def view(lat_range = [9.,15.], lon_range = [140., 150.] ):
'''View GIS Eleveation data for the ocean floor.
kwargs:
lat_range [9,13]
lon_range [140,144]
'''
nc = sio.netcdf_file(config.dataPath('random/ocean_topo.nc'))
xvals = nc.variables['x'].data
yvals = nc.variables['y'].data
idxs = [nonzero(logical_and(greater(xvals,lon_range[0]),
less(xvals, lon_range[1])))[0],
nonzero(logical_and(greater(yvals,lat_range[0]),
less(yvals, lat_range[1])))[0]]
cmap = mycolors.blackbody()
arr = array(nc.variables['z'].data)
subimg = arr[:,idxs[0]][idxs[1],:]
plt.imshow(subimg[::-1], cmap = cmap, interpolation = 'nearest')
def cluster_tissues(nx = 20,ny = 500, timepoint = -1,
step = 4,
sim = 'neg_dist',
imshow_sims = False,
scatter_sims = False,
hist_sims = False,
do_cluster= True,
do_show = True, cstep = -1):
'''Cluster ny nuclei by the values of the nx mRNAs with highest
variance. Uses the medioids method with number of clusters set
by exemplar self simalarity as outlined in 6.874 and implemented
at http://www.psi.toronto.edu/affinitypropagation
imaging:
imshow_sims
scatter_sims
hist_sims
do_show
numerics:
nx: number of genes to cluster upon
ny: number of cells in the clusterin
timepoint: which time to use for cluster computation
step: how many genes to skip when showing results
So far I have implemented a distance based similarity and a
'''
mrnas = nio.getBDTNP()
misc = nio.getBDTNP(misc = True)
shp = shape(mrnas.values()[0]['vals'])
#choose to look only at one timepoint
stds = [std(m['vals'][:,timepoint]) for m in mrnas.values()]
vsort = argsort(stds)[::-1]
xinds = vsort[:nx]
#Choose the most variable factors and use them as the
#underlying variables from which to construct a similarity
nuclei =array([ mrnas.values()[idx]['vals'][:,timepoint]
for idx in xinds]).T
t = [ mean(nuclei, 0), std(nuclei,0)]
t[1][equal(t[1],0)] = 0
sims = similarity(nuclei, transform = t, method = sim)
cluster_inds = array(floor(linspace(0,len(nuclei)-1, ny)), int)
cluster_training = sims[cluster_inds,:][:,cluster_inds]
f = plt.figure(0)
#, projection = '3d')
if scatter_sims:
ax = f.add_subplot(111)
scatterx = [cluster_sims[i] for i in range(ny) for j in range(ny)]
scattery = [cluster_sims[j] for i in range(ny) for j in range(ny)]
ax.scatter(scatterx, scattery, s =3, alpha = .1)
if imshow_sims:
ax = f.add_subplot(111)
cmap = mycolors.blackbody()
ax.imshow(cluster_sims, cmap = cmap, interpolation = 'nearest')
if hist_sims:
ax = f.add_subplot(111)
csf = cluster_sims.flatten()
csf -= max(csf)
csf *= -1
h = histogram(log10(1+csf), bins = 100)
ax.plot(h[1][:-1],h[0])
cluster(cluster_training, ss.scoreatpercentile(cluster_training,.2) )
fopen = open(cfg.dataPath('bdtnp/clustering/nuclei/idxs'))
lines = fopen.readlines()
c = [int(l.strip()) for l in lines]
c_training_exemplars = set(c)
exemplar_inds = [cluster_inds[i] for i in c_training_exemplars]
#I am being a bit lazy with subscripting here because I just assume
#that the similarity is symmetric... I suppose I could let it be
#asymmetric if I liked
exemplars = nuclei[exemplar_inds,:]
all_sims = similarity(nuclei, exemplars,
transform = t,
transform_exemplars = True,
method = sim)
assignments = np.argmax(all_sims,1)
ne = len(c_training_exemplars)
colors = array(mycolors.getct(len(c)))
colors = array(colors)
if do_show:
for tp in range(shape(mrnas.values()[0]['vals'])[1])[-1:]:
try: f.clear()
except Exception, e: print 'Weird 3d plotting error. Alas'
nuclei =array([ mrnas.values()[idx]['vals'][:,tp]
for idx in xinds]).T
all_sims = similarity(nuclei, exemplars,
transform = t, transform_exemplars = True,
method = sim)
assignments = np.argmax(all_sims,1)
ax = f.add_subplot(111)
#colors = [colors[i] for i in c]
xs = misc['x']['vals'][::step,0]
ys = misc['y']['vals'][::step,0]
zs = misc['z']['vals'][::step,0]
ax.scatter(xs, zs,s= 50, color =colors[assignments[::step]])
#ax.set_title('''Virtual embryo cell (n={2}) clusters
#from similarities derived from {0} genes.
