) * 1.94
u_rot, v_rot = map.rotate_vector(u, v, obs['longitude'], obs['latitude'])
pylab.barbs(obs_x[good_idxs], obs_y[good_idxs], u_rot[good_idxs],
v_rot[good_idxs])
drawPolitical(map, scale_len=5)
pylab.title(title)
pylab.savefig(file_name)
return
def plotProbability(vortex_prob, map, lower_bound, grid_spacing, tornado_track, title, file_name, obs=None, centers=None, min_prob=0.1, objects=None):
nx, ny = vortex_prob.shape
gs_x, gs_y = grid_spacing
lb_x, lb_y = lower_bound
# print lb_x, lb_y
xs, ys = np.meshgrid(gs_x * np.arange(nx), gs_y * np.arange(ny))
prob_color_map = matplotlib.cm.RdYlBu_r
prob_color_map.set_under('#ffffff')
track_xs, track_ys = map(*reversed(tornado_track))
pylab.figure(figsize=(10, 8))
pylab.axes((0.025, 0.025, 0.95, 0.925))
# Plot Vortex Probability
pylab.pcolormesh(xs, ys, vortex_prob, cmap=prob_color_map, vmin=min_prob, vmax=1.0)
pylab.colorbar()#orientation='horizontal', aspect=40)
pylab.plot(track_xs, track_ys, 'mv-', lw=2.5, mfc='k', ms=8)
if centers:
lines_x, lines_y = reorganizeCenters(centers, range(14400, 18300, 300))
for line_x, line_y in zip(lines_x, lines_y):
pylab.plot(line_x - lb_y, line_y - lb_x, 'ko-', markersize=2, linewidth=1)
if obs is not None:
pylab.contour(x, y, obs, levels=[0.95], colors='k')
if objects:
for obj in objects:
obj_t, obj_y, obj_x = removeNones(obj, (13,) + vortex_prob.shape)
if findSize(obj) >= 8:
pylab.plot(1000 * np.array([obj_x.start, obj_x.start, obj_x.stop, obj_x.stop, obj_x.start]), 1000 * np.array([obj_y.start, obj_y.stop, obj_y.stop, obj_y.start, obj_y.start]), color='k', zorder=10)
drawPolitical(map, scale_len=0) # scale_len=(xs[-1, -1] - xs[0, 0]) / 10000.)
pylab.title(title)
pylab.savefig(file_name)
pylab.close()
return
def plotVorticity(vorticity, winds, map, title, file_name, stride=4):
u, v = winds
pylab.figure()
thin_data = tuple([ slice(None, None, stride) ] * 2)
gs_x, gs_y = goshen_1km_gs
nx, ny = vorticity.shape
xs, ys = np.meshgrid(gs_x * np.arange(nx), gs_y * np.arange(ny))
pylab.contourf(xs, ys, vorticity, levels=np.arange(0.0075, 0.034, 0.0025))
pylab.colorbar()
pylab.quiver(xs[thin_data], ys[thin_data], u[thin_data], v[thin_data])
drawPolitical(map)
pylab.suptitle(title)
pylab.savefig(file_name)
pylab.close()
return
def plotVorticity(vorticity, winds, map, title, file_name, stride=4):
u, v = winds
pylab.figure()
thin_data = tuple([slice(None, None, stride)] * 2)
gs_x, gs_y = goshen_1km_gs
nx, ny = vorticity.shape
xs, ys = np.meshgrid(gs_x * np.arange(nx), gs_y * np.arange(ny))
pylab.contourf(xs, ys, vorticity, levels=np.arange(0.0075, 0.034, 0.0025))
pylab.colorbar()
pylab.quiver(xs[thin_data], ys[thin_data], u[thin_data], v[thin_data])
drawPolitical(map)
pylab.suptitle(title)
pylab.savefig(file_name)
pylab.close()
return
def plotObservationsComposite(obs, map, title, file_name):
pylab.clf()
colors = ['r', 'g', 'b', 'c', 'm', '#660099', '#ff9900', '#006666']
ob_ids = np.unique1d(obs['id'])
ttu_label = False
for ob_id in ob_ids:
ob_idxs = np.where(obs['id'] == ob_id)[0]
these_obs = obs[ob_idxs]
ob_xs, ob_ys = map(these_obs['longitude'], these_obs['latitude'])
if ob_id[0] == "P":
ob_num = int(ob_id[1]) - 1
pylab.plot(ob_xs,
ob_ys,
'o',
mfc=colors[ob_num],
mec=colors[ob_num],
ms=3,
label="NSSL MM (%s)" % ob_id)
else:
if not ttu_label:
label = "TTU Sticknet"
