def plot_val_table(fsp, func, freq, quantile, stationlist):
#
freq = climpy.check_freq(freq)
if freq == _c.FR_MTH:
(periodtype, periodnames) = ("monthly", hdf5io.monthenum._names.keys())
periodnames = ['Dec', 'Jan', 'Feb', 'Mar', 'Apr', 'May',
'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov']
elif (freq // _c.FR_QTR == 1):
(periodtype, periodnames) = ("quarterly", fqtr2names[freq])
else:
raise NotImplementedError, "Unrecognized frequency %s" % freq
#
funcdict = dict(med='median',
mean='mean',
)
funcname = funcdict[func]
qtlname = '%03i' % (100 * quantile)
qtlleg = {'000':'Minimum',
'025':'Lower Quartile'}['%03i' % (100 * quantile)]
#
freq = climpy.check_freq(freq)
if freq == _c.FR_MTH:
(periodtype, periodnames) = ("monthly", hdf5io.monthenum._names.keys())
periodnames = ['Dec', 'Jan', 'Feb', 'Mar', 'Apr', 'May',
'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov']
elif (freq // _c.FR_QTR == 1):
(periodtype, periodnames) = ("quarterly", fqtr2names[freq])
else:
raise NotImplementedError, "Unrecognized frequency %s" % freq
#
#
flow_basename = "USGS_Q%s_%s%s.hdf5" % (qtlname, fqname, func)
# Define the HDF5 files ....................................
hdf5flow_filename = os.path.join(hdf5_archive_dir, flow_basename)
file = hdf5io.openFile(hdf5flow_filename, 'r+')
hdf5flow = hdf5io.remove_duplicates(file)
#
val = hdf5flow.getNode("/".join(['', 'values', 'modified', 'noenso'])).read()
for (marker, color, station) in zip(['o'] * 3 + ['s'] * 4,
['k'] * 3 + ['r'] * 4,
stationlist):
locval = val[val['id'] == station]
fsp.plot(range(12), [locval[p] for p in periodnames],
marker=marker, label=station, c=color)
fsp.set_yscale('log')
fsp.xaxis.set_major_locator(pylab.MultipleLocator(1))
fsp.set_xticklabels(periodnames)
fsp.set_ylabel("Flows (cfs)", fontweight='bold')
fsp.set_title("%s flows - %s" % (qtlleg, funcname), fontweight='bold')
pylab.show()
def plot_val(fsp, station, func, quantile, group='modified'):
#
freq = _c.FR_MTH
freq = climpy.check_freq(freq)
if freq == _c.FR_MTH:
# (periodtype, periodnames) = ("monthly", hdf5io.monthenum._names.keys())
periodnames = ['Dec', 'Jan', 'Feb', 'Mar', 'Apr', 'May',
'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov']
elif (freq // _c.FR_QTR == 1):
(periodtype, periodnames) = ("quarterly", fqtr2names[freq])
else:
raise NotImplementedError, "Unrecognized frequency %s" % freq
#
funcdict = dict(med='median',
mean='mean',
)
funcname = funcdict[func]
qtlname = '%03i' % (100 * quantile)
qtlleg = {'000':'Minimum',
'025':'Lower Quartile'}['%03i' % (100 * quantile)]
#
#
flow_basename = "USGS_Q%s_%s%s.hdf5" % (qtlname, fqname, func)
