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Posted to commits@climate.apache.org by jo...@apache.org on 2014/02/21 05:38:22 UTC
svn commit: r1570450 - /incubator/climate/trunk/ocw/plotter.py
Author: joyce
Date: Fri Feb 21 04:38:21 2014
New Revision: 1570450
URL: http://svn.apache.org/r1570450
Log:
CLIMATE-343 - Integrate utils.taylor into OCW
- Add TaylorDiagram class to ocw.plotter. Note that this is contingent
upon the decisions made in [1].
- Cleaned up trailing whitespace in ocw.plotter.
[1] https://issues.apache.org/jira/browse/LEGAL-193
Modified:
incubator/climate/trunk/ocw/plotter.py
Modified: incubator/climate/trunk/ocw/plotter.py
URL: http://svn.apache.org/viewvc/incubator/climate/trunk/ocw/plotter.py?rev=1570450&r1=1570449&r2=1570450&view=diff
==============================================================================
--- incubator/climate/trunk/ocw/plotter.py (original)
+++ incubator/climate/trunk/ocw/plotter.py Fri Feb 21 04:38:21 2014
@@ -23,7 +23,6 @@ from mpl_toolkits.axes_grid1 import Imag
import scipy.stats.mstats as mstats
import numpy as np
import numpy.ma as ma
-from utils.taylor import TaylorDiagram
# Set the default colormap to coolwarm
mpl.rc('image', cmap='coolwarm')
@@ -35,7 +34,7 @@ def set_cmap(name):
for a list of possible colormaps.
Appending '_r' to a matplotlib colormap name will give you a reversed
version of it.
-
+
:param name: The name of the colormap.
:type name: str
'''
@@ -43,19 +42,19 @@ def set_cmap(name):
# the cmap rc value improperly
cmap = plt.get_cmap(name)
mpl.rc('image', cmap=cmap.name)
-
+
def _nice_intervals(data, nlevs):
'''
Purpose::
Calculates nice intervals between each color level for colorbars
and contour plots. The target minimum and maximum color levels are
calculated by taking the minimum and maximum of the distribution
- after cutting off the tails to remove outliers.
-
+ after cutting off the tails to remove outliers.
+
Input::
data - an array of data to be plotted
nlevs - an int giving the target number of intervals
-
+
Output::
clevs - A list of floats for the resultant colorbar levels
'''
@@ -66,7 +65,7 @@ def _nice_intervals(data, nlevs):
mxlvl = mstats.scoreatpercentile(data, 95)
locator = mpl.ticker.MaxNLocator(nlevs)
clevs = locator.tick_values(mnlvl, mxlvl)
-
+
# Make sure the bounds of clevs are reasonable since sometimes
# MaxNLocator gives values outside the domain of the input data
clevs = clevs[(clevs >= mnlvl) & (clevs <= mxlvl)]
@@ -77,12 +76,12 @@ def _best_grid_shape(nplots, oldshape):
Purpose::
Calculate a better grid shape in case the user enters more columns
and rows than needed to fit a given number of subplots.
-
+
Input::
nplots - an int giving the number of plots that will be made
oldshape - a tuple denoting the desired grid shape (nrows, ncols) for arranging
- the subplots originally requested by the user.
-
+ the subplots originally requested by the user.
