def meshplot(x, y, z, log=False, levels=100, aligned=False, **kwargs):
labels, kwargs = get_labels(x, y, z, **kwargs)
if aligned == False:
x, y = uf.broadcast(x, y)
subp_kwargs = {
k: v
for k, v in kwargs.items() if k in common.get_kwarg_names(pl.subplots)
}
cont_kwargs = {k: v for k, v in kwargs.items() if k not in subp_kwargs}
if x.dtype.kind == 'M':
x = x.astype(datetime)
if y.dtype.kind == 'M':
y = y.astype(datetime)
fig, ax = pl.subplots(**subp_kwargs)
levels = np.linspace(0, uf.nanmax(z).values, levels)
img = ax.pcolormesh(x.values, y.values, z.values, **cont_kwargs)
# layout
if x.dtype.kind == 'M':
fig.autofmt_xdate()
cbar = fig.colorbar(img, ax=ax)
cbar.set_label(labels[2])
ax.set_xlabel(labels[0])
ax.set_ylabel(labels[1])
pl.grid = True
pl.tight_layout()
return fig, ax
def semivar(*args, **kwargs):
"""
semivariance
"""
args = uf.broadcast(*args)
X = da.stack([x.task.flatten() for x in args])
out = 0.5 * da.nanmean((X[0] - X[1])**2)
return out
def bootstrap(*args, func='corr', size=100):
if func == 'corr':
func = uf.corr
args = uf.broadcast(*args)
args = tuple(x.task if hasattr(x, 'task') else x for x in args)
args = tuple(x.compute() if hasattr(x, 'compute') else x for x in args)
indices = np.random.randint(len(args[0]), size=(size, len(args[0])))
return func(*tuple(x[indices] for x in args), axis=1)
def linregress(x, y, **kwargs):
"""
linear least-squares regression
"""
x, y = uf.broadcast(x, y)
x = x.task.flatten().compute()
y = y.task.flatten().compute()
cond = ~(np.isnan(x) | np.isnan(y))
x = x[cond]
y = y[cond]
return stats.linregress(x, y)
def pixplot(x, y, z, log=False, interp='nearest', aligned=False, **kwargs):
labels, kwargs = get_labels(x, y, z, **kwargs)
if aligned == False:
x, y = uf.broadcast(x, y)
subp_kwargs = {
k: v
for k, v in kwargs.items() if k in common.get_kwarg_names(pl.subplots)
}
imsh_kwargs = {k: v for k, v in kwargs.items() if k not in subp_kwargs}
if x.dtype.kind == 'M':
xlims = mdates.date2num([
x.min().values.astype(datetime).item(),
x.max().values.astype(datetime).item()
])
else:
xlims = (min(x).values.item(), max(x).values.item())
if y.dtype.kind == 'M':
ylims = mdates.date2num([
y.min().values.astype(datetime).item(),
y.max().values.astype(datetime).item()
])
else:
ylims = [y.min().values.item(), y.max().values.item()]
fig, ax = pl.subplots(**subp_kwargs)
if log:
z = uf.log10(z)
img = ax.imshow(z.values,
interpolation=interp,
extent=[xlims[0], xlims[1], ylims[0], ylims[1]],
origin='lower',
aspect='auto',
**imsh_kwargs)
# layout
if x.dtype.kind == 'M':
ax.xaxis_date()
fig.autofmt_xdate()
cbar = fig.colorbar(img, ax=ax)
cbar.set_label(labels[2])
ax.set_xlabel(labels[0])
ax.set_ylabel(labels[1])
pl.grid = True
pl.tight_layout()
return fig, ax
def barplot(x, y, width=None, ax=None, fig=None, aligned=False, **kwargs):
labels, kwargs = get_labels(x, y, **kwargs)
if aligned == False:
x, y = uf.broadcast(x, y)
fig_kwargs = {
k: v
for k, v in kwargs.items() if k in common.get_kwarg_names(pl.figure)
}
subp_kwargs = {
k: v
for k, v in kwargs.items() if k in common.get_kwarg_names(pl.subplot)
}
plot_kwargs = {k: v for k, v in kwargs.items() if k not in subp_kwargs}
if x.dtype.kind == 'M':
x = x.astype(datetime)
if y.dtype.kind == 'M':
y = y.astype(datetime)
if ax is None:
if fig is None:
fig = pl.figure(**fig_kwargs)
ax = fig.add_subplot(111, **subp_kwargs)
x = x.values
