Esempio n. 1
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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()
Esempio n. 2
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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
Esempio n. 3
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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
Esempio n. 4
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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
Esempio n. 5
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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
Esempio n. 6
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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()
Esempio n. 7
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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()
Esempio n. 8
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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()
Esempio n. 9
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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()