+
Output::
newshape - the smallest possible subplot grid shape needed to fit nplots
'''
@@ -100,24 +99,24 @@ def _best_grid_shape(nplots, oldshape):
nrows -= 1
size = nrows * ncols
diff = size - nplots
-
+
# Don't forget to remove unnecessary columns too
if nrows == 1:
ncols = nplots
-
+
newshape = nrows, ncols
return newshape
-
+
def _fig_size(gridshape, aspect=None):
'''
Purpose::
Calculates the figure dimensions from a subplot gridshape
-
+
Input::
gridshape - Tuple denoting the subplot gridshape
- aspect - Float denoting approximate aspect ratio of each subplot
+ aspect - Float denoting approximate aspect ratio of each subplot
(width / height). Default is 8.5 / 5.5
-
+
Output::
width - float for width of the figure in inches
height - float for height of the figure in inches
@@ -134,16 +133,16 @@ def _fig_size(gridshape, aspect=None):
else:
# If more columns keep height constant
width, height = (aspect * 5.5) * (ncols / nrows), 5.5
-
+
return width, height
-
-def draw_taylor_diagram(results, names, refname, fname, fmt='png',
- gridshape=(1,1), ptitle='', subtitles=None,
+
+def draw_taylor_diagram(results, names, refname, fname, fmt='png',
+ gridshape=(1,1), ptitle='', subtitles=None,
pos='upper right', frameon=True, radmax=1.5):
'''
Purpose::
Draws a Taylor diagram
-
+
Input::
results - an Nx2 array containing normalized standard deviations,
correlation coefficients, and names of evaluation results
@@ -152,24 +151,24 @@ def draw_taylor_diagram(results, names,
fname - a string specifying the filename of the plot
fmt - an optional string specifying the filetype, default is .png
gridshape - optional tuple denoting the desired grid shape (nrows, ncols) for arranging
- the subplots.
+ the subplots.
ptitle - an optional string specifying the plot title
subtitles - an optional list of strings specifying the title for each subplot
- pos - an optional string or tuple of float for determining
+ pos - an optional string or tuple of float for determining
the position of the legend
frameon - an optional boolean that determines whether to draw a frame
around the legend box
radmax - an optional float to adjust the extent of the axes in terms of
standard deviation.
'''
- # Handle the single plot case.
+ # Handle the single plot case.
if results.ndim == 2:
results = results.reshape(1, *results.shape)
# Make sure gridshape is compatible with input data
nplots = results.shape[0]
gridshape = _best_grid_shape(nplots, gridshape)
-
+
# Set up the figure
fig = plt.figure()
fig.set_size_inches((8.5, 11))
@@ -182,31 +181,31 @@ def draw_taylor_diagram(results, names,
# Create Taylor Diagram object
dia = TaylorDiagram(1, fig=fig, rect=rect, label=refname, radmax=radmax)
for i, (stddev, corrcoef) in enumerate(data):
- dia.add_sample(stddev, corrcoef, marker='$%d$' % (i + 1), ms=6,
+ dia.add_sample(stddev, corrcoef, marker='$%d$' % (i + 1), ms=6,
label=names[i])
if subtitles is not None:
dia._ax.set_title(subtitles[i])
-
+
# Add legend
- legend = fig.legend(dia.samplePoints,
- [p.get_label() for p in dia.samplePoints],
- handlelength=0., prop={'size': 10}, numpoints=1,
+ legend = fig.legend(dia.samplePoints,
+ [p.get_label() for p in dia.samplePoints],
+ handlelength=0., prop={'size': 10}, numpoints=1,
loc=pos)
legend.draw_frame(frameon)
- plt.subplots_adjust(wspace=0)
-
+ plt.subplots_adjust(wspace=0)
+
# Add title and save the figure
fig.suptitle(ptitle)
plt.tight_layout(.05, .05)
- fig.savefig('%s.%s' %(fname, fmt), bbox_inches='tight', dpi=fig.dpi)
+ fig.savefig('%s.%s' %(fname, fmt), bbox_inches='tight', dpi=fig.dpi)
fig.clf()
-
+
def draw_subregions(subregions, lats, lons, fname, fmt='png', ptitle='',
parallels=None, meridians=None, subregion_masks=None):
'''
Purpose::
Function to draw subregion domain(s) on a map
-
+
Input::
subregions - a list of subRegion objects
lats - array of latitudes
@@ -214,18 +213,18 @@ def draw_subregions(subregions, lats, lo
fname - a string specifying the filename of the plot
fmt - an optional string specifying the filetype, default is .png
ptitle - an optional string specifying plot title
- parallels - an optional list of ints or floats for the parallels to be drawn
+ parallels - an optional list of ints or floats for the parallels to be drawn
meridians - an optional list of ints or floats for the meridians to be drawn
subregion_masks - optional dictionary of boolean arrays for each subRegion
for giving finer control of the domain to be drawn, by default
- the entire domain is drawn.
