def __latvtimea(data, cdata, variable, ncdf, cncdf, ax):
#format data
data = ncdf.formatdata(data, 'latvtime', cncdf)
cdata = ncdf.formatdata(cdata, 'latvtime')
#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), calc.gety(ncdf)
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', colors=None, cmap=cmap,
vmin=datamin, vmax=datamax)
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, format='%.2g')
cbar.ax.set_xlabel(label)
#label & title
plt.xlabel('Years after eruption')
plt.ylabel('Degrees latitude')
#add control stats
cmean = stats.nanmean(cdata.flat)
cstd = stats.nanstd(cdata.flat)
string = ('Control mean: ' + '%.2g'%cmean +
'\nControl standard deviation: ' + '%.2g'%cstd)
plt.figtext(.02,.02,string, fontsize=12)
#additional formating
ax = plt.gca()
plt.yticks(np.linspace(-90, 90, 7, endpoint=True))
#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 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()