def plotdict(datadict,
labels=None,
outfig='./somefig.png',
title='A boxplot from a dictionary'):
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.set_title(title)
#sys.path.append('/usr/people/plas/python/tools')
import boxplot_class_JK as mbox
if labels == None: labels = sorted(datadict.keys())
if len(labels) < len(datadict.keys()):
print 'Warning: too few labels, using keys, \nlabels:', labels, '\nkeys: ', datadict.keys(
)
labels = sorted(datadict.keys())
pb = mbox.MultiBoxPlot(ax)
for f, lab in zip(sorted(datadict.keys()), labels):
pb.addmultibox([datadict[f]], label=str(lab))
pb.render()
plotfile = outfig
sf = fig.savefig(plotfile)
print 'plotted ', plotfile
plt.close(fig)
def plotmultidict(datadict,
labels=None,
outfig='./somefig.png',
title='Grouped box plot'):
'''
makes a boxplot of different datasets, expects a dict
'''
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.set_title(title)
#sys.path.append('/usr/people/plas/python/tools')
import boxplot_class_JK as mbox
if labels == None: labels = datadict.keys()
if len(labels) < len(datadict.keys()):
print 'Warning: too few labels, using keys, ' + '\nlabels:', labels, '\nkeys: ', datadict.keys(
)
labels = datadict.keys()
pb = mbox.MultiBoxPlot(ax)
cases = sorted(datadict.keys()) ## NB cases are sorted alphabetically!
if str in [type(c) for c in cases]:
print 'multidict: ', cases, datadict[cases[0]].keys()
labels = sorted(datadict[cases[0]].keys())
# take the first case as determining the labels etc(?)
for f, lab in zip(sorted(datadict[cases[0]].keys()), labels):
# [datadict[c1][f],datadict[c2][f],...]
pb.addmultibox([datadict[c][f] for c in cases], label=str(lab))
pb.addlegend(cases, loc=4)
else: # assume it is a 1 case dict
print 'one case dict: ', datadict.keys()
for f, lab in zip(sorted(datadict[c].keys()), labels):
pb.addmultibox([datadict[f]], label=str(lab))
pb.render()
plotfile = outfig
sf = fig.savefig(plotfile)
print 'plotted ', plotfile
plt.close(fig)
def boxplot_leadtime(df, par='t2m', outdir='./', outfile=None):
import matplotlib.pyplot as plt
import boxplot_class_JK as mbox
# create figure canvas, axes
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
pb = mbox.MultiBoxPlot(ax)
data = [
df[df.leadtime_x == leadtime][par]
for leadtime in df.leadtime_x.unique()
]
pb.addmultibox(data, label=par)
if outfile == None:
outfile = 'leadtime_{p}.png'.format(p=par)
fig.savefig(os.path.join(outdir, outfile))
return 0
def boxplotNoDict(dbfiles,
cases,
thresholds,
nbpts,
param='fss',
tab='stats',
colors=None,
baserateThreshold=0.2,
outdir='./'):
'''
makes comparative boxplots of param (default 'fss') based on the
HARP spatial databases "dbfiles" (eg 'spatial_BULL_3H_201310.db'),
described by "cases" (eg 'BULL','D11' etc), where the results are
generated with given thresholds and neighbourhoods (nbpts).
The table where this is set is default set to 'stats'.
A baserateThreshold (default set to 0.2, so 20% of the pixels has
rain > threshold) is used to filter out so-called 'non-events' as
this gives a lot of points with value 0 in the distribution.
