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Brain Modeling Toolkit

Home Page: https://alleninstitute.github.io/bmtk/

License: BSD 3-Clause "New" or "Revised" License

Python 98.65% AMPL 1.09% Dockerfile 0.22% Shell 0.04% Batchfile 0.01%

neuroscience modeling simulation python neural-networks neuroinformatics neuron

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bmtk's Issues

Single-compartment Bionet cells

Hey Kael et al.! Firstly, thanks for doing an incredible amount of work on developing BMTK and the SONATA standard!

I'm trying to test-run BMTK on simple, single-compartment neurons, but after some digging into the code of both BMTK and BBP's libSONATA, it seems like this model_type (single_compartment) is either not yet or will not be implemented. Are there concrete plans to implement the single_compartment model_type, or is there an official roadmap for the future of BMTK in general? I also noticed that there may be plans to implement a model_type called point_soma, but I'm not sure if that's only meant to work with Pointnet/NEST instead of Bionet/NEURON.

I also tried using the default model_type of biophysical and then just using a different NEURON/hoc model_template, but I couldn't get this to work. I'm not sure, but from reading the code, it seems like which model_template is allowed are hardcoded, rather than being able to provide your own cell.hoc template -- am I right that the model_templates are hardcoded for each model_type?

Thanks,
Austin

save_nodes fails to overwrite (nodes file already open)

When trying to modify and re-run a network interactively in ipython/jupyter, NetworkBuilder.save_nodes fails with the message (from h5py): IOError: Unable to create file (unable to truncate a file which is already open)
It seems like the file is left open on loading the network when BioNetwork.from_config is called, maybe in SonataNodes.load()?. Not sure if that's intentional, or could be fixed?

assert statement for expected/spikes.txt

There is an assert error statement error occurring when I run 14cells_nsyns in the bionet examples. The simulation is running and outputting correctly, but the spike output is not identical to the expected/spikes.txt file which is causing the error. Please see the error message and my pandas version below:

step:115000 t_sim:2875.000 ms -- t_wall: 118.63 s
step:120000 t_sim:3000.000 ms -- t_wall: 123.42 s

Simulation completed (119.4 seconds of simulation time) -- on 2017/09/29 at 14:05:57
Traceback (most recent call last):
File "run_bionet.py", line 42, in
run()
File "run_bionet.py", line 38, in run
assert (spike_files_equal(conf['output']['spikes_ascii'], 'expected/spikes.txt'))
File "/allen/aibs/mat/yazan/bmtk/bmtk/analyzer/spikes_analyzer.py", line 41, in spike_files_equal
trial_1 = spikes2dict(spikes_txt_1)
File "/allen/aibs/mat/yazan/bmtk/bmtk/analyzer/spikes_analyzer.py", line 33, in spikes2dict
spikes_sorted = spikes_df.sort_values(['gid', 'time'])
File "/shared/utils.x86_64/python-2.7/lib/python2.7/site-packages/pandas/core/generic.py", line 2150, in getattr
(type(self).name, name))
AttributeError: 'DataFrame' object has no attribute 'sort_values'
[yazanb@ibs-yazanb-ux1 14cells_nsyns]$ nano output/spikes.txt
[yazanb@ibs-yazanb-ux1 14cells_nsyns]$
[yazanb@ibs-yazanb-ux1 14cells_nsyns]$ pip show pandas

Name: pandas
Version: 0.14.1
Location: /shared/utils.x86_64/python-2.7/lib/python2.7/site-packages
Requires:

The Python 3.7 builds are failing

Hi, I also posted this somewhere obscurer but thought it was worth bringing to your attention in a seperate issue: All the Travis Python 3.7 pipelines are failing. The errors that are listed seem quite familiar and iirc are caused by a Cython version that is too old. You should try upgrading to the latest Cython before the step that errors out 👍

Docker build broken

Error when running docker build -t alleninstitute/bmtk .:

...
...
Step 11/31 : RUN conda install -y -c kaeldai neuron
 ---> Running in d5436094c58c
Collecting package metadata (current_repodata.json): ...working... done
Solving environment: ...working... failed with initial frozen solve. Retrying with flexible solve.
Solving environment: ...working... failed with repodata from current_repodata.json, will retry with next repodata source.
Collecting package metadata (repodata.json): ...working... done
Solving environment: ...working... failed with initial frozen solve. Retrying with flexible solve.
                                                                          
Found conflicts! Looking for incompatible packages.                                     
This can take several minutes.  Press CTRL-C to abort.                                                               
failed

UnsatisfiableError: The following specifications were found
to be incompatible with the existing python installation in your environment:

Specifications:

  - neuron -> python[version='>=2.7,<2.8.0a0']

Your python: python=3.7

If python is on the left-most side of the chain, that's the version you've asked for.
When python appears to the right, that indicates that the thing on the left is somehow
not available for the python version you are constrained to. Note that conda will not
change your python version to a different minor version unless you explicitly specify
that.

The following specifications were found to be incompatible with each other:

Package pip conflicts for:
python=3.7 -> pip
neuron -> python[version='>=2.7,<2.8.0a0'] -> pip
Package certifi conflicts for:
neuron -> python[version='>=2.7,<2.8.0a0'] -> pip -> setuptools -> certifi[version='>=2016.09|>=2016.9.26']
python=3.7 -> pip -> setuptools -> certifi[version='>=2016.09|>=2016.9.26']
Package wheel conflicts for:
neuron -> python[version='>=2.7,<2.8.0a0'] -> pip -> wheel
python=3.7 -> pip -> wheel
Package ca-certificates conflicts for:
python=3.7 -> openssl[version='>=1.1.1b,<1.1.2a'] -> ca-certificates
neuron -> python[version='>=2.7,<2.8.0a0'] -> ca-certificates
Package setuptools conflicts for:
python=3.7 -> pip -> setuptools
neuron -> python[version='>=2.7,<2.8.0a0'] -> pip -> setuptools

The command '/bin/sh -c conda install -y -c kaeldai neuron' returned a non-zero code: 1

Bmtk requires optional mechanisms_dir parameter

According to the Sonata specification, the "mechanisms_dir" field of the circuit config is optional.
However, due to I think this line
https://github.com/AllenInstitute/bmtk/blob/develop/bmtk/simulator/bionet/config.py#L68
bmtk fails with an exception when I try to load a config that doesn't have mechanisms specification

Add triadic synapses

Would it be possible to be more specific where external nodes synapse such that it targets the segment in which a synapse is already present? Thus, creating a triadic synapse?

MPI support for FilterNet

Since LGN Model is none-recurrent we should be able to implement parallelization in FilterNet. The main places this should happen are at filternetwork.build_nodes() and filtersimulator.run().

Documentation

Some more documentation in the readme file would be great. What levels of resolution are described? What is its logic for building and running models and storing results, and what need does the package fill? Is there a jupyter notebook tutorial or something to help people get started?

Possible issues with latest versions of H5py and Pandas

I kept running into the two following errors until I downgraded h5py from 2.8.0 to 2.7.0 and pandas from 0.24.1 to 0.20.3

pip install h5py==2.7.0 pandas==0.23.0

(base) C:\Users\Tyler\Desktop\git_stage\project>python run_bionet.py
2019-03-12 22:19:58,928 [INFO] Created log file
2019-03-12 22:19:59,038 [INFO] Building cells.
2019-03-12 22:19:59,773 [INFO] Building recurrent connections
2019-03-12 22:20:38,338 [INFO] Building virtual cell stimulations for exc_spikes
Traceback (most recent call last):
  File "C:\Users\Tyler\Anaconda3\lib\site-packages\bmtk-0.0.7-py3.7.egg\bmtk\utils\sonata\population.py", line 586, in _get_index
    raise StopIteration
StopIteration

