def test_complex(self):
a = rand(13,4) + 1j*rand(13,4)
fname = tempfile.mktemp('.dat')
io.write_array(fname,a)
b = io.read_array(fname,atype=N.Complex)
assert_array_almost_equal(a,b,decimal=4)
os.remove(fname)
def test_complex(self):
a = rand(13, 4) + 1j * rand(13, 4)
fname = tempfile.mktemp('.dat')
io.write_array(fname, a)
b = io.read_array(fname, atype=N.Complex)
assert_array_almost_equal(a, b, decimal=4)
os.remove(fname)
def test_float(self):
a = rand(3,4)*30
fname = tempfile.mktemp('.dat')
io.write_array(fname,a)
b = io.read_array(fname)
assert_array_almost_equal(a,b,decimal=4)
os.remove(fname)
def test_float(self):
a = rand(3, 4) * 30
fname = tempfile.mktemp('.dat')
io.write_array(fname, a)
b = io.read_array(fname)
assert_array_almost_equal(a, b, decimal=4)
os.remove(fname)
def test_integer(self):
from scipy import stats
a = stats.randint.rvs(1,20,size=(3,4))
fname = tempfile.mktemp('.dat')
io.write_array(fname,a)
b = io.read_array(fname,atype=a.dtype.char)
assert_array_equal(a,b)
os.remove(fname)
def test_integer(self):
from scipy import stats
a = stats.randint.rvs(1, 20, size=(3, 4))
fname = tempfile.mktemp('.dat')
io.write_array(fname, a)
b = io.read_array(fname, atype=a.dtype.char)
assert_array_equal(a, b)
os.remove(fname)
def walk_and_run(top_dir, tempdir='', action=default_action):
"""recurse through directory structure, looking for .LS8 files.
Each .LS8 file is run.
"""
if tempdir == '': # let tempfile library choose a tempdir by default
tempdir = tempfile.mkdtemp()
sys.stderr.write( "Temporary directory will be %s.\n" %
os.path.abspath(tempdir) )
top_dir = os.path.abspath(top_dir)
not_converged = file('not_converged', 'w')
for dirpath, dirnames, filenames in os.walk(top_dir):
sys.stderr.write( "Searching %s...\n" % dirpath )
for filename in filenames:
if filename.upper().endswith('LS8'):
sys.stderr.write( "Found %s.\n" % filename )
lisfile = LisrelInput(os.path.join(dirpath, filename))
# adjust input to output matrices separately
lisfile.write_to_file(lisfile.get_modified_input())
try:
lisfile.run_lisrel(tempdir) # run lisrel to write output to tempdir
except:
print "LISREL encountered an error, skipping...\n"
break
finally:
if os.path.exists(lisfile.path + '.backup'):
os.remove(lisfile.path)
os.rename(lisfile.path + '.backup', lisfile.path)
else:
sys.stderr.write('WARNING: Could not restore backup LS8 file.\n')
if solution_obtained(os.path.join(tempdir, 'OUT')):
smats = lisfile.standardize_matrices()
for igrp in range(len(smats)):
for matname, stanmat in smats[igrp].iteritems():
sys.stderr.write( "INPUT %s, GROUP %d, MATRIX %s:" % \
(filename, igrp+1, matname))
action(stanmat, matname=matname, group_num = igrp+1,
filename=filename, dirpath=dirpath)
# try to write the variance matrix of
# the standardized estimates
try:
vnames, vmat = lisfile.get_var_standardized(path = \
tempdir)
vfile = open(os.path.join(dirpath,
'vcov_standardized.txt'), 'w')
io.write_array(vfile, vmat, separator='\t',
linesep='\n', precision=10,) # closes vfile
write_tuple_to_file(vnames,
path=(dirpath, 'vcov_standardized.names'))
except:
sys.stderr.write('ERROR writing or getting vcov matrix of standardized estimates for group %d. Error: %s\n' % (igrp, str(e.args)))
else:
print "No solution could be obtained, skipping...\n"
not_converged.write(os.path.join(dirpath, filename) + "\n")
not_converged.close()
def saveSample(self, path):
outData = self.dataContainer.data
if path[-3:] == 'csv':
outFile = file(path, 'wb')
csvWriter = csv.writer(outFile, dialect='excel')
#csvWriter.writerow([self.dataContainer.dimensions[0].label, self.dataContainer.label])
csvWriter.writerows(outData.tolist())
else:
outFile = file(path, 'w')
#outFile.write(str([self.dataContainer.dimensions[0].label, self.dataContainer.label])+"\n")
write_array(outFile, outData.tolist())
outFile.close()
def saveSample(self, path):
outData = self.dataContainer.data
if path[-3:] == 'csv':
outFile = file(path,'wb')
csvWriter = csv.writer(outFile,dialect='excel')
#csvWriter.writerow([self.dataContainer.dimensions[0].label, self.dataContainer.label])
csvWriter.writerows(outData.tolist())
else:
outFile = file(path,'w')
#outFile.write(str([self.dataContainer.dimensions[0].label, self.dataContainer.label])+"\n")
write_array(outFile,outData.tolist())
outFile.close()
def saveField(self, path):
if not isQuantity(self.dataContainer.unit):
self.ordinate = self.dataContainer.data*self.dataContainer.unit
else:
self.ordinate = self.dataContainer.data*self.dataContainer.unit.value
self.abscissa = self.dataContainer.dimensions[0].data
outData = scipy.transpose(scipy.array([self.abscissa,self.ordinate]))
if path[-3:] == 'csv':
outFile = file(path,'wb')
csvWriter = csv.writer(outFile,dialect='excel')
csvWriter.writerow([self.dataContainer.dimensions[0].label, self.dataContainer.label])
csvWriter.writerows(outData.tolist())
else:
outFile = file(path,'w')
outFile.write(str([self.dataContainer.dimensions[0].label, self.dataContainer.label])+"\n")
write_array(outFile,outData)
outFile.close()
def convert_to_midi(reffile,savefile):
""" converts REF files from mirex05 to MIDI data
"""
target_file = DATA_PATH + reffile
data = read_array(target_file)
# remove time axes and convert to midi
midi = np.round( audiotools.freq_to_midi(data[:,1]) )
# calc chromas
chromas = np.ones(len(midi)) * (-1)
for n in range(len(midi)):
if midi[n]>0:
chromas[n] = midi[n] % 12
# write data to disk
writedata = np.c_[data[:,0], midi, chromas]
FILE = open(DATA_PATH + savefile,"w")
write_array(FILE,writedata,',')
FILE.close()
def saveField(self, path):
if not isQuantity(self.dataContainer.unit):
self.ordinate = self.dataContainer.data * self.dataContainer.unit
else:
self.ordinate = self.dataContainer.data * self.dataContainer.unit.value
self.abscissa = self.dataContainer.dimensions[0].data
outData = scipy.transpose(scipy.array([self.abscissa, self.ordinate]))
if path[-3:] == 'csv':
outFile = file(path, 'wb')
csvWriter = csv.writer(outFile, dialect='excel')
csvWriter.writerow([
self.dataContainer.dimensions[0].label,
self.dataContainer.label
])
csvWriter.writerows(outData.tolist())
else:
outFile = file(path, 'w')
outFile.write(
str([
self.dataContainer.dimensions[0].label,
self.dataContainer.label
]) + "\n")
write_array(outFile, outData)
outFile.close()
def deact_batch(filename=""):
"""Fits deactivation time constants: Monoexponential until <=-70,
biexponential for >-70 mV.
