name = sys.argv[1]
f = Level3File(name)
# Pull the data out of the file object
datadict = f.sym_block[0][0]
# Turn into an array, then mask
data = np.ma.array(datadict['data'])
data[data == 0] = np.ma.masked
# Grab azimuths and calculate a range based on number of gates
az = np.array(datadict['start_az'] + [datadict['end_az'][-1]])
rng = np.linspace(0, f.max_range, data.shape[-1] + 1)
# Convert az,range to x,y
xlocs = rng * np.sin(np.deg2rad(az[:, np.newaxis]))
ylocs = rng * np.cos(np.deg2rad(az[:, np.newaxis]))
# Plot the data
norm, cmap = colortables.get_with_steps(ctable, 16, 16)
ax.pcolormesh(xlocs, ylocs, data, norm=norm, cmap=cmap)
ax.set_aspect('equal', 'datalim')
ax.set_xlim(xmin, xmax)
ax.set_ylim(xmin, xmax)
add_timestamp(ax, f.metadata['prod_time'], y=0.02, high_contrast=True)
if len(sys.argv) < 3 or sys.argv[2] == 'plot':
plt.show()
else:
plt.savefig(sys.argv[1] + '.png')
('NWS8bitVel', -100, 1.0)) # m/s
for v, ctable, ax in zip(('N0Q', 'N0U'), ctables, axes):
# Open the file
name = get_test_data('nids/KOUN_SDUS54_{}TLX_201305202016'.format(v),
as_file_obj=False)
f = Level3File(name)
# Pull the data out of the file object
datadict = f.sym_block[0][0]
# Turn into an array using the scale specified by the file
data = f.map_data(datadict['data'])
# Grab azimuths and calculate a range based on number of gates
az = np.array(datadict['start_az'] + [datadict['end_az'][-1]])
rng = np.linspace(0, f.max_range, data.shape[-1] + 1)
# Convert az,range to x,y
xlocs = rng * np.sin(np.deg2rad(az[:, np.newaxis]))
ylocs = rng * np.cos(np.deg2rad(az[:, np.newaxis]))
# Plot the data
norm, cmap = colortables.get_with_steps(*ctable)
ax.pcolormesh(xlocs, ylocs, data, norm=norm, cmap=cmap)
ax.set_aspect('equal', 'datalim')
ax.set_xlim(-40, 20)
ax.set_ylim(-30, 30)
add_timestamp(ax, f.metadata['prod_time'], y=0.02, high_contrast=True)
plt.show()
def main():
manager = mp.Manager()
results = manager.dict()
pool = mp.Pool(12)
jobs = []
for filepath in glob.glob(join(args["NEXRADL3"], '*')):
job = pool.apply_async(calculate_radar_stats, (results, filepath))
jobs.append(job)
for job in tqdm(jobs, desc="Bounding & Searching Data"):
job.get()
pool.close()
pool.join()
print('Sorting...')
columns = [
'datetime', 'metadata', 'sensorData', 'indices', 'xlocs', 'ylocs',
'data', 'polyVerts', 'offset', 'areaValue', 'refValue', 'varRefValue'
]
resultsDF = pd.DataFrame.from_dict(results,
orient='index',
columns=columns)
resultsDF['datetime'] = pd.to_datetime(resultsDF.datetime)
resultsDF.sort_values(by='datetime', inplace=True)
#resultsDF.to_csv(args["output"] + '.csv', index = False)
print(resultsDF[['areaValue', 'refValue']].head(5))
# --- Plot time series---
print('Plotting Slices...')