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "run_bionet.py", line 34, in <module>
    run('simulation_config.json')
  File "run_bionet.py", line 22, in run
    sim = bionet.BioSimulator.from_config(conf, network=graph)
  File "C:\Users\Tyler\Anaconda3\lib\site-packages\bmtk-0.0.7-py3.7.egg\bmtk\simulator\bionet\biosimulator.py", line 291, in from_config
    network.add_spike_trains(spikes, node_set)
  File "C:\Users\Tyler\Anaconda3\lib\site-packages\bmtk-0.0.7-py3.7.egg\bmtk\simulator\bionet\bionetwork.py", line 260, in add_spike_trains
    for edge in edge_pop.get_target(trg_nid):
  File "C:\Users\Tyler\Anaconda3\lib\site-packages\bmtk-0.0.7-py3.7.egg\bmtk\simulator\core\sonata_reader\network_reader.py", line 174, in get_target
    for edge in self._edge_pop.get_target(node_id):
RuntimeError: generator raised StopIteration

Pandas error:

(base) C:\Users\Tyler\Desktop\git_stage\project>python run_bionet.py
C:\Users\Tyler\Anaconda3\lib\site-packages\h5py\__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
2019-03-12 23:17:37,341 [INFO] Created log file
Traceback (most recent call last):
  File "run_bionet.py", line 34, in <module>
    run('simulation_config.json')
  File "run_bionet.py", line 21, in run
    graph = bionet.BioNetwork.from_config(conf)
  File "C:\Users\Tyler\Anaconda3\lib\site-packages\bmtk-0.0.7-py3.6.egg\bmtk\simulator\core\simulator_network.py", line 169, in from_config
    network.add_nodes(node_pop)
  File "C:\Users\Tyler\Anaconda3\lib\site-packages\bmtk-0.0.7-py3.6.egg\bmtk\simulator\core\simulator_network.py", line 87, in add_nodes
    node_population.initialize(self)
  File "C:\Users\Tyler\Anaconda3\lib\site-packages\bmtk-0.0.7-py3.6.egg\bmtk\simulator\core\sonata_reader\network_reader.py", line 44, in initialize
    model_types.update(set(np.unique(grp.get_values(self._adaptor.COL_MODEL_TYPE))))
  File "C:\Users\Tyler\Anaconda3\lib\site-packages\bmtk-0.0.7-py3.6.egg\bmtk\utils\sonata\group.py", line 182, in get_values
    self.build_indicies()
  File "C:\Users\Tyler\Anaconda3\lib\site-packages\bmtk-0.0.7-py3.6.egg\bmtk\utils\sonata\group.py", line 178, in build_indicies
    self._parent_indicies = self._parent.group_indicies(self.group_id, build_cache=True)
  File "C:\Users\Tyler\Anaconda3\lib\site-packages\bmtk-0.0.7-py3.6.egg\bmtk\utils\sonata\population.py", line 125, in group_indicies
    tmp_index['grp_id'] = pd.Series(self._group_id_ds, dtype=self._group_id_ds.dtype)
  File "C:\Users\Tyler\Anaconda3\lib\site-packages\pandas\core\series.py", line 262, in __init__
    raise_cast_failure=True)
  File "C:\Users\Tyler\Anaconda3\lib\site-packages\pandas\core\internals\construction.py", line 625, in sanitize_array
    subarr = _try_cast(data, False, dtype, copy, raise_cast_failure)
  File "C:\Users\Tyler\Anaconda3\lib\site-packages\pandas\core\internals\construction.py", line 695, in _try_cast
    subarr = maybe_cast_to_integer_array(arr, dtype)
  File "C:\Users\Tyler\Anaconda3\lib\site-packages\pandas\core\dtypes\cast.py", line 1307, in maybe_cast_to_integer_array
    casted = arr.astype(dtype, copy=copy)
TypeError: astype() got an unexpected keyword argument 'copy'

Error re-running bionet build_env (removing output directory)

In bionet, running a network multiple times (specifically, the build_env step) can give an error OSError: [Errno 39] Directory not empty: './output'. Should be able to reproduce by simply opening an example ipynb and executing the "run" cell twice.
Seems to be an issue with Python's shutil.rmtree, which should be able to delete non-empty directories but is failing in certain cases.
This is not hard to work around (just delete manually), but slightly annoying - I'm curious if it's specific to my installation or if others have seen it too (I'm using Anaconda Python 2.7.15).