if width is None:
width = 0.5 * x[1] - x[0]
else:
try:
width = width.values
except AttributeError:
pass
ax.bar(x, y.values, align='center', width=width, **plot_kwargs)
ax.set_xlabel(labels[0])
ax.set_ylabel(labels[1])
pl.grid = True
pl.tight_layout()
return fig, ax
def plot(x,
y,
style='o',
dim=None,
log=False,
semilogx=False,
semilogy=False,
ax=None,
aligned=False,
fig=None,
lloc='best',
**kwargs):
xdim = x.dims
ydim = y.dims
if xdim != ydim:
err = 'unequal dimensions: %s and %s' % (str(xdim), str(ydim))
raise Exception(err)
if len(xdim) > 1 and dim is None:
err = 'more than 1 dimension: %s' % str(xdim)
raise Exception(err)
labels, kwargs = get_labels(x, y, **kwargs)
if x.attrs['quantity'] == y.attrs['quantity']:
kwargs['figsize'] = pl.figaspect(1)
elif x.attrs['quantity'] == 'time':
kwargs['figsize'] = (15, 5)
if aligned == False:
x, y = uf.broadcast(x, y)
fig_kwargs = {
k: v
for k, v in kwargs.items() if k in common.get_kwarg_names(pl.figure)
}
subp_kwargs = {
k: v
for k, v in kwargs.items() if k in common.get_kwarg_names(pl.subplot)
}
plot_kwargs = {
k: v
for k, v in kwargs.items() if k not in {
**subp_kwargs,
**fig_kwargs
}
}
if x.dtype.kind == 'M':
x = x.astype(datetime)
if y.dtype.kind == 'M':
y = y.astype(datetime)
if ax is None:
if fig is None:
fig = pl.figure(**fig_kwargs)
ax = fig.add_subplot(111, **subp_kwargs)
if log or semilogx:
x = uf.log(x)
if log or semilogy:
y = uf.log(y)
if dim is not None:
x = x.split(dim)
y = y.split(dim)
for ix, iy in zip(x, y):
line, = ax.plot(ix[1].values,
iy[1].values,
style,
label=ix[0],
**plot_kwargs)
else:
line, = ax.plot(x.values, y.values, style, **plot_kwargs)
# layout
ax.set_xlabel(labels[0])
ax.set_ylabel(labels[1])
if log:
def f(x, pos):
return '{:0.2e}'.format(uf.exp(x))
ax.xaxis.set_major_formatter(mpl.ticker.FuncFormatter(f))
ax.yaxis.set_major_formatter(mpl.ticker.FuncFormatter(f))
ax.legend(loc=lloc)
pl.tight_layout()
return fig, ax
def contourplot(x,
y,
z,
log=False,
levels=100,
aligned=False,
maxz=None,
minz=None,
zlog=False,
ax=None,
fig=None,
**kwargs):
labels, kwargs = get_labels(x, y, z, **kwargs)
if aligned == False:
x, y = uf.broadcast(x, y)
fig_kwargs = {
k: v
for k, v in kwargs.items() if k in common.get_kwarg_names(pl.figure)
}
subp_kwargs = {
k: v
for k, v in kwargs.items() if k in common.get_kwarg_names(pl.subplots)
}
if x.dtype.kind == 'M':
x = x.astype(datetime)
if y.dtype.kind == 'M':
y = y.astype(datetime)
if ax is None:
if fig is None:
fig = pl.figure(**fig_kwargs)
ax = fig.add_subplot(111, **subp_kwargs)
else:
fig = ax.figure
if ax.get_xlabel() == labels[1] or ax.get_ylabel() == labels[0]:
tmp = x.copy()
x = y.copy()
y = tmp
tmp = labels[0]
labels[0] = labels[1]
labels[1] = tmp
z = z.T
if maxz is None:
minz = 0
maxz = uf.nanmax(z).values
if zlog:
levels = np.logspace(minz, maxz, levels)
else:
levels = np.linspace(minz, maxz, levels)
z = z.values
z = np.ma.masked_array(z, mask=np.isnan(z))
img = ax.contour(x.values, y.values, z, levels=levels, **kwargs)
# layout
if log:
def f(x, pos):
return '{:0.2e}'.format(uf.exp(x))
ax.xaxis.set_major_formatter(mpl.ticker.FuncFormatter(f))
ax.yaxis.set_major_formatter(mpl.ticker.FuncFormatter(f))
if x.dtype.kind == 'M':
fig.autofmt_xdate()
cbar = fig.colorbar(img, ax=ax, format='%.1e')
cbar.set_label(labels[2])
ax.set_xlabel(labels[0])
ax.set_ylabel(labels[1])
pl.grid = True
pl.tight_layout()
return fig, ax