fig, axes = plt.subplots(8, 8, figsize=(30, 30))
date_format = mpl_dates.DateFormatter('%H:%Mz')
for i, (dt, record) in tqdm(enumerate(resultsDF.iterrows()),
desc='Plotting Slices'):
plotx = i % 8
ploty = int(i / 8)
negXLim = -.5
posXLim = 1.5
negYLim = -1.0
posYLim = 1.0
norm, cmap = colortables.get_with_steps('NWSReflectivity', 18, 16)
tempdata = record[
'data'] # create a deep copy of data to maipulate for plotting
tempdata[tempdata == 0] = np.ma.masked # mask out 0s for plotting
axes[ploty][plotx].pcolormesh(record['xlocs'],
record['ylocs'],
tempdata,
norm=norm,
cmap=cmap)
axes[ploty][plotx].set_aspect('equal', 'datalim')
axes[ploty][plotx].set_xlim(negXLim, posXLim)
axes[ploty][plotx].set_ylim(negYLim, posYLim)
pVXs, pVYs = zip(
*record['polyVerts']
) # create lists of x and y values for transformed polyVerts
axes[ploty][plotx].plot(pVXs, pVYs)
if negXLim < record['offset'][1] < posXLim and negYLim < record[
'offset'][0] < posYLim:
axes[ploty][plotx].plot(record['offset'][1], record['offset'][0],
'o') # Location of the radar
axes[ploty][plotx].text(
record['offset'][1], record['offset'][0],
record['sensorData']['siteID']
) # will plot outside limits of subplot if site falls outside range
axes[ploty][plotx].plot(0.0, 0.0, 'bx') # Location of the convection
axes[ploty][plotx].text(0.0, 0.0, str(args["convLatLon"]))
add_timestamp(axes[ploty][plotx],
record['datetime'],
y=0.02,
high_contrast=True)
axes[ploty][plotx].tick_params(axis='both', which='both')
print('Calculating Statistics...')
# pull data out of DF to make code cleaner
datetimes = resultsDF['datetime'].tolist()
#elapsedtimes = list(map(lambda x: x - min(datetimes), datetimes)) # not currently used, need to get this working
areaValues = resultsDF['areaValue'].tolist() # area ≥ 35dbz within ROI
refValues = (
np.array(resultsDF['refValue'].tolist()) - 65
) * 0.5 # mean reflectivity ≥ 35dbz within ROI (conversion: (val-65)*0.5) [https://mesonet.agron.iastate.edu/GIS/rasters.php?rid=2]
if np.nan in refValues:
warnings.warn(
"Radar inputs contains instance with no ref values >= thresh",
UserWarning)
#areaRefValues = np.multiply(areaValues, refValues) # product of area and reflectivity
varValues = resultsDF['varRefValue'].tolist(
) # variance of mean reflectivity ≥ 35dbz within ROI
cvValues = np.array([
a / b for a, b in zip(varValues, refValues)
]) * 0.5 # coeff. of variation for mean reflectivity ≥ 35dbz within ROI
# Frequency
N = len(refValues)
T = 1.0 / N
yf = fft(refValues)
w = blackman(N)
ywf = fft(refValues * w)
# Normalization
areaNorm = areaValues / np.max(areaValues)
xf = np.linspace(0, 1.0 / (2.0 * T), N // 2)
cvNorm = cvValues / np.max(cvValues)
areaCVValuesNormalized = np.multiply(areaNorm, cvNorm)
# Curve Smoothing
window = len(
resultsDF.index
) // 8 # ~2 hours/8 = ~15 mins ----> number of samples in moving average ( helps counteract more visible noise in higher temporal resolution data)
yAreaAvg = movingaverage(
areaValues, window)[window // 2:-window //
2] # create moving averages for time series'
yRefAvg = movingaverage(refValues, window)[window // 2:-window // 2]
yCVAvg = movingaverage(cvValues, window)[window // 2:-window // 2]
yAreaCVNormAvg = movingaverage(areaCVValuesNormalized,
window)[window // 2:-window // 2]
# local minima & maxima on smoothed curves
minTemporalwindow = window * 2
areaLocalMax = argrelmax(yAreaAvg)
areaLocalMin = argrelmin(yAreaAvg)
endpoints = []
if yAreaAvg[0] <= np.all(yAreaAvg[1:window]) or yAreaAvg[0] >= np.all(
yAreaAvg[1:window]):
endpoints.append(0)
if yAreaAvg[-1] <= np.all(
yAreaAvg[len(yAreaAvg - 1) - window:-2]) or yAreaAvg[-1] >= np.all(
yAreaAvg[len(yAreaAvg - 1) - window:-2]):
endpoints.append(len(yAreaAvg) - 1)
areaExtremaRaw = sorted(
areaLocalMax[0].tolist() + areaLocalMin[0].tolist() + endpoints
) # combine mins, maxes, and endpoints (if endpoints are an extreme) then sort