Note that this doesn't occur from simply running run_bionet.py twice, but it's also not restricted to jupyter or ipython (can reproduce by executing the relevant lines directly in a python console. Strange!

Slow merging of spike output files

Hi. I noticed in some of my simulations of a large scale network set up in bmtk that a substantial amount of the overall simulation time for a job is spent reading in and converting temporary spike files from each MPI rank (w. MPI pool size 1536). The issue at hand seems to be due to the serial function _merge_files at https://github.com/AllenInstitute/bmtk/blob/develop/bmtk/utils/io/spike_trains.py#L116, where cvs.reader(...) returns an iterable over each line in the input files. Reading in files as typed arrays and sorting and dumping everything in the end is many times faster as the double for loop containing a function call to write stuff is avoided; something like:

import numpy as np
import h5py
from glob import glob
import os

outputdir = 'output'
dtype=[('timestamps', 'f8'), ('gids', 'u8')]

spikes = np.empty(shape=(), dtype=dtype)
for fname in glob(os.path.join(outputdir, '_bmtk_tmp_spikes_*.csv')):
    spikes = np.r_[spikes, np.loadtxt(fname, delimiter=' ', dtype=dtype)]

sort = np.argsort(spikes['timestamps'])
spikes  = spikes[sort]

np.savetxt(os.path.join(outputdir, 'spikes.csv'), spikes)

f = h5py.File(os.path.join(outputdir, 'spikes.h5'), 'w')
grp = f.create_group('spikes')
grp.attrs['sorting'] = 'time'
grp['gids'] = spikes['gids']
grp['timestamps'] = spikes['timestamps']
f.close()

Any plans on addressing this issue? In my case with somewhat long simulation durations (20s biological time) the utilization of resources is poor as 1 process converts files while 1535 other processes are dormant for 1-2 hours before it to finishes.

pip installable

Just a suggestion, maybe you can also tell users they can install bmtk using:
pip install git+https://github.com/AllenInstitute/bmtk.git

Maybe you could also upload it to pypi ? (I saw the bmtk name is still available, could be useful to register it at least so that no other python project can use it)

Problem with adding cells that do not have a morphology file

Currently, the only way to create a cell without a morphology file that I can find is to use morphology=None in add_nodes. This does not create a visible issue when running most simulations, but a bug is uncovered when reporting ecp.

The bug is in make_morphologies:

bmtk/bmtk/simulator/bionet/bionetwork.py

Lines 188 to 217 in 85e8aa2

 def make_morphologies(self): 

 """Creating a Morphology object for each biophysical model""" 

 # TODO: Let Morphology take care of the cache 

 # TODO: Let other types have morphologies 

 # TODO: Get all available morphologies from TypesTable or group 

 for gid, cell in self._rank_node_gids.items(): 

 if not isinstance(cell, BioCell): 

 continue 

 morphology_file = cell.morphology_file 

 if morphology_file in self._morphologies_cache: 

 # create a single morphology object for each model_group which share that morphology 

 morph = self._morphologies_cache[morphology_file] 

 # associate morphology with a cell 

 cell.set_morphology(morph) 

 self._morphology_lookup[morphology_file].append(cell.gid) 

 else: 

 hobj = cell.hobj # get hoc object (hobj) from the first cell with a new morphologys 

 morph = Morphology(hobj) 

 # associate morphology with a cell 

 cell.set_morphology(morph) 

 # create a single morphology object for each model_group which share that morphology 

 self._morphologies_cache[morphology_file] = morph 

 self._morphology_lookup[morphology_file] = [cell.gid] 

 self.io.barrier()

The issue is that there is a morphology cache that uses morphology_file as a key. Therefore, if multiple types of cells were created without a morphology file (meaning they all used morphology=None), they will all be assigned the same morphology (from whichever cell type was added first).