ttu_label = True
else:
label = None
pylab.plot(ob_xs[0], ob_ys[0], 'ko', ms=3, label=label)
drawPolitical(map, scale_len=5)
pylab.legend(loc=3, numpoints=1, prop={'size': 'medium'})
pylab.title(title)
pylab.savefig(file_name)
return
def main():
base_time = datetime(2009, 6, 5, 18, 0, 0)
epoch = datetime(1970, 1, 1, 0, 0, 0)
base_epoch = (base_time - epoch).total_seconds()
times_seconds = range(14700, 18300, 300)
times = [ base_time + timedelta(seconds=t) for t in times_seconds ]
bounds = (slice(100, 180), slice(90, 170))
rev_bounds = [ 0 ]
rev_bounds.extend(bounds[::-1])
rev_bounds = tuple(rev_bounds)
proj = setupMapProjection(goshen_1km_proj, goshen_1km_gs, bounds=bounds)
map = Basemap(**proj)
obs_file_names = ['psu_straka_mesonet.pkl', 'ttu_sticknet.pkl', 'asos.pkl', 'soundings_clip.pkl']
all_obs = loadObs(obs_file_names, times, map, (goshen_1km_proj['width'], goshen_1km_proj['height']), sounding_obs=['soundings_clip.pkl'])
refl_base = "hdf/KCYS/1km/goshen.hdfrefl2d"
refl_times = np.array([ int(f[-6:]) for f in glob.glob("%s??????" % refl_base) ])
refl_keep_times = []
refl_data = {}
for tt in times_seconds:
idx = np.argmin(np.abs(refl_times - tt))
if refl_times[idx] > tt and idx > 0:
idx -= 1
file_name = "%s%06d" % (refl_base, refl_times[idx])
hdf = nio.open_file(file_name, mode='r', format='hdf')
refl_keep_times.append(refl_times[idx])
refl_data[tt] = hdf.variables['refl2d'][rev_bounds]
for time, reg in inflow_stations.iteritems():
pylab.figure()
gs_x, gs_y = goshen_1km_gs
nx, ny = refl_data[time].shape
xs, ys = np.meshgrid(gs_x * np.arange(nx), gs_y * np.arange(ny))
pylab.contourf(xs, ys, refl_data[time], levels=np.arange(10, 80, 10))
for region, stations in reg.iteritems():
if region != 'sounding':
for station in stations:
idxs = np.where((all_obs['id'] == station) & (all_obs['time'] == base_epoch + time))
ob_xs, ob_ys = map(all_obs['longitude'][idxs], all_obs['latitude'][idxs])
if region == 'inflow': color='r'
elif region == 'outflow': color='b'
wdir = all_obs['wind_dir'][idxs]
wspd = all_obs['wind_spd'][idxs]
u = -wspd * np.sin(wdir * np.pi / 180.) * 1.94
v = -wspd * np.cos(wdir * np.pi / 180.) * 1.94
pylab.plot(ob_xs, ob_ys, "%so" % color)
pylab.barbs(ob_xs, ob_ys, u, v)
drawPolitical(map, scale_len=10)
pylab.savefig("inflow_stations_%06d.png" % time)
pylab.close()
return
def makeplot(refl, winds, w, stride, map, gs, title, file_name, box=None):
pylab.figure()
axmain = pylab.axes((0, 0.025, 1, 0.9))
gs_x, gs_y = gs
nx, ny = refl.shape
xs, ys = np.meshgrid(gs_x * np.arange(nx), gs_y * np.arange(ny))
pylab.contourf(xs, ys, refl, levels=np.arange(10, 80, 10))
pylab.colorbar()
pylab.contour(xs,
ys,
w,
levels=np.arange(-10, 0, 2),
colors='#666666',
style='--')
pylab.contour(xs,
ys,
w,
levels=np.arange(2, 12, 2),
colors='#666666',
style='-')
u, v = winds
wind_slice = tuple([slice(None, None, stride)] * 2)
pylab.quiver(xs[wind_slice], ys[wind_slice], u[wind_slice], v[wind_slice])
if box:
lb_y, lb_x = [b.start for b in box]
ub_y, ub_x = [b.stop for b in box]
box_xs = gs_x * np.array([lb_x, lb_x, ub_x, ub_x, lb_x])
box_ys = gs_y * np.array([lb_y, ub_y, ub_y, lb_y, lb_y])
map.plot(box_xs, box_ys, '#660099')
axins = zoomed_inset_axes(pylab.gca(), 4, loc=4)
pylab.sca(axins)
pylab.contourf(xs[box],
ys[box],
refl[box],
levels=np.arange(10, 80, 10))