areaExtrema = [
x for x in areaExtremaRaw[1:]
if x - areaExtremaRaw[0] >= minTemporalwindow
] # remove maxima that are within threshold of first one
areaExtrema = [areaExtremaRaw[0]
] + areaExtrema # add back in forst one to begining
print(f'Area Extrema: {areaExtrema}')
refLocalMax = argrelmax(yRefAvg)
refLocalMin = argrelmin(yRefAvg)
endpoints = []
if yRefAvg[0] <= np.all(yRefAvg[1:window]) or yRefAvg[0] >= np.all(
yRefAvg[1:window]):
endpoints.append(0)
if yRefAvg[-1] <= np.all(
yRefAvg[len(yRefAvg - 1) - window:-2]) or yRefAvg[-1] >= np.all(
yRefAvg[len(yRefAvg - 1) - window:-2]):
endpoints.append(len(yRefAvg) - 1)
refExtremaRaw = sorted(refLocalMax[0].tolist() + refLocalMin[0].tolist() +
endpoints)
refExtrema = [
x for x in refExtremaRaw[1:]
if x - refExtremaRaw[0] >= minTemporalwindow
]
refExtrema = [refExtremaRaw[0]] + refExtrema
print(f'Ref Extrema: {refExtrema}')
#cvLocalMax = argrelmax(yCVAvg)
#cvLocalMin = argrelmin(yCVAvg)
#endpoints = []
#if yCVAvg[0] <= np.all(yCVAvg[1:window]) or yCVAvg[0] >= np.all(yCVAvg[1:window]):
# endpoints.append(0)
#if yCVAvg[-1] <= np.all(yCVAvg[len(yCVAvg-1)-window:-2]) or yCVAvg[-1] >= np.all(yCVAvg[len(yCVAvg-1)-window:-2]):
# endpoints.append(len(yCVAvg)-1)
#cvExtremaRaw = sorted(cvLocalMax[0].tolist()+cvLocalMin[0].tolist()+endpoints)
#cvExtrema = [x for x in cvExtremaRaw[1:] if x-cvExtremaRaw[0]>=minTemporalwindow]
#cvExtrema = [cvExtremaRaw[0]]+cvExtrema
#print(f'CV Extrema: {cvExtrema}')
yAreaCVNormLocalMax = argrelmax(yAreaCVNormAvg)
yAreaCVNormLocalMin = argrelmin(yAreaCVNormAvg)
endpoints = []
if yAreaCVNormAvg[0] <= np.all(
yAreaCVNormAvg[1:window]) or yAreaCVNormAvg[0] >= np.all(
yAreaCVNormAvg[1:window]):
endpoints.append(0)
if yAreaCVNormAvg[-1] <= np.all(
yAreaCVNormAvg[len(yAreaCVNormAvg - 1) -
window:-2]) or yAreaCVNormAvg[-1] >= np.all(
yAreaCVNormAvg[len(yAreaCVNormAvg - 1) -
window:-2]):
endpoints.append(len(yAreaCVNormAvg) - 1)
yAreaCVNormExtremaRaw = sorted(yAreaCVNormLocalMax[0].tolist() +
yAreaCVNormLocalMin[0].tolist() + endpoints)
yAreaCVNormExtrema = [
x for x in yAreaCVNormExtremaRaw[1:]
if x - yAreaCVNormExtremaRaw[0] >= minTemporalwindow
]
yAreaCVNormExtrema = [yAreaCVNormExtremaRaw[0]] + yAreaCVNormExtrema
print(f'AreaCVNorm Extrema: {yAreaCVNormExtrema}')
# Find slopes of Build-up Lines
# Area
xArea = np.array(datetimes[window // 2:-window // 2])[np.array(
[areaExtrema[0], areaExtrema[1]]
)] # grab datetime (x component) of the leftmost bounds (determined by window size), and the first extreme on the smoothed curve (sm curve is already bound by window, we need to apply bounds to datetimes)
xAreaDiff = xArea[1] - xArea[
0] # subtract the later value from the former to get our delta x
yArea = yAreaAvg[np.array(
[areaExtrema[0], areaExtrema[1]]
)] # grab the values (y component) of the sm curve at the begining and at the first extreme
yAreaDiff = yArea[1] - yArea[0] # subtract to find delta y
slopeArea = np.arctan(yAreaDiff /
xAreaDiff.seconds) # calc the slope angle
print(f'Slope Area: {slopeArea}')
# Reflectivity
xRef = np.array(datetimes[window // 2:-window // 2])[np.array(
[refExtrema[0], refExtrema[1]])]
xRefDiff = xRef[1] - xRef[0]
yRef = yRefAvg[np.array([refExtrema[0], refExtrema[1]])]
yRefDiff = yRef[1] - yRef[0]
slopeRef = np.arctan(yRefDiff / xRefDiff.seconds)
print(f'Slope Reflectivity: {slopeRef}')
# Product of Area and CV of ref
xProduct = np.array(datetimes[window // 2:-window // 2])[np.array(
[yAreaCVNormExtrema[0], yAreaCVNormExtrema[1]])]
XProductDiff = xProduct[1] - xProduct[0]
yProduct = yAreaCVNormAvg[np.array(
[yAreaCVNormExtrema[0], yAreaCVNormExtrema[1]])]
yProductDiff = yProduct[1] - yProduct[0]
slopeProduct = np.arctan(yProductDiff / XProductDiff.seconds)
print(f'Slope Product: {slopeProduct}')
print('Plotting Additional Data and Saving Output...')