+ the entire domain is drawn.
'''
# Set up the figure
fig = plt.figure()
fig.set_size_inches((8.5, 11.))
fig.dpi = 300
ax = fig.add_subplot(111)
-
+
# Determine the map boundaries and construct a Basemap object
lonmin = lons.min()
lonmax = lons.max()
@@ -233,12 +232,12 @@ def draw_subregions(subregions, lats, lo
latmax = lats.max()
m = Basemap(projection='cyl', llcrnrlat=latmin, urcrnrlat=latmax,
llcrnrlon=lonmin, urcrnrlon=lonmax, resolution='l', ax=ax)
-
+
# Draw the borders for coastlines and countries
m.drawcoastlines(linewidth=1)
m.drawcountries(linewidth=.75)
m.drawstates()
-
+
# Create default meridians and parallels. The interval between
# them should be 1, 5, 10, 20, 30, or 40 depending on the size
# of the domain
@@ -249,12 +248,12 @@ def draw_subregions(subregions, lats, lo
dlatlon = 5
else:
dlatlon = np.round(length, decimals=-1)
-
+
if meridians is None:
meridians = np.r_[np.arange(0, -180, -dlatlon)[::-1], np.arange(0, 180, dlatlon)]
if parallels is None:
parallels = np.r_[np.arange(0, -90, -dlatlon)[::-1], np.arange(0, 90, dlatlon)]
-
+
# Draw parallels / meridians
m.drawmeridians(meridians, labels=[0, 0, 0, 1], linewidth=.75, fontsize=10)
m.drawparallels(parallels, labels=[1, 0, 0, 1], linewidth=.75, fontsize=10)
@@ -262,54 +261,54 @@ def draw_subregions(subregions, lats, lo
# Set up the color scaling
cmap = plt.cm.rainbow
norm = mpl.colors.BoundaryNorm(np.arange(1, len(subregions) + 3), cmap.N)
-
- # Process the subregions
+
+ # Process the subregions
for i, reg in enumerate(subregions):
if subregion_masks is not None and reg.name in subregion_masks.keys():
domain = (i + 1) * subregion_masks[reg.name]
- else:
+ else:
domain = (i + 1) * np.ones((2, 2))
-
+
nlats, nlons = domain.shape
domain = ma.masked_equal(domain, 0)
reglats = np.linspace(reg.latmin, reg.latmax, nlats)
- reglons = np.linspace(reg.lonmin, reg.lonmax, nlons)
+ reglons = np.linspace(reg.lonmin, reg.lonmax, nlons)
reglons, reglats = np.meshgrid(reglons, reglats)
-
+
# Convert to to projection coordinates. Not really necessary
# for cylindrical projections but keeping it here in case we need
# support for other projections.
x, y = m(reglons, reglats)
-
+
# Draw the subregion domain
m.pcolormesh(x, y, domain, cmap=cmap, norm=norm, alpha=.5)
-
+
# Label the subregion
xm, ym = x.mean(), y.mean()
m.plot(xm, ym, marker='$%s$' %(reg.name), markersize=12, color='k')
-
+
# Add the title
ax.set_title(ptitle)
-
+
# Save the figure
fig.savefig('%s.%s' %(fname, fmt), bbox_inches='tight', dpi=fig.dpi)
fig.clf()
-def draw_time_series(results, times, labels, fname, fmt='png', gridshape=(1, 1),
- xlabel='', ylabel='', ptitle='', subtitles=None,
+def draw_time_series(results, times, labels, fname, fmt='png', gridshape=(1, 1),
+ xlabel='', ylabel='', ptitle='', subtitles=None,
label_month=False, yscale='linear', aspect=None):
'''
Purpose::
Function to draw a time series plot
-
- Input::
+
+ Input::
results - a 3d array of time series
times - a list of python datetime objects
labels - a list of strings with the names of each set of data
fname - a string specifying the filename of the plot
fmt - an optional string specifying the output filetype
gridshape - optional tuple denoting the desired grid shape (nrows, ncols) for arranging
- the subplots.