'''
try:
os.makedirs(outdir)
except:
print 'output to ', outdir
if colors == None: colors = ['blue', 'red', 'green', 'purple', 'orange']
sys.path.append('/usr/people/plas/python/tools')
import boxplot_class_JK as mbox
print cases
print dbfiles
#sys.exit(1)
outfiles = []
for thr in thresholds:
fignb = plt.figure()
axnb = fignb.add_subplot(1, 1, 1)
pbnb = mbox.MultiBoxPlot(axnb, colors=colors)
axnb.set_title(
'{PAR} (> {THR} mm/3h) as a function of NBR SIZE'.format(PAR=param,
THR=thr))
for nbp in nbpts:
## nbp plot: eg FSS as a function of neighbourhood size
datanbp = []
for c, db in zip(cases, dbfiles):
sql_fss_nbp = 'select {PAR} from {TAB} ' + 'where threshold = {THR} and nbpts = {NBP} and baserate > {BR}'
sqlin = sql_fss_nbp.format(PAR=param,
TAB=tab,
THR=str(thr),
NBP=str(nbp),
BR=str(baserateThreshold))
print sqlin
inf, sdat = get_data(db,
tab,
sqlin=sql_fss_nbp.format(
PAR=param,
TAB=tab,
THR=str(thr),
NBP=str(nbp),
BR=str(baserateThreshold)))
datanbp.append(np.array(
[d[0]
for d in sdat])) # append to make new list within a list
pbnb.addmultibox(datanbp, label=str(nbp))
## leadtime plot: eg FSS as a function of leadtime
figlt = plt.figure()
axlt = figlt.add_subplot(1, 1, 1)
pblt = mbox.MultiBoxPlot(axlt, colors=colors)
axlt.set_title(
'{PAR} (> {THR} mm/3h) as a function of leadtime, size {NBPTS}'
.format(PAR=param, THR=thr, NBPTS=nbp))
for lt in range(3, 49, 3):
datalt = []
for c, db in zip(cases, dbfiles):
sql_gen = 'select fss from stats '+\
'where threshold = {THR} and nbpts = {NBP} '+\
'and leadtime = {LT} and baserate > {BR}'
lts = 3600 * lt # POSIX: from hours to seconds
# getting the actual data
sql_fss_lt = sql_gen.format(THR=str(thr),
NBP=str(nbp),
LT=str(lts),
BR=str(baserateThreshold))
inf, sdat = get_data(db, tab, sqlin=sql_fss_lt)
datalt.append(np.array([d[0] for d in sdat]))
print 'Case:', c, db, sql_fss_lt
print 'for leadtime {LT} data: {DAT}'.format(LT=str(lt),
DAT=datalt)
pblt.addmultibox(datalt, label=str(lt))
pblt.addlegend(cases, loc=3)
pblt.render()
outfig = os.path.join(
outdir, '{PAR}_thr{THR}_nbp{NBP}_lt.png'.format(PAR=param,
THR=thr,
NBP=nbp))
plotfile = outfig
sf = figlt.savefig(plotfile)
print '+++++++++ plotted ', plotfile
plt.close(figlt)
outfiles.append(plotfile)
# end loop over NBPTS
pbnb.addlegend(cases, loc=4)
pbnb.render()
outfig = os.path.join(
outdir, '{PAR}_thr{THR}_nb.png'.format(PAR=param, THR=thr))
plotfile = outfig
sf = fignb.savefig(plotfile)
print '+++++++++ plotted ', plotfile
outfiles.append(plotfile)
plt.close(fignb)
# end loop over thresholds
return outfiles
#print fssdat
#sys.exit(0)
import matplotlib.pyplot
fig = matplotlib.pyplot.figure()
ax = fig.add_subplot(2, 1, 1)
bp = matplotlib.pyplot.boxplot(fssdat, notch=0, sym='+', vert=1, whis=1.5)
ax = fig.add_subplot(2, 1, 2)
#home = os.getenv('HOME')
#sys.path.append(os.path.join(home,'python','tools')
import boxplot_class_JK as mbox
pb = mbox.MultiBoxPlot(ax)
for f, b, d, lt in zip(fssdat, fssdat_M, fssdat_D, range(3, 49, 3)):
print len(f), len(b)
if len(f) == 0 or len(b) == 0 or len(d) == 0: break
pb.addmultibox([f, b, d], label=str(lt))
#pb.addlegend(['FSS','Baserate'])
pb.addlegend(['FSS BULL', 'FSS Mode-S', 'FSS D11'])
pb.render()
#bp = matplotlib.pyplot.boxplot(fssdat+baserdat, notch=0, sym='+', vert=1, whis=1.5)
plotfile = 'box_pyclass.png'
sf = fig.savefig(plotfile)