filename -- If not an empty string, stores the best-fit parameters
in this file."""
stf = __import__("stf")
# Some ugly definitions for the time being
gNMono = 5 # Monoexponential fits
gNBi = 4 # Biexponential fits
gFMono = 0 # id of monoexponential function
gFBi = 3 # id of biexponential function
gMonoDictSize = stf.leastsq_param_size(gFMono) + 1 # Parameters, chisqr
gBiDictSize = stf.leastsq_param_size(gFBi) + 1 # Parameters, chisqr
if (gMonoDictSize < 0 or gBiDictSize < 0):
print('Couldn\'t retrieve function; aborting now.')
return False
if (not (stf.check_doc())):
print('Couldn\'t find an open file; aborting now.')
return False
# set the test pulse window cursors:
if (not (stf.set_peak_start(70.84, True))):
return False
if (not (stf.set_peak_end(74.84, True))):
return False
if (not (stf.set_base_start(69.5, True))):
return False
if (not (stf.set_base_end(70.5, True))):
return False
if (not (stf.set_peak_mean(1))):
return False
if (not (stf.set_peak_direction("down"))):
return False
# Monoexponential loop ---------------------------------------------------
firstpass = True
# A list for dictionary keys...
mono_keys = []
# ... and values:
mono_values = np.empty((gMonoDictSize, gNMono))
if not filename == "":
ls_file = np.empty((gNMono, stf.leastsq_param_size(gFMono)))
# Monoexponential fits:
for n in range(0, gNMono):
if (stf.set_trace(n) == False):
print("Couldn't set a new trace; aborting now.")
return False
print('Analyzing trace %d of %d' % (n + 1, stf.get_size_channel()))
# set the fit window cursors:
# use the index for the start cursor:
if (not (stf.set_fit_start(stf.peak_index(True)))):
return False
# fit 1.5 ms:
fit_end_time = stf.get_fit_start(True) + 1.0
if (not (stf.set_fit_end(fit_end_time, True))):
return False
# Least-squares fitting:
p_dict = stf.leastsq(gFMono)
if not filename == "":
ls_file[n][0] = p_dict["Amp_0"]
ls_file[n][1] = p_dict["Tau_0"]
ls_file[n][2] = p_dict["Offset"]
if (p_dict == 0):
print('Couldn\'t perform a fit; aborting now.')
return False
# Create an empty list:
tempdict_entry = []
row = 0
for k, v in p_dict.iteritems():
if (firstpass == True):
mono_keys.append(k)
mono_values[row][n] = v
row = row + 1
firstpass = False
monoDict = dict()
# Create the dictionary for the table:
entry = 0
for elem in mono_keys:
monoDict[elem] = mono_values[entry].tolist()
entry = entry + 1
if (not (stf.show_table_dictlist(monoDict))):
return False
# Biexponential loop ---------------------------------------------------
firstpass = True
# A list for dictionary keys...
bi_keys = []
# ... and values:
bi_values = np.empty((gBiDictSize, gNBi))
# Monoexponential fits:
for n in range(gNMono, gNBi + gNMono):
if (stf.set_trace(n) == False):
print('Couldn\'t set a new trace; aborting now.')
return False
print('Analyzing trace %d of %d' % (n + 1, stf.get_size_channel()))
# set the fit window cursors:
# use the index for the start cursor:
if (not (stf.set_fit_start(stf.peak_index(True)))):
return False
# fit 4 ms:
fit_end_time = stf.get_fit_start(True) + 3.5
if (not (stf.set_fit_end(fit_end_time, True))):
return False
# Least-squares fitting:
p_dict = stf.leastsq(gFBi)
if (p_dict == 0):
print('Couldn\'t perform a fit; aborting now.')