+ the subplots.
xlabel - a string specifying the x-axis title
ylabel - a string specifying the y-axis title
ptitle - a string specifying the plot title
@@ -317,23 +316,23 @@ def draw_time_series(results, times, lab
label_month - optional bool to toggle drawing month labels
yscale - optional string for setting the y-axis scale, 'linear' for linear
and 'log' for log base 10.
- aspect - Float denoting approximate aspect ratio of each subplot
+ aspect - Float denoting approximate aspect ratio of each subplot
(width / height). Default is 8.5 / 5.5
'''
- # Handle the single plot case.
+ # Handle the single plot case.
if results.ndim == 2:
results = results.reshape(1, *results.shape)
# Make sure gridshape is compatible with input data
nplots = results.shape[0]
gridshape = _best_grid_shape(nplots, gridshape)
-
+
# Set up the figure
width, height = _fig_size(gridshape)
fig = plt.figure()
fig.set_size_inches((width, height))
fig.dpi = 300
-
+
# Make the subplot grid
grid = ImageGrid(fig, 111,
nrows_ncols=gridshape,
@@ -348,7 +347,7 @@ def draw_time_series(results, times, lab
cbar_size=.05,
cbar_pad=.20
)
-
+
# Make the plots
for i, ax in enumerate(grid):
data = results[i]
@@ -357,14 +356,14 @@ def draw_time_series(results, times, lab
xloc = mpl.dates.MonthLocator()
ax.xaxis.set_major_formatter(xfmt)
ax.xaxis.set_major_locator(xloc)
-
+
# Set the y-axis scale
ax.set_yscale(yscale)
-
+
# Set up list of lines for legend
lines = []
ymin, ymax = 0, 0
-
+
# Plot each line
for tSeries in data:
line = ax.plot_date(times, tSeries, '')
@@ -372,56 +371,56 @@ def draw_time_series(results, times, lab
cmin, cmax = tSeries.min(), tSeries.max()
ymin = min(ymin, cmin)
ymax = max(ymax, cmax)
-
+
# Add a bit of padding so lines don't touch bottom and top of the plot
ymin = ymin - ((ymax - ymin) * 0.1)
ymax = ymax + ((ymax - ymin) * 0.1)
ax.set_ylim((ymin, ymax))
-
+
# Set the subplot title if desired
if subtitles is not None:
ax.set_title(subtitles[i], fontsize='small')
-
+
# Create a master axes rectangle for figure wide labels
fax = fig.add_subplot(111, frameon=False)
fax.tick_params(labelcolor='none', top='off', bottom='off', left='off', right='off')
fax.set_ylabel(ylabel)
fax.set_title(ptitle, fontsize=16)
fax.title.set_y(1.04)
-
+
# Create the legend using a 'fake' colorbar axes. This lets us have a nice
# legend that is in sync with the subplot grid
cax = ax.cax
cax.set_frame_on(False)
cax.set_xticks([])
cax.set_yticks([])
- cax.legend((lines), labels, loc='upper center', ncol=10, fontsize='small',
+ cax.legend((lines), labels, loc='upper center', ncol=10, fontsize='small',
mode='expand', frameon=False)
-
+
# Note that due to weird behavior by axes_grid, it is more convenient to
# place the x-axis label relative to the colorbar axes instead of the
# master axes rectangle.
cax.set_title(xlabel, fontsize=12)
cax.title.set_y(-1.5)
-
+
# Rotate the x-axis tick labels
for ax in grid:
for xtick in ax.get_xticklabels():
xtick.set_ha('right')
xtick.set_rotation(30)
-
+
# Save the figure
fig.savefig('%s.%s' %(fname, fmt), bbox_inches='tight', dpi=fig.dpi)
fig.clf()
def draw_contour_map(dataset, lats, lons, fname, fmt='png', gridshape=(1, 1),
- clabel='', ptitle='', subtitles=None, cmap=None,
+ clabel='', ptitle='', subtitles=None, cmap=None,
clevs=None, nlevs=10, parallels=None, meridians=None,
extend='neither', aspect=8.5/2.5):
'''
Purpose::
Create a multiple panel contour map plot.