# Define the HDF5 files ....................................
hdf5flow_filename = os.path.join(hdf5_archive_dir, flow_basename)
file = hdf5io.openFile(hdf5flow_filename, 'r+')
hdf5flow = hdf5io.remove_duplicates(file)
#
if fsp.is_first_col():
labels = {'G':u"Global",
'C':u"La Ni\xf1a",
'N':u"Neutral",
'W':u"El Ni\xf1o",
'NS':"N/S",
'90': '_nolabel_',
'95': '_nolabel_',
'99': '_nolabel_'}
else:
labels = {'G':'_nolabel_',
'C':'_nolabel_',
'N':'_nolabel_',
'W':'_nolabel_',
'NS':"N/S",
'90': "90%",
'95': "95%",
'99': "99%"}
#
val = hdf5flow.getNode("/".join(['', 'values', group, 'noenso'])).read()
val = val[val['id'] == station]
fsp.plot(range(12), [val[p] for p in periodnames],
marker='^', ms=6, label=labels['G'], c=ENSOlines['G'])
#
val = hdf5flow.getNode("/".join(['', 'values', group, 'cold'])).read()
val = val[val['id'] == station]
val = numpy.array([val[p] for p in periodnames])
apx = hdf5flow.getNode("/".join(['', 'apxpvalues', group, 'CN'])).read()
apx = apx[apx['id'] == station]
apx = numpy.array([apx[p] for p in periodnames])
col_C = ENSOlines['C']
fsp.plot(range(12), val, marker='', label='_nolabel_', c=col_C)
fsp.plot(range(12), ma.array(val, mask=(apx < 0.1)),
marker='^', label=labels['C'], c=col_C)
fsp.plot(range(12), ma.array(val, mask=(apx >= 0.1) | (apx < 0.05)) ,
marker='d', label='_nolabel_', ls='', c=col_C, ms=10)
fsp.plot(range(12), ma.array(val, mask=(apx >= 0.05) | (apx < 0.01)) ,
marker='s', label='_nolabel_', ls='', c=col_C, ms=10)
fsp.plot(range(12), ma.array(val, mask=(apx > 0.01)) ,
marker='o', label='_nolabel_', ls='', c=col_C, ms=10)
#
val = hdf5flow.getNode("/".join(['', 'values', group, 'neutral'])).read()
val = val[val['id'] == station]
fsp.plot(range(12), [val[p] for p in periodnames],
marker='o', ms=6, label=labels['N'], c=ENSOlines['N'])
#
val = hdf5flow.getNode("/".join(['', 'values', group, 'warm'])).read()
val = val[val['id'] == station]
val = numpy.array([val[p] for p in periodnames])
apx = hdf5flow.getNode("/".join(['', 'apxpvalues', group, 'WN'])).read()
apx = apx[apx['id'] == station]
apx = numpy.array([apx[p] for p in periodnames])
col_W = ENSOlines['W']
fsp.plot(range(12), val, marker='', label='_nolabel_', c=col_W)
fsp.plot(range(12), ma.array(val, mask=(apx < 0.1)),
marker='^', label=labels['W'], c=col_W)
fsp.plot(range(12), ma.array(val, mask=(apx >= 0.1) | (apx < 0.05)) ,
marker='d', label=labels['90'], ls='', c=col_W, ms=10)
fsp.plot(range(12), ma.array(val, mask=(apx >= 0.05) | (apx < 0.01)) ,
marker='s', label=labels['95'], ls='', c=col_W, ms=10)
fsp.plot(range(12), ma.array(val, mask=(apx > 0.01)) ,
marker='o', label=labels['99'], ls='', c=col_W, ms=10)
#
#fsp.set_yscale('log')
#
fsp.xaxis.set_major_locator(pylab.MultipleLocator(1))
if fsp.is_last_row():
fsp.set_xticklabels(periodnames, fontsize=10)
else:
fsp.set_xticklabels([])
fsp.set_ylabel("Flows (cfs)", fontweight='bold', fontsize=10)
fsp.yaxis.major.formatter.set_powerlimits((-3, 3))
# fsp.set_title("%s Q%02i flows - %s" % (funcname.capitalize(),
# 100.*quantile,
# station), fontweight='bold')
fsp.set_title("%s" % (station), fontweight='bold', fontsize=10)
if fsp.is_last_row():
fsp.legend(numpoints=1, prop=dict(size='x-small'))
hdf5flow.close()
return
def plot_jma_vs_oni_comparison(freq='M'):
"""
Plots the comparison of ONI vs JMAI ENSO indices at the given frequency.
