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assemble_rmsd.py
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assemble_rmsd.py
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import numpy as np
import matplotlib
matplotlib.use('agg')
import pylab
from datetime import datetime, timedelta
import cPickle
radar_id_1km_goshen = [ "KTLX" ]
experiments_cov_infl = [ "mult=1.15", "mult=1.15,noise=0.5", "mult=1.15,noise=0.75", "adapt=0.80", "adapt=0.80,noise=0.5", "adapt=0.80,noise=0.75", "relax=0.50", "relax=0.50,noise=0.5", "relax=0.50,noise=0.75", "no-infl" ]
#experiments_cov_infl = [ "mult=1.15", "adapt=0.80", "adapt=0.80,noise=0.5", "adapt=0.80,noise=0.75", "relax=0.50", "relax=0.50,noise=0.5", "relax=0.50,noise=0.75", "no-infl" ]
colors_cov_infl = {
"mult=1.15:KTLX":'r', "mult=1.15,noise=0.5:KTLX":'r', "mult=1.15,noise=0.75:KTLX":'r',
"adapt=0.80:KTLX":'g', "adapt=0.80,noise=0.5:KTLX":'g', "adapt=0.80,noise=0.75:KTLX":'g',
"relax=0.50:KTLX":'b', "relax=0.50,noise=0.5:KTLX":'b', "relax=0.50,noise=0.75:KTLX":'b',
"no-infl:KTLX":'m'
}
styles_cov_infl = {
"mult=1.15:KTLX":'-', "mult=1.15,noise=0.5:KTLX":'--', "mult=1.15,noise=0.75:KTLX":':',
"adapt=0.80:KTLX":'-', "adapt=0.80,noise=0.5:KTLX":'--', "adapt=0.80,noise=0.75:KTLX":':',
"relax=0.50:KTLX":'-', "relax=0.50,noise=0.5:KTLX":'--', "relax=0.50,noise=0.75:KTLX":':',
"no-infl:KTLX":'-'
}
radar_id_1km_goshen = [ "KCYS" ] #, "KFTG", "05XP" ]
experiments_1km_goshen = [ "1kmf-sndr0h=25km", "1kmf-zs25-no-05XP", "1kmf-zs25-no-mm", "1kmf-zs25-no-mm-05XP", "1kmf-z-no-v2", "1kmf-z-no-snd" ] #, "1kmf-prtrgn=1", "1kmf-newbc", "1kmf-snd-no-w", "1kmf-no-05XP", "1kmf-no-mm-05XP" ]
colors_1km_goshen = {
"1kmf-sndr0h=25km:KCYS":'k',
"1kmf-sndr0h=25km:KFTG":'k',
"1kmf-sndr0h=25km:05XP":'k',
"1kmf-zs25-no-05XP:KCYS":'r',
"1kmf-zs25-no-05XP:KFTG":'r',
"1kmf-zs25-no-05XP:05XP":'r',
"1kmf-zs25-no-mm:KCYS":'g',
"1kmf-zs25-no-mm:KFTG":'g',
"1kmf-zs25-no-mm:05XP":'g',
"1kmf-zs25-no-mm-05XP:KCYS":'b',
"1kmf-zs25-no-mm-05XP:KFTG":'b',
"1kmf-zs25-no-mm-05XP:05XP":'b',
"1kmf-z-no-v2:KCYS":'m',
"1kmf-z-no-v2:KFTG":'m',
"1kmf-z-no-v2:05XP":'m',
"1kmf-z-no-snd:KCYS":'c',
"1kmf-z-no-snd:KFTG":'c',
"1kmf-z-no-snd:05XP":'c',
}
styles_1km_goshen = {
"1km-control-mod-05XP:KCYS":'-',
"1km-control-mod-05XP:KFTG":'--',
"1km-control-mod-05XP:05XP":':',
"1km-control-no-mm:KCYS":'-',
"1km-control-no-mm:KFTG":'--',
"1km-control-no-mm:05XP":':',
"1km-control-mm:KCYS":'-',
"1km-control-mm:KFTG":'--',
"1km-control-mm:05XP":':',
}
exp_names_1km_goshen = {
"1kmf-control":"Control Boundary Conditions",
"1kmf-prtrgn=1":"Initial Perturbations in Storm Only",
"1kmf-newbc":"Control",
"1kmf-r0h=4km":"$r_{0h}$ = 4 km",
"1kmf-snd-no-w":"Sounding obs do not update $w$",
"1kmf-no-05XP":"No MWR-05XP data",
"1kmf-no-mm-05XP":"No MM or MWR-05XP data",
"1kmf-bc7dBZ,5ms":r"BC: $\sigma_Z$ = 7 dBZ, $\sigma_{v_r}$ = 5 m s$^{-1}$",
"1kmf-bcmult=1.03":r"BC: Multiplicative $\alpha$ = 1.03",