return False
# Create an empty list:
tempdict_entry = []
row = 0
for k, v in p_dict.iteritems():
if (firstpass == True):
bi_keys.append(k)
bi_values[row][n - gNMono] = v
row = row + 1
firstpass = False
biDict = dict()
# Create the dictionary for the table:
entry = 0
for elem in bi_keys:
biDict[elem] = bi_values[entry].tolist()
entry = entry + 1
if not filename == "":
write_array(file(filename, 'w'), ls_file, precision=15)
if (not (stf.show_table_dictlist(biDict))):
return False
return True
def save( ar, fileName ):
from scipy.io import write_array
write_array( fileName, ar, precision = 8 )
def exportPointset(thepointset, infodict, separator=' ', linesep='\n',
precision=12, suppress_small=0, varvaldir='col',
ext='', append=False):
assert varvaldir in ['col', 'row'], \
"invalid variable value write direction"
# in order to avoid import cycles, cannot explicitly check that
# thepointset is of type Pointset, because Points.py imports this file
# (utils.py), so check an attribute instead.
try:
thepointset.coordnames
except AttributeError:
raise TypeError, "Must pass Pointset to this function: use arrayToPointset first!"
infodict_usedkeys = []
for key, info in infodict.iteritems():
if isinstance(info, str):
infodict_usedkeys += [info]
elif info == []:
infodict[key] = copy.copy(thepointset.coordnames)
infodict_usedkeys.extend(thepointset.coordnames)
else:
infodict_usedkeys += list(info)
allnames = copy.copy(thepointset.coordnames)
if thepointset._parameterized:
allnames.append(thepointset.indepvarname)
remlist = remain(infodict_usedkeys, allnames+range(len(allnames)))
if remlist != []:
print "Coords not found in pointset:", remlist
raise ValueError, \
"invalid keys in infodict - some not present in thepointset"
assert isinstance(ext, str), "'ext' extension argument must be a string"
if ext != '':
if ext[0] != '.':
ext = '.'+ext
if append:
assert varvaldir == 'col', ("append mode not supported for row"
"format of data ordering")
modestr = 'a'
else:
modestr = 'w'
totlen = len(thepointset)
if totlen == 0:
raise ValueError, ("Pointset is empty")
for fname, tup in infodict.iteritems():
try:
f = open(fname+ext, modestr)
except IOError:
print "There was a problem opening file "+fname+ext
raise
try:
if isinstance(tup, str):
try:
varray = thepointset[tup]
except TypeError:
raise ValueError, "Invalid specification of coordinates"
elif isinstance(tup, int):
try:
varray = thepointset[:,tup].toarray()
except TypeError:
raise ValueError, "Invalid specification of coordinates"
elif type(tup) in [list, tuple]:
if alltrue([type(ti)==str for ti in tup]):
thetup=list(tup)
if thepointset.indepvarname in tup:
tix = thetup.index(thepointset.indepvarname)
thetup.remove(thepointset.indepvarname)
try:
vlist = thepointset[thetup].toarray().tolist()
except TypeError:
raise ValueError, "Invalid specification of coordinates"
if len(thetup)==1:
vlist = [vlist]
if thepointset.indepvarname in tup:
vlist.insert(tix, thepointset.indepvararray.tolist())
varray = array(vlist)
elif alltrue([type(ti)==int for ti in tup]):
try:
varray = thepointset[:,tup].toarray()
except TypeError:
raise ValueError, "Invalid specification of coordinates"
else:
raise ValueError, "Invalid specification of coordinates"
else:
f.close()
raise TypeError, \
"infodict values must be singletons or tuples/lists of strings or integers"
except IOError:
f.close()
print "Problem writing to file"+fname+ext
raise
except KeyError:
f.close()
raise KeyError, ("Keys in infodict not found in pointset")
if varvaldir == 'row':
write_array(f, varray, separator, linesep,
precision, suppress_small, keep_open=0)
else:
write_array(f, transpose(varray), separator, linesep,
precision, suppress_small, keep_open=0)
def act_batch(nFunc=5, filename="", lat=60):
"""Fits activation and inactivation of 15 iv pulses
using a biexponential funtion with a delay, creates a
table showing the results.
Keyword argument:
nFunc -- Index of function used for fitting. At present,
10 is the HH gNa function,
5 is a sum of two exponentials with a delay.
filename -- If not an empty string, stores the best-fit parameters
in this file."""
stf = __import__("stf")
# Some ugly definitions for the time being
gFitStart = 70.5 + lat / 1000.0 # fit end cursor is variable
gFSelect = nFunc # HH function
gDictSize = stf.leastsq_param_size(
gFSelect) + 2 # Parameters, chisqr, peak value
gBaseStartCtrl = 69.5 # Start and end of the baseline before the control pulse, in ms
gBaseEndCtrl = 70.5
gPeakStartCtrl = 70.64 # Start and end of the peak cursors for the control pulse, in ms
gPeakWindowSizes = (2.5, 2, 1.5, 1, 1, 0.8, 0.8, 0.8, 0.6, 0.6, 0.5, 0.5,
0.4, 0.4, 0.4)
gFitDurations = (8, 8, 7, 6, 5.5, 5, 4.5, 3.5, 2.5, 2, 1.5, 1.5, 1.0, 0.8,
0.8)
gPulses = len(gFitDurations) # Number of traces
if (gDictSize < 0):
print("Couldn\'t retrieve function id=%d; aborting now." % gFSelect)
return False
if (not (stf.check_doc())):
print("Couldn\'t find an open file; aborting now.")
return False
# set cursors:
if (not (stf.set_peak_start(gPeakStartCtrl, True))):
return False
if (not (stf.set_peak_end(stf.get_size_trace(0) - 1))):
return False
if (not (stf.set_base_start(gBaseStartCtrl, True))):
return False
if (not (stf.set_base_end(gBaseEndCtrl, True))):
return False
if (not (stf.set_peak_mean(3))):
return False
if (not (stf.set_peak_direction("both"))):
return False
firstpass = True
# A list for dictionary keys and values:
dict_keys = []
dict_values = np.empty((gDictSize, stf.get_size_channel()))
if not filename == "":
ls_file = np.empty((gPulses, stf.leastsq_param_size(nFunc)))
for n in range(0, gPulses):
if (stf.set_trace(n) == False):
print('Couldn\'t set a new trace; aborting now.')