pylab.contour(xs[box],
ys[box],
w[box],
levels=np.arange(-10, 0, 2),
colors='#666666',
style='--')
pylab.contour(xs[box],
ys[box],
w[box],
levels=np.arange(2, 12, 2),
colors='#666666',
style='-')
pylab.quiver(xs[box], ys[box], u[box], v[box])
drawPolitical(map)
pylab.xlim([lb_x * gs_x, ub_x * gs_x - 1])
pylab.ylim([lb_y * gs_y, ub_y * gs_y - 1])
mark_inset(axmain, axins, loc1=1, loc2=3, fc='none', ec='k')
pylab.sca(axmain)
drawPolitical(map)
pylab.suptitle(title)
pylab.savefig(file_name)
pylab.close()
return
def plotConfusion(confusion, map, grid_spacing, title, file_name, inset=None, fudge=16):
pylab.figure()
axmain = pylab.axes()
gs_x, gs_y = grid_spacing
confusion_cmap_dict = {
'red':(
(0.0, 0.5, 0.5),
(0.25, 0.5, 1.0),
(0.5, 1.0, 0.0),
(0.75, 0.0, 1.0),
(1.0, 1.0, 1.0),
),
'green':(
(0.0, 0.5, 0.5),
(0.25, 0.5, 0.0),
(0.5, 0.0, 1.0),
(0.75, 1.0, 1.0),
(1.0, 1.0, 1.0),
),
'blue':(
(0.0, 0.5, 0.5),
(0.25, 0.5, 1.0),
(0.5, 1.0, 0.0),
(0.75, 0.0, 1.0),
(1.0, 1.0, 1.0),
),
}
confusion_cmap = LinearSegmentedColormap('confusion', confusion_cmap_dict, 256)
nx, ny = confusion.shape
xs, ys = np.meshgrid( gs_x * np.arange(nx), gs_y * np.arange(ny) )
pylab.pcolormesh(xs, ys, confusion, cmap=confusion_cmap, vmin=0, vmax=3)
tick_locs = [ 0.375 + 0.75 * l for l in range(4) ]
tick_labels = [ "Correct\nNegative", "False\nAlarm", "Miss", "Hit" ]
bar = pylab.colorbar()
bar.locator = FixedLocator(tick_locs)
bar.formatter = FixedFormatter(tick_labels)
pylab.setp(pylab.getp(bar.ax, 'ymajorticklabels'), fontsize='large')
bar.update_ticks()
drawPolitical(map, scale_len=25)
if inset:
lb_y, lb_x = [ b.start for b in inset ]
ub_y, ub_x = [ b.stop + fudge for b in inset ]
inset_exp = (slice(lb_y, ub_y), slice(lb_x, ub_x))
axins = zoomed_inset_axes(pylab.gca(), 2, loc=4)
pylab.sca(axins)
pylab.pcolormesh(xs[inset_exp], ys[inset_exp], confusion[inset_exp], cmap=confusion_cmap, vmin=0, vmax=3)
drawPolitical(map)
pylab.xlim([lb_x * gs_x, (ub_x - 1) * gs_x])
pylab.ylim([lb_y * gs_y, (ub_y - 1) * gs_y])
mark_inset(axmain, axins, loc1=1, loc2=3, fc='none', ec='k')
pylab.sca(axmain)
pylab.suptitle(title)
pylab.savefig(file_name)
pylab.close()
return
def plotSoundingObservationsComposite(obs, map, scale_len, dimensions, title, file_name, radars=None):
figure = pylab.figure(figsize=(10,8))
x_size, y_size = dimensions
grid_spec = GridSpec(2, 2, width_ratios=[3, 1], height_ratios=[1, 3])
colors = [ 'r', 'g', 'b', 'c', 'm', '#660099', '#006666', '#99ff00', '#ff9900', '#666666' ]
names = [ "Sounding (NSSL)", "Sounding (NCAR)", "Sounding (NCAR)", "Sounding (NCAR)", "Sounding (NSSL)", "Sounding (NSSL)" ]
ob_ids = np.unique1d(obs['id'])
bottom = 0.075
top = 0.95
stretch_fac = (1 - top + bottom) / 2. - 0.011
pylab.subplots_adjust(left=0.1 + stretch_fac, bottom=bottom, right=0.9 - stretch_fac, top=top, hspace=0.075, wspace=0.075)
ax_xy = figure.add_subplot(grid_spec[2])
ax_xz = figure.add_subplot(grid_spec[0], sharex=ax_xy)
ax_yz = figure.add_subplot(grid_spec[3], sharey=ax_xy)
def labelMe(do_label, label):
if do_label:
return not do_label, label
else:
return do_label, None
color_num = 0
prof_label = True
sfc_label = True
asos_label = True
ttu_label = True
for ob_id in ob_ids:
plot_vertical = False
ob_idxs = np.where(obs['id'] == ob_id)[0]
these_obs = obs[ob_idxs]