"1kmf-sndr0h=25km":r"Control (All VORTEX2 Obs)",
"1kmf-zs25-no-05XP":"No MWR-05XP",
"1kmf-zs25-no-mm":"No MM",
"1kmf-zs25-no-mm-05XP":"No MWR-05XP or MM",
"1kmf-z-no-v2":"No VORTEX2 Obs",
"1kmf-z-no-snd":"No Soundings"
}
radar_id_3km_goshen = [ "KCYS", "KFTG", "KRIW" ]
experiments_3km_goshen = [ "3km-fixed-radar", "3kmf-7dBZ,5ms", "3kmf-r0h=12km", "3kmf-mult=1.03" ] #"3kmf-r0h=18km", "3kmf-pr0h=16km", "3kmf-posnegpt", "3kmf-pr0h=16km,r0h=12km", "3kmf-n0r=2e6" ]
colors_3km_goshen = {
"3kmf-mult=1.03:KCYS":'r',
"3kmf-mult=1.03:KFTG":'r',
"3kmf-mult=1.03:KRIW":'r',
"3kmf-r0h=12km:KCYS":'g',
"3kmf-r0h=12km:KFTG":'g',
"3kmf-r0h=12km:KRIW":'g',
"3kmf-r0h=18km:KCYS":'r',
"3kmf-r0h=18km:KFTG":'r',
"3kmf-r0h=18km:KRIW":'r',
"3kmf-pr0h=16km,r0h=12km:KCYS":'b',
"3kmf-pr0h=16km,r0h=12km:KFTG":'b',
"3kmf-pr0h=16km,r0h=12km:KRIW":'b',
"3kmf-7dBZ,5ms:KCYS":'m',
"3kmf-7dBZ,5ms:KFTG":'m',
"3kmf-7dBZ,5ms:KRIW":'m',
"3kmf-n0r=2e6:KCYS":'c',
"3kmf-n0r=2e6:KFTG":'c',
"3kmf-n0r=2e6:KRIW":'c',
"3kmf-pr0h=16km:KCYS":'#999999',
"3kmf-pr0h=16km:KFTG":'#999999',
"3kmf-pr0h=16km:KRIW":'#999999',
"3kmf-posnegpt:KCYS":'#ff6600',
"3kmf-posnegpt:KFTG":'#ff6600',
"3kmf-posnegpt:KRIW":'#ff6600',
"3km-fixed-radar:KCYS":'k',
"3km-fixed-radar:KFTG":'k',
"3km-fixed-radar:KRIW":'k',
}
styles_3km_goshen = {
"3km-control:KCYS":'-',
"3km-control:KFTG":'--',
"3km-control:KRIW":':',
"3km-control-adapt=0.80:KCYS":'-',
"3km-control-adapt=0.80:KFTG":'--',
"3km-control-adapt=0.80:KRIW":':',
"3km-control-adapt=1.00:KCYS":'-',
"3km-control-adapt=1.00:KFTG":'--',
"3km-control-adapt=1.00:KRIW":':',
}
exp_names_3km_goshen = {
"3kmf-control":r"Control (New)",
"3km-control-adapt=0.80":r"RTPS $\alpha$ = 0.80",
"3km-control-adapt=1.00":r"RTPS $\alpha$ = 1.00",
"3km-control-r0h=12km":r"$r_{0h}$ = 12 km",
"3kmf-r0h=12km":r"$r_{0h}$ = 12 km",
"3kmf-r0h=18km":r"$r_{0h}$ = 18 km",
"3kmf-pr0h=16km":r"$r_{0h,i}$ = 16 km",
"3kmf-pr0h=16km,r0h=12km":r"$r_{0h,i}$ = 16 km, $r_{0h}$ = 12 km",
"3kmf-7dBZ,5ms":r"$\sigma_Z$ = 7 dBZ, $\sigma_{v_r}$ = 5 m s$^{-1}$",
"3km-n0r=8e5":r"$N_{0r}$ = 8 $\times$ 10$^5$ m$^{-4}$",
"3kmf-n0r=2e6":r"$N_{0r}$ = 2 $\times$ 10$^6$ m$^{-4}$",
"3km-alladapt":"RTPS only",
"3km-fixed-radar":"Control",
"3kmf-posnegpt":r"+ and - $\theta$ perturbations",
"3kmf-mult=1.03":r"Multiplicative $\alpha$ = 1.03 domain-wide",
}
def _loadRMSDFile(file_name):
try:
return [ float(v) for v in open(file_name, 'r').read().strip().split() ]
except IOError:
return tuple([np.nan, np.nan])
def _loadSpreadFile(file_name):
try:
return [ float(v) for v in open(file_name, 'r').read().strip().split() ]
except IOError:
return tuple([np.nan, np.nan])
def _loadconsistencyparameterfile(file_name):
try:
return [ float(v) for v in open(file_name, 'r').readline().strip().split() ]
except IOError:
return tuple([np.nan, np.nan])
def plotSubplots(times, data, legend_loc, y_label, y_lim, colors, styles, exp_names, title, file_name):
pylab.figure(figsize=(10, 8))
