def curvelinear_test1(fig):
"""
grid for custom transform.
"""
def tr(x, y):
x, y = np.asarray(x), np.asarray(y)
return x, y - x
def inv_tr(x, y):
x, y = np.asarray(x), np.asarray(y)
return x, y + x
grid_helper = GridHelperCurveLinear((tr, inv_tr))
ax1 = Subplot(fig, 1, 2, 1, grid_helper=grid_helper)
# ax1 will have a ticks and gridlines defined by the given
# transform (+ transData of the Axes). Note that the transform of
# the Axes itself (i.e., transData) is not affected by the given
# transform.
fig.add_subplot(ax1)
xx, yy = tr([3, 6], [5.0, 10.])
ax1.plot(xx, yy, linewidth=2.0)
ax1.set_aspect(1.)
ax1.set_xlim(0, 10.)
ax1.set_ylim(0, 10.)
ax1.axis["t"] = ax1.new_floating_axis(0, 3.)
ax1.axis["t2"] = ax1.new_floating_axis(1, 7.)
ax1.grid(True, zorder=0)
def curvelinear_test1(fig):
"""
grid for custom transform.
"""
def tr(x, y):
x, y = np.asarray(x), np.asarray(y)
return x, y - x
def inv_tr(x, y):
x, y = np.asarray(x), np.asarray(y)
return x, y + x
grid_helper = GridHelperCurveLinear((tr, inv_tr))
ax1 = Subplot(fig, 1, 2, 1, grid_helper=grid_helper)
# ax1 will have a ticks and gridlines defined by the given
# transform (+ transData of the Axes). Note that the transform of
# the Axes itself (i.e., transData) is not affected by the given
# transform.
fig.add_subplot(ax1)
xx, yy = tr([3, 6], [5.0, 10.])
ax1.plot(xx, yy, linewidth=2.0)
ax1.set_aspect(1.)
ax1.set_xlim(0, 10.)
ax1.set_ylim(0, 10.)
ax1.axis["t"] = ax1.new_floating_axis(0, 3.)
ax1.axis["t2"] = ax1.new_floating_axis(1, 7.)
ax1.grid(True, zorder=0)
def curvelinear_test1(fig):
"""
grid for custom transform.
"""
def tr(x, y):
x, y = np.asarray(x), np.asarray(y)
return x, y-x
def inv_tr(x,y):
x, y = np.asarray(x), np.asarray(y)
return x, y+x
grid_helper = GridHelperCurveLinear((tr, inv_tr))
ax1 = Subplot(fig, 1, 2, 1, grid_helper=grid_helper)
fig.add_subplot(ax1)
xx, yy = tr([3, 6], [5.0, 10.])
ax1.plot(xx, yy)
ax1.set_aspect(1.)
ax1.set_xlim(0, 10.)
ax1.set_ylim(0, 10.)
ax1.axis["t"]=ax1.new_floating_axis(0, 3.)
ax1.axis["t2"]=ax1.new_floating_axis(1, 7.)
ax1.grid(True)
def plotXcorr(qdata,save=False,fname='hmmSortingUnits.pdf'):
unitTimePoints = qdata['unitTimePoints']
samplingRate = qdata.get('samplingRate',30000.0)
units = unitTimePoints.keys()
nunits = len(units)
xsize = max(10,nunits*2)
fig = plt.figure(figsize=(xsize,(6.0/8)*xsize) )
fig.subplots_adjust(left=0.03,right=0.97,bottom=0.03,top=0.97)
i = 1
if not 'XCorr' in qdata:
if isinstance(qdata,dict):
qdata['XCorr'] = {}
else:
qdata.create_group('XCorr')
for k1 in xrange(len(units)-1) :
if not units[k1] in qdata['XCorr']:
qdata['XCorr'].create_group(units[k1])
for k2 in xrange(k1+1,len(units)):
if not units[k2] in qdata['XCorr'][units[k1]]:
T1 = unitTimePoints[units[k1]][:]/(samplingRate/1000)
T2 = unitTimePoints[units[k2]][:]/(samplingRate/1000)
#compute differences less than 50 ms
C = pdist_threshold2(T1,T2,50)
qdata['XCorr'][units[k1]].create_dataset(units[k2],data=C,compression=2,fletcher32=True,shuffle=True)
else:
C = qdata['XCorr'][units[k1]][units[k2]][:]
n,b = np.histogram(C,np.arange(-50,50),normed=True)
ax = Subplot(fig,nunits-1,nunits,k1*nunits+k2)
fig.add_axes(ax)
formatAxis(ax)
ax.plot(b[:-1],n,'k')
ax.fill_betweenx([0,n.max()],-1.0,1.0,color='r',alpha=0.3)
if not (k1 == len(units)-2 and k2 == len(units)-1):
ax.set_xticklabels('')
ax.set_yticklabels('')
if save:
fig.savefig(os.path.expanduser('~/Documents/research/figures/SpikeSorting/hmm/%s' %( fname,)),bbox='tight')
else:
plt.draw()
def curvelinear_test1(fig):
"""
Grid for custom transform.