ob_xs, ob_ys = map(these_obs['longitude'], these_obs['latitude'])
if np.any(ob_xs >= 0) and np.any(ob_xs <= x_size) and np.any(ob_ys >= 0) and np.any(ob_ys <= y_size):
if these_obs['obtype'][0] == "PROF":
prof_label, label = labelMe(prof_label, "Profiler")
color = "#ff9900"
marker = 'o'
ms = 3
plot_vertical = True
elif these_obs['obtype'][0] == "SNDG":
color = colors[color_num]
label = "Sounding"# % ob_id
marker = 'o'
ms = 3
plot_vertical = True
color_num += 1
elif ob_id[0] == "P":
color = "#999999"
sfc_label, label = labelMe(sfc_label, "Sounding")
marker = 'o'
ms = 3
elif ob_id[0] == "K":
asos_label, label = labelMe(asos_label, "ASOS")
color = 'k'
marker = '*'
ms = 5
elif ob_id[0] == "1" or ob_id[0] == "2":
ttu_label, label = labelMe(ttu_label, "TTU Sticknet")
color = "#003300"
marker = 'o'
ms = 3
ax_xy.plot(ob_xs, ob_ys, marker, mfc=color, mec=color, ms=ms, label=label)[0]
if plot_vertical:
ax_xz.plot(ob_xs, these_obs['elevation'] / 1000., marker, mfc=color, mec=color, ms=ms)
ax_yz.plot(these_obs['elevation'] / 1000., ob_ys, marker, mfc=color, mec=color, ms=ms)
dummy, zlim = ax_xz.get_ylim()
for ob_id in ob_ids:
ob_idxs = np.where(obs['id'] == ob_id)[0]
these_obs = obs[ob_idxs]
ob_xs, ob_ys = map(these_obs['longitude'], these_obs['latitude'])
plot_vertical = False
if np.any(ob_xs >= 0) and np.any(ob_xs <= x_size) and np.any(ob_ys >= 0) and np.any(ob_ys <= y_size):
if these_obs['obtype'][0] == "PROF":
color = "#ff9900"
plot_vertical = True
elif these_obs['obtype'][0] == "SNDG":
color = "#999999"
plot_vertical = True
if plot_vertical:
ax_xy.plot(ob_xs[0], ob_ys[0], 'x', color=color, ms=6)
ax_xz.plot([ob_xs[0], ob_xs[0]], [0, zlim], '--', color=color, lw=0.5)
ax_yz.plot([0, zlim], [ob_ys[0], ob_ys[0]], '--', color=color, lw=0.5)
if radars:
rad_label = True
for radar in radars:
rad_label, label = labelMe(rad_label, "Radar")
(lat, lon), range = radar
radar_x, radar_y = map(lon, lat)
ax_xy.plot(radar_x, radar_y, 'b<', ms=4, label=label)
ax_xy.add_patch(Circle((radar_x, radar_y), range, fc='none', ec='k'))
ax_xz.set_ylim(0, zlim)
ax_yz.set_xlim(0, zlim)
pylab.sca(ax_xy)
drawPolitical(map, scale_len=scale_len)
ax_xz.set_xlabel("x")
ax_xz.set_ylabel("z (km)")
ax_yz.set_xlabel("z (km)")
ax_yz.set_ylabel("y", rotation=-90)
line_objects = [ l for l in sorted(ax_xy.lines, key=lambda x: x.get_label()) if l.get_label()[0] != "_" ]
ax_xy.legend(line_objects, [ l.get_label() for l in line_objects], loc=1, numpoints=1, ncol=2, prop={'size':'medium'}, bbox_to_anchor=(1.28, 1.07, 0.33, 0.33), handletextpad=0, columnspacing=0)
pylab.suptitle(title)
pylab.savefig(file_name)
pylab.close()
return
def plotConfusion(confusion,
map,
grid_spacing,
title,
file_name,
inset=None,
fudge=16):
pylab.figure()
axmain = pylab.axes()
gs_x, gs_y = grid_spacing
confusion_cmap_dict = {
'red': (
(0.0, 0.5, 0.5),
(0.25, 0.5, 1.0),
(0.5, 1.0, 0.0),
(0.75, 0.0, 1.0),
(1.0, 1.0, 1.0),
),
'green': (
(0.0, 0.5, 0.5),
(0.25, 0.5, 0.0),
(0.5, 0.0, 1.0),
(0.75, 1.0, 1.0),
(1.0, 1.0, 1.0),
),
'blue': (
(0.0, 0.5, 0.5),
(0.25, 0.5, 1.0),
(0.5, 1.0, 0.0),
(0.75, 0.0, 1.0),
(1.0, 1.0, 1.0),
),
}
confusion_cmap = LinearSegmentedColormap('confusion', confusion_cmap_dict,
256)
nx, ny = confusion.shape
xs, ys = np.meshgrid(gs_x * np.arange(nx), gs_y * np.arange(ny))
