def corr(stats, var, p=[.05],
figsize=(6.,4.),
axrect=[.08,.15,.9,.75],
legend_ncol=4,
):
"""
plots the correlation between a timeseries and a variable over time
"""
# if iscollection(stats):
# stats = [stats]
# assert all(iscollection(s) for s in stats)
corr = [segment_ops.corr(s, var) for s in stats]
if np.isscalar(p):
p = [p]
# P (http://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient#Inference)
n = len(corr[0]['slist'])
if not all([len(corr[i]['slist']) == n for i in range(1, len(corr))]):
raise ValueError("StatsSegments have different N --> p threshold cannot be drawn correctly")
Ps = np.array(p) / 2 # 2 tailed
df = n-2
Ts = sp.stats.t.isf(Ps, df)
R_thresholds = Ts / np.sqrt(df + Ts**2)
P.figure(figsize=figsize)
ax = P.axes(axrect)
legend_names = []
legend_handles = []
# corr
T = stats[0].t
for c in corr:
R = c.param
handle = P.plot(T, R, label=c.name)
legend_names.append(fmtxt.texify(c.name))
legend_handles.append(handle)
# thresholds
P.axhline(0, c='k')
for p in R_thresholds:
P.axhline(p)
P.axhline(-p)
# Figure stuff
P.ylabel('r')
P.xlabel('time')
P.suptitle("Correlation with {v}".format(v=fmtxt.texify(var.name)), fontsize=16)
P.figlegend(legend_handles, legend_names, 'lower center', ncol=legend_ncol)
def _ax_im_array(ax, layers, title=None, tick_spacing=.5):
"""
plots segment data as im
define a colorspace by supplying one of those kwargs: ``colorspace`` OR
``p`` OR ``vmax``
"""
handles = []
epoch = layers[0]
map_kwargs = {'extent': [epoch.time[0], epoch.time[-1], 0, len(epoch.sensor)],
'aspect': 'auto'}
# plot
for l in layers:
h = _plt_im_array(ax, l, **map_kwargs)
handles.append(h)
ax.set_ylabel("Sensor")
ax.set_xlabel("Time [s]")
#ticks
tickstart = int((-epoch.time[0] - 1e-3) / tick_spacing)
ticklabels = np.r_[-tickstart * tick_spacing : \
epoch.time[-1] + 1e-3 : tick_spacing]
ax.xaxis.set_ticks(ticklabels)
#title
if title is None:
if _plt.rcParams['text.usetex']:
title = fmtxt.texify(epoch.name)
else:
title = epoch.name
ax.set_title(title)
return handles
def plot(self, fig_num=None):#, tmax=1000):
if self._example_path == None:
ui.message("No files specified", "Before an import preview can be "
"plotted, at least one source file has "
"to be specified through the p.source parameter.", '!')