"""
def tr(x, y):
x, y = numpy.asarray(x), numpy.asarray(y)
return x, y - (2 * x) # return x + (5 * y), (7 * y) + (3 * x)
def inv_tr(x, y):
x, y = numpy.asarray(x), numpy.asarray(y)
return x, y + (2 * x)
grid_helper = GridHelperCurveLinear((tr, inv_tr))
ax1 = Subplot(fig, 1, 1, 1, grid_helper=grid_helper)
# ax1 will have a ticks and gridlines defined by the given
# transform (+ transData of the Axes). Note that the transform of
# the Axes itself (i.e., transData) is not affected by the given
# transform.
fig.add_subplot(ax1)
xx, yy = tr([0, 1], [0, 2])
ax1.plot(xx, yy, linewidth=2.0)
ax1.set_aspect(1)
ax1.set_xlim(-3, 3)
ax1.set_ylim(-3, 3)
ax1.axis["t"] = ax1.new_floating_axis(
0, 0
) # first argument appears to be slope, second argument appears to be starting point on vertical
ax1.axis["t2"] = ax1.new_floating_axis(1, 0)
ax1.axhline(y=0, color='r')
ax1.axvline(x=0, color='r')
ax1.grid(True, zorder=0)
]
mesh_T, mesh_p = np.meshgrid(
np.arange(-60.0,
T_levels.max() - C_to_K + 0.1, 0.1), p_all)
theta_ep_mesh = Bolton.theta_ep_field(mesh_T, mesh_p)
# Plotting Code!
skew_grid_helper = GridHelperCurveLinear((from_thermo, to_thermo))
fig = plt.figure()
ax = Subplot(fig, 1, 1, 1, grid_helper=skew_grid_helper)
fig.add_subplot(ax)
for yi in y_p_levels:
ax.plot((x_min, x_max), (yi, yi), color=(1.0, 0.8, 0.8))
for x_T in x_T_levels:
ax.plot(x_T, y_all_p, color=(1.0, 0.5, 0.5))
for x_theta in x_thetas:
ax.plot(x_theta, y_all_p, color=(1.0, 0.7, 0.7))
for x_mixing_ratio in x_mixing_ratios:
good = p_all >= 600 # restrict mixing ratio lines to below 600 mb
ax.plot(x_mixing_ratio[good], y_all_p[good], color=(0.8, 0.8, 0.6))
n_moist = len(theta_ep_mesh)
moist_colors = ((0.6, 0.9, 0.7), ) * n_moist
ax.contour(x_from_Tp(mesh_T + C_to_K, mesh_p),
y_from_p(mesh_p),
from mpl_toolkits.axisartist import Subplot
from mpl_toolkits.axisartist.grid_helper_curvelinear import \
GridHelperCurveLinear
def tr(x, y): # source (data) to target (rectilinear plot) coordinates
x, y = numpy.asarray(x), numpy.asarray(y)
return x + 0.2 * y, y - x
def inv_tr(x, y):
x, y = numpy.asarray(x), numpy.asarray(y)
return x - 0.2 * y, y + x
grid_helper = GridHelperCurveLinear((tr, inv_tr))
ax6 = Subplot(fig, nrow, ncol, 6, grid_helper=grid_helper)
fig.add_subplot(ax6)
ax6.set_title('non-ortho axes')
xx, yy = tr([3, 6], [5.0, 10.])
