def plot_sta(self, ax, vname, station, date, label):
print vname, station, date, label
if label == 'Free':
nc = self.free
if self.t_free is None:
ocean_time = nc.variables['ocean_time'][:]
time = netCDF4.date2num(date, self.sta_JST)
if type(time) == np.int64:
t = np.where(ocean_time == time)[0][0]
else:
t0 = np.where(ocean_time == time[0])[0][0]
t1 = np.where(ocean_time == time[1])[0][0]
t = np.arange(t0, t1)
self.t_free = t
else:
t = self.t_free
elif label == 'Assi':
nc = self.assi
if self.t_assi is None:
ocean_time = nc.variables['ocean_time'][:]
time = netCDF4.date2num(date, self.sta_JST)
t = np.where(ocean_time == time)[0][0]
self.t_assi = t
else:
t = self.t_assi
if vname == 'DIN':
NH4 = nc.variables['NH4'][t,station-1,:]
NO3 = nc.variables['NO3'][t,station-1,:]
var = NH4 + NO3
elif vname == 'PON':
LDeN = nc.variables['LdetritusN'][t,station-1,:]
SDeN = nc.variables['SdetritusN'][t,station-1,:]
var = LDeN + SDeN
elif vname == 'POP':
LDeP = nc.variables['LdetritusP'][t,station-1,:]
SDeP = nc.variables['SdetritusP'][t,station-1,:]
var = LDeP + SDeP
else:
var = nc.variables[vname][t,station-1,:]
line = {'Free': '--', 'Assi': '-'}
if type(t) == np.int64:
depth = self.calculate_depth(nc, t, station)
ax.plot(var, depth, line[label], label=label)
else:
depth = self.calculate_depth(nc, t[0], station)
#ax.errorbar(np.mean(var, axis=0), depth, xerr=np.std(var, axis=0), fmt=line[label], label=label)
mean = np.mean(var, axis=0)
std = np.std(var, axis=0)
#ax.fill_betweenx(depth, mean-std, mean+std, label=label)
#ax.plot(mean, depth, line[label])
special.errorfill(mean, depth, xerr=std, label=label, ax=ax, alpha_fill=0.1)
stim_perm = stim[perm]
pred_perm = clf.fit(stim_perm, voxels).predict(stim_perm)
# compute the test statistic
T_perm.append(linalg.norm(pred_perm - voxels, 'fro'))
pvals_ls.append(perm_p_values(T_a_i, np.array(T_perm)))
# plot the resulting p-value distributions
from mpltools import special
pvals_rsa = np.array(pvals_rsa)
pvals_ls = np.array(pvals_ls)
power_rsa = 1. * (pvals_rsa < .05)
power_ls = 1. * (pvals_ls < .05)
plt.figure(figsize=(4, 4))
special.errorfill(a_space, np.mean(power_rsa, axis=1),
np.std(power_rsa, axis=1), label='RSA')
special.errorfill(a_space, np.mean(power_ls, axis=1),
np.std(power_ls, axis=1), label='LS')
plt.xlim((0, np.max(a_space)))
plt.ylim((0, 1.01))
plt.legend(loc=4)
plt.plot(a_space, power_rsa.mean(1), color='b', linewidth=2)
plt.plot(a_space, power_ls.mean(1), color='g', linewidth=2)
plt.ylabel('Power')
plt.xlabel('Effect size')
plt.subplots_adjust(bottom=.15, left=.15)
plt.savefig('power_%s.png' % random_effects)
plt.show()
def wplot(typ,*args,**kwargs):
save=kwargs.pop('save',0)
hold=kwargs.pop('hold',False)
title=kwargs.pop('title',False)
legs=kwargs.pop('legends',False)
invert=kwargs.pop('invert',0)
if not legs:
legs=kwargs.pop('legend',False)
if not legs:
legs=kwargs.pop('leg',False)
lkw=kwargs.pop('legopt',{})
if 'xlabel' in kwargs:
plt.xlabel(kwargs.pop('xlabel'))
if 'ylabel' in kwargs:
plt.ylabel(kwargs.pop('ylabel'))
if 'labels' in kwargs:
labs=kwargs.pop('xlabel')
plt.xlabel(labs[0])
plt.ylabel(labs[1])
if 'log' in kwargs:
lg=kwargs.pop('log')
if not isinstance(lg,str):
if typ!= 'hist':
kwargs['log']='xy'
else:
kwargs['log']=lg
else:
if 'x' in lg:
plt.xscale('log')
if typ!= 'hist':