-
+
Input::
dataset - 3d array of the field to be plotted with shape (nT, nLon, nLat)
lats - array of latitudes
@@ -429,7 +428,7 @@ def draw_contour_map(dataset, lats, lons
fname - a string specifying the filename of the plot
fmt - an optional string specifying the filetype, default is .png
gridshape - optional tuple denoting the desired grid shape (nrows, ncols) for arranging
- the subplots.
+ the subplots.
clabel - an optional string specifying the colorbar title
ptitle - an optional string specifying plot title
subtitles - an optional list of strings specifying the title for each subplot
@@ -437,12 +436,12 @@ def draw_contour_map(dataset, lats, lons
denoting the colormap
clevs - an optional list of ints or floats specifying contour levels
nlevs - an optional integer specifying the target number of contour levels if
- clevs is None
- parallels - an optional list of ints or floats for the parallels to be drawn
+ clevs is None
+ parallels - an optional list of ints or floats for the parallels to be drawn
meridians - an optional list of ints or floats for the meridians to be drawn
extend - an optional string to toggle whether to place arrows at the colorbar
boundaries. Default is 'neither', but can also be 'min', 'max', or
- 'both'. Will be automatically set to 'both' if clevs is None.
+ 'both'. Will be automatically set to 'both' if clevs is None.
'''
# Handle the single plot case. Meridians and Parallels are not labeled for
# multiple plots to save space.
@@ -454,16 +453,16 @@ def draw_contour_map(dataset, lats, lons
else:
mlabels = [0, 0, 0, 0]
plabels = [0, 0, 0, 0]
-
+
# Make sure gridshape is compatible with input data
nplots = dataset.shape[0]
gridshape = _best_grid_shape(nplots, gridshape)
-
+
# Set up the figure
fig = plt.figure()
fig.set_size_inches((8.5, 11.))
fig.dpi = 300
-
+
# Make the subplot grid
grid = ImageGrid(fig, 111,
nrows_ncols=gridshape,
@@ -477,7 +476,7 @@ def draw_contour_map(dataset, lats, lons
cbar_size=.15,
cbar_pad='0%'
)
-
+
# Determine the map boundaries and construct a Basemap object
lonmin = lons.min()
lonmax = lons.max()
@@ -485,20 +484,20 @@ def draw_contour_map(dataset, lats, lons
latmax = lats.max()
m = Basemap(projection = 'cyl', llcrnrlat = latmin, urcrnrlat = latmax,
llcrnrlon = lonmin, urcrnrlon = lonmax, resolution = 'l')
-
+
# Convert lats and lons to projection coordinates
if lats.ndim == 1 and lons.ndim == 1:
lons, lats = np.meshgrid(lons, lats)
-
+
# Calculate contour levels if not given
if clevs is None:
# Cut off the tails of the distribution
# for more representative contour levels
clevs = _nice_intervals(dataset, nlevs)
extend = 'both'
-
+
cmap = plt.get_cmap(cmap)
-
+
# Create default meridians and parallels. The interval between
# them should be 1, 5, 10, 20, 30, or 40 depending on the size
# of the domain
@@ -513,24 +512,24 @@ def draw_contour_map(dataset, lats, lons
meridians = np.r_[np.arange(0, -180, -dlatlon)[::-1], np.arange(0, 180, dlatlon)]
if parallels is None:
parallels = np.r_[np.arange(0, -90, -dlatlon)[::-1], np.arange(0, 90, dlatlon)]
-
+
x, y = m(lons, lats)
- for i, ax in enumerate(grid):
+ for i, ax in enumerate(grid):
# Load the data to be plotted
data = dataset[i]
m.ax = ax
-
+
# Draw the borders for coastlines and countries
m.drawcoastlines(linewidth=1)
m.drawcountries(linewidth=.75)
-
+
# Draw parallels / meridians
m.drawmeridians(meridians, labels=mlabels, linewidth=.75, fontsize=10)
m.drawparallels(parallels, labels=plabels, linewidth=.75, fontsize=10)
-
+
# Draw filled contours
cs = m.contourf(x, y, data, cmap=cmap, levels=clevs, extend=extend)
-
+
# Add title
if subtitles is not None:
ax.set_title(subtitles[i], fontsize='small')
@@ -542,7 +541,7 @@ def draw_contour_map(dataset, lats, lons
cbar.set_ticks(clevs)
cbar.ax.xaxis.set_ticks_position('none')
cbar.ax.yaxis.set_ticks_position('none')
