def timeseriesa(data, cdata, variable, ncdf, cncdf):
#construct strings
label = labels[variable]
if variable in names:
title = names[variable]
else:
title = variable
subtitle = ncdf.filetitle + ' - ' + cncdf.filetitle
filename = (variable + '_' + filenames[ncdf.filename] + '_tsa_' +
filenames[cncdf.filename] + '.eps')
#create plot
x = calc.getx(ncdf)
y = calc.anomalize(data, cdata, ncdf, cncdf)
lp = plt.plot(x, y, 'k')
plt.xlim(x[0], x[-1])
#label & title
plt.xlabel('Years after eruption')
plt.ylabel(label)
ax.yaxis.ticklabel_format(style='sci', axis='y', scilimits=(-2,2))
ax.yaxis.locator_params(nbins=3, axis='y')
#additional formatting
ax = plt.gca()
plt.rc('font', **font)
plt.title(subtitle)
ax.spines['bottom'].set_position('zero')
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
plt.savefig(filename)
plt.close()
def __timeseriesa(data, cdata, variable, ncdf, cncdf, ax):
#construct strings
label = labels[variable]
#format data
data = ncdf.formatdata(data, 'timeseries', cncdf)
cdata = ncdf.formatdata(cdata, 'timeseries')
#create plot
x = calc.getx(ncdf)
y = calc.anomalize(data, cdata, ncdf, cncdf)
lp = plt.plot(x, y, 'k')
plt.xlim(x[0], x[-1])
#label & title
plt.xlabel('Years after eruption')
plt.ylabel(label)
#additional formatting
ax = plt.gca()
ax.yaxis.set_major_locator(ticker.MaxNLocator(nbins=5))
ax.ticklabel_format(axis='y', style='sci', scilimits=(-1,1))
ax.spines['bottom'].set_position('zero')
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
return ax
def __prvtimea(data, cdata, variable, ncdf, cncdf, ax):
#format data
data = ncdf.formatdata(data, 'prvtime', cncdf)
cdata = ncdf.formatdata(cdata, 'prvtime')
#anomaly
data = calc.anomalize(data, cdata, ncdf, cncdf)
#construct strings
label = labels[variable]
#colormap
cmap = LinearSegmentedColormap('bwr', colors)
#calculate params
x, y = calc.getx(ncdf), ple
z = data.transpose()
datamax, datamin = calc.getmax(data, True), calc.getmin(data, True)
v = np.linspace(datamin, datamax, 41)
#create plot
cp = plt.contourf(x, y, z, v, extend='both', color=None, cmap=cmap,
vmin=datamin, vmax=datamax)
plt.yscale('log')
plt.xlim(x[0], x[-1])
plt.ylim(.1, 200)
#create cbar
ticks = np.linspace(datamin, datamax, 3)
cbar = plt.colorbar(cp, orientation='horizontal', extend='both',
aspect=40, ticks=ticks, format='%.2g')
cbar.ax.set_xlabel(label)
#label & title
plt.xlabel('Years after eruption')
plt.ylabel('Pressure (hPa)')
#additional formatting
ax = plt.gca()
ax.invert_yaxis()
#add control stats
cmean = stats.nanmean(cdata.flat)
#cmean = calc.anlmean(cdata)
cstd = stats.nanstd(cdata.flat)
#cstd = calc.anlstd(cdata)
string = ('Control mean: ' + '%.2g'%cmean +
'\nControl standard deviation: ' + '%.2g'%cstd)
plt.figtext(.02,.02,string, fontsize=12)
#show/save plot
return ax
def __prvtime(data, variable, ncdf, cncdf, ax):
#construct strings
label = labels[variable]
#format data
data = ncdf.formatdata(data, 'prvtime', cncdf)
#calculate params
x, y = calc.getx(ncdf), ple
z = data.transpose()
datamax, datamin = calc.getmax(data), calc.getmin(data)
v = np.linspace(datamin, datamax, 41)
cmap = LinearSegmentedColormap('red', colors2)
#create plot
cp = plt.contourf(x, y, z, v, extend='both', vmin=datamin, vmax=datamax,
cmap=cmap)
plt.yscale('log')
plt.xlim(x[0], x[-1])
plt.ylim(.1, 200)
#create cbar
ticks = np.linspace(datamin, datamax, 3)