pylab.subplots_adjust(left=0.075, right=0.95, top=0.925, bottom=0.1, wspace=0.175, hspace=0.275)
radars = sorted(list(set([ name.split(":")[-1] for name in data.keys() ])))
n_rows = 1
n_cols = (len(radars) + 1) / 2
lines = {}
for idx, radar in enumerate(radars):
all_good = []
pylab.subplot(n_rows, n_cols, idx + 1)
for exp in sorted([ e for e in data.keys() if e.split(":")[-1] == radar ]):
good_idxs = np.where(~np.isnan(data[exp]))[0]
if len(good_idxs) > 0:
name = exp.split(":")[0]
line = pylab.plot(times[good_idxs], data[exp][good_idxs], color=colors[exp], label=exp_names[name])
lines[exp_names[name]] = line
all_good.append(good_idxs)
all_good_idxs = np.unique1d(np.concatenate(tuple(all_good)))
pylab.plot([times.min(), times.max()], [0, 0], color='k', linestyle=':')
pylab.axvline(14400, color='k', linestyle=':')
pylab.xlabel(r"Time (UTC)", size='xx-large')
pylab.ylabel(y_label, size='xx-large')
pylab.xlim((times.min(), times.max()))
pylab.ylim(y_lim)
unique_times = np.sort(np.unique1d(times))
pylab.xticks(unique_times[::2], [ (datetime(2009, 6, 5, 18, 0, 0) + timedelta(seconds=int(t))).strftime("%H%M") for t in unique_times ][::2], rotation=30, size='xx-large')
pylab.yticks(size='xx-large')
# pylab.title(radar)
labels, line_objs = zip(*lines.items())
# pylab.gcf().legend(line_objs, labels, 'lower right', prop={'size':'medium'})
pylab.legend(line_objs, labels, loc=legend_loc, prop={'size':'medium'})
pylab.suptitle(title)
pylab.savefig(file_name)
pylab.close()
return
def plot(times, rms_difference, legend_loc, y_label, y_lim, colors, styles, title, file_name):
# exp_names = { "1km-control-mod-05XP":"MM + MWR05XP", "1km-control-no-mm":"No MM", "1km-control-mm":"MM" }
exp_names = { "3km-control":r"5 dBZ, 3 m s$^{-1}$", "3km-control-adapt=0.80":r"RTPS $\alpha$ = 0.80", "3km-control-adapt=1.00":r"RTPS $\alpha$ = 1.00",
"3km-control-r0h=12km":r"$r_{0h}$ = 12 km", "3km-control-7dBZ,5ms":r'$\sigma_Z$ = 7 dBZ, $\sigma_{v_r}$ = 5 m s$^{-1}$' }
pylab.figure()
pylab.axes((0.1, 0.125, 0.85, 0.8))
all_good = []
for exp_name in sorted(rms_difference.keys()):
good_idxs = np.where(~np.isnan(rms_difference[exp_name]))[0]
name, radar= exp_name.split(':')
if len(good_idxs) > 0:
pylab.plot(times[good_idxs], rms_difference[exp_name][good_idxs], color=colors[exp_name], linestyle=styles[exp_name], label="%s (%s)" % (exp_names[name], radar))
all_good.append(good_idxs)
all_good_idxs = np.unique1d(np.concatenate(tuple(all_good)))
pylab.plot([times.min(), times.max()], [0, 0], color='k', linestyle=':')
pylab.xlabel(r"Time (UTC)", size='large')
pylab.ylabel(y_label, size='large')
pylab.xlim((times.min(), times.max()))
pylab.ylim(y_lim)
pylab.xticks(times[all_good_idxs], [ (datetime(2009, 6, 5, 18, 0, 0) + timedelta(seconds=int(t))).strftime("%H%M") for t in times[all_good_idxs] ], size='large', rotation=30)
pylab.yticks(size='large')
pylab.legend(loc=legend_loc, prop={'size':'small'})