pylab.pcolormesh(xs, ys, confusion, cmap=confusion_cmap, vmin=0, vmax=3)
tick_locs = [0.375 + 0.75 * l for l in range(4)]
tick_labels = ["Correct\nNegative", "False\nAlarm", "Miss", "Hit"]
bar = pylab.colorbar()
bar.locator = FixedLocator(tick_locs)
bar.formatter = FixedFormatter(tick_labels)
pylab.setp(pylab.getp(bar.ax, 'ymajorticklabels'), fontsize='large')
bar.update_ticks()
drawPolitical(map, scale_len=25)
if inset:
lb_y, lb_x = [b.start for b in inset]
ub_y, ub_x = [b.stop + fudge for b in inset]
inset_exp = (slice(lb_y, ub_y), slice(lb_x, ub_x))
axins = zoomed_inset_axes(pylab.gca(), 2, loc=4)
pylab.sca(axins)
pylab.pcolormesh(xs[inset_exp],
ys[inset_exp],
confusion[inset_exp],
cmap=confusion_cmap,
vmin=0,
vmax=3)
drawPolitical(map)
pylab.xlim([lb_x * gs_x, (ub_x - 1) * gs_x])
pylab.ylim([lb_y * gs_y, (ub_y - 1) * gs_y])
mark_inset(axmain, axins, loc1=1, loc2=3, fc='none', ec='k')
pylab.sca(axmain)
pylab.suptitle(title)
pylab.savefig(file_name)
pylab.close()
return
def plotObservationsComposite(obs, map, scale_len, title, file_name):
pylab.figure(figsize=(10, 8))
pylab.axes((0, 0, 1, 0.95))
colors = ['r', 'g', 'b', 'c', 'm', '#660099', '#ff9900', '#006666']
ob_ids = np.unique1d(obs['id'])
ttu_label = False
asos_label = False
sndg_label = False
for ob_id in ob_ids:
ob_idxs = np.where(obs['id'] == ob_id)[0]
these_obs = obs[ob_idxs]
ob_xs, ob_ys = map(these_obs['longitude'], these_obs['latitude'])
if ob_id[0] == "P":
ob_num = int(ob_id[1]) - 1
pylab.plot(ob_xs,
ob_ys,
'o',
mfc=colors[ob_num],
mec=colors[ob_num],
ms=3,
label="NSSL MM (%s)" % ob_id)
elif ob_id[0] == "K":
if not asos_label:
label = "ASOS"
asos_label = True
else:
label = None
pylab.plot(ob_xs[0], ob_ys[0], 'k*', ms=5, label=label)
elif ob_id[0] == "1" or ob_id[0] == "2":
if not ttu_label:
label = "TTU Sticknet"
ttu_label = True
else:
label = None
pylab.plot(ob_xs[0],
ob_ys[0],
'o',
mfc="#999999",
mec="#999999",
ms=3,
label=label)
elif these_obs[0]['obtype'] == "SNDG":
if not sndg_label:
label = "Sounding"
sndg_label = True
else:
label = None
pylab.plot(ob_xs[0], ob_ys[0], 'k^', ms=4, label=label)
drawPolitical(map, scale_len=scale_len)
line_objects = [
l for l in sorted(pylab.gca().lines, key=lambda x: x.get_label())
if l.get_label()[0] != "_"
]
pylab.legend(line_objects, [l.get_label() for l in line_objects],
loc=2,
numpoints=1,
prop={'size': 'medium'})
pylab.suptitle(title)
pylab.savefig(file_name)
pylab.close()
return
def plotSoundingObservationsComposite(obs,
map,
scale_len,
dimensions,
title,
file_name,
radars=None):
figure = pylab.figure(figsize=(10, 8))
x_size, y_size = dimensions
grid_spec = GridSpec(2, 2, width_ratios=[3, 1], height_ratios=[1, 3])
colors = [
'r', 'g', 'b', 'c', 'm', '#660099', '#006666', '#99ff00', '#ff9900',
'#666666'
]
names = [
"Sounding (NSSL)", "Sounding (NCAR)", "Sounding (NCAR)",
"Sounding (NCAR)", "Sounding (NSSL)", "Sounding (NSSL)"
]
ob_ids = np.unique1d(obs['id'])
bottom = 0.075
top = 0.95
stretch_fac = (1 - top + bottom) / 2. - 0.011
pylab.subplots_adjust(left=0.1 + stretch_fac,
bottom=bottom,
right=0.9 - stretch_fac,
top=top,
hspace=0.075,
wspace=0.075)
ax_xy = figure.add_subplot(grid_spec[2])
ax_xz = figure.add_subplot(grid_spec[0], sharex=ax_xy)
ax_yz = figure.add_subplot(grid_spec[3], sharey=ax_xy)
def labelMe(do_label, label):
if do_label:
return not do_label, label
