def saveVarsInMat(filename, varNamesStr, outOf=None, **opts):
"""Hacky convinience function to dump a couple of python variables in a
.mat file. See `awmstools.saveVars`.
"""
from mlabwrap import mlab
filename, varnames, outOf = __saveVarsHelper(
filename, varNamesStr, outOf, '.mat', **opts)
try:
for varname in varnames:
mlab._set(varname, outOf[varname])
mlab._do("save('%s','%s')" % (filename, "', '".join(varnames)), nout=0)
finally:
assert varnames
mlab._do("clear('%s')" % "', '".join(varnames), nout=0)
def saveVarsInMat(filename, varNamesStr, outOf=None, **opts):
"""Hacky convinience function to dump a couple of python variables in a
.mat file. See `awmstools.saveVars`.
"""
from mlabwrap import mlab
filename, varnames, outOf = __saveVarsHelper(filename, varNamesStr, outOf,
'.mat', **opts)
try:
for varname in varnames:
mlab._set(varname, outOf[varname])
mlab._do("save('%s','%s')" % (filename, "', '".join(varnames)), nout=0)
finally:
assert varnames
mlab._do("clear('%s')" % "', '".join(varnames), nout=0)
def dumpPathsToMatlab(self, fileName, **trackOptions):
self.track(**trackOptions)
from mlabwrap import mlab
allPaths = []
for info in self.infos:
try:
paths = self._loadPaths(info)
except:
continue
print info.uniqueName
frames = _makeCells([v.array for v in paths.velocities], mlab)
allPaths.append(mlab.struct("info", _infoToMatlab(paths.info, mlab),
"frames", _wrapCellValue(frames)))
mlab._set("paths", _toStructArray(allPaths))
mlab.save(fileName, "paths")
def dumpPathsToMatlab(self, fileName, **trackOptions):
self.track(**trackOptions)
from mlabwrap import mlab
allPaths = []
for info in self.infos:
try:
paths = self._loadPaths(info)
except:
continue
print info.uniqueName
frames = _makeCells([v.array for v in paths.velocities], mlab)
allPaths.append(
mlab.struct("info", _infoToMatlab(paths.info, mlab), "frames",
_wrapCellValue(frames)))
mlab._set("paths", _toStructArray(allPaths))
mlab.save(fileName, "paths")
def dumpCorrelationToMatlab(self, correlation, fileName, **analysisOptions):
results = self._performAnalysis(correlation, **analysisOptions)
results = self._organizeByGroup(results)
from mlabwrap import mlab
groups = []
for group, correlations in results.iteritems():
wrapped = []
for i, data in enumerate(correlations):
info = _infoToMatlab(data.info, mlab)
points = np.array(data.points)
wrapped.append(mlab.struct("info", info,
"points", points,
"name", str(data.name)))
groups.append(mlab.struct("group", group,
"correlations", _toStructArray(wrapped)))
mlab._set(correlation, _toStructArray(groups))
mlab.save(fileName, correlation)
def dumpCorrelationToMatlab(self, correlation, fileName,
**analysisOptions):
results = self._performAnalysis(correlation, **analysisOptions)
results = self._organizeByGroup(results)
from mlabwrap import mlab
groups = []
for group, correlations in results.iteritems():
wrapped = []
for i, data in enumerate(correlations):
info = _infoToMatlab(data.info, mlab)
points = np.array(data.points)
wrapped.append(
mlab.struct("info", info, "points", points, "name",
str(data.name)))
groups.append(
mlab.struct("group", group, "correlations",
_toStructArray(wrapped)))
mlab._set(correlation, _toStructArray(groups))
mlab.save(fileName, correlation)
# This is a primitive way to check for memory leaks.
# Run with ``python EatMem.py`` and use some memory watching tool
# (like ``top`` under unix) to see if the memory consumpiton goes up
# by steps while the program is running (which would suggest a memory leak).
import gc
from mlabwrap import mlab
try:
import numpy
from numpy.random import rand, randn
toscalar = lambda a: a.item()
except ImportError:
import Numeric as numpy
from MLab import rand, randn
toscalar = lambda a: a.toscalar()
import time
#gc.set_debug(gc.DEBUG_LEAK)
myMat = numpy.ones((1000**2, 2), 'd')
mlab._set('mymat', myMat)
raw_input("Press return when ready!")
for i in range(10):
mlab.sum(myMat, nout=0)
for i in range(1000):
s = mlab.sin(rand(100, 100))
#print loads of crap to make sure this won't screw up
for i in range(20):
mlab.sin(rand(100, 100), nout=0)
assert numpy.alltrue(myMat.flat == mlab._get('mymat', 1).flat)
gc.collect()
raw_input("Press return to finish")
# Run with ``python EatMem.py`` and use some memory watching tool
# (like ``top`` under unix) to see if the memory consumpiton goes up
# by steps while the program is running (which would suggest a memory leak).
import gc
from mlabwrap import mlab
try:
import numpy
from numpy.random import rand, randn
toscalar = lambda a:a.item()
except ImportError:
import Numeric as numpy
from MLab import rand, randn
toscalar = lambda a:a.toscalar()
import time
#gc.set_debug(gc.DEBUG_LEAK)
myMat = numpy.ones((1000**2,2), 'd')
mlab._set('mymat', myMat)
input("Press return when ready!")
for i in range(10):
mlab.sum(myMat,nout=0)
for i in range(1000):
s = mlab.sin(rand(100,100))
#print loads of crap to make sure this won't screw up
for i in range(20):
mlab.sin(rand(100,100), nout=0)
assert numpy.alltrue(myMat.flat == mlab._get('mymat',1).flat)
gc.collect()
input("Press return to finish")