cbar = plt.colorbar(cp, orientation='horizontal', extend='both',
aspect=40, ticks=ticks, format='%.2g')
cbar.ax.set_xlabel(label)
#label & title
plt.xlabel('Years after eruption')
plt.ylabel('Pressure (hPa)')
#additional formatting
ax = plt.gca()
ax.invert_yaxis()
#show/save plot
return ax
def __latvtime(data, variable, ncdf, cncdf, ax):
#construct strings
label = labels[variable]
#format data
data = ncdf.formatdata(data, 'latvtime', cncdf)
#calculate params
x, y = calc.getx(ncdf), calc.gety(ncdf)
z = data.transpose()
datamax, datamin = calc.getmax(data), calc.getmin(data)
v = np.linspace(datamin, datamax, 41)
cmap = LinearSegmentedColormap('red', colors2)
#create plot
cp = plt.contourf(x, y, z, v, extend='both', vmin=datamin, vmax=datamax,
cmap=cmap)
plt.xlim(x[0], x[-1])
plt.ylim(-90, 90)
#create colorbar
ticks = np.linspace(datamin, datamax, 3)
cbar = plt.colorbar(cp, orientation='horizontal', extend='both',
aspect=40, ticks=ticks)
cbar.ax.set_xlabel(label)
#label & title
plt.xlabel('Years after eruption')
plt.ylabel('Degrees latitude')
#additional formating
ax = plt.gca()
plt.yticks(np.linspace(-90, 90, 7, endpoint=True))
#show/save plot
return ax
def prvtimea(data, cdata, variable, ncdf, controlncdf):
fig = plt.figure(figsize=figsize)
ax = fig.add_subplot(111)
fig.subplots_adjust(left=.2)
#anomaly
data = calc.anomalize(data, cdata, ncdf, controlncdf)
#construct strings
label = labels[variable]
if variable in names:
title = names[variable]
else:
title = variable
if variable in variablefilenames:
variabletitle = variablefilenames[variable]
else:
variabletitle = variable
filename = (variabletitle + '_' + filenames[ncdf.filename] + '_pvta_' +
filenames[controlncdf.filename] + '.eps')
#colormap
cmap = LinearSegmentedColormap('bwr', colors)
#calculate params
x, y = calc.getx(ncdf), ple
z = data.transpose()
datamax, datamin = 0, 0
if ((filenames[ncdf.filename] != 'q150') or
(filenames[controlncdf.filename] != 'q0')):
try:
datamax = minmax[variable + 'a'][1]
datamin = minmax[variable + 'a'][0]
except KeyError:
datamax, datamin = calc.getmax(data, True), calc.getmin(data, True)
except Error:
print Error
else:
try:
datamax = minmax[variable + 'aa'][1]
datamin = minmax[variable + 'aa'][0]
except KeyError:
datamax, datamin = calc.getmax(data, True), calc.getmin(data, True)
except Error:
print Error
v = np.linspace(datamin, datamax, 17)
#create plot
cp = ax.contourf(x, y, z, v, extend='both', color=None, cmap=cmap,
vmin=datamin, vmax=datamax)
plt.yscale('log')
plt.xlim(x[0], x[-1])
plt.ylim(.1, 200)
#create cbar
cbar = None
ticks = np.linspace(datamin, datamax, 5, endpoint=True)
format = ScalarFormatter()
format.set_powerlimits((-2,2))
cbar = plt.colorbar(cp, orientation='horizontal',
aspect=15, ticks=ticks, format=format,
pad = .2)
cbar.ax.set_xlabel(label)
#label & title
plt.xlabel('Years after eruption')
plt.ylabel('Pressure (hPa)')
#additional formatting
ax = plt.gca()
ax.invert_yaxis()
plt.rc('font', **font)
plt.title(title)
#show/save plot
plt.rc({'figure.autolayout': True})
#show/save plot
ax.get_xaxis().majorTicks[-1].label1.set_horizontalalignment('right')
ax.get_yaxis().majorTicks[0].label1.set_verticalalignment('top')
plt.savefig(filename)
plt.close()
def latvtimegiss(data, variable, ncdf):
plt.figure(figsize=figsize)
#construct strings
label = labels[variable]
if variable in names:
title = names[variable]
else:
title = variable
subtitle = ncdf.filetitle
filename = variable + '_' + filenames[ncdf.filename] + '_lvt' + '.eps'
#calculate params
x, y = calc.getx(ncdf), calc.gety(ncdf)
z = data.transpose()
datamax, datamin = calc.getmax(data), calc.getmin(data)
v = np.linspace(datamin, datamax, 17)
#colormap
#cmap = plt.get_cmap('jet')
#cmap.set_under(color='w')
cdict = matplotlib.cm.get_cmap('jet')._segmentdata
if cdict['red'][1]!=(0.05,1,1):
cdict['red'] = list(cdict['red'])
cdict['red'][0]=(0,1,1)
cdict['red'].insert(1, (0.05,1,1))
cdict['red'] = tuple(cdict['red'])
cdict['blue'] = list(cdict['blue'])
cdict['blue'][0]=(0,1,1)
cdict['blue'].insert(1, (0.05,1,1))
cdict['blue'] = tuple(cdict['blue'])
cdict['green'] = list(cdict['green'])
cdict['green'][0]=(0,1,1)
cdict['green'].insert(1, (0.05,1,1))
cdict['green'] = tuple(cdict['green'])
cmap = LinearSegmentedColormap('jet2', cdict)
#create plot
extend = ''
if datamin == 0:
extend='max'
else:
extend='both'
norm = Normalize(vmin=datamin, vmax=datamax, clip=False)
cp = plt.contourf(x, y, z, v, extend=extend, vmin=datamin, vmax=datamax,
cmap=cmap, origin='lower', norm=norm)
plt.xlim(x[0], x[-1])
plt.ylim(-90, 90)
#create colorbar
cbar = plt.colorbar(cp, orientation='horizontal',
aspect=15, format='%.2g', pad=0.2)
cbar.ax.set_xlabel(label)
#label & title
plt.xlabel('Years after eruption')
plt.ylabel('Latitude')
'''for label in cbar.ax.xaxis.get_ticklabels()[1::2]:
label.set_visible(False)'''
newlabels=[]
for label in cbar.ax.xaxis.get_ticklabels()[::2]:
newlabels.append(label.get_text())
cbar.ax.xaxis.set_ticklabels(newlabels)
newticks=[]
for tick in cbar.ax.get_xticks()[::2]:
newticks.append(tick)
cbar.ax.set_xticks(newticks)
#additional formating
ax = plt.gca()
plt.yticks(np.linspace(-90, 90, 7, endpoint=True))
ax.get_xaxis().majorTicks[-1].label1.set_horizontalalignment('right')
ax.get_yaxis().majorTicks[-1].label1.set_verticalalignment('top')
plt.rc('font', **font)
plt.title(title)
#show/save plot
plt.savefig(filename, bbox_inches='tight')
#print str(max(z.flat)) + " + " + str(min(z.flat))
plt.close()
def latvtimea(data, cdata, variable, ncdf, controlncdf):
#anomaly
plt.figure(figsize=figsize)
data = calc.anomalize(data, cdata, ncdf, controlncdf)
#construct strings
label = labels[variable]
if variable in names:
title = names[variable]
else:
title = variable
subtitle = title
filename = (variable + '_' + filenames[ncdf.filename] + '_lvta_' +
filenames[controlncdf.filename] + '.eps')
#colormap
cmap = LinearSegmentedColormap('bwr', colors)
#calculate params
x, y = calc.getx(ncdf), calc.gety(ncdf)
z = data.transpose()
datamax, datamin = calc.getmax(data, True), calc.getmin(data, True)
v = np.linspace(datamin, datamax, 17)
#create plot
cp = plt.contourf(x, y, z, v, extend='both', colors=None, cmap=cmap,
vmin=datamin, vmax=datamax)
plt.xlim(x[0], x[-1])
plt.ylim(-90, 90)
#create colorbar
cbar = plt.colorbar(cp, orientation='horizontal',
aspect=15, format="%.2g", pad = .2)
cbar.ax.set_xlabel(label)
#label & title
plt.xlabel('Years after eruption')
plt.ylabel('Latitude')
#add control stats
'''cmean = stats.nanmean(cdata.flat)
#cmean = calc.anlmean(cdata)
cstd = stats.nanstd(cdata.flat)
#cstd = calc.anlstd(cdata)
string = ('Control mean: ' + '%.2g'%cmean +
'\nControl standard deviation: ' + '%.2g'%cstd)
plt.figtext(.02,.02,string, fontsize=12)'''