if 'y' in lg :
plt.yscale('log')
else:
if 'y' in lg:
kwargs['log']=1
if 'x' in lg:
if min(args[0])<=0:
args=([ar for ar in args[0] if ar>0],)+args[1:]
if not 'bins' in kwargs or isinstance(kwargs['bins'],int):
kwargs['bins']=np.logspace(log10(min(args[0])),
log10(max(args[0])),kwargs.get('bins',100),10)
elif typ=='hist' and min(args[0])>0 and max(args[0])/min(args[0])>10**3:
kwargs['log']=1
if 'xs' in kwargs :
xs=kwargs.pop('xs')
args=list(itertools.chain.from_iterable([[xs[:len(a)],a] for a in args]))
if not args:
return
try:
if typ=='plot':
handle=plt.plot(*args,**kwargs)
if typ =='hist':
if not 'bins' in kwargs:
kwargs['bins']=100
if kwargs.pop('cleverbins',False):
if isinstance(kwargs['bins'],int):
kwargs['bins']=min(kwargs['bins'],len(args[0])/20)
kwargs['hold']=1
handle=[plt.hist(a,**kwargs)[2][0] for a in args]
if typ=='scatter':
handle=plt.scatter(*args,**kwargs)
if typ=='error':
handle=plt.errorbar(*args,**kwargs)
if typ=='errorfill':
handle=pltspecial.errorfill(*args,**kwargs)
if typ=='contour':
handle=plt.contour(*args,**kwargs)
if typ=='contourf':
handle=plt.contourf(*args,**kwargs)
if typ=='bar':
handle=plt.bar(*args,**kwargs)
except e:
print "Cannot plot:", sys.exc_info()[0],e
return
if title:
plt.title(title)
if legs:
plt.legend(handle,legs,**lkw)
if invert:
if 'x' in invert:
plt.xlim(plt.xlim()[::-1])
elif 'y' in invert:
plt.ylim(plt.ylim()[::-1])
if save:
plt.savefig(save)
plt.clf()
plt.close()
elif not hold:
plt.show()
return handle
""" ================ Plot `errorfill` ================ When you have continuous data measurement and errors associated with every data point, plotting error bars can get really noisy. `special.errorfill` plots a filled region to represent the error values instead of using individual bars. """ import numpy as np import matplotlib.pyplot as plt from mpltools import special x = np.linspace(0, 2 * np.pi) y_sin = np.sin(x) y_cos = np.cos(x) y_err = 0.2 special.errorfill(x, y_sin, y_err) special.errorfill(x, y_cos, y_err, alpha_fill=0.1) plt.show()
"""
================
Plot `errorfill`
================
When you have continuous data measurement and errors associated with every data point, plotting error bars can get really noisy. `special.errorfill` plots a filled region to represent the error values instead of using individual bars.
"""
import numpy as np
import matplotlib.pyplot as plt
from mpltools import special
x = np.linspace(0, 2 * np.pi)
y_sin = np.sin(x)
y_cos = np.cos(x)
y_err = 0.2
special.errorfill(x, y_sin, y_err, label='sin', label_fill='sin error')
special.errorfill(x, y_cos, y_err, label='cos', label_fill='cos error',
alpha_fill=0.1)
plt.legend()
plt.show()
""" ================ Plot `errorfill` ================ When you have continuous data measurement and errors associated with every data point, plotting error bars can get really noisy. `special.errorfill` plots a filled region to represent the error values instead of using individual bars. """ import numpy as np import matplotlib.pyplot as plt from mpltools import special x = np.linspace(0, 2 * np.pi) y_sin = np.sin(x) y_cos = np.cos(x) y_err = 0.2 special.errorfill(x, y_sin, y_err, label="sin", label_fill="sin error") special.errorfill(x, y_cos, y_err, label="cos", label_fill="cos error", alpha_fill=0.1) plt.legend() plt.show()