return False
print('Analyzing trace %d of %d' % (n + 1, stf.get_size_channel()))
# set the fit window cursors:
if (not (stf.set_peak_end(gPeakStartCtrl + gPeakWindowSizes[n],
True))):
return False
if (not (stf.set_fit_start(gFitStart, True))):
return False
if (not (stf.set_fit_end(gFitStart + gFitDurations[n], True))):
return False
stf.measure()
# Least-squares fitting:
p_dict = stf.leastsq(gFSelect)
if not filename == "":
ls_file[n][0] = p_dict["gprime_na"]
ls_file[n][1] = p_dict["tau_m"]
ls_file[n][2] = p_dict["tau_h"]
ls_file[n][3] = p_dict["offset"]
if (p_dict == 0):
print('Couldn\'t perform a fit; aborting now.')
return False
# Create an empty list:
tempdict_entry = []
row = 0
for k, v in p_dict.iteritems():
if (firstpass == True):
dict_keys.append(k)
dict_values[row][n] = v
row = row + 1
if (firstpass):
dict_keys.append("Peak amplitude")
dict_values[row][n] = stf.get_peak() - stf.get_base()
firstpass = False
if not filename == "":
write_array(file(filename, 'w'), ls_file, precision=15)
retDict = dict()
# Create the dictionary for the table:
entry = 0
for elem in dict_keys:
retDict[elem] = dict_values[entry].tolist()
entry = entry + 1
return stf.show_table_dictlist(retDict)
def gc_homeo_af():
import contrib.jsldefs
import topo.command.pylabplots
import contrib.jacommands
from topo.command.analysis import save_plotgroup
from topo.analysis.featureresponses import FeatureResponses , PatternPresenter, FeatureMaps
#FeatureResponses.repetitions=10
FeatureMaps.selectivity_multiplier=20
PatternPresenter.duration=0.2
PatternPresenter.apply_output_fns=False
import topo.command.pylabplots
reload(topo.command.pylabplots)
on = topo.sim["LGNOn"].in_connections[0].strength
off = topo.sim["LGNOff"].in_connections[0].strength
if __main__.__dict__.get("GC",False):
topo.sim["LGNOn"].in_connections[0].strength=0
topo.sim["LGNOff"].in_connections[0].strength=0
contrib.jsldefs.homeostatic_analysis_function()
topo.command.pylabplots.fftplot(topo.sim["V1"].sheet_views["OrientationPreference"].view()[0],filename="V1ORMAPFFT")
from topo.misc.filepath import normalize_path, application_path
from scipy.io import write_array
import numpy
write_array(normalize_path(str(topo.sim.time())+"orprefmap.txt"), topo.sim["V1"].sheet_views["OrientationPreference"].view()[0])
write_array(normalize_path(str(topo.sim.time())+"orselmap.txt"), topo.sim["V1"].sheet_views["OrientationSelectivity"].view()[0])
topo.sim["LGNOn"].in_connections[0].strength = on
topo.sim["LGNOff"].in_connections[0].strength = off
print float(topo.sim.time())
if(float(topo.sim.time()) > 19002.0):
#topo.sim["V1"].output_fns[2].scale=0.0
save_plotgroup("Position Preference")
PatternPresenter.duration=1.0
PatternPresenter.apply_output_fns=True
import topo.command.pylabplots
reload(topo.command.pylabplots)
topo.command.pylabplots.measure_or_tuning_fullfield.instance(sheet=topo.sim["V1"],repetitions=10)(repetitions=10)
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0,0]",sheet=topo.sim["V1"],coords=[(0,0)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0,0]",sheet=topo.sim["V1"],coords=[(0.1,0)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0,0]",sheet=topo.sim["V1"],coords=[(0.1,0.1)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0,0]",sheet=topo.sim["V1"],coords=[(0,0.1)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.1,0.1]",sheet=topo.sim["V1"],coords=[(0.1,0.1)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.1,-0.1]",sheet=topo.sim["V1"],coords=[(0.1,-0.1)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.1,0.1]",sheet=topo.sim["V1"],coords=[(-0.1,0.1)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.1,-0.1]",sheet=topo.sim["V1"],coords=[(-0.1,-0.1)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.2,0.2]",sheet=topo.sim["V1"],coords=[(0.2,0.2)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.2,-0.2]",sheet=topo.sim["V1"],coords=[(0.2,-0.2)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.2,0.2]",sheet=topo.sim["V1"],coords=[(-0.2,0.2)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.2,-0.2]",sheet=topo.sim["V1"],coords=[(-0.2,-0.2)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0,0.1]",sheet=topo.sim["V1"],coords=[(0.0,0.1)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0,-0.1]",sheet=topo.sim["V1"],coords=[(0.0,-0.1)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.1,0]",sheet=topo.sim["V1"],coords=[(-0.1,0.0)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.1,0]",sheet=topo.sim["V1"],coords=[(0.1,-0.0)])()
if(float(topo.sim.time()) > 20000.0):
topo.sim["V1"].output_fns[1].plastic=False
contrib.jacommands.measure_histogram(iterations=1000)
obj = GpuSpace() obj.cuda_make_context( mpi.rank ) t1 = datetime.now() for i, m in enumerate( m_list ): mpi.world.barrier() t2 = datetime.now() print t2-t1, m, 'MB' obj.set_nx( m*mbytes_float ) if mpi.rank == 0: t_rate_htod[0][i], t_rate_dtoh[0][i] = obj.get_transfer_rate() for dev in xrange( 1, num_gpus ): t_rate_htod[dev][i], t_rate_dtoh[dev][i] = mpi.world.recv( source=dev ) else: mpi.world.send( dest=0, value=obj.get_transfer_rate() ) if mpi.rank == 0: from scipy.io import write_array fpath = './bandwidth-mpi_barrier-%dgpus.ascii' % num_gpus data_list = [m_list] for dev in xrange( num_gpus ): data_list.append( t_rate_htod[dev] ) data_list.append( t_rate_dtoh[dev] ) write_array( fpath, sc.transpose(data_list), separator='\t', linesep='\n' ) obj.cuda_context_pop()