newlabels=[]
for label in cbar.ax.xaxis.get_ticklabels()[::2]:
newlabels.append(label.get_text())
cbar.ax.xaxis.set_ticklabels(newlabels)
newticks=[]
for tick in cbar.ax.get_xticks()[::2]:
newticks.append(tick)
cbar.ax.set_xticks(newticks)
#additional formating
ax = plt.gca()
ax.get_xaxis().majorTicks[-1].label1.set_horizontalalignment('right')
plt.yticks(np.linspace(-90, 90, 7, endpoint=True))
ax.get_yaxis().majorTicks[-1].label1.set_verticalalignment('top')
plt.rc('font', **font)
plt.title(subtitle)
#show/save plot
plt.savefig(filename, bbox_inches='tight')
plt.close()
def prvtime(data, variable, ncdf, regionmin=None, regionmax=None):
#construct figure object
fig = plt.figure(figsize=figsize)
ax = fig.add_subplot(111)
fig.subplots_adjust(left=.2)
#construct strings
label = labels[variable]
if variable in names:
title = names[variable]
else:
title = variable
if variable in variablefilenames:
variabletitle = variablefilenames[variable]
else:
variabletitle = variable
if regionmin != None and regionmax != None:
if regionmin < 0:
regionminstr = str(regionmin)[1:] + 'S'
else:
regionminstr = str(regionmin) + 'N'
if regionmax < 0:
regionmaxstr = str(regionmax)[1:] + 'S'
else:
regionmaxstr = str(regionmax) + 'N'
variabletitle = regionminstr + '-' + regionmaxstr + '_' + variabletitle
filename = variabletitle + '_' + filenames[ncdf.filename] + '_pvt' + '.eps'
#calculate params
x, y = calc.getx(ncdf), ple
z = data.transpose()
datamax, datamin = 0,0
try:
datamax = minmax[variable][1]
datamin = minmax[variable][0]
except KeyError:
datamax, datamin = calc.getmax(data), calc.getmin(data)
except Error:
print Error
v = np.linspace(datamin, datamax, 17)
norm = Normalize(vmin=datamin, vmax=datamax)
#construct colormap
#for info on how this works:
#http://matplotlib.org/examples/pylab_examples/custom_cmap.html
cdict = matplotlib.cm.get_cmap('jet')._segmentdata
if cdict['red'][1]!=(0.05,1,1):
cdict['red'] = list(cdict['red'])
cdict['red'][0]=(0,1,1)
cdict['red'].insert(1, (0.05,1,1))
cdict['red'] = tuple(cdict['red'])
cdict['blue'] = list(cdict['blue'])
cdict['blue'][0]=(0,1,1)
cdict['blue'].insert(1, (0.05,1,1))
cdict['blue'] = tuple(cdict['blue'])
cdict['green'] = list(cdict['green'])
cdict['green'][0]=(0,1,1)
cdict['green'].insert(1, (0.05,1,1))
cdict['green'] = tuple(cdict['green'])
cmap = LinearSegmentedColormap('jet2', cdict)
#create plot
cp = ax.contourf(x, y, z, v, extend='max', norm=norm, vmin=datamin, vmax=datamax,
cmap=cmap)
#fix x,y axes
plt.yscale('log')
plt.xlim(x[0], x[-1])
plt.ylim(.1, 200)
#create cbar
cbar = None
ticks = np.linspace(float(datamin), float(datamax), 5, endpoint=True)
format = ScalarFormatter()
#set power limits, any e^+-x will be scientific notation
format.set_powerlimits((-2,2))
cbar = plt.colorbar(cp, norm=norm, orientation='horizontal',
aspect=15, extend='max', ticks = ticks, format=format,
pad = .2)
cbar.ax.set_xlabel(label)
#label & title
plt.xlabel('Years after eruption')
plt.ylabel('Pressure (hPa)')
#additional formatting
ax = plt.gca()
#invert pressure axis
ax.invert_yaxis()
#bring in last labels
ax.get_xaxis().majorTicks[-1].label1.set_horizontalalignment('right')
ax.get_yaxis().majorTicks[0].label1.set_verticalalignment('top')
#fix font
plt.rc('font', **font)
#add title
plt.title(title)
#show/save plot
plt.savefig(filename, format='eps')
plt.close()