ax6.plot(xx, yy)
ax6.set_aspect(1.)
ax6.set_xlim(0, 10.)
ax6.set_ylim(0, 10.)
ax6.axis["t"] = ax6.new_floating_axis(0, 3.)
ax6.axis["t2"] = ax6.new_floating_axis(1, 7.)
ax6.grid(True)
plt.show()
def plotwitherrors(time, timeunit, means, meansunit, std):
# ---> basic settings
title = input('Title: ')
y_name = input('Observable name: ')
if title == '' or y_name == '':
title = 'test'
y_name = 'test'
axistop_bool = False
axisright_bool = False
axisbottom_bool = True
axisleft_bool = True
# <--- basic settings
# ---> analyse data
# <--- analyse data
# plot --->
fig = plt.figure()
ax = Subplot(fig, 111)
fig.add_subplot(ax)
ax.errorbar(time,
means,
yerr=std,
marker='o',
markersize=2,
c='black',
linestyle='none',
ecolor='black',
elinewidth=1,
capthick=1,
capsize=3)
ax.plot(time,
means,
color='black',
alpha=0.5,
linewidth=1.0,
linestyle='--')
#legend and labels
ax.legend(loc='best')
plt.xlabel('time in ' + timeunit, **hfont)
plt.ylabel(y_name + ' in ' + meansunit, **hfont)
# visible axis
ax.axis["right"].set_visible(axisright_bool)
ax.axis["top"].set_visible(axistop_bool)
ax.axis["bottom"].set_visible(axisbottom_bool)
ax.axis["left"].set_visible(axisleft_bool)
# <--- plot
# save plot --->
pdfdirectory = '../../figures/' + title + '.pdf'
pgfdirectory = '../../figures/' + title + '.pgf'
fig.savefig(pdfdirectory)
plt.savefig(pgfdirectory)
# <--- save plot
print('test run successful')
return None
def plotSpikes(qdata,save=False,fname='hmmSorting.pdf',tuning=False,figsize=(10,6)):
allSpikes = qdata['allSpikes']
unitSpikes = qdata['unitSpikes']
spikeIdx = qdata['spikeIdx']
spikeIdx = qdata['unitTimePoints']
units = qdata['unitTimePoints']
spikeForms = qdata['spikeForms']
channels = qdata['channels']
uniqueIdx = qdata['uniqueIdx']
samplingRate = qdata.get('samplingRate',30000.0)
"""
mustClose = False
if isinstance(dataFile,str):
dataFile = h5py.File(dataFile,'r')
mustClose = True
data = dataFile['data'][:]
"""
keys = np.array(units.keys())
x = np.arange(32)[None,:] + 42*np.arange(spikeForms.shape[1])[:,None]
xt = np.linspace(0,31,spikeForms.shape[-1])[None,:] + 42*np.arange(spikeForms.shape[1])[:,None]
xch = 10 + 42*np.arange(len(channels))
for c in units.keys():
ymin,ymax = (5000,-5000)
fig = plt.figure(figsize=figsize)
fig.subplots_adjust(hspace=0.3)
print "Unit: %s " %(str(c),)
print "\t Plotting waveforms..."