def gc_homeo_af():
import contrib.jsldefs
import topo.command.pylabplot
import contrib.jacommands
from topo.command.analysis import save_plotgroup
from topo.analysis.featureresponses import FeatureResponses, PatternPresenter, FeatureMaps
#FeatureResponses.repetitions=10
FeatureMaps.selectivity_multiplier = 20
PatternPresenter.duration = 0.2
PatternPresenter.apply_output_fns = False
import topo.command.pylabplot
reload(topo.command.pylabplot)
on = topo.sim["LGNOn"].in_connections[0].strength
off = topo.sim["LGNOff"].in_connections[0].strength
if __main__.__dict__.get("GC", False):
topo.sim["LGNOn"].in_connections[0].strength = 0
topo.sim["LGNOff"].in_connections[0].strength = 0
contrib.jsldefs.homeostatic_analysis_function()
topo.command.pylabplot.fftplot(
topo.sim["V1"].sheet_views["OrientationPreference"].view()[0],
filename="V1ORMAPFFT")
from topo.misc.filepath import normalize_path, application_path
from scipy.io import write_array
import numpy
write_array(normalize_path(str(topo.sim.time()) + "orprefmap.txt"),
topo.sim["V1"].sheet_views["OrientationPreference"].view()[0])
write_array(normalize_path(str(topo.sim.time()) + "orselmap.txt"),
topo.sim["V1"].sheet_views["OrientationSelectivity"].view()[0])
topo.sim["LGNOn"].in_connections[0].strength = on
topo.sim["LGNOff"].in_connections[0].strength = off
print float(topo.sim.time())
if (float(topo.sim.time()) > 19002.0):
#topo.sim["V1"].output_fns[2].scale=0.0
save_plotgroup("Position Preference")
PatternPresenter.duration = 1.0
PatternPresenter.apply_output_fns = True
import topo.command.pylabplot
reload(topo.command.pylabplot)
topo.command.pylabplot.measure_or_tuning_fullfield.instance(
sheet=topo.sim["V1"], repetitions=10)(repetitions=10)
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[0,0]",
sheet=topo.sim["V1"],
coords=[(0, 0)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[0,0]",
sheet=topo.sim["V1"],
coords=[(0.1, 0)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[0,0]",
sheet=topo.sim["V1"],
coords=[(0.1, 0.1)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[0,0]",
sheet=topo.sim["V1"],
coords=[(0, 0.1)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[0.1,0.1]",
sheet=topo.sim["V1"],
coords=[(0.1, 0.1)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[0.1,-0.1]",
sheet=topo.sim["V1"],
coords=[(0.1, -0.1)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[-0.1,0.1]",
sheet=topo.sim["V1"],
coords=[(-0.1, 0.1)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[-0.1,-0.1]",
sheet=topo.sim["V1"],
coords=[(-0.1, -0.1)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[0.2,0.2]",
sheet=topo.sim["V1"],
coords=[(0.2, 0.2)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[0.2,-0.2]",
sheet=topo.sim["V1"],
coords=[(0.2, -0.2)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[-0.2,0.2]",
sheet=topo.sim["V1"],
coords=[(-0.2, 0.2)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[-0.2,-0.2]",
sheet=topo.sim["V1"],
coords=[(-0.2, -0.2)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[0,0.1]",
sheet=topo.sim["V1"],
coords=[(0.0, 0.1)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[0,-0.1]",
sheet=topo.sim["V1"],
coords=[(0.0, -0.1)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[-0.1,0]",
sheet=topo.sim["V1"],
coords=[(-0.1, 0.0)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[0.1,0]",
sheet=topo.sim["V1"],
coords=[(0.1, -0.0)])()
if (float(topo.sim.time()) > 20000.0):
topo.sim["V1"].output_fns[1].plastic = False
contrib.jacommands.measure_histogram(iterations=1000)
def deact_batch( filename="" ):
"""Fits deactivation time constants: Monoexponential until <=-70,
biexponential for >-70 mV.
filename -- If not an empty string, stores the best-fit parameters
in this file."""
stf = __import__("stf")
# Some ugly definitions for the time being
gNMono = 5 # Monoexponential fits
gNBi = 4 # Biexponential fits
gFMono = 0 # id of monoexponential function
gFBi = 3 # id of biexponential function
gMonoDictSize = stf.leastsq_param_size( gFMono ) + 1 # Parameters, chisqr
gBiDictSize = stf.leastsq_param_size( gFBi ) + 1 # Parameters, chisqr
if ( gMonoDictSize < 0 or gBiDictSize < 0 ):
print('Couldn\'t retrieve function; aborting now.')
return False
if ( not(stf.check_doc()) ):
print('Couldn\'t find an open file; aborting now.')
return False
# set the test pulse window cursors:
if ( not(stf.set_peak_start( 70.84, True )) ):
return False
if ( not(stf.set_peak_end( 74.84, True )) ):
return False
if ( not(stf.set_base_start( 69.5, True )) ):
return False
if ( not(stf.set_base_end( 70.5, True )) ):
return False
if ( not(stf.set_peak_mean( 1 )) ):
return False
if ( not(stf.set_peak_direction( "down" )) ):
return False
# Monoexponential loop ---------------------------------------------------
firstpass = True
# A list for dictionary keys...
mono_keys = []
# ... and values:
mono_values = np.empty( (gMonoDictSize, gNMono) )
if not filename=="":
ls_file=np.empty((gNMono,stf.leastsq_param_size(gFMono)))
# Monoexponential fits:
for n in range( 0, gNMono ):
if ( stf.set_trace( n ) == False ):
print("Couldn't set a new trace; aborting now.")