# Area for Reflectivity ≥ 35dbz
axes[-1][-5].plot_date(datetimes, areaValues, linestyle='solid', ms=2)
axes[-1][-5].plot_date(datetimes[window // 2:-window // 2],
yAreaAvg,
linestyle='solid',
ms=2)
axes[-1][-5].plot_date(
np.array(datetimes[window // 2:-window // 2])[np.array(
[areaExtrema[0], areaExtrema[1]])],
yAreaAvg[np.array([areaExtrema[0], areaExtrema[1]])],
linestyle="solid",
ms=2)
axes[-1][-5].legend(['Area Delta', 'Sm. Area Delta', 'Build-up Rate'])
axes[-1][-5].xaxis.set_major_formatter(date_format)
plt.setp(axes[-1][-5].xaxis.get_majorticklabels(),
rotation=45,
ha="right",
rotation_mode="anchor")
axes[-1][-5].set_title('Area of Reflectivity ≥ 35dbz (km^2)')
# TODO: map y axis to dbz for output
# Mean of Reflectivity ≥ 35dbz
axes[-1][-4].plot_date(datetimes, refValues, linestyle='solid', ms=2)
#axes[-1][-4].plot_date(datetimes[window//2:-window//2], yRefAvg, linestyle='solid', ms=2)
#axes[-1][-4].plot_date(np.array(datetimes[window//2:-window//2])[np.array([0,refLocalMax[0][0]])], yRefAvg[np.array([0,refLocalMax[0][0]])], linestyle="solid", ms=2)
axes[-1][-4].plot_date(datetimes[window // 2:-window // 2],
yRefAvg,
linestyle='solid',
ms=2)
axes[-1][-4].plot_date(np.array(
datetimes[window // 2:-window // 2])[np.array(
[refExtrema[0], refExtrema[1]])],
yRefAvg[np.array([refExtrema[0], refExtrema[1]])],
linestyle="solid",
ms=2)
axes[-1][-4].legend(['Ref Delta', 'Sm. Ref Delta', 'Build-up Rate'])
axes[-1][-4].xaxis.set_major_formatter(date_format)
plt.setp(axes[-1][-4].xaxis.get_majorticklabels(),
rotation=45,
ha="right",
rotation_mode="anchor")
axes[-1][-4].set_title('Mean of Reflectivity ≥ 35dbz')
# Product of cv reflectivity and area
axes[-1][-3].plot_date(datetimes,
areaCVValuesNormalized,
linestyle='solid',
ms=2)
axes[-1][-3].plot_date(datetimes[window // 2:-window // 2],
yAreaCVNormAvg,
linestyle='solid',
ms=2)
axes[-1][-3].plot_date(
np.array(datetimes[window // 2:-window // 2])[np.array(
[yAreaCVNormExtrema[0], yAreaCVNormExtrema[1]])],
yAreaCVNormAvg[np.array([yAreaCVNormExtrema[0],
yAreaCVNormExtrema[1]])],
linestyle="solid",
ms=2)
axes[-1][-3].legend(
['Area*cv_Ref Delta', 'Sm. Area*cv_Ref Delta', 'Build-up Rate'])
axes[-1][-3].xaxis.set_major_formatter(date_format)
plt.setp(axes[-1][-3].xaxis.get_majorticklabels(),
rotation=45,
ha="right",
rotation_mode="anchor")
axes[-1][-3].set_title('Norm Product: CV Reflectivity * Area ≥ 35dbz')
# Coeff. of Variance of Reflectivity ≥ 35dbz
axes[-1][-2].plot_date(datetimes, cvValues, linestyle='solid', ms=2)
axes[-1][-2].plot_date(datetimes[window // 2:-window // 2],
yCVAvg,
linestyle='solid',
ms=2)
axes[-1][-2].legend(['CV Delta', 'Sm. CV Delta'])
axes[-1][-2].xaxis.set_major_formatter(date_format)
plt.setp(axes[-1][-2].xaxis.get_majorticklabels(),
rotation=45,
ha="right",
rotation_mode="anchor")
axes[-1][-2].set_title('CV of Reflectivity ≥ 35dbz')
# Testing plot
axes[-1][-1].semilogy(xf[1:N // 2], 2.0 / N * np.abs(yf[1:N // 2]), '-b')
axes[-1][-1].semilogy(xf[1:N // 2], 2.0 / N * np.abs(ywf[1:N // 2]), '-r')
axes[-1][-1].legend(['FFT', 'FFT w. Window'])