-
+
# This is an ugly hack to make the title show up at the correct height.
# Basically save the figure once to achieve tight layout and calculate
# the adjusted heights of the axes, then draw the title slightly above
@@ -552,22 +551,22 @@ def draw_contour_map(dataset, lats, lons
for ax in grid:
bbox = ax.get_position()
ymax = max(ymax, bbox.ymax)
-
+
# Add figure title
fig.suptitle(ptitle, y=ymax + .06, fontsize=16)
fig.savefig('%s.%s' %(fname, fmt), bbox_inches='tight', dpi=fig.dpi)
fig.clf()
-def draw_portrait_diagram(results, rowlabels, collabels, fname, fmt='png',
- gridshape=(1, 1), xlabel='', ylabel='', clabel='',
- ptitle='', subtitles=None, cmap=None, clevs=None,
+def draw_portrait_diagram(results, rowlabels, collabels, fname, fmt='png',
+ gridshape=(1, 1), xlabel='', ylabel='', clabel='',
+ ptitle='', subtitles=None, cmap=None, clevs=None,
nlevs=10, extend='neither', aspect=None):
'''
Purpose::
Makes a portrait diagram plot.
-
+
Input::
- results - 3d array of the field to be plotted. The second dimension
+ results - 3d array of the field to be plotted. The second dimension
should correspond to the number of rows in the diagram and the
third should correspond to the number of columns.
rowlabels - a list of strings denoting labels for each row
@@ -575,7 +574,7 @@ def draw_portrait_diagram(results, rowla
fname - a string specifying the filename of the plot
fmt - an optional string specifying the output filetype
gridshape - optional tuple denoting the desired grid shape (nrows, ncols) for arranging
- the subplots.
+ the subplots.
xlabel - an optional string specifying the x-axis title
ylabel - an optional string specifying the y-axis title
clabel - an optional string specifying the colorbar title
@@ -585,34 +584,34 @@ def draw_portrait_diagram(results, rowla
denoting the colormap
clevs - an optional list of ints or floats specifying colorbar levels
nlevs - an optional integer specifying the target number of contour levels if
- clevs is None
+ clevs is None
extend - an optional string to toggle whether to place arrows at the colorbar
boundaries. Default is 'neither', but can also be 'min', 'max', or
- 'both'. Will be automatically set to 'both' if clevs is None.
- aspect - Float denoting approximate aspect ratio of each subplot
+ 'both'. Will be automatically set to 'both' if clevs is None.
+ aspect - Float denoting approximate aspect ratio of each subplot
(width / height). Default is 8.5 / 5.5
- '''
+ '''