return False
print('Analyzing trace %d of %d'%( n+1, stf.get_size_channel()))
# set the fit window cursors:
# use the index for the start cursor:
if ( not(stf.set_fit_start( stf.peak_index( True ) )) ):
return False
# fit 1.5 ms:
fit_end_time = stf.get_fit_start( True )+1.0
if ( not(stf.set_fit_end( fit_end_time, True)) ):
return False
# Least-squares fitting:
p_dict = stf.leastsq( gFMono )
if not filename=="":
ls_file[n][0]=p_dict["Amp_0"]
ls_file[n][1]=p_dict["Tau_0"]
ls_file[n][2]=p_dict["Offset"]
if ( p_dict == 0 ):
print('Couldn\'t perform a fit; aborting now.')
return False
# Create an empty list:
tempdict_entry = []
row = 0
for k, v in p_dict.iteritems():
if ( firstpass == True ):
mono_keys.append( k )
mono_values[row][n] = v
row = row+1
firstpass = False
monoDict = dict()
# Create the dictionary for the table:
entry = 0
for elem in mono_keys:
monoDict[ elem ] = mono_values[entry].tolist()
entry = entry+1
if ( not(stf.show_table_dictlist( monoDict )) ):
return False
# Biexponential loop ---------------------------------------------------
firstpass = True
# A list for dictionary keys...
bi_keys = []
# ... and values:
bi_values = np.empty( (gBiDictSize, gNBi) )
# Monoexponential fits:
for n in range( gNMono, gNBi+gNMono ):
if ( stf.set_trace( n ) == False ):
print('Couldn\'t set a new trace; aborting now.')
return False
print('Analyzing trace %d of %d'%( n+1, stf.get_size_channel()))
# set the fit window cursors:
# use the index for the start cursor:
if ( not(stf.set_fit_start( stf.peak_index( True ) )) ):
return False
# fit 4 ms:
fit_end_time = stf.get_fit_start( True )+3.5
if ( not(stf.set_fit_end( fit_end_time, True)) ):
return False
# Least-squares fitting:
p_dict = stf.leastsq( gFBi )
if ( p_dict == 0 ):
print('Couldn\'t perform a fit; aborting now.')
return False
# Create an empty list:
tempdict_entry = []
row = 0
for k, v in p_dict.iteritems():
if ( firstpass == True ):
bi_keys.append( k )
bi_values[row][n-gNMono] = v
row = row+1
firstpass = False
biDict = dict()
# Create the dictionary for the table:
entry = 0
for elem in bi_keys:
biDict[ elem ] = bi_values[entry].tolist()
entry = entry+1
if not filename=="":
write_array(file(filename,'w'), ls_file, precision=15)
if ( not(stf.show_table_dictlist( biDict )) ):
return False
return True
from mystic.metropolis import *
import time
t1 = time.time()
for i in xrange(L):
scem(Ck, ak, Sk, Sak, target, 0.1)
t2 = time.time()
print "SCEM 1 chain for x[%d] took %0.3f ms" % (len(Sk), (t2 - t1) * 1000)
Sk = array(Sk)
t1 = time.time()
x = [[0, 10]]
for i in xrange(L):
x.append(metropolis_hastings(proposal, target, x[-1]))
t2 = time.time()
print "2D Metropolis for x[%d] took %0.3f ms" % (len(x), (t2 - t1) * 1000)
x = array(x)
# have a look at vrugt.nb for reading and postprocessing
# the datafile below
from scipy.io import write_array
#write_array(open('twisted1.dat','w'),x)
write_array(open('twisted1.dat', 'w'), Sk)
import pylab
pylab.plot(Sk[:, 0], Sk[:, 1], 'r.')
pylab.plot(x[:, 0] + 30, x[:, 1], 'b.')
pylab.show()
# end of file
def save( ar, fileName ):
io.write_array( fileName, ar, precision = 8 )
cc.logging(fname,dt)
else :
print 'discarded cell # %d ' % run
del cc
angle_moyen = S.array(angle_moyen)
longueur_moyenne= S.array(longueur_moyenne)
ecartype = S.array(ecartype)
duree2 = S.array(duree2)
duree1 = S.array(duree1)
angle_onset = S.array(angle_onset)*180/pi
angle_maxi = S.array(angle_maxi)*180/pi
angle_moyen = fabs(angle_moyen)*180/pi
data = S.column_stack((duree1, duree2, angle_moyen,ecartype,longueur_moyenne))
io.write_array("results.txt", data, separator=' ', linesep='\n')
figure(3)
plot(duree2,angle_moyen,'o')
show()
def save_event(self):
dataout = np.vstack((self.x, self.y, self.z))
dataout = dataout.transpose()
write_array("/home/pcuser/data/RVsT1.txt", dataout)
def exportPointset(thepointset,
infodict,
separator=' ',
linesep='\n',
precision=12,
suppress_small=0,
varvaldir='col',
ext='',
append=False):
assert varvaldir in ['col', 'row'], \
"invalid variable value write direction"
# in order to avoid import cycles, cannot explicitly check that
# thepointset is of type Pointset, because Points.py imports this file
# (utils.py), so check an attribute instead.
try:
thepointset.coordnames
except AttributeError:
raise TypeError, "Must pass Pointset to this function: use arrayToPointset first!"