#axes[-1][-1].plot(xf, 2.0/N * np.abs(yf[0:N//2]),linestyle='solid', ms=2)
#axes[-1][-1].plot_date(datetimes, yCVAvg, linestyle='solid')
#axes[-1][-1].xaxis.set_major_formatter(date_format)
plt.setp(axes[-1][-1].xaxis.get_majorticklabels(),
rotation=45,
ha="right",
rotation_mode="anchor")
axes[-1][-1].set_title('Testing Plot (Frequency)')
plt.tight_layout()
plt.savefig(args["output"] + 'Nexrad.png') # Set the output file name
#plt.show()
f_o = open(args["output"] + 'log_stats_area_nexrad.txt', 'a')
f_o.write(datetimes[0].strftime("%Y%m%d%H%M%S") + '\t' +
str(args["convLatLon"]) + '\t' + str(args["convBearing"]) +
'\t' + str(args["scaleFactor"]) + '\t' +
str(np.max(areaValues)) + '\t' + str(np.max(refValues)) + '\t' +
str(slopeArea) # std dev of LIS aligned data
+ '\t' + str(slopeRef) + '\t' + str(slopeProduct) + '\n')
f_o.close()
add_metpy_logo(fig, 190, 85, size='large')
for v, ctable, ax in zip(('N0Q', 'N0U'), ('NWSReflectivity', 'NWSVelocity'), axes):
# Open the file
name = get_test_data('nids/KOUN_SDUS54_{}TLX_201305202016'.format(v), as_file_obj=False)
f = Level3File(name)
# Pull the data out of the file object
datadict = f.sym_block[0][0]
# Turn into an array, then mask
data = np.ma.array(datadict['data'])
data[data == 0] = np.ma.masked
# Grab azimuths and calculate a range based on number of gates
az = np.array(datadict['start_az'] + [datadict['end_az'][-1]])
rng = np.linspace(0, f.max_range, data.shape[-1] + 1)
# Convert az,range to x,y
xlocs = rng * np.sin(np.deg2rad(az[:, np.newaxis]))
ylocs = rng * np.cos(np.deg2rad(az[:, np.newaxis]))
# Plot the data
norm, cmap = colortables.get_with_steps(ctable, 16, 16)
ax.pcolormesh(xlocs, ylocs, data, norm=norm, cmap=cmap)
ax.set_aspect('equal', 'datalim')
ax.set_xlim(-40, 20)
ax.set_ylim(-30, 30)
add_timestamp(ax, f.metadata['prod_time'], y=0.02, high_contrast=True)
plt.show()
def plot_U_W_P_T(u, w, p, t, time, x, y, plot_prefix="workshop"):
plot_filename = "%s.%s" % (plot_prefix, output_format)
fig, ((ax1, ax2, ax3, ax4)) = P.subplots(4,
1,
sharey=True,
sharex=True,
figsize=(6, 8))
yy, xx = N.meshgrid(y, x)
yy = yy / 1000.
xx = xx / 1000.
clevels = N.arange(-35., 40., 5.)
norm, cmap = colortables.get_with_steps('viridis', clevels.shape[0], 5.)
plot = ax1.contourf(xx, yy, u, clevels, cmap=cmap)
# cbar = ax1.colorbar(plot,location='right',pad="5%")
# cbar.set_label("U")
plot = ax1.contour(xx, yy, u, clevels[::2], colors='k', linewidths=0.5)
title = ("U-Wind (m/s)")
ax1.set_title(title, loc='left', fontsize=8)
start, end = ax1.get_xlim()
# ax1.xaxis.set_ticks(N.arange(start, end+6, 6))
ax1.xaxis.set_ticks(N.arange(start, end + 11, 10))
ax1.xaxis.set_major_formatter(ticker.FormatStrFormatter('%d'))
start, end = ax1.get_ylim()
ax1.yaxis.set_ticks(N.arange(start, end, 2))
ax1.yaxis.set_major_formatter(ticker.FormatStrFormatter('%d'))
at = AnchoredText(
"Max U: %4.1f \n Min U: %4.1f" % (u.max(), u.min()),
loc=1,
prop=dict(size=10),
frameon=True,
)
at.patch.set_boxstyle("round,pad=0.,rounding_size=0.2")
ax1.add_artist(at)
if N.abs(t).max() > 5.0:
clevels = N.arange(-30., 32., 2.)