This bug is uncovered on line 206 of ecp.py:

bmtk/bmtk/simulator/bionet/modules/ecp.py

Line 206 in 85e8aa2

ecp = np.dot(tr, im)

It is caused by the fact that tr gets its second dimension from the number of segments in the morphology object assigned to the cell, where as im gets its length from the actual counted number of segments in the cell's hobj.

I am not sure of the implications this bug has on the overall simulation results, but it atleast stops ecp reporting from working if a cell is added without a morphology file.

Failure when no segments match distance_range

Currently the user gets an obscure error message from mtrand.RandomState.choice(): ValueError: a must be non-empty.
This should maybe be caught as a warning, with the result that no synapses are inserted?

Calculate LFP

Has anyone made a function to calculate the local field potential (LFP)? I will write it if not. We use the line-source approximation from

Schomburg, E. W., Anastassiou, C. A., Buzsaki, G., & Koch, C. (2012). The Spiking Component of Oscillatory Extracellular Potentials in the Rat Hippocampus. Journal of Neuroscience, 32(34), 11798–11811.

plot_report single plot issue

Minor and may not be worth the time to fix. Feel free to close.
When plotting a single variable for a gid other than 0, using bmtk/analyzer/cell_vars.plot_report() I get the following error.

Traceback (most recent call last):
  File "/home/tbg28/anaconda3/envs/py36/lib/python3.6/runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "/home/tbg28/anaconda3/envs/py36/lib/python3.6/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/home/tbg28/git_stage/bmtools/bmtools/plot.py", line 621, in <module>
    handling_func(**v)
  File "/home/tbg28/git_stage/bmtools/bmtools/plot.py", line 375, in plot_report_default
    plot_report(config_file=config, report_file=report_file, report_name=report_name, variables=variables, gids=gids);
  File "/home/tbg28/git_stage/bmtk/bmtk/analyzer/cell_vars.py", line 71, in plot_report
    plt.plot(time_steps, var_report.data(gid=0, var_name=variables[0]), label='gid {}'.format(gid))
  File "/home/tbg28/git_stage/bmtk/bmtk/utils/cell_vars/var_reader.py", line 121, in data
    gid_slice = self._gid2data_table[gid][0]
KeyError: 0

It appears there's a hard-coded 0 that should be replaced with gid here:
https://github.com/AllenInstitute/bmtk/blob/develop/bmtk/analyzer/cell_vars.py#L71

        for gid in gids:
            plt.plot(time_steps, var_report.data(gid=0, var_name=variables[0]), label='gid {}'.format(gid))

Support dynamics_params at node level (overriding node type params)

Should support setting dynamics_params for individual nodes, both at network building (saving to h5) and simulation instantiation (loading from h5).

BMTK seems to depend on a dummy PyPI package called "nest"

Somehow https://libraries.io/pypi/nest/dependents thinks the BMTK needs nest, but nest on PyPI is this piece of junk source code:

def print_list(the_list, level=0):

    for each_item in the_list:
        if isinstance(each_item, indent = False ,list):
            print_list(each_item, indent, level +1)
        else:
            if indent:
              for tab_stop in range(level):
                pritn("/t", end='')
            print(each_item)

(Which contains abundant typo's and syntax errors)

Could you look into where this link comes from, do you specify it in any setup.py or requirement files? Would you be willing, if necessary (see nest/nest-simulator#2007) to clarify the misunderstanding to PyPA during the removal procedure as well?

Problem with tutorial chapter 03, unable to move on to next step after [INFO] building recurrent connections.