infodict_usedkeys = []
for key, info in infodict.iteritems():
if isinstance(info, str):
infodict_usedkeys += [info]
elif info == []:
infodict[key] = copy.copy(thepointset.coordnames)
infodict_usedkeys.extend(thepointset.coordnames)
else:
infodict_usedkeys += list(info)
allnames = copy.copy(thepointset.coordnames)
if thepointset._parameterized:
allnames.append(thepointset.indepvarname)
remlist = remain(infodict_usedkeys, allnames + range(len(allnames)))
if remlist != []:
print "Coords not found in pointset:", remlist
raise ValueError, \
"invalid keys in infodict - some not present in thepointset"
assert isinstance(ext, str), "'ext' extension argument must be a string"
if ext != '':
if ext[0] != '.':
ext = '.' + ext
if append:
assert varvaldir == 'col', ("append mode not supported for row"
"format of data ordering")
modestr = 'a'
else:
modestr = 'w'
totlen = len(thepointset)
if totlen == 0:
raise ValueError, ("Pointset is empty")
for fname, tup in infodict.iteritems():
try:
f = open(fname + ext, modestr)
except IOError:
print "There was a problem opening file " + fname + ext
raise
try:
if isinstance(tup, str):
try:
varray = thepointset[tup]
except TypeError:
raise ValueError, "Invalid specification of coordinates"
elif isinstance(tup, int):
try:
varray = thepointset[:, tup].toarray()
except TypeError:
raise ValueError, "Invalid specification of coordinates"
elif type(tup) in [list, tuple]:
if alltrue([type(ti) == str for ti in tup]):
thetup = list(tup)
if thepointset.indepvarname in tup:
tix = thetup.index(thepointset.indepvarname)
thetup.remove(thepointset.indepvarname)
try:
vlist = thepointset[thetup].toarray().tolist()
except TypeError:
raise ValueError, "Invalid specification of coordinates"
if len(thetup) == 1:
vlist = [vlist]
if thepointset.indepvarname in tup:
vlist.insert(tix, thepointset.indepvararray.tolist())
varray = array(vlist)
elif alltrue([type(ti) == int for ti in tup]):
try:
varray = thepointset[:, tup].toarray()
except TypeError:
raise ValueError, "Invalid specification of coordinates"
else:
raise ValueError, "Invalid specification of coordinates"
else:
f.close()
raise TypeError, \
"infodict values must be singletons or tuples/lists of strings or integers"
except IOError:
f.close()
print "Problem writing to file" + fname + ext
raise
except KeyError:
f.close()
raise KeyError, ("Keys in infodict not found in pointset")
if varvaldir == 'row':
write_array(f,
varray,
separator,
linesep,
precision,
suppress_small,
keep_open=0)
else:
write_array(f,
transpose(varray),
separator,
linesep,
precision,
suppress_small,
keep_open=0)
def writeAscii(self, filename):
from scipy.io import write_array
self.info('Writing data to file: ' + filename + '.asc')
write_array(filename + ".asc", np.transpose(self.data[1:-1, :]), separator=' ', linesep='\n')
self.done()
# print 'i', i
for input in range(T):
# print 'j', j
col = a[:,input]
if options.bylab:
if options.sum:
v[maxindices[output]][input] += sum([abs(k) for k in col])
else:
v[maxindices[output]][input] = sum([abs(k) for k in col])
else:
v[output][input] = sum([abs(k) for k in col])
# print 'v[i][j]', v[i][j]
if options.softmax:
for t in range(T):
col = v[:,t]
Z = sum([exp(x) for x in col])
for y in range(len(col)):
v[y][t] = exp(col[y]) / Z
if options.max:
for t in range(T):
c = list(v[:,t])
i = c.index(max(c))
v[:,t] = 0
v[i][t] = 1
print shape(v)
out = file(outfile, 'w')
if options.bylab:
print >> out,'LABELS:', ' '.join(labels)
print >> out,'DIMENSIONS:', T
io.write_array(out, v)
def act_batch( nFunc = 5, filename="", lat=60 ):
"""Fits activation and inactivation of 15 iv pulses
using a biexponential funtion with a delay, creates a
table showing the results.
Keyword argument:
nFunc -- Index of function used for fitting. At present,
10 is the HH gNa function,
5 is a sum of two exponentials with a delay.
filename -- If not an empty string, stores the best-fit parameters
in this file."""
stf = __import__("stf")
# Some ugly definitions for the time being
gFitStart = 70.5 + lat/1000.0 # fit end cursor is variable
gFSelect = nFunc # HH function
gDictSize = stf.leastsq_param_size( gFSelect ) + 2 # Parameters, chisqr, peak value
gBaseStartCtrl = 69.5 # Start and end of the baseline before the control pulse, in ms
gBaseEndCtrl = 70.5
gPeakStartCtrl = 70.64 # Start and end of the peak cursors for the control pulse, in ms
gPeakWindowSizes = ( 2.5, 2, 1.5, 1, 1, 0.8, 0.8, 0.8, 0.6, 0.6, 0.5, 0.5, 0.4, 0.4, 0.4 )
gFitDurations = ( 8, 8, 7, 6, 5.5, 5, 4.5, 3.5, 2.5, 2, 1.5, 1.5, 1.0, 0.8, 0.8 )
gPulses = len( gFitDurations ) # Number of traces
if ( gDictSize < 0 ):
print("Couldn\'t retrieve function id=%d; aborting now." % gFSelect)
return False
if ( not(stf.check_doc()) ):
print("Couldn\'t find an open file; aborting now.")
return False
# set cursors:
if ( not(stf.set_peak_start( gPeakStartCtrl, True )) ):
return False
if ( not(stf.set_peak_end( stf.get_size_trace(0)-1 )) ):
return False
if ( not(stf.set_base_start( gBaseStartCtrl, True )) ):
return False
if ( not(stf.set_base_end( gBaseEndCtrl, True )) ):
return False
if ( not(stf.set_peak_mean( 3 )) ):
return False
if ( not(stf.set_peak_direction( "both" )) ):
return False
firstpass = True
# A list for dictionary keys and values:
dict_keys = []
dict_values = np.empty( (gDictSize, stf.get_size_channel()) )
if not filename=="":
ls_file=np.empty((gPulses,stf.leastsq_param_size(nFunc)))
for n in range( 0, gPulses ):
if ( stf.set_trace( n ) == False ):
print('Couldn\'t set a new trace; aborting now.')