pylab.suptitle(title)
pylab.savefig(file_name)
pylab.close()
return
def main():
base_path = "/caps2/tsupinie/"
# base_path = "/data6/tsupinie/goshen/"
tag = "1km"
if tag == "cov_infl":
radar_id = radar_id_cov_infl
experiments = experiments_cov_infl
colors = colors_cov_infl
styles = styles_cov_infl
t_ens_start = 0
t_ens_end = 3600
t_ens_step = 300
elif tag == "1km":
radar_id = radar_id_1km_goshen
experiments = experiments_1km_goshen
colors = colors_1km_goshen
styles = styles_1km_goshen
exp_names = exp_names_1km_goshen
t_ens_start = 10800
t_ens_end = 18000
t_ens_step = 300
elif tag == "3km":
radar_id = radar_id_3km_goshen
experiments = experiments_3km_goshen
colors = colors_3km_goshen
styles = styles_3km_goshen
exp_names = exp_names_3km_goshen
t_ens_start = 10800
t_ens_end = 18000
t_ens_step = 300
rmsd = {'vr':{}, 'ref':{}}
spread = {'vr':{}, 'ref':{}}
consistency = {'vr':{}, 'ref':{}}
rmsd_sawtooth = {'vr':{}, 'ref':{}}
spread_sawtooth = {'vr':{}, 'ref':{}}
for rid in radar_id:
for exp_name in experiments:
key = "%s:%s" % (exp_name, rid)
for quant in ['vr', 'ref']:
rmsd[quant][key] = []
spread[quant][key] = []
consistency[quant][key] = []
rmsd_sawtooth[quant][key] = []
spread_sawtooth[quant][key] = []
for t_ens in xrange(t_ens_start + t_ens_step, t_ens_end + t_ens_step, t_ens_step):
# if exp_name[:4] == "mult":
# rmsd_file_name = "%s/%s/%srmsdbg%06d" % (base_path, exp_name, rid, t_ens)
# else:
# rmsd_file_name = "%s/%s/%srmsdbg%06d" % (base_path, exp_name, rid, t_ens)
rmsd_file_name = "%s/%s/%srmsdbg%06d" % (base_path, exp_name, rid, t_ens)
vr_rmsd, ref_rmsd = _loadRMSDFile(rmsd_file_name)
rmsd['vr'][key].append(vr_rmsd)
rmsd['ref'][key].append(ref_rmsd)
spread_file_name = "%s/%s/%sspreadfcs%06d" % (base_path, exp_name, rid, t_ens)
vr_spread, ref_spread = _loadSpreadFile(spread_file_name)
spread['vr'][key].append(vr_spread)
spread['ref'][key].append(ref_spread)
consistency_file_name = "%s/%s/%sinnov%06d" % (base_path, exp_name, rid, t_ens)
vr_consistency, ref_consistency = _loadConsistencyParameterFile(consistency_file_name)
consistency['vr'][key].append(vr_consistency)
consistency['ref'][key].append(ref_consistency)
for t_ens in xrange(t_ens_start, t_ens_end + t_ens_step, t_ens_step):
rmsd_file_name = "%s/%s/%srmsdbg%06d" % (base_path, exp_name, rid, t_ens)
vr_rmsd, ref_rmsd = _loadRMSDFile(rmsd_file_name)
rmsd_sawtooth['vr'][key].append(vr_rmsd)
rmsd_sawtooth['ref'][key].append(ref_rmsd)
rmsd_file_name = "%s/%s/%srmsdan%06d" % (base_path, exp_name, rid, t_ens)
vr_rmsd, ref_rmsd = _loadRMSDFile(rmsd_file_name)
rmsd_sawtooth['vr'][key].append(vr_rmsd)
rmsd_sawtooth['ref'][key].append(ref_rmsd)
spread_file_name = "%s/%s/%sspreadfcs%06d" % (base_path, exp_name, rid, t_ens)
vr_spread, ref_spread = _loadRMSDFile(spread_file_name)
spread_sawtooth['vr'][key].append(vr_spread)
spread_sawtooth['ref'][key].append(ref_spread)
spread_file_name = "%s/%s/%sspreadana%06d" % (base_path, exp_name, rid, t_ens)