"""
# Check that we have a valid frequency
freq = climpy.check_freq(freq)
# Get the ENSO indices: annual for 12 months period, monthly on a monthly basis
JMAi = JMA.indices(full_year=False, reference_season='NDJ', minimum_size=6)
ONIi = ONI.indices(full_year=False, reference_season=None, minimum_size=5)
# Convert to the new frequency
if freq != climpy._c.FR_MTH:
JMAi = np.round_(JMAi.convert(freq, func=ma.mean))
ONIi = np.round_(ONIi.convert(freq, func=ma.mean))
# Convert the indices to a Nx12 series (N the nb of years)
JMAi = JMAi.convert('A')
ONIi = ONIi.convert('A')
#
JMAi = JMAi[JMAi.year >= 1900]
ONIi = ONIi[ONIi.year >= 1900]
#
j_colors = get_colormatrix(JMAi)
o_colors = get_colormatrix(ONIi)
#
fig = cpl.figure(num=1, figsize=(13, 5))
fig.clear()
fsp = fig.add_axes([0.05, 0.1, 0.85, 0.85])
(nrows, ncols) = j_colors.shape
edgecolors = 'none'
#
j_vert = np.array([[((i, j + 1), (i, j), (i + 1, j)) for i in range(ncols)]
for j in range(nrows)]).reshape(-1, 3, 2)
j_coll = matplotlib.collections.PolyCollection(j_vert,
facecolors=j_colors.ravel(),
edgecolors=edgecolors)
fsp.add_collection(j_coll)
#............................................
o_vert = np.array([[((i, j + 1), (i + 1, j + 1), (i + 1, j)) for i in range(ncols)]
for j in range(nrows)]).reshape(-1, 3, 2)
o_coll = matplotlib.collections.PolyCollection(o_vert,
facecolors=o_colors.ravel(),
edgecolors=edgecolors)
fsp.add_collection(o_coll)
#............................................
xy_vert = [((i, 0), (i, nrows)) for i in range(0, ncols, 12)]
xy_vert += [((0, j), (ncols, j)) for j in range(nrows)]
xy_coll = matplotlib.collections.LineCollection(xy_vert,
colors='k')
fsp.add_collection(xy_coll)
#............................................
fsp.xaxis.set_major_locator(cpl.MultipleLocator(12))
fsp.set_xticks(np.arange(0, ncols, 12) + 6)
fsp.set_xticklabels(["%02i" % i for i in range(12)])
fsp.set_xlim(0, ncols)
#............................................
fsp.set_yticks(np.arange(0, nrows) + 0.5)
fsp.set_yticklabels(["%i" % (i * 10 + 1900) for i in range(nrows + 1)])
fsp.set_ylim(0, nrows)
fsp.set_ylim(fsp.get_ylim()[::-1])
add_plot_legend(fig, lableft='JMA', labright='ONI')
cpl.show()
return fig
"%s Q%02i flows" % (funcname.capitalize(), quantile * 100),
fontweight='bold', ha='center')
return fig
#####-------------------------------------------------------------------------
if 1:
freq_names = dict([(v, k) for (k, v) in _c.freq_constants.iteritems()])
onlyGA = False
func = 'mean'
funclist = ('med', 'mean')
freq = 'M'
# freq = _c.FR_QTRSFEB
fqname = freq_names[climpy.check_freq(freq)][3:]
orientation = 'horizontal'
reference = 'G'
reference = 'N'
#
quantilelist = (0, 0.25)
stationlist = ('02226500', '02228000', '02314500',
'02353000', '02353500', '02356000', '02357000')
phasenames = ('CN', 'WN')
if 0:
fig = pylab.figure()
fsp = fig.add_subplot(221)
plot_val_table(fsp, 'mean', freq, 0, stationlist)
fsp = fig.add_subplot(222)
plot_val_table(fsp, 'med', freq, 0, stationlist)
def plot_jma_vs_jma_comparison(freq='M'):
"""
Plots the comparison of monthly and annual indices for the JMA index.