return False
print('Analyzing trace %d of %d'%( n+1, stf.get_size_channel()))
# set the fit window cursors:
if ( not(stf.set_peak_end( gPeakStartCtrl + gPeakWindowSizes[n], True )) ):
return False
if ( not(stf.set_fit_start( gFitStart, True )) ):
return False
if ( not(stf.set_fit_end( gFitStart + gFitDurations[n], True )) ):
return False
stf.measure()
# Least-squares fitting:
p_dict = stf.leastsq( gFSelect )
if not filename=="":
ls_file[n][0]=p_dict["gprime_na"]
ls_file[n][1]=p_dict["tau_m"]
ls_file[n][2]=p_dict["tau_h"]
ls_file[n][3]=p_dict["offset"]
if ( p_dict == 0 ):
print('Couldn\'t perform a fit; aborting now.')
return False
# Create an empty list:
tempdict_entry = []
row = 0
for k, v in p_dict.iteritems():
if ( firstpass == True ):
dict_keys.append( k )
dict_values[row][n] = v
row = row+1
if ( firstpass ):
dict_keys.append( "Peak amplitude" )
dict_values[row][n] = stf.get_peak()-stf.get_base()
firstpass = False
if not filename=="":
write_array(file(filename,'w'), ls_file, precision=15)
retDict = dict()
# Create the dictionary for the table:
entry = 0
for elem in dict_keys:
retDict[ elem ] = dict_values[entry].tolist()
entry = entry+1
return stf.show_table_dictlist( retDict )
from mystic.metropolis import *
import time
t1 = time.time()
for i in xrange(L):
scem(Ck, ak, Sk, Sak, target, 0.1)
t2 = time.time()
print "SCEM 1 chain for x[%d] took %0.3f ms" % (len(Sk), (t2-t1)*1000)
Sk = array(Sk)
t1 = time.time()
x = [ [0,10] ]
for i in xrange(L):
x.append(metropolis_hastings(proposal, target, x[-1]))
t2 = time.time()
print "2D Metropolis for x[%d] took %0.3f ms" % (len(x), (t2-t1)*1000)
x = array(x)
# have a look at vrugt.nb for reading and postprocessing
# the datafile below
from scipy.io import write_array
#write_array(open('twisted1.dat','w'),x)
write_array(open('twisted1.dat','w'),Sk)
import pylab
pylab.plot(Sk[:,0],Sk[:,1],'r.')
pylab.plot(x[:,0] + 30,x[:,1],'b.')
pylab.show()
# end of file
def save(ar, fileName):
from scipy.io import write_array
write_array(fileName, ar, precision=8)
t1 = datetime.now()
for i, m in enumerate(m_list):
mpi.world.barrier()
t2 = datetime.now()
print t2 - t1, m, 'MB'
obj.set_nx(m * mbytes_float)
if mpi.rank == 0:
t_rate_htod[0][i], t_rate_dtoh[0][i] = obj.get_transfer_rate()
for dev in xrange(1, num_gpus):
t_rate_htod[dev][i], t_rate_dtoh[dev][i] = mpi.world.recv(
source=dev)
else:
mpi.world.send(dest=0, value=obj.get_transfer_rate())
if mpi.rank == 0:
from scipy.io import write_array
fpath = './bandwidth-mpi_barrier-%dgpus.ascii' % num_gpus
data_list = [m_list]
for dev in xrange(num_gpus):
data_list.append(t_rate_htod[dev])
data_list.append(t_rate_dtoh[dev])
write_array(fpath,
sc.transpose(data_list),
separator='\t',
linesep='\n')
obj.cuda_context_pop()
if max_tavg < max_tavg_val: max_tavg_count += 1 else: max_tavg_count = 0 # save the field at last period if last_period == 'off': if max_tavg_count == max_tavg_count_val: last_period = 'on' elif last_period == 'on': field_tavg = field_tsum/period field2_tavg = field2_tsum/period # write the binary file filename = dirname + '/' + 'tavg_Ez_%.4dperiod.scbin' % (tstep/period) fd = open(filename, 'wb') fwrite(fd, Nx, field_tavg) fd.close() filename = dirname + '/' + 'tavg_Ez2_%.4dperiod.scbin' % (tstep/period) fd = open(filename, 'wb') fwrite(fd, Nx, field2_tavg) fd.close() # write the ascii file filename = dirname + '/' + 'i_epr_tavgEz_tavgEz2.txt' write_array(filename, transpose((range(Nx+1), epr, field_tavg, field2_tavg)), separator='\t', linesep='\n') sys.exit()
# print 'i', i
for input in range(T):
# print 'j', j
col = a[:, input]
if options.bylab:
if options.sum:
v[maxindices[output]][input] += sum([abs(k) for k in col])
else:
v[maxindices[output]][input] = sum([abs(k) for k in col])
else:
v[output][input] = sum([abs(k) for k in col])
# print 'v[i][j]', v[i][j]
if options.softmax:
for t in range(T):
col = v[:, t]
Z = sum([exp(x) for x in col])
for y in range(len(col)):
v[y][t] = exp(col[y]) / Z
if options.max:
for t in range(T):
c = list(v[:, t])
i = c.index(max(c))
v[:, t] = 0
v[i][t] = 1
print shape(v)
out = file(outfile, 'w')
if options.bylab:
print >> out, 'LABELS:', ' '.join(labels)
print >> out, 'DIMENSIONS:', T
io.write_array(out, v)