else:
return do_label, None
color_num = 0
prof_label = True
sfc_label = True
asos_label = True
ttu_label = True
for ob_id in ob_ids:
plot_vertical = False
ob_idxs = np.where(obs['id'] == ob_id)[0]
these_obs = obs[ob_idxs]
ob_xs, ob_ys = map(these_obs['longitude'], these_obs['latitude'])
if np.any(ob_xs >= 0) and np.any(ob_xs <= x_size) and np.any(
ob_ys >= 0) and np.any(ob_ys <= y_size):
if these_obs['obtype'][0] == "PROF":
prof_label, label = labelMe(prof_label, "Profiler")
color = "#ff9900"
marker = 'o'
ms = 3
plot_vertical = True
elif these_obs['obtype'][0] == "SNDG":
color = colors[color_num]
label = "Sounding" # % ob_id
marker = 'o'
ms = 3
plot_vertical = True
color_num += 1
elif ob_id[0] == "P":
color = "#999999"
sfc_label, label = labelMe(sfc_label, "Sounding")
marker = 'o'
ms = 3
elif ob_id[0] == "K":
asos_label, label = labelMe(asos_label, "ASOS")
color = 'k'
marker = '*'
ms = 5
elif ob_id[0] == "1" or ob_id[0] == "2":
ttu_label, label = labelMe(ttu_label, "TTU Sticknet")
color = "#003300"
marker = 'o'
ms = 3
ax_xy.plot(ob_xs,
ob_ys,
marker,
mfc=color,
mec=color,
ms=ms,
label=label)[0]
if plot_vertical:
ax_xz.plot(ob_xs,
these_obs['elevation'] / 1000.,
marker,
mfc=color,
mec=color,
ms=ms)
ax_yz.plot(these_obs['elevation'] / 1000.,
ob_ys,
marker,
mfc=color,
mec=color,
ms=ms)
dummy, zlim = ax_xz.get_ylim()
for ob_id in ob_ids:
ob_idxs = np.where(obs['id'] == ob_id)[0]
these_obs = obs[ob_idxs]
ob_xs, ob_ys = map(these_obs['longitude'], these_obs['latitude'])
plot_vertical = False
if np.any(ob_xs >= 0) and np.any(ob_xs <= x_size) and np.any(
ob_ys >= 0) and np.any(ob_ys <= y_size):
if these_obs['obtype'][0] == "PROF":
color = "#ff9900"
plot_vertical = True
elif these_obs['obtype'][0] == "SNDG":
color = "#999999"
plot_vertical = True
if plot_vertical:
ax_xy.plot(ob_xs[0], ob_ys[0], 'x', color=color, ms=6)
ax_xz.plot([ob_xs[0], ob_xs[0]], [0, zlim],
'--',
color=color,
lw=0.5)
ax_yz.plot([0, zlim], [ob_ys[0], ob_ys[0]],
'--',
color=color,
lw=0.5)
if radars:
rad_label = True
for radar in radars:
rad_label, label = labelMe(rad_label, "Radar")
(lat, lon), range = radar
radar_x, radar_y = map(lon, lat)
ax_xy.plot(radar_x, radar_y, 'b<', ms=4, label=label)
ax_xy.add_patch(
Circle((radar_x, radar_y), range, fc='none', ec='k'))
ax_xz.set_ylim(0, zlim)
ax_yz.set_xlim(0, zlim)
pylab.sca(ax_xy)
drawPolitical(map, scale_len=scale_len)
ax_xz.set_xlabel("x")
ax_xz.set_ylabel("z (km)")
ax_yz.set_xlabel("z (km)")
ax_yz.set_ylabel("y", rotation=-90)
line_objects = [
l for l in sorted(ax_xy.lines, key=lambda x: x.get_label())
if l.get_label()[0] != "_"
]
ax_xy.legend(line_objects, [l.get_label() for l in line_objects],
loc=1,
numpoints=1,
ncol=2,
prop={'size': 'medium'},
bbox_to_anchor=(1.28, 1.07, 0.33, 0.33),
handletextpad=0,
columnspacing=0)
pylab.suptitle(title)
pylab.savefig(file_name)
pylab.close()
return
def main():
base_time = datetime(2009, 6, 5, 18, 0, 0)
epoch = datetime(1970, 1, 1, 0, 0, 0)
base_epoch = (base_time - epoch).total_seconds()
times_seconds = range(14700, 18300, 300)
times = [base_time + timedelta(seconds=t) for t in times_seconds]
bounds = (slice(100, 180), slice(90, 170))
rev_bounds = [0]
rev_bounds.extend(bounds[::-1])
rev_bounds = tuple(rev_bounds)
proj = setupMapProjection(goshen_1km_proj, goshen_1km_gs, bounds=bounds)
