#* FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT. IN NO EVENT
#* SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE
#* FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE,
#* ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
#* DEALINGS IN THE SOFTWARE.
#*
#*****************************************************************************/
import pyalps
# This is an example of how to easily calculate the mean of data stored in a hdf5 file.
filename = "testfile.h5"
# create the correct MCData object to load the data.
obs = pyalps.alea.MCScalarData()
# load the variable E saved in the file testfile.h5 into the mcdata object.
obs.load(filename, "simulation/results/" + pyalps.hdf5_name_encode("E"))
# calculate the mean
mean = pyalps.alea.mean(obs)
# print the result
print("The mean of E is: " + str(mean))
# write the result back to the file
ar = pyalps.hdf5.archive(filename, 1)
ar.write("simulation/results/" + pyalps.hdf5_name_encode("E") + "/mean/value",
mean)
#
# You should have received a copy of the ALPS Library License along with
# the ALPS Libraries; see the file LICENSE.txt. If not, the license is also
# available from http://alps.comp-phys.org/.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT. IN NO EVENT
# SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE
# FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE,
# ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.
#
# ****************************************************************************
import pyalps as alps
import pyalps.hdf5 as h5
import pyalps.alea as alea
iar = h5.archive('loadobs.h5', 'r')
for name in iar.list_children('/simulation/results'):
if iar.is_scalar('/simulation/results/' + alps.hdf5_name_encode(name) +
'/mean/value'):
obs = alea.MCScalarData()
else:
obs = alea.MCVectorData()
obs.load('loadobs.h5',
'/simulation/results/' + alps.hdf5_name_encode(name))
print name + ": " + str(obs)
#* SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE
#* FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE,
#* ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
#* DEALINGS IN THE SOFTWARE.
#*
#*****************************************************************************/
import pyalps
# This is an example of how to easily calculate the variance of data stored in a hdf5 file.
filename = "testfile.h5"
# create the correct MCData object to load the data.
obs = pyalps.alea.MCScalarData()
# load the variable E saved in the file testfile.h5 into the mcdata object.
obs.load(filename, "simulation/results/" + pyalps.hdf5_name_encode("E"))
# calculate the mean
variance = pyalps.alea.variance(obs)
# print the result
print("The variance of E is: " + str(variance))
# write the result back to the file
ar = pyalps.hdf5.archive(filename, 1)
ar.write(
"simulation/results/" + pyalps.hdf5_name_encode("E") + "/variance/value",
variance)
#* ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
#* DEALINGS IN THE SOFTWARE.
#*
#*****************************************************************************/
import pyalps
# This is an example of how to calculate the running mean and the reverse running mean of data stored in a hdf5 file.
filename = "testfile.h5"
# create the correct MCData object to load the data.
obs = pyalps.alea.MCScalarData()
# load the variable E saved in the file testfile.h5 into the mcdata object.
obs.load(filename, "simulation/results/" + pyalps.hdf5_name_encode("E"))
# calculate the running mean
running_mean = pyalps.alea.running_mean(obs)
# calculate the reverse running mean
reverse_running_mean = pyalps.alea.reverse_running_mean(obs)
# print the result
print("The running mean of E is: " + str(running_mean))
print()
print("The reverse running mean of E is: " + str(reverse_running_mean))
print()
def impl_calculation(name, save_path, calculate):
usage = "Usage: %prog [options] FILE [FILE [...]]"
parser = OptionParser(usage=usage)
parser.add_option("-v",
"--verbose",
action="store_true",
dest="verbose",
help="print detailed information")
parser.add_option("-w",
"--write",
action="store_true",
dest="write",
help="write the result(s) back into the file(s)")
parser.add_option(
"-n",
"--name",
action="append",
metavar="VAR",
dest="variables",
help=
"variable name, can be specified multiple times [default: all variables]"
)
parser.add_option(
"-p",
"--path",
action="store",
metavar="HDF5-PATH",
dest="path",
help=
"hdf5-path where the data is stored [default: \"/simulation/results\"]"
)
parser.set_defaults(verbose=False,
write=False,
variables=[],
path="/simulation/results")
(options, args) = parser.parse_args()
if len(args) == 0:
parser.print_help()
exit()
variables = options.variables
for filestring in args:
ar = h5.archive(filestring, 1)
if len(options.variables) == 0:
variables = ar.list_children(options.path)
if options.verbose:
print("Variables in file " + filestring + ": " +
" ".join(variables))
for variablestring in variables:
if ar.dimensions(options.path + "/" +
pyalps.hdf5_name_encode(variablestring) +
"/timeseries/data") == 1:
obs = alea.MCScalarData()
#_save = mcanalyze.write_dim_0
#vector_save = mcanalyze.write_dim_1
else:
obs = alea.MCVectorData()
#scalar_save = mcanalyze.write_dim_1
#vector_save = mcanalyze.write_dim_2
obs.load(
filestring,
options.path + "/" + pyalps.hdf5_name_encode(variablestring))
result = calculate(obs)
if options.verbose:
print("The " + name + " of variable " + variablestring +
" in file " + filestring + " is: " + str(result))
if options.write:
ar.write(
options.path + "/" +
pyalps.hdf5_name_encode(variablestring) + "/" + save_path,
result)
print("Done")
# the terms of the license, either version 1 or (at your option) any later
# version.
#
# You should have received a copy of the ALPS Library License along with
# the ALPS Libraries; see the file LICENSE.txt. If not, the license is also
# available from http://alps.comp-phys.org/.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT. IN NO EVENT
# SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE
# FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE,
# ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.
#
# ****************************************************************************
import pyalps as alps
import pyalps.hdf5 as h5
import pyalps.alea as alea
iar = h5.archive('loadobs.h5', 'r')
for name in iar.list_children('/simulation/results'):
if iar.is_scalar('/simulation/results/' + alps.hdf5_name_encode(name) + '/mean/value'):
obs = alea.MCScalarData()
else:
obs = alea.MCVectorData()
obs.load('loadobs.h5', '/simulation/results/' + alps.hdf5_name_encode(name))
print name + ": " + str(obs)