def processItem(self, item):
hash = item.hash()
from cache import cacheFolder, cached
cacheFile = join(cacheFolder, hash)
try:
data = urlopen(item.url).read()
open(cacheFile, "wb").write(data)
cached(item)
except URLError:
item.failed = True
item.save()
def processItem(self,item):
hash = item.hash()
from cache import cacheFolder, cached
cacheFile = join(cacheFolder, hash)
try:
data = urlopen(item.url).read()
open(cacheFile, "wb").write(data)
cached(item)
except URLError:
item.failed = True
item.save()
def __init__(self, xx):
'''
Inspired by PCA in matplotlib.mlab
Compute the principle components of a dataset and stores the
mean, sigma, and SVD of sigma for the data. Use toPC and
fromPC to project the data onto a reduced set of dimensions
and back. This version takes the SVD of the covariance matrix.
Inputs:
*xx*: a numobservations x numdims array
Attrs:
*nn*, *mm*: the dimensions of xx
*mu* : a numdims array of means of xx
*sigma* : the covariance matrix
*var* : the average amount of variance of each of the principal components
*std* : sqrt of var
*fracVar* : the fractional amount of variance from each principal component
*fracStd* : sqrt of fracVar
'''
self.nn, self.mm = xx.shape
if self.nn < self.mm:
raise RuntimeError('we assume data in a is organized with numrows>numcols')
self.mu = xx.mean(axis=0)
centeredXX = self.center(xx)
#self.sigma = dot(centeredXX.T, centeredXX) / self.nn
self.sigma = cached(dot, centeredXX.T, centeredXX) / self.nn
# Columns of UU are the eigenvectors of self.sigma, i.e. the
# principle components. UU and VV are transpose of each other;
# we don't use VV. ss is the diagonal of the true S matrix.
#self.UU, self.ss, self.VV = linalg.svd(self.sigma, full_matrices = False)
self.UU, self.ss, self.VV = cached(linalg.svd, self.sigma, full_matrices = False)
self.var = self.ss / float(self.nn)
self.std = sqrt(self.var)
self.fracVar = self.var / self.var.sum()
self.fracStd = self.std / self.std.sum()
def diff(self):
# Doing this weird to make caching work.
def differ(a, b):
return Diff(a, b)
prev = (Revision.q.filter(Revision.svn_id < self.svn_id)
.order_by(Revision.svn_id.desc()).first())
return cached(differ)(self, prev)
# In[2]:
sns.set(context='talk')
# In[3]:
model_name = 'model5'
by = 'SubSet'
sample_n = 1000
# ## load files for all cell types
# In[4]:
df = cache.cached(data.prep_annotated_data)
# In[5]:
assert all(pd.notnull(df['log1p_tpm_rescaled']))
# In[6]:
print(df.columns)
#apply(lambda x: x.startswith('C'))
# ## sample genes for analysis
# In[7]:
except ImportError:
from StringIO import StringIO
import simplejson
from lanshark.config import config
import logging
logger = logging.getLogger('lanshark')
from lanshark import icons
from lanshark import network
from lanshark import sendfile
from cache import cached
socket.getaddrinfo = cached(config.CACHE_TIMEOUT, stats=config.debug)(
socket.getaddrinfo)
iconpath = os.path.join(config.DATA_PATH, "icons", "32x32")
iconfactory = icons.URLIconFactory(iconpath, "/__data__/icons/128x128/", ".png")
hidden_files = [re.compile(pattern) for pattern in config.HIDDEN_FILES]
def hidden(filename):
return any(pattern.match(filename) for pattern in hidden_files)
class FileIndex(threading.Thread):
"""
The fileindex offers fast searching over
a periodicaly updated file index
"""
def __init__(self, path):
# In[1]:
import data
import models
import cache
import seaborn as sns
# In[2]:
model_name = 'model5.2'
by = 'cell_type'
sample_n = 500
# In[3]:
sample_df = cache.cached(models.prep_sample_df, sample_n=sample_n)
(training_df, test_df) = models.split_sample_df(sample_df=sample_df,
test_sample_n=1)
# In[4]:
model_file = models.get_model_file(model_name=model_name)
#print(cache._read_file(model_file))
# In[5]:
stan_data = models.prep_stan_data(sample_df=training_df,
test_df=test_df,
by=by)
# In[ ]:
# In[3]: model_name = 'model5.3' by = 'SubSet' sample_n = 100 # ## get data, as we did in earlier examples # This will help in case we want to compare estimates for particular genes or samples # In[4]: sample_df = cache.cached(models.prep_sample_df, sample_n=sample_n) # In[5]: stan_data1 = models.prep_stan_data(sample_df, by=by, nu=1) stan_data2 = models.prep_stan_data(sample_df, by=by, nu=2) stan_data3 = models.prep_stan_data(sample_df, by=by, nu=3) stan_data4 = models.prep_stan_data(sample_df, by=by, nu=4) stan_data5 = models.prep_stan_data(sample_df, by=by, nu=5) stan_data6 = models.prep_stan_data(sample_df, by=by, nu=6) # In[6]: model = models.get_model_file(model_name=model_name)