# Handle the single plot case.
if results.ndim == 2:
results = results.reshape(1, *results.shape)
-
+
nplots = results.shape[0]
-
+
# Make sure gridshape is compatible with input data
gridshape = _best_grid_shape(nplots, gridshape)
-
+
# Row and Column labels must be consistent with the shape of
# the input data too
prows, pcols = results.shape[1:]
if len(rowlabels) != prows or len(collabels) != pcols:
raise ValueError('rowlabels and collabels must have %d and %d elements respectively' %(prows, pcols))
-
+
# Set up the figure
width, height = _fig_size(gridshape)
fig = plt.figure()
fig.set_size_inches((width, height))
fig.dpi = 300
-
+
# Make the subplot grid
grid = ImageGrid(fig, 111,
nrows_ncols=gridshape,
@@ -627,22 +626,22 @@ def draw_portrait_diagram(results, rowla
cbar_size=.15,
cbar_pad='3%'
)
-
+
# Calculate colorbar levels if not given
if clevs is None:
# Cut off the tails of the distribution
# for more representative colorbar levels
clevs = _nice_intervals(results, nlevs)
extend = 'both'
-
- cmap = plt.get_cmap(cmap)
+
+ cmap = plt.get_cmap(cmap)
norm = mpl.colors.BoundaryNorm(clevs, cmap.N)
-
+
# Do the plotting
for i, ax in enumerate(grid):
data = results[i]
cs = ax.matshow(data, cmap=cmap, aspect='auto', origin='lower', norm=norm)
-
+
# Add grid lines
ax.xaxis.set_ticks(np.arange(data.shape[1] + 1))
ax.yaxis.set_ticks(np.arange(data.shape[0] + 1))
@@ -650,41 +649,157 @@ def draw_portrait_diagram(results, rowla
y = (ax.yaxis.get_majorticklocs() - .5)
ax.vlines(x, y.min(), y.max())
ax.hlines(y, x.min(), x.max())
-
+
# Configure ticks
ax.xaxis.tick_bottom()
ax.xaxis.set_ticks_position('none')
ax.yaxis.set_ticks_position('none')
ax.set_xticklabels(collabels, fontsize='xx-small')
ax.set_yticklabels(rowlabels, fontsize='xx-small')
-
+
# Add axes title
if subtitles is not None:
- ax.text(0.5, 1.04, subtitles[i], va='center', ha='center',
+ ax.text(0.5, 1.04, subtitles[i], va='center', ha='center',
transform = ax.transAxes, fontsize='small')
-
+
# Create a master axes rectangle for figure wide labels
fax = fig.add_subplot(111, frameon=False)
fax.tick_params(labelcolor='none', top='off', bottom='off', left='off', right='off')
fax.set_ylabel(ylabel)
fax.set_title(ptitle, fontsize=16)
fax.title.set_y(1.04)
-
+
# Add colorbar
cax = ax.cax
- cbar = fig.colorbar(cs, cax=cax, norm=norm, boundaries=clevs, drawedges=True,
+ cbar = fig.colorbar(cs, cax=cax, norm=norm, boundaries=clevs, drawedges=True,
extend=extend, orientation='horizontal', extendfrac='auto')
cbar.set_label(clabel)
cbar.set_ticks(clevs)
cbar.ax.xaxis.set_ticks_position('none')
cbar.ax.yaxis.set_ticks_position('none')
-
+
# Note that due to weird behavior by axes_grid, it is more convenient to
# place the x-axis label relative to the colorbar axes instead of the
# master axes rectangle.
cax.set_title(xlabel, fontsize=12)
cax.title.set_y(1.5)
-
+
# Save the figure
fig.savefig('%s.%s' %(fname, fmt), bbox_inches='tight', dpi=fig.dpi)
- fig.clf()
\ No newline at end of file
+ fig.clf()
+
+class TaylorDiagram(object):
+ """ Taylor diagram helper class
+
+ Plot model standard deviation and correlation to reference (data)
+ sample in a single-quadrant polar plot, with r=stddev and
+ theta=arccos(correlation).
+ """
+
+ def __init__(self, refstd, radmax=1.5, fig=None, rect=111, label='_'):
+ """Set up Taylor diagram axes, i.e. single quadrant polar
+ plot, using mpl_toolkits.axisartist.floating_axes. refstd is
+ the reference standard deviation to be compared to.