return
data = self._example_data#[:tmax]
# range correct data
t, n = data.shape
data = data - np.min(data, 0)
data /= np.max(data, 0) * 1.1
data += np.arange(n-1, -1, -1) # channel 1 on the top
# collect properties
name = os.path.basename(self._example_path)
samplingrate = self.p.samplingrate.get()
T = np.arange(0, t)
if samplingrate:
T /= samplingrate
# start plot
P.figure(fig_num)
P.subplots_adjust(.2, .05, .99, .9)
P.suptitle("Example: %s"%fmtxt.texify(name))
ax = P.axes()
lines = [ax.plot(data[:,i], c='.75', antialiased=False)[0] for i in xrange(n)]
y_ticks = np.arange(n-1, -1, -1) + .45
y_ticklabels = range(n)
# get channels data
for index, settings in self.p.channels.iterchannels():
# ax = P.subplot(n, 1, i+1, sharex=ax)
name, ds_type, arg1, arg2 = settings
# y_ticklabels.append(name)
if isinstance(index, int):
ls = [lines[index]]
y_ticklabels[index] = name
# y_ticks.append(i)
else:
if isinstance(index, slice):
start, stop = index.start, index.stop
ls = lines[index]
indexes = xrange(start+1, stop+1)
else:
start = index[0]
indexes = index[1:]
ls = [lines[i] for i in index]
y_ticklabels[start] = name
for i in indexes:
y_ticklabels[i] = ''
# tick_y = start + (stop-start)//2
# y_ticks.append(tick_y)
if ds_type == 'uts':
c = 'k'
elif ds_type == 'topo':
c = 'r'
elif ds_type == 'evt':
c = 'b'
for l in ls:
l.set_color(c)
if hasattr(ax, 'tick_params'):
ax.tick_params(axis='y', length=0)
ax.set_xlim(0, len(data))
ax.set_yticks(y_ticks)
ax.set_yticklabels(y_ticklabels)
# ax.set_xlim(T[0], T[-1])
# ax.set_xlim(0, tmax)
P.show()
def _ax_im_array(ax, layers, x='time', # vmax=None,
xlabel=True, ylabel=True, title=None, tick_spacing=.3):
"""
plots segment data as im
define a colorspace by supplying one of those kwargs: ``colorspace`` OR
``p`` OR ``vmax``
"""
handles = []
epoch = layers[0]
xdim = epoch.get_dim(x)
if epoch.ndim == 2:
xdim_i = epoch.dimnames.index(x)
ydim_i = {1:0, 0:1}[xdim_i]
y = epoch.dimnames[ydim_i]
else:
err = ("Need 2 dimensions, got %i" % epoch.ndim)
raise ValueError(err)
ydim = epoch.get_dim(y)
if y == 'sensor':
ydim = _dta.var(np.arange(len(ydim)), y)
map_kwargs = {'extent': [xdim[0], xdim[-1], ydim[0], ydim[-1]],
'aspect': 'auto'}
# plot
for l in layers:
h = _plt_im_array(ax, l, dims=(y, x), **map_kwargs)
handles.append(h)
if xlabel:
if xlabel is True:
xlabel = xdim.name
ax.set_xlabel(xlabel)
if ylabel:
if ylabel is True:
ylabel = ydim.name
ax.set_ylabel(ylabel)
# x-ticks
tickstart = math.ceil(xdim[0] / tick_spacing) * tick_spacing
tickend = xdim[-1] + tick_spacing / 1e4
ticklabels = np.arange(tickstart, tickend, tick_spacing)
ax.xaxis.set_ticks(ticklabels)
ax.x_fmt = "t = %.3f s"
# y-ticks
if y == 'sensor': # make sure y-ticklabels are all integers
locs = ax.yaxis.get_ticklocs()
if any(locs != locs.round()):
idx = np.where(locs == locs.round())[0]
locs = locs[idx]
labels = map(lambda x: str(int(x)), locs)
ax.yaxis.set_ticks(locs)
ax.yaxis.set_ticklabels(labels)
# title
if title is None:
if plt.rcParams['text.usetex']:
title = fmtxt.texify(epoch.name)
else:
title = epoch.name
ax.set_title(title)
return handles
def _get_attributes(statsSegments, sensor=None):
""
raise DeprecationWarning
if sensor:
try:
sensor_name = statsSegments[0].sensors[sensor].name
except Exception, exc:
print Exception, exc
sensor_name = None
else:
sensor_name = None
seg_t_start = statsSegments[0].tstart
seg_t_end = statsSegments[0].tend
if P.rcParams['text.usetex']:
names = [fmtxt.texify(s.name) for s in statsSegments]
else:
names = [s.name for s in statsSegments]
return names, sensor_name, seg_t_start, seg_t_end
# MARK: figure composition
def _loc(name, size=(0,0), title_space=0, frame=.01):
"""
takes a loc argument and returns x,y of bottom left edge
"""
if isinstance(name, basestring):