else:
clevels = N.arange(-1., 1.1, 0.1) / 10.
norm, cmap = colortables.get_with_steps('viridis', clevels.shape[0],
clevels[1] - clevels[0])
wmask = N.ma.masked_array(w, mask=[N.abs(w) <= _min_w])
plot = ax2.contourf(xx, yy, wmask, clevels, cmap=cmap)
# cbar = plot.colorbar(plot,location='right',pad="5%")
plot = ax2.contour(xx, yy, wmask, clevels[::2], colors='k', linewidths=0.5)
# cbar.set_label('%s' % ("$m s^{-1}$"))
title = ("Vertical Velocity (m/s)")
ax2.set_title(title, loc='left', fontsize=8)
at = AnchoredText(
"Max W: %4.1f \n Min W: %4.1f" % (w.max(), w.min()),
loc=1,
prop=dict(size=10),
frameon=True,
)
at.patch.set_boxstyle("round,pad=0.,rounding_size=0.2")
ax2.add_artist(at)
if N.abs(t).max() > 5.0:
clevels = N.arange(-16., 10., 1.)
else:
clevels = N.arange(-100, 105, 5)
clevels = 0.01 * clevels[clevels != 0]
norm, cmap = colortables.get_with_steps('viridis', clevels.shape[0],
clevels[1] - clevels[0])
tmask = N.ma.masked_array(t, mask=[N.abs(t) <= 0.00001])
plot = ax3.contourf(xx, yy, tmask, clevels, cmap=cmap)
# cbar = ax3.colorbar(plot,location='right',pad="5%")
plot = ax3.contour(xx, yy, tmask, clevels, colors='k', linewidths=0.5)
# cbar.set_label('%s' % ("K"))
title = ("Pert. Potential Temperature (K)")
ax3.set_title(title, loc='left', fontsize=8)
at = AnchoredText(
"Max TH: %5.3f \n Min TH: %5.3f" % (t.max(), t.min()),
loc=1,
prop=dict(size=10),
frameon=True,
)
at.patch.set_boxstyle("round,pad=0.,rounding_size=0.2")
ax3.add_artist(at)
if N.abs(t).max() > 5.0:
clevels = N.arange(-1000., 1000., 100.0)
# clevels = N.arange(-3.,3.,0.1)
else:
clevels = N.arange(-15., 15.5, 0.5)
norm, cmap = colortables.get_with_steps('viridis', clevels.shape[0],
clevels[1] - clevels[0])
plot = ax4.contourf(xx, yy, p, clevels, cmap=cmap)
# cbar = ax4.colorbar(plot,location='right',pad="5%")
plot = ax4.contour(xx, yy, p, clevels[::2], colors='k', linewidths=0.5)
# cbar.set_label('%s' % ("mb"))
title = ("Pressure (mb)")
ax4.set_title(title, fontsize=8)
ax4.set_xlabel('X km', fontsize=8)
at = AnchoredText(
"Max P: %4.1f \n Min P: %4.1f" % (p.max(), p.min()),
loc=1,
prop=dict(size=10),
frameon=True,
)
at.patch.set_boxstyle("round,pad=0.,rounding_size=0.2")
ax4.add_artist(at)
if int(time) < 1000:
title = ("Time = %3d sec" % (int(time)))
else:
title = ("Time = %4d sec" % (int(time)))
fig.suptitle(title, y=1.0, fontsize=10)
P.tight_layout(h_pad=0.5)
if output_format == "pdf":
print("\n Saving file %s" % (plot_filename))
fig.savefig(plot_filename, format="pdf", dpi=300)
if output_format == "png":
print("\n Saving file %s" % (plot_filename))
fig.savefig(plot_filename, format="png", dpi=300)
if interactive:
print(plot_filename)
os.system("open %s" % plot_filename)
return plot_filename
def cref_uv850(initial_time, fhour=0, model='ShangHai',
map_center=(117, 39), map_width=12, draw_wind=False):
"""
Analysis composite reflectivity and 850hPa wind.