The monthly indices are defined for each month using Nov-Jan as reference
season and a minimum of 6 consecutive months in the same ENSO conditions.
The annual indices are defined for each period of 12 months, starting in Oct.,
using Oct-Dec as reference season and a minimum of 6 consecutive months in
the same ENSO conditions
Parameters
----------
freq : {int, string}
Valid frequency specifier.
"""
# Check that we have a valid frequency
freq = climpy.check_freq(freq)
# Get the ENSO indices: annual for 12 months period, monthly on a monthly basis
logger.debug("Get ENSO JMAI...")
annual = JMA.set_indices(full_year=True, reference_season='OND',
minimum_size=6)
monthly = JMA.set_indices(full_year=False, reference_season='NDJ',
minimum_size=6)
# Convert to the new frequency
if freq != climpy._c.FR_MTH:
annual = np.round_(annual.convert(freq, func=ma.mean))
monthly = np.round_(monthly.convert(freq, func=ma.mean))
# Convert the indices to a Nx12 series (N the nb of years)
annual = annual.convert('A')
monthly = monthly.convert('A')
# Trim to select only the data after 1900.
annual = annual[annual.year >= 1900]
monthly = monthly[monthly.year >= 1900]
# Get the colors
# logger.debug("Get color matrices")
# a_colors = get_colormatrix(annual)
# m_colors = get_colormatrix(monthly)
# edegecolors = 'none'
ensomap = ENSOmap
#
logger.debug("Prepare figure...")
fig = cpl.figure(num=1, figsize=(13, 5))
fig.clear()
fsp = fig.add_axes([0.05, 0.1, 0.85, 0.85])
(nrows, ncols) = annual.shape #a_colors.shape
logger.debug("Get vertices #1...")
# Get the vertices of the polygons corresponding to standard indices
a_vert = np.array([[((i, j + 1), (i, j), (i + 1, j)) for i in range(ncols)]
for j in range(nrows)])
a_vert.shape = (-1, 3, 2)
# Create a PolyCollection for the polygons.
a_coll = matplotlib.collections.PolyCollection(a_vert,
norm=ENSOmap)
fsp.add_collection(a_coll)
# Same process, this time with the monthly indices
logger.debug("Get vertices #2...")
m_vert = np.array([[((i, j + 1), (i + 1, j + 1), (i + 1, j)) for i in range(ncols)]
for j in range(nrows)])
m_vert.shape = (-1, 3, 2)
m_coll = matplotlib.collections.PolyCollection(m_vert,
norm=ENSOmap)
fsp.add_collection(m_coll)
# Plot the underlying grid.
logger.debug("Plot grid...")
xy_vert = [((i, 0), (i, nrows)) for i in range(0, ncols, 12)]
xy_vert += [((0, j), (ncols, j)) for j in range(nrows)]
xy_coll = matplotlib.collections.LineCollection(xy_vert, colors='k')
fsp.add_collection(xy_coll)
fsp.grid(True, which='minor', ls=':', c='#999999', lw=0.5)
# Set the ticks on the
logger.debug("Set ticks...")
fsp.xaxis.set_major_locator(cpl.MultipleLocator(12))
fsp.xaxis.set_minor_locator(cpl.MultipleLocator(1))
fsp.set_xticks(np.arange(0, ncols, 12) + 6, minor=False)
fsp.set_xticklabels(["%02i" % i for i in range(10)])
fsp.set_xlim(0, ncols)
#............................................
fsp.set_yticks(np.arange(0, nrows) + 0.5)
fsp.set_yticklabels(["%i" % (i * 10 + 1900) for i in range(nrows + 1)])
fsp.set_ylim(0, nrows)
fsp.set_ylim(fsp.get_ylim()[::-1])
#............................................
logger.debug("Add legend")
add_plot_legend(fig)
#
#cpl.show()
return fig