Sorry to bother again. I am studying the tutorial chapter 3: Multi-cell, single population network (with BioNet). At the sim.run() step, there returned a value error said: " mtrand.pyx in numpy.random.mtrand.RandomState.choice(), ValueError: 'a' cannot be empty unless no samples are taken ", and cannot move onto next step.

Looking forward to your reply, thank you very much!

Problem in building a one compartment or several compartments cell

Hello, I try to use BMTK to build a one compartment cell, I deleted all lines in morphology file except the first line for soma:

1 1 356.5848 666.8376 28.56 6.4681 -1

it comes the error:

NEURON: x : object prefix is NULL
near line 0
^
Import3d_SWC_read[0].mksections()
Import3d_SWC_read[0].input("./biophys_...")
Biophys1[0].init("./biophys_...")

If I left two more lines in .swc file to build a two compartments cell:

1 1 356.5848 666.8376 28.56 6.4681 -1
2 3 354.1858 672.2133 28.4684 0.1608 1

It comes another error:

NEURON: section in the object was deleted
near line 0
{soma { pt3dadd(363.053, 666.838, 28.56, 12.9362) }}
^
*** longjmp causes uninitialized stack frame ***:

This is my method to build the network:

net = NetworkBuilder('BL')
net.add_nodes(cell_name='BL',
potental='exc',
model_type='biophysical',
model_template='ctdb:Biophys1.hoc',
model_processing='aibs_perisomatic',
dynamics_params='fit_parameters.json',
morphology='reconstruction.swc')
net.build()
net.save_nodes(output_dir='network')

How to solve these problems and is there any right way to build a one compartment or several compartments cell?

Problem with ECP calculation when using Neuron 7.8.2

Hi,

There seems to be an issue with the calculation of the ECP when using the latest version of Neuron (7.8.2). Running, for example, the bio_450cells_exact example provided with BMTK I get the following warning:

/Users/atleeskelandrimehaug/sources/bmtk_test_py3_6/bmtk/bmtk/simulator/bionet/modules/ecp.py:283: RuntimeWarning: invalid value encountered in true_divide
rll = abs(rlldl / dlmag) # component of r parallel to the segment axis it must be always positive

And in the ecp.h5 output file all values are NaN.

There are no problems with earlier versions of Neuron (I haven't testet all, of course, but 7.8.0 f. ex. works fine), so I'm merely posting this to bring it to your attention.

spikes_csv.py iterator not python3 friendly

When running the tutorials users running Python3 will be met with the following error after executing a section of code:

from bmtk.utils.spike_trains import SpikesGenerator

sg = SpikesGenerator(nodes='network/mthalamus_nodes.h5', t_max=3.0)
sg.set_rate(10.0)
sg.save_csv('thalamus_spikes.csv', in_ms=True)

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-5-c6b681b962ce> in <module>
      3 sg = SpikesGenerator(nodes='network/mthalamus_nodes.h5', t_max=3.0)
      4 sg.set_rate(10.0)
----> 5 sg.save_csv('thalamus_spikes.csv', in_ms=True)

~\AppData\Local\Continuum\anaconda3\lib\site-packages\bmtk-0.0.7-py3.7.egg\bmtk\utils\spike_trains\spikes_csv.py in save_csv(self, csv_file_name, in_ms)
     91             csv_writer.writerow(['gid', 'spike-times'])
     92             for gid, rate_gen in self._nodes.items():
---> 93                 csv_writer.writerow([gid, ','.join(str(r*conv) for r in rate_gen)])
     94 

TypeError: iter() returned non-iterator of type 'NormalRates'

Getting wrong resting potential in cell models from Allen Brain Atlas

Hi,
I'm sorry if this is the wrong place to post this problem.

