BALTRAD Quality Control
Import the file I/O module along with the main RAVE module containing useful constants
%matplotlib inline
import matplotlib
import _raveio, _rave
Create a simple plotter for B-scans, elaborating the example from the I/O exercise
# Two color palettes, one used in GoogleMapsPlugin, and the other from RAVE
from GmapColorMap import dbzh as dbzp
from rave_win_colors import continuous_MS as vradp
# Convert a 768-list palette to a matplotlib colorlist
def make_colorlist(pal):
colorlist = []
for i in range(0, len(pal), 3):
colorlist.append([pal[i]/255.0, pal[i+1]/255.0, pal[i+2]/255.0])
return colorlist
# Convert lists to colormaps
dbzcl = make_colorlist(dbzp)
vradcl = make_colorlist(vradp)
# Then create a simple plotter
import matplotlib.pyplot as plt
#from types import StringType
StringType = type('')
def plot(data, colorlist=dbzcl, title="B-scan"):
mini, maxi = data.shape.index(min(data.shape)), data.shape.index(max(data.shape))
figsize=(16,12) if mini == 0 else (12,8)
fig = plt.figure(figsize=figsize)
plt.title(title)
clist=colorlist if type(colorlist)==StringType else matplotlib.colors.ListedColormap(colorlist)
plt.imshow(data, cmap=clist, clim=(0,255))
plt.colorbar(shrink=float(data.shape[mini])/data.shape[maxi])
Access the polar volume and plot VRAD data from the lowest scan
pvol = rio.object
plot(pvol.getScan(0).getParameter("VRADH").getData(), vradcl, "Original VRAD")
Dealias the volume
import _dealias
ret = _dealias.dealias(pvol)
Shift gears - back to reflectivity
rio = _raveio.open("data/plrze_pvol_20120205T0430Z.h5")
pvol = rio.object
plot(pvol.getScan(0).getParameter("DBZH").getData(), title="Original DBZH")
Use the bRopo package’s quality plugin to identify and remove non-precipitation echoes
import odc_polarQC
import warnings
warnings.filterwarnings('ignore') # Suppress SyntaxWarning from Python2 code
odc_polarQC.algorithm_ids = ["ropo"]
pvol = odc_polarQC.QC(pvol)
Topographical beam-blockage QC using the beamb package’s quality plugin
import time
odc_polarQC.algorithm_ids = ["beamb"]
before = time.time()
pvol = odc_polarQC.QC(pvol)
after = time.time()
print("beamb runtime = %2.2f seconds" % (after-before))
beamb runtime = 6.69 seconds
Probability of overshooting
odc_polarQC.algorithm_ids = ["rave-overshooting"]
pvol = odc_polarQC.QC(pvol)
Accessing and manging data quality fields
scan = pvol.getScan(0)
print("Scan contains %i quality fields" % scan.getNumberOfQualityFields())
Scan contains 3 quality fields
for i in range(scan.getNumberOfQualityFields()):
qf = scan.getQualityField(i)
print("Quality field %i has identifier %s" % (i, qf.getAttribute("how/task")))
Quality field 0 has identifier fi.fmi.ropo.detector.classification
Quality field 1 has identifier se.smhi.detector.beamblockage
Quality field 2 has identifier se.smhi.detector.poo
Plot quality fields
Beam blockage
bb = scan.getQualityFieldByHowTask("se.smhi.detector.beamblockage")
plot(bb.getData(), "binary", "Quality indicator for beam blockage")
Chaining algorithms - new data
rio = _raveio.open("data/sekir.h5")
pvol = rio.object
odc_polarQC.algorithm_ids = ["ropo", "beamb", "radvol-att", "radvol-broad", "rave-overshooting"]
pvol = odc_polarQC.QC(pvol)
scan = pvol.getScan(0)
att = scan.getQualityField(2)
plot(att.getData(), "binary", "Attenuation")