Arguments:
initial_time {string or datetime}} --
initial time, string or datetime ojbect.
like '18042008' or datetime(2018, 4, 20, 8).
Keyword Arguments:
fhour {int} -- forecast hour (default: {0})
model {str} -- model name (default: {'ShangHai'})
map_center {tuple} -- map center (default: {(117, 39)})
map_width {int} -- map width (default: {12})
draw_wind {bool} -- draw 850hPa wind or not (default: {False})
Raises:
ValueError -- [description]
"""
# micaps data directory
data_dirs = {
'SHANGHAI': ['SHANGHAI_HR/COMPOSITE_REFLECTIVITY/ENTIRE_ATMOSPHERE',
'SHANGHAI_HR/UGRD/850', 'SHANGHAI_HR/VGRD/850'],
'BEIJING': ['BEIJING_MR/COMPOSITE_REFLECTIVITY/ENTIRE_ATMOSPHERE',
'BEIJING_MR/UGRD/850', 'BEIJING_MR/VGRD/850'],
'GRAPES_MESO': ['GRAPES_MESO_HR/RADAR_COMBINATION_REFLECTIVITY',
'GRAPES_MESO_HR/UGRD/850', 'GRAPES_MESO_HR/VGRD/850'],
'GRAPES_3KM': ['GRAPES_3KM/RADAR_COMBINATION_REFLECTIVITY',
'GRAPES_3KM/UGRD/850', 'GRAPES_3KM/VGRD/850']}
try:
data_dir = data_dirs[model.strip().upper()]
except KeyError:
raise ValueError('Unknown model, choose ShangHai, BeiJing, Grapes_meso of Grapes_3km.')
# get filename
filename = model_filename(initial_time, fhour)
# retrieve data from micaps server
cref = get_model_grid(data_dir[0], filename=filename)
if cref is None:
return
init_time = cref.coords['init_time'].values[0]
if draw_wind:
u850 = get_model_grid(data_dir[1], filename=filename)
if u850 is None:
return
v850 = get_model_grid(data_dir[2], filename=filename)
if v850 is None:
return
# prepare data
data = np.ma.masked_array(cref.values)
data[data == 9999] = np.ma.masked
data[data < 10] = np.ma.masked
cref_data = {'lon':cref.coords['lon'].values,
'lat':cref.coords['lat'].values,
'data':np.squeeze(data)}
if draw_wind:
uv850 = {'lon': u850.coords['lon'].values,
'lat': u850.coords['lat'].values,
'udata': np.squeeze(u850.values),
'vdata': np.squeeze(v850.values)}
# set up map projection
datacrs = ccrs.PlateCarree()
plotcrs = ccrs.AlbersEqualArea(
central_latitude=map_center[1], central_longitude=map_center[0],
standard_parallels=[30., 60.])
# set up figure
fig = plt.figure(figsize=(12, 9))
gs = mpl.gridspec.GridSpec(
1, 2, width_ratios=[1, .03],
bottom=.01, top=.99, hspace=0.01, wspace=0.01)
ax = plt.subplot(gs[0], projection=plotcrs)
# add model title
add_model_title(
'CREF (dBz), 850-hPa Winds', init_time, model=model,
fhour=fhour, fontsize=18, multilines=True)
# add map background
map_extent = (
map_center[0] - map_width/2.0, map_center[0] + map_width/2.0,
map_center[1] - map_width/3.0, map_center[1] + map_width/3.0)
ax.set_extent(map_extent, crs=datacrs)
add_china_map_2cartopy(
ax, name='province', edgecolor='black',
lw=2, zorder=100)
# draw composite reflectivity
x, y = np.meshgrid(cref_data['lon'], cref_data['lat'])
norm, cmap = colortables.get_with_steps('NWSReflectivity', 12, 4)
pm = ax.pcolormesh(x, y, cref_data['data'], norm=norm, cmap=cmap, transform=datacrs)
cax = plt.subplot(gs[1])
cb = plt.colorbar(pm, cax=cax, orientation='vertical', extendrect='True')
cb.set_label('Composite reflectivity', size=12)
# draw wind vector
if draw_wind:
x, y = np.meshgrid(uv850['lon'], uv850['lat'])
ax.quiver(x, y, uv850['udata'], uv850['vdata'],
transform=datacrs, regrid_shape=25)
# show figure
gs.tight_layout(fig)
plt.show()
def cref_uv850_compare(initial_time, fhour=0, map_center=(117, 39), map_width=12, draw_wind=False):
"""
Compare mesoscale model's composite reflectivity.