vr_spread, ref_spread = _loadRMSDFile(spread_file_name)
spread_sawtooth['vr'][key].append(vr_spread)
spread_sawtooth['ref'][key].append(ref_spread)
for quant in ['vr', 'ref']:
rmsd[quant][key] = np.array(rmsd[quant][key])
spread[quant][key] = np.array(spread[quant][key])
consistency[quant][key] = np.log10(np.array(consistency[quant][key]))
rmsd_sawtooth[quant][key] = np.array(rmsd_sawtooth[quant][key])
spread_sawtooth[quant][key] = np.array(spread_sawtooth[quant][key])
if quant == 'ref' and rid == "05XP":
rmsd[quant][key] = np.nan * np.zeros(rmsd[quant][key].shape)
spread[quant][key] = np.nan * np.zeros(spread[quant][key].shape)
consistency[quant][key] = np.nan * np.zeros(consistency[quant][key].shape)
rmsd_sawtooth[quant][key] = np.nan * np.zeros(rmsd_sawtooth[quant][key].shape)
spread_sawtooth[quant][key] = np.nan * np.zeros(spread_sawtooth[quant][key].shape)
times = np.arange(t_ens_start + t_ens_step, t_ens_end + t_ens_step, t_ens_step)
# plot(times, rmsd['ref'], 1, "RMS Difference (dBZ)", (0, 25), colors, styles, "RMS Analysis Innovation for Reflectivity", "rmsd_ref_%s.png" % tag)
# plot(times, spread['ref'], 2, "Spread (dBZ)", (0, 25), colors, styles, "Ensemble Analysis Spread in Reflectivity", "spread_ref_%s.png" % tag)
plotSubplots(times, consistency['ref'], 1, "Log Consistency Parameter (unitless)", (-1, 1), colors, styles, exp_names, "Log Consistency Parameter for Reflectivity", "consistency_ref_%s.png" % tag)
# plot(times, rmsd['vr'], 1, "RMS Difference (m s$^{-1}$)", (0, 10), colors, styles, "RMS Analysis Innovation for Radial Velocity", "rmsd_vr_%s.png" % tag)
# plot(times, spread['vr'], 2, "Spread (m s$^{-1}$)", (0, 10), colors, styles, "Ensemble Analysis Spread in Radial Velocity", "spread_vr_%s.png" % tag)
plotSubplots(times, consistency['vr'], 3, "Log Consistency Parameter (unitless)", (-1, 1), colors, styles, exp_names, "Log Consistency Parameter for Radial Velocity", "consistency_vr_%s.png" % tag)
times = np.arange(t_ens_start, t_ens_end + t_ens_step, t_ens_step)
plotSubplots(times[:, np.newaxis].repeat(2, axis=1).flatten('C'), rmsd_sawtooth['ref'], 4, "RMS Difference (dBZ)", (0, 20), colors, styles, exp_names, "RMS Innovation for Reflectivity", "rmsd_sawtooth_ref_%s.png" % tag)
plotSubplots(times[:, np.newaxis].repeat(2, axis=1).flatten('C'), rmsd_sawtooth['vr'], 2, "RMS Difference (m s$^{-1}$)", (0, 10), colors, styles, exp_names, "RMS Innovation for Radial Velocity", "rmsd_sawtooth_vr_%s.png" % tag)
plotSubplots(times[:, np.newaxis].repeat(2, axis=1).flatten('C'), spread_sawtooth['ref'], 1, "Spread (dBZ)", (0, 20), colors, styles, exp_names, "Spread for Reflectivity", "spread_sawtooth_ref_%s.png" % tag)
plotSubplots(times[:, np.newaxis].repeat(2, axis=1).flatten('C'), spread_sawtooth['vr'], 1, "Spread (m s$^{-1}$)", (0, 10), colors, styles, exp_names, "Spread for Radial Velocity", "spread_sawtooth_vr_%s.png" % tag)
return
if __name__ == "__main__":
main()