map = Basemap(**proj)
obs_file_names = [
'psu_straka_mesonet.pkl', 'ttu_sticknet.pkl', 'asos.pkl',
'soundings_clip.pkl'
]
all_obs = loadObs(obs_file_names,
times,
map,
(goshen_1km_proj['width'], goshen_1km_proj['height']),
sounding_obs=['soundings_clip.pkl'])
refl_base = "hdf/KCYS/1km/goshen.hdfrefl2d"
refl_times = np.array(
[int(f[-6:]) for f in glob.glob("%s??????" % refl_base)])
refl_keep_times = []
refl_data = {}
for tt in times_seconds:
idx = np.argmin(np.abs(refl_times - tt))
if refl_times[idx] > tt and idx > 0:
idx -= 1
file_name = "%s%06d" % (refl_base, refl_times[idx])
hdf = nio.open_file(file_name, mode='r', format='hdf')
refl_keep_times.append(refl_times[idx])
refl_data[tt] = hdf.variables['refl2d'][rev_bounds]
for time, reg in inflow_stations.iteritems():
pylab.figure()
gs_x, gs_y = goshen_1km_gs
nx, ny = refl_data[time].shape
xs, ys = np.meshgrid(gs_x * np.arange(nx), gs_y * np.arange(ny))
pylab.contourf(xs, ys, refl_data[time], levels=np.arange(10, 80, 10))
for region, stations in reg.iteritems():
if region != 'sounding':
for station in stations:
idxs = np.where((all_obs['id'] == station)
& (all_obs['time'] == base_epoch + time))
ob_xs, ob_ys = map(all_obs['longitude'][idxs],
all_obs['latitude'][idxs])
if region == 'inflow': color = 'r'
elif region == 'outflow': color = 'b'
wdir = all_obs['wind_dir'][idxs]
wspd = all_obs['wind_spd'][idxs]
u = -wspd * np.sin(wdir * np.pi / 180.) * 1.94
v = -wspd * np.cos(wdir * np.pi / 180.) * 1.94
pylab.plot(ob_xs, ob_ys, "%so" % color)
pylab.barbs(ob_xs, ob_ys, u, v)
drawPolitical(map, scale_len=10)
pylab.savefig("inflow_stations_%06d.png" % time)
pylab.close()
return
def plot_map(radar_data, grid_spacing, title, file_name, color_bar='refl', topo=None, aux_field=None, vectors=None, obs=None):
# bounds = (slice(80, 160), slice(90, 170))
bounds = (slice(None), slice(None))
# bounds = (slice(242, 327), slice(199, 284))
pylab.figure()
pylab.subplots_adjust(left=0.02, right=0.98, top=0.90, bottom=0.02)
nx, ny = radar_data[bounds[::-1]].shape
proj = setupMapProjection(goshen_1km_proj, goshen_1km_gs, bounds=bounds)
map = Basemap(**proj)
radar_x, radar_y = map([-104.299004], [41.561497])
if topo is not None:
topo_data, topo_lats, topo_lons = topo
topo_lats, topo_lons = np.meshgrid(topo_lats, topo_lons)
topo_x, topo_y = map(topo_lons, topo_lats)
map.contourf(topo_x, topo_y, topo_data, cmap=pylab.get_cmap('gray'))
if color_bar == 'refl':
levels = range(10, 85, 5)
color_map = NWSRef #pylab.get_cmap('jet')
elif color_bar == 'radv':
levels = range(-35, 40, 5)
color_map = pylab.get_cmap('RdBu')
elif color_bar == 'pt':
levels = range(296, 321, 2)
color_map = pylab.get_cmap('jet')
print nx, ny
x, y = np.meshgrid(grid_spacing * np.arange(nx), grid_spacing * np.arange(ny))
map.contourf(x, y, radar_data[bounds[::-1]], levels=levels, cmap=color_map)
# map.plot(radar_x, radar_y, 'ko')
pylab.colorbar()
if aux_field is not None:
aux_levels, aux_data = aux_field
CS = map.contour(x, y, aux_data[bounds[::-1]], levels=aux_levels, colors='k', lw=0.75)
# pylab.clabel(CS, fmt='%.0f', inline_spacing=1, fontsize="12px")
drawPolitical(map, scale_len=25)
if obs is not None:
for stid, ob in obs.iteritems():
potential_temperature = (5. / 9. * (ob['temperature'] - 32) + 273.15) * (29.5250192 / ob['pressure']) ** (2. / 7.)