sys.stdout.flush()
#allspikes = data[units[c][:,None]+np.arange(-10,22)[None,:],:]
#allspikes = allSpikes[spikeIdx[c]]
allspikes = qdata['unitSpikes'][c]
otherunits = keys[keys!=c]
#nonOverlapIdx = np.prod(np.array([~np.lib.arraysetops.in1d(spikeIdx[c],spikeIdx[c1]) for c1 in otherunits]),axis=0).astype(np.bool)
#nonOverlapIdx = np.prod(np.array([pdist_threshold(spikeIdx[c],spikeIdx[c1],3) for c1 in otherunits]),axis=0).astype(np.bool)
#nonOverlapIdx = uniqueIdx[c]
nonOverlapIdx = qdata['nonOverlapIdx'][c]
overlapIdx = np.lib.arraysetops.setdiff1d(np.arange(qdata['unitTimePoints'][c].shape[0]),nonOverlapIdx)
#allspikes = allSpikes[np.lib.arraysetops.union1d(nonOverlapIdx,overlapIdx)]
ax = Subplot(fig,2,3,1)
fig.add_axes(ax)
formatAxis(ax)
#plt.plot(x.T,sp,'b')
m = allspikes[:].mean(0)
s = allspikes[:].std(0)
plt.plot(x.T,m,'k',lw=1.5)
#find the minimum point for this template
ich = spikeForms[int(c)].min(1).argmin()
ix = spikeForms[int(c)][ich,:].argmin()
#plt.plot(x.T,spikeForms[int(c)][:,ix-10:ix+22].T,'r')
plt.plot(x.T,np.roll(spikeForms[int(c)],10-ix,axis=1)[:,:32].T,'r')
for i in xrange(x.shape[0]):
plt.fill_between(x[i],m[:,i]-s[:,i],m[:,i]+s[:,i],color='b',alpha=0.5)
yl = ax.get_ylim()
ymin = min(ymin,yl[0])
ymax = max(ymax,yl[1])
ax.set_title('All spikes (%d)' % (allspikes.shape[0],))
ax = Subplot(fig,2,3,2)
fig.add_axes(ax)
formatAxis(ax)
if len(nonOverlapIdx)>0:
m = allspikes[:][nonOverlapIdx,:,:].mean(0)
s = allspikes[:][nonOverlapIdx,:,:].std(0)
plt.plot(x.T,m,'k',lw=1.5)
for i in xrange(x.shape[0]):
plt.fill_between(x[i],m[:,i]-s[:,i],m[:,i]+s[:,i],color='b',alpha=0.5)
#plt.plot(x.T,spikeForms[int(c)][:,ix-10:ix+22].T,'r')
plt.plot(x.T,np.roll(spikeForms[int(c)],10-ix,axis=1)[:,:32].T,'r')
yl = ax.get_ylim()
ymin = min(ymin,yl[0])
ymax = max(ymax,yl[1])
#for sp in allspikes[nonOverlapIdx,:,:]:
# plt.plot(x.T,sp,'r')
ax.set_title('Non-overlap spikes (%d)' %(nonOverlapIdx.shape[0],))
ax = Subplot(fig,2,3,3)
fig.add_axes(ax)
formatAxis(ax)
if len(overlapIdx)>0:
m = allspikes[:][overlapIdx,:,:].mean(0)
s = allspikes[:][overlapIdx,:,:].std(0)
plt.plot(x.T,m,'k',lw=1.5)
for i in xrange(x.shape[0]):
plt.fill_between(x[i],m[:,i]-s[:,i],m[:,i]+s[:,i],color='b',alpha=0.5)
#plt.plot(x.T,spikeForms[int(c)][:,ix-10:ix+22].T,'r')
plt.plot(x.T,np.roll(spikeForms[int(c)],10-ix,axis=1)[:,:32].T,'r')
yl = ax.get_ylim()
ymin = min(ymin,yl[0])
ymax = max(ymax,yl[1])
#for sp in allspikes[~nonOverlapIdx,:,:]:
# plt.plot(x.T,sp,'g')
ax.set_title('Overlap spikes (%d)' % ((overlapIdx).shape[0],))
for a in fig.axes:
a.set_ylim((ymin,ymax))
a.set_xticks(xch)
a.set_xticklabels(map(str,channels))
a.set_xlabel('Channels')
for a in fig.axes[1:]:
a.set_yticklabels([])
fig.axes[0].set_ylabel('Amplitude')
"""
isi distribution
"""
print "\t ISI distribution..."
sys.stdout.flush()
timepoints = qdata['unitTimePoints'][c][:]/(samplingRate/1000)
if len(timepoints)<2:
print "Too few spikes. Aborting..."
