def setup_hdf5_output(model, neuron, filename=None, compartments=DEFAULT_HDF5_COMPARTMENTS):
# Make sure /hdf5 exists
if not moose.exists(HDF5WRITER_NAME):
print('creating', HDF5WRITER_NAME)
writer = moose.HDF5DataWriter(HDF5WRITER_NAME)
#writer = moose.NSDFWriter(HDF5WRITER_NAME)
writer.mode = 2 # Truncate existing file
if filename is not None:
writer.filename = filename
moose.useClock(8, HDF5WRITER_NAME, 'process')
else:
print('using', HDF5WRITER_NAME)
writer = moose.element(HDF5WRITER_NAME)
for typenum,neur_type in enumerate(neuron.keys()):
for ii,compname in enumerate(compartments): #neur_comps):
comp=moose.element(neur_type+'/'+compname)
moose.connect(writer, 'requestOut', comp, 'getVm')
if model.calYN:
for typenum,neur_type in enumerate(neuron.keys()):
for ii,compname in enumerate(compartments): #neur_comps):
comp=moose.element(neur_type+'/'+compname)
for child in comp.children:
if child.className in {"CaConc", "ZombieCaConc"}:
cal = moose.element(comp.path+'/'+child.name)
moose.connect(writer, 'requestOut', cal, 'getCa')
elif child.className == 'DifShell':
cal = moose.element(comp.path+'/'+child.name)
moose.connect(writer, 'requestOut', cal, 'getC')
return writer
def example():
model = moose.Neutral('/model')
comp = moose.Compartment('/model/c')
hdfwriter = moose.HDF5DataWriter('h')
hdfwriter.mode = 2 # Truncate existing file
moose.connect(hdfwriter, 'requestOut', comp, 'getVm')
moose.connect(hdfwriter, 'requestOut', comp, 'getIm')
hdfwriter.filename = 'output_hdfdemo.h5'
hdfwriter.compressor = 'zlib'
hdfwriter.compression = 7
# Flush data from memory to disk after accumulating every 1K entries.
hdfwriter.flushLimit = 1024
# We allow simple attributes of type string, double and long.
# This allows for file-level metadata/annotation.
hdfwriter.stringAttr['note'] = 'This is a test.'
# All paths are taken relative to the root. The last token is the name
# of the attribute.
hdfwriter.doubleAttr['{}/vm/a_double_attribute'.format(
comp.path)] = 3.141592
hdfwriter.longAttr['an_int_attribute'] = 8640
# In addition, vectors of string, long and double can also be stored
# as attributes.
hdfwriter.stringVecAttr['stringvec'] = ['I wonder', 'why', 'I wonder']
hdfwriter.doubleVecAttr['{}/dvec'.format(comp.path)] = [3.141592, 2.71828]
hdfwriter.longVecAttr['{}/lvec'.format(comp.path)] = [3, 14, 1592, 271828]
vm_tab = moose.Table('Vm')
moose.connect(vm_tab, 'requestOut', comp, 'getVm')
moose.setClock(0, 1e-3)
moose.setClock(1, 1e-3)
moose.setClock(2, 1e-3)
moose.useClock(0, '/model/c', 'init')
moose.useClock(1, '/##[TYPE!=HDF5DataWriter]', 'process')
moose.useClock(2, '/##[TYPE=HDF5DataWriter]', 'process')
moose.reinit()
comp.inject = 0.1
moose.start(30.0)
hdfwriter.close()
vm_tab.plainPlot('hdfdemo_Vm.csv')
print(('Finished simulation. Data was saved in', hdfwriter.filename))
def example():
"""In this example
1. We create a passive neuronal compartment `comp`.
2. We create an HDF5DataWriter object `hdfwriter` for writing to a
file ``output_hdfdemo.h5``. The `mode` of the HDF5DataWriter
is set to `2` which means if a file of the same name exists, it
will be overwritten.
3. The membrane voltage `Vm` and membrane current `Im` of `comp`
are connected to `hdfwriter` for recording.
4. We set some attributes of the datasets and the file.
5. We run the simulation, `hdfwriter` records and stores the `Vm`
and `Im` values over time.
6. We close the file by calling close() method of `hdfwriter`.
Running this snippet creates the file ``output_hdfdemo.h5`` which
reflects the structure of the model::
model
|
|
c[0]
|
|___im
|
|___vm
`im` and `vm` are datasets containing `Im` and `Vm` field values
recorded from `comp`.
"""
model = moose.Neutral('/model')
comp = moose.Compartment('/model/c')
hdfwriter = moose.HDF5DataWriter('h')
hdfwriter.mode = 2 # Truncate existing file
moose.connect(hdfwriter, 'requestOut', comp, 'getVm')
moose.connect(hdfwriter, 'requestOut', comp, 'getIm')
hdfwriter.filename = 'output_hdfdemo.h5'
hdfwriter.compressor = 'zlib'
hdfwriter.compression = 7
# Flush data from memory to disk after accumulating every 1K entries.
hdfwriter.flushLimit = 1024
# We allow simple attributes of type string, double and long.
# This allows for file-level metadata/annotation.
hdfwriter.stringAttr['note'] = 'This is a test.'
# All paths are taken relative to the root. The last token is the name
# of the attribute.
hdfwriter.doubleAttr['{}/vm/a_double_attribute'.format(
comp.path)] = 3.141592
hdfwriter.longAttr['an_int_attribute'] = 8640
# In addition, vectors of string, long and double can also be stored
# as attributes.
hdfwriter.stringVecAttr['stringvec'] = ['I wonder', 'why', 'I wonder']
hdfwriter.doubleVecAttr['{}/dvec'.format(comp.path)] = [3.141592, 2.71828]
hdfwriter.longVecAttr['{}/lvec'.format(comp.path)] = [3, 14, 1592, 271828]
vm_tab = moose.Table('Vm')
moose.connect(vm_tab, 'requestOut', comp, 'getVm')
moose.setClock(0, 1e-3)
moose.setClock(1, 1e-3)
moose.setClock(2, 1e-3)
moose.useClock(0, '/model/c', 'init')
moose.useClock(1, '/##[TYPE!=HDF5DataWriter]', 'process')
moose.useClock(2, '/##[TYPE=HDF5DataWriter]', 'process')
moose.reinit()
comp.inject = 0.1
moose.start(30.0)
hdfwriter.close()
vm_tab.plainPlot('hdfdemo_Vm.csv')
print 'Finished simulation. Data was saved in', hdfwriter.filename
# Author: Subhasis Ray
"""
HDF5 is a self-describing file format for storing large
datasets. MOOSE has an utility :class:`HDF5DataWriter` for saving
simulations data in HDF5 files.
"""
import sys
import os
import numpy
import moose
try:
hdfwriter = moose.HDF5DataWriter('h')
except AttributeError as e:
print( "[INFO ] This build of moose does not have HDF5 support." )
print( " Please compile and reinstall: \
https://github.com/BhallaLab/moose-core/blob/master/INSTALL.md"
)
quit()
def example():
model = moose.Neutral('/model')
comp = moose.Compartment('/model/c')
hdfwriter = moose.HDF5DataWriter('h')
hdfwriter.mode = 2 # Truncate existing file
moose.connect(hdfwriter, 'requestOut', comp, 'getVm')
moose.connect(hdfwriter, 'requestOut', comp, 'getIm')