ob_x, ob_y = map(ob['Longitude'], ob['Latitude'])
ob_ax_x, ob_ax_y = (pylab.gca().transData + pylab.gca().transAxes.inverted()).transform(np.array([ob_x, ob_y]))
if ob_ax_x > 0 and ob_ax_x <= 1 and ob_ax_y > 0 and ob_ax_y <= 1:
pylab.gca().add_patch(Circle((ob_x, ob_y), 4000, fc='none', ec='k'))
pylab.text(ob_x, ob_y, "%5.1f" % potential_temperature, size='x-small', ha='right', va='bottom')
if vectors is not None:
stride = 4
u, v = vectors
pylab.quiver(x[::stride, ::stride], y[::stride, ::stride], u[::stride, ::stride], v[::stride, ::stride])
pylab.title(title)
pylab.savefig(file_name)
return
def plot_map(radar_data,
grid_spacing,
title,
file_name,
color_bar='refl',
topo=None,
aux_field=None,
vectors=None,
obs=None):
# bounds = (slice(80, 160), slice(90, 170))
bounds = (slice(None), slice(None))
# bounds = (slice(242, 327), slice(199, 284))
pylab.figure()
pylab.subplots_adjust(left=0.02, right=0.98, top=0.90, bottom=0.02)
nx, ny = radar_data[bounds[::-1]].shape
proj = setupMapProjection(goshen_1km_proj, goshen_1km_gs, bounds=bounds)
map = Basemap(**proj)
radar_x, radar_y = map([-104.299004], [41.561497])
if topo is not None:
topo_data, topo_lats, topo_lons = topo
topo_lats, topo_lons = np.meshgrid(topo_lats, topo_lons)
topo_x, topo_y = map(topo_lons, topo_lats)
map.contourf(topo_x, topo_y, topo_data, cmap=pylab.get_cmap('gray'))
if color_bar == 'refl':
levels = range(10, 85, 5)
color_map = NWSRef #pylab.get_cmap('jet')
elif color_bar == 'radv':
levels = range(-35, 40, 5)
color_map = pylab.get_cmap('RdBu')
elif color_bar == 'pt':
levels = range(296, 321, 2)
color_map = pylab.get_cmap('jet')
print nx, ny
x, y = np.meshgrid(grid_spacing * np.arange(nx),
grid_spacing * np.arange(ny))
map.contourf(x, y, radar_data[bounds[::-1]], levels=levels, cmap=color_map)
# map.plot(radar_x, radar_y, 'ko')
pylab.colorbar()
if aux_field is not None:
aux_levels, aux_data = aux_field
CS = map.contour(x,
y,
aux_data[bounds[::-1]],
levels=aux_levels,
colors='k',
lw=0.75)
# pylab.clabel(CS, fmt='%.0f', inline_spacing=1, fontsize="12px")
drawPolitical(map, scale_len=25)
if obs is not None:
for stid, ob in obs.iteritems():
potential_temperature = (5. / 9. * (ob['temperature'] - 32) +
273.15) * (29.5250192 /
ob['pressure'])**(2. / 7.)
ob_x, ob_y = map(ob['Longitude'], ob['Latitude'])
ob_ax_x, ob_ax_y = (pylab.gca().transData +
pylab.gca().transAxes.inverted()).transform(
np.array([ob_x, ob_y]))
if ob_ax_x > 0 and ob_ax_x <= 1 and ob_ax_y > 0 and ob_ax_y <= 1:
pylab.gca().add_patch(
Circle((ob_x, ob_y), 4000, fc='none', ec='k'))
pylab.text(ob_x,
ob_y,
"%5.1f" % potential_temperature,
size='x-small',
ha='right',
va='bottom')
if vectors is not None:
stride = 4
u, v = vectors
pylab.quiver(x[::stride, ::stride], y[::stride, ::stride],
u[::stride, ::stride], v[::stride, ::stride])
pylab.title(title)
pylab.savefig(file_name)
return