continue
isi = np.log(np.diff(timepoints))
n,b = np.histogram(isi,100)
ax = Subplot(fig,2,3,4)
fig.add_axes(ax)
formatAxis(ax)
ax.plot(b[:-1],n,'k')
yl = ax.get_ylim()
ax.vlines(0.0,0,yl[1],'r',lw=1.5)
ax.set_xlabel('ISI [ms]')
#get xticklabels
xl,xh = int(np.round((b[0]-0.5)*2))/2,int(np.round((b[-1]+0.5)*2))/2
xl = -0.5
dx = np.round(10.0*(xh-xl)/5.0)/10
xt_ = np.arange(xl,xh+1,dx)
ax.set_xticks(xt_)
ax.set_xticklabels(map(lambda s: r'$10^{%.1f}$' % (s,),xt_))
"""
auto-correlogram
"""
print "\t auto-correllogram..."
sys.stdout.flush()
if not 'autoCorr' in qdata:
if isinstance(qdata,dict):
qdata['autoCorr'] = {}
else:
qdata.create_group('autoCorr')
if not c in qdata['autoCorr']:
C = pdist_threshold2(timepoints,timepoints,50)
qdata['autoCorr'][c] = C
if not isinstance(qdata,dict):
qdata.flush()
else:
C = qdata['autoCorr'][c][:]
n,b = np.histogram(C[C!=0],np.arange(-50,50))
ax = Subplot(fig,2,3,5)
fig.add_axes(ax)
formatAxis(ax)
ax.plot(b[:-1],n,'k')
ax.fill_betweenx([0,n.max()],-1.0,1.0,color='r',alpha=0.3)
ax.set_xlabel('Lag [ms]')
if tuning:
print "\tPlotting tuning..."
sys.stdout.flush()
#attempt to get tuning for the current session, based on PWD
stimCounts,isiCounts,angles,spikedata = gt.getTuning(sptrain=timepoints)
#reshape to number of orientations X number of reps, collapsing
#across everything else
#angles = np.append(angles,[angles[0]])
C = stimCounts['0'].transpose((1,0,2,3))
C = C.reshape(C.shape[0],C.size/C.shape[0])
ax = plt.subplot(2,3,6,polar=True)
ax.errorbar(angles*np.pi/180,C.mean(1),C.std(1))
if save:
if not os.path.isabs(fname):
fn = os.path.expanduser('~/Documents/research/figures/SpikeSorting/hmm/%s' % (fname.replace('.pdf','Unit%s.pdf' %(str(c),)),))
else:
fn = fname.replace('.pdf','Unit%s.pdf' %(str(c),))
fig.savefig(fn,bbox='tight')
if not save:
plt.draw()
"""
fig = plt.figure()
ax = Subplot(fig, 1, 1, 1, grid_helper=skew_grid_helper)
fig.add_subplot(ax)
def format_coord(x, y):
"""format ticks with physical values"""
T, p = to_thermo(x, y)
return "{0: 5.1f} C {1: 5.1f} mb".format(float(T), float(p))
ax.format_coord = format_coord
for yi in y_p_levels:
ax.plot((x_min, x_max), (yi, yi), color=(1.0, 0.8, 0.8))
for x_T in x_T_levels:
ax.plot(x_T, y_all_p, color=(1.0, 0.5, 0.5))
for x_theta in x_thetas:
ax.plot(x_theta, y_all_p, color=(1.0, 0.7, 0.7))
for x_mixing_ratio in x_mixing_ratios:
good = p_all >= 600 #restrict mixing ratio lines to below 600 mb
ax.plot(x_mixing_ratio[good], y_all_p[good], color=(0.8, 0.8, 0.6))
n_moist = len(theta_ep_levels)
moist_colors = ((0.6, 0.9, 0.7), ) * n_moist
ax.contour(x_from_Tp(mesh_T + C_to_K, mesh_p),
y_from_p(mesh_p),