#Clusters derived at T = {1}, shown at T = {3}.'''\
# .format(nx,timepoint, len(xs),tp))
f.savefig(cfg.dataPath('figs/bdtnp/cluster_movie{0:02d}.tiff'.format(tp)), format = 'tiff')
def heatMap(grid,
ann = [],
xlabel = 'none',
ylabel = 'none',
**kwargs):
'''
for now, this is a primordial heatmap script.
The optimal use case is as in the function heatmapGene from
compbio.projects.predict.py. In this case, we input a grid and
a list of annotations that happen to annotate each and every
grid point. In particular, the dictionary contains an xvalue
and a yvalue for each point as well as an entry, 'pkeys' that
give the names of the elements being plotted on the x and the y.
e.g: ann[0] = {'pvalue':0.,
'cvalue':0.,
'pkeys':['pvalue','cvalue']
}
Of course, this assumes a standard form for annotations.
I guess its not so bad for now.
'''
cmap = mycolors.blackbody()
ax = kwargs.get('axes', None)
if not ax:
f = kwargs.get('fig', 0)
figure = plt.figure(f)
ax = figure.add_subplot(111)
ax.imshow(grid.T, cmap = cmap, interpolation = 'nearest',
origin = 'lower', aspect = 'auto', **kwargs)
xticks, xticklabels, yticklabels = [], [], []
yticks = []
edgept = lambda i,j: ( i == 0 and j == 0) \
or ( i == len(ann) -1 and j == len(ann[0])-1)
lowpt = argmin(grid)
highpt = argmax(grid)
ishigh = lambda i,j : (i,j) == unravel_index(highpt, shape(grid))
islow = lambda i,j: (i,j) == unravel_index(lowpt, shape(grid))
for i in range(shape(ann)[0]):
for j in range(shape(ann)[1]):
if random.random() < ( 0./product(shape(ann))) \
or edgept(i,j) or islow(i,j) or ishigh(i,j):
d = ann[i][j]
dkeys = d['pkeys']
s = dictString(ann[i][j])
xy = [i,j]
color = 'black'
xytext=(30,10)
textcoords='offset pixels'
bbox = None #bbox=dict(boxstyle="round4", fc="none")
arrowprops=dict(arrowstyle="-|>",
connectionstyle="arc3,rad=-0.2",
relpos=(0., 0.),
shrinkA = 0,
shrinkB = 10,
fc="none")
ax.scatter(xy[0],xy[1], 100,
color = 'none',
edgecolor = 'black')
if islow(i,j):
color ='red'
s+= '\nLow Point: {0}'.format(strFormat(grid[i,j]))
elif ishigh(i,j):
color = 'blue'
s+= '\nHigh Point: {0}'.format(strFormat(grid[i,j]))
if xlabel == 'none': xlabel = dkeys[0]
if ylabel == 'none': ylabel = dkeys[1]
if not edgept(i,j): ax.annotate(s,xy,xytext = xytext,
textcoords = textcoords,
bbox = bbox,
arrowprops =arrowprops)
xticks.append(i)
xticklabels.append(strFormat(d[dkeys[0]]))
yticks.append(j)
yticklabels.append(strFormat(d[dkeys[1]]))
ax.set_xticks(xticks)
ax.set_xticklabels(xticklabels)
ax.set_yticks(yticks)
ax.set_yticklabels(yticklabels)
if xlabel != 'none': ax.set_xlabel(xlabel)
if ylabel != 'none': ax.set_ylabel(ylabel)
return ax