+ """
+
+ from matplotlib.projections import PolarAxes
+ import mpl_toolkits.axisartist.floating_axes as FA
+ import mpl_toolkits.axisartist.grid_finder as GF
+
+ self.refstd = refstd # Reference standard deviation
+
+ tr = PolarAxes.PolarTransform()
+
+ # Correlation labels
+ rlocs = np.concatenate((np.arange(10)/10.,[0.95,0.99]))
+ tlocs = np.arccos(rlocs) # Conversion to polar angles
+ gl1 = GF.FixedLocator(tlocs) # Positions
+ tf1 = GF.DictFormatter(dict(zip(tlocs, map(str,rlocs))))
+
+ # Standard deviation axis extent
+ self.smin = 0
+ self.smax = radmax*self.refstd
+
+ ghelper = FA.GridHelperCurveLinear(tr,
+ extremes=(0,np.pi/2, # 1st quadrant
+ self.smin,self.smax),
+ grid_locator1=gl1,
+ tick_formatter1=tf1,
+ )
+
+ if fig is None:
+ fig = plt.figure()
+
+ ax = FA.FloatingSubplot(fig, rect, grid_helper=ghelper)
+ fig.add_subplot(ax)
+
+ # Adjust axes
+ ax.axis["top"].set_axis_direction("bottom") # "Angle axis"
+ ax.axis["top"].toggle(ticklabels=True, label=True)
+ ax.axis["top"].major_ticklabels.set_axis_direction("top")
+ ax.axis["top"].label.set_axis_direction("top")
+ ax.axis["top"].label.set_text("Correlation")
+
+ ax.axis["left"].set_axis_direction("bottom") # "X axis"
+ ax.axis["left"].label.set_text("Standard deviation")
+
+ ax.axis["right"].set_axis_direction("top") # "Y axis"
+ ax.axis["right"].toggle(ticklabels=True)
+ ax.axis["right"].major_ticklabels.set_axis_direction("left")
+
+ ax.axis["bottom"].set_visible(False) # Useless
+
+ # Contours along standard deviations
+ ax.grid(False)
+
+ self._ax = ax # Graphical axes
+ self.ax = ax.get_aux_axes(tr) # Polar coordinates
+
+ # Add reference point and stddev contour
+ # print "Reference std:", self.refstd
+ l, = self.ax.plot([0], self.refstd, 'k*',
+ ls='', ms=10, label=label)
+ t = np.linspace(0, np.pi/2)
+ r = np.zeros_like(t) + self.refstd
+ self.ax.plot(t,r, 'k--', label='_')
+
+ # Collect sample points for latter use (e.g. legend)
+ self.samplePoints = [l]
+
+ def add_sample(self, stddev, corrcoef, *args, **kwargs):
+ """Add sample (stddev,corrcoeff) to the Taylor diagram. args
+ and kwargs are directly propagated to the Figure.plot
+ command."""
+
+ l, = self.ax.plot(np.arccos(corrcoef), stddev,
+ *args, **kwargs) # (theta,radius)
+ self.samplePoints.append(l)
+
+ return l
+
+ def add_rms_contours(self, levels=5, **kwargs):
+ """Add constant centered RMS difference contours."""
+
+ rs,ts = np.meshgrid(np.linspace(self.smin,self.smax),
+ np.linspace(0,np.pi/2))
+ # Compute centered RMS difference
+ rms = np.sqrt(self.refstd**2 + rs**2 - 2*self.refstd*rs*np.cos(ts))
+
+ contours = self.ax.contour(ts, rs, rms, levels, **kwargs)
+
+ def add_stddev_contours(self, std, corr1, corr2, **kwargs):
+ """Add a curved line with a radius of std between two points
+ [std, corr1] and [std, corr2]"""
+
+ t = np.linspace(np.arccos(corr1), np.arccos(corr2))
+ r = np.zeros_like(t) + std
+ return self.ax.plot(t,r,'red', linewidth=2)
+
+ def add_contours(self,std1,corr1,std2,corr2, **kwargs):
+ """Add a line between two points
+ [std1, corr1] and [std2, corr2]"""
+
+ t = np.linspace(np.arccos(corr1), np.arccos(corr2))
+ r = np.linspace(std1, std2)
+
+ return self.ax.plot(t,r,'red',linewidth=2)