Arguments:
initial_time {string or datetime}} --
initial time, string or datetime ojbect.
like '18042008' or datetime(2018, 4, 20, 8).
Keyword Arguments:
fhour {int} -- forecast hour (default: {0})
map_center {tuple} -- map center (default: {(117, 39)})
map_width {int} -- map width (default: {12})
draw_wind {bool} -- draw 850hPa wind or not (default: {False})
"""
# micaps data directory
data_dirs = {'SHANGHAI': ['SHANGHAI_HR/COMPOSITE_REFLECTIVITY/ENTIRE_ATMOSPHERE',
'SHANGHAI_HR/UGRD/850', 'SHANGHAI_HR/VGRD/850'],
'BEIJING': ['BEIJING_MR/COMPOSITE_REFLECTIVITY/ENTIRE_ATMOSPHERE',
'BEIJING_MR/UGRD/850', 'BEIJING_MR/VGRD/850'],
'GRAPES_MESO': ['GRAPES_MESO_HR/RADAR_COMBINATION_REFLECTIVITY',
'GRAPES_MESO_HR/UGRD/850', 'GRAPES_MESO_HR/VGRD/850'],
'GRAPES_3KM': ['GRAPES_3KM/RADAR_COMBINATION_REFLECTIVITY',
'GRAPES_3KM/UGRD/850', 'GRAPES_3KM/VGRD/850']}
# get filename
filename = model_filename(initial_time, fhour)
# set up map projection
datacrs = ccrs.PlateCarree()
plotcrs = ccrs.AlbersEqualArea(
central_latitude=map_center[1], central_longitude=map_center[0],
standard_parallels=[30., 60.])
# set up figure
fig = plt.figure(figsize=(16, 12))
axes_class = (GeoAxes, dict(map_projection=plotcrs))
grid = AxesGrid(fig, 111, axes_class=axes_class, nrows_ncols=(2, 2),
axes_pad=0.05, cbar_location='right', cbar_mode='single',
cbar_pad=0.05, label_mode='')
# loop every data directory
for index, key in enumerate(data_dirs):
# get axis and data directory
ax = grid[index]
data_dir = data_dirs[key]
# retrieve data from micaps server
cref = get_model_grid(data_dir[0], filename=filename)
if cref is None:
return
init_time = cref.coords['init_time'].values[0]
if draw_wind:
u850 = get_model_grid(data_dir[1], filename=filename)
if u850 is None:
return
v850 = get_model_grid(data_dir[2], filename=filename)
if v850 is None:
return
# add title
if index == 0:
initial_str, fhour_str, valid_str = get_model_time_stamp(init_time, fhour)
fig.suptitle('CREF (dBz), 850-hPa Winds ' + initial_str + '; ' + fhour_str + '; ' + valid_str,
x=0.5, y=0.9, fontsize=16)
# prepare data
data = np.ma.masked_array(cref.values)
data[data == 9999] = np.ma.masked
data[data < 10] = np.ma.masked
cref_data = {'lon':cref.coords['lon'].values,
'lat':cref.coords['lat'].values,
'data':np.squeeze(data)}
if draw_wind:
uv850 = {'lon': u850.coords['lon'].values,
'lat': u850.coords['lat'].values,
'udata': np.squeeze(u850.values),
'vdata': np.squeeze(v850.values)}
# add map background
map_extent = (
map_center[0] - map_width/2.0, map_center[0] + map_width/2.0,
map_center[1] - map_width/3.0, map_center[1] + map_width/3.0)
ax.set_extent(map_extent, crs=datacrs)
add_china_map_2cartopy(
ax, name='province', edgecolor='black',
lw=2, zorder=100)
# draw composite reflectivity
x, y = np.meshgrid(cref_data['lon'], cref_data['lat'])
norm, cmap = colortables.get_with_steps('NWSReflectivity', 12, 4)
pm = ax.pcolormesh(x, y, cref_data['data'], norm=norm, cmap=cmap, transform=datacrs)
# draw wind vector
if draw_wind:
x, y = np.meshgrid(uv850['lon'], uv850['lat'])
ax.quiver(x, y, uv850['udata'], uv850['vdata'],
transform=datacrs, regrid_shape=25)
# add title
add_titlebox(ax, key)
# add color bar
cbar = grid.cbar_axes[0].colorbar(pm)
# show figure
plt.show()