np.arange(-60.0, T_levels.max()-C_to_K+0.1, 0.1), p_all) theta_ep_mesh = Bolton.theta_ep_field(mesh_T, mesh_p) # Plotting Code! skew_grid_helper = GridHelperCurveLinear((from_thermo, to_thermo)) fig = plt.figure() ax = Subplot(fig, 1, 1, 1, grid_helper=skew_grid_helper) fig.add_subplot(ax) for yi in y_p_levels: ax.plot((x_min, x_max), (yi,yi), color = (1.0, 0.8, 0.8)) for x_T in x_T_levels: ax.plot(x_T, y_all_p, color=(1.0, 0.5, 0.5)) for x_theta in x_thetas: ax.plot(x_theta, y_all_p, color=(1.0, 0.7, 0.7)) for x_mixing_ratio in x_mixing_ratios: good = p_all >= 600 # restrict mixing ratio lines to below 600 mb ax.plot(x_mixing_ratio[good], y_all_p[good], color = (0.8, 0.8, 0.6)) n_moist = len(theta_ep_mesh) moist_colors = ((0.6, 0.9, 0.7),)*n_moist ax.contour(x_from_Tp(mesh_T+C_to_K, mesh_p), y_from_p(mesh_p), theta_ep_mesh, theta_ep_levels, colors = moist_colors)
def plot_univariate_inequality(xdata,
inclusive=False,
raycolor='blue',
bgcolor='white',
xlim=None,
figsize=(5, 0.5),
figure=None,
title=None,
**kwargs):
"""Plot a chart of a univariate inequality ray with matplotlib
Args:
xdata (list, tuple): endpoints of the ray: ``[start, ..., end]``
Kwargs:
inclusive (bool): if True, draw a filled circle; if False, draw a circle without fill
raycolor (str): matplotlib color of the line and markers
bgcolor (str): matplotlib color for the background and circle without fill
xlim (None, True, or list/tuple): if True, calculate defaults for xmin and xmax of the x-axis
figsize (tuple): matplotlib figsize
figure (None or matplotlib.figure.Figure): Figure to add a plot to
title (None or str): Title to add the the plot
kwargs: all other kwargs will be passed to `plt.figure()`
Returns:
matplotlib.figure.Figure: inequality figure
"""
_xdata = sorted(xdata)
xstart = _xdata[0]
xend = _xdata[-1]
lefttoright = xstart <= xend
lefttoright2 = xdata[0] <= xdata[-1]
fig1 = figure or plt.figure(figsize=figsize, facecolor=bgcolor, **kwargs)
ax1 = Subplot(fig1, 111)
fig1.add_subplot(ax1)
style = {}
style['linewidth'] = 4
style['linestyle'] = 'solid'
style['color'] = raycolor
style['marker'] = 'o'
style['markersize'] = 12
style['markevery'] = [0] if lefttoright else [-1]
style['markerfacecolor'] = raycolor if inclusive else bgcolor
style['markeredgecolor'] = raycolor
style['markeredgewidth'] = 4
arrowstyle = {}
arrowstyle['marker'] = '>' if lefttoright2 else '<'
arrowstyle['markersize'] = 12
arrowstyle['markerfacecolor'] = raycolor
arrowstyle['markeredgecolor'] = raycolor
arrowy = xdata[-1] if lefttoright else xdata[0]
ax1.set(ylim=(-0.5, 1))
if xlim is True:
xlim = (float(xstart - 1), float(xend + 0.5))
if xlim:
ax1.set(xlim=xlim)
ax1.axis["left"].set_visible(False)
ax1.axis["right"].set_visible(False)
ax1.axis["top"].set_visible(False)
#ax1.axis["bottom"].set_axisline_style(axisline_style="->", size=2)
ax1.plot(1, -0.5, ">k", transform=ax1.get_yaxis_transform(), clip_on=False)
ax1.plot(0, -0.5, "<k", transform=ax1.get_yaxis_transform(), clip_on=False)
_x = [xval for xval in xdata]
_y = [0 for xval in xdata]
plt.plot(_x, _y, **style)
if title:
ax1.set_title(title)
plt.plot(arrowy, 0, **arrowstyle)
return fig1