I'm trying to reproduce the Model Response of this cell:
http://celltypes.brain-map.org/experiment/electrophysiology/490387590
I have downloaded the model, and I try to run it in BMTK, using the code below.
("fit_parameters.json", and "reconstruction.swc" were put in the folders where BMTK seemed to expect to find them)

In general, the results look good, and the simulated cell-behavior seems reasonable, except that the resting potential is very different. In my attempted recreation, the resting potential is -93 mV (which seems very low), while on the web page it seems it should be about -79 mV? I noticed that this difference, -14 mV, exactly equals the given 'junction_potential' in 'fit_parameters.json', but presumably I should not have to correct for the liquid junction potential in the simulations?

Any idea about what could be causing this?

'''
from bmtk.builder.networks import NetworkBuilder

net = NetworkBuilder("mcortex")

net.add_nodes(cell_name="491623973",
potental="exc",
model_type="biophysical",
model_template="ctdb:Biophys1.hoc",
model_processing="aibs_perisomatic",
dynamics_params="fit_parameters.json",
morphology="reconstruction.swc")

net.build()
net.save_nodes(output_dir="network")
for node in net.nodes():
print(node)

from bmtk.utils.sim_setup import build_env_bionet
build_env_bionet(base_dir="neuronal_model", # Where to save the scripts and config files
network_dir="network", # Location of directory containing network files
tstop=1200.0, dt=0.1, # Run a simulation for 2000 ms at 0.1 ms intervals
report_vars=["v"], # Tells simulator we want to record membrane potential and calcium traces
current_clamp={ # Creates a step current from 500.ms to 1500.0 ms
"amp": 0.61,
"delay": 100.0,
"duration":
1000.0
},
include_examples=False, # Copies components files
compile_mechanisms=False # Will try to compile NEURON mechanisms
)

from bmtk.simulator import bionet

conf = bionet.Config.from_json("neuronal_model/simulation_config.json")
conf.build_env()
net = bionet.BioNetwork.from_config(conf)
sim = bionet.BioSimulator.from_config(conf, network=net)
sim.run()

from bmtk.analyzer.spike_trains import to_dataframe
to_dataframe(config_file="neuronal_model/simulation_config.json")

from bmtk.analyzer.cell_vars import plot_report

plot_report(config_file="neuronal_model/simulation_config.json")
'''

Can't create folder named "aux" on Windows

I'm attempting to install bmtk for the first time by cloning the git repo. When I do git clone https://github.com/AllenInstitute/bmtk.git, I get the following error:

fatal: cannot create directory at 'bmtk/builder/aux': Invalid argument warning: Clone succeeded, but checkout failed.

I even tried downloading the zip and extracting it and the same thing happens. I checked around and apparently that folder name isn't aloud on Windows. I'm going to try to work around it, but just thought I would mention it for others who use Windows.

VirtualCell set_stim fails when spikes is a list

Depending on the spike train buffer, the spikes may be a list instead of a numpy array. This leads the line assert(np.all(spikes >= 0)) to fail because you cannot compare a list with an integer.

Tutorial 05 fails on Analysis (no spikes?)

Tutorials 1-4 run fine but it appears that no spikes are being recorded by the simulator in tutorial 05. I'm familiar with NEST but not Sonata and bmtk so I haven't had much luck troubleshooting. This was on a clean conda environment on Linux x86-64 with Python 3.6.12, pandas 0.23.0 and h5py 2.7.0:

from bmtk.analyzer.spike_trains import plot_raster, plot_rates
plot_raster(config_file='sim_ch05/simulation_config.json', group_by='pop_name')

IndexError Traceback (most recent call last)
in
1 from bmtk.analyzer.spike_trains import plot_raster, plot_rates
2
----> 3 plot_raster(config_file='sim_ch05/simulation_config.json', group_by='pop_name')

~/anaconda3/envs/newenv/lib/python3.6/site-packages/bmtk/analyzer/spike_trains.py in plot_raster(config_file, population, with_histogram, times, title, show, group_by, group_excludes, spikes_file, nodes_file, node_types_file)
185 config_file=config_file, population=population, times=times, title=title, show=show,
186 group_by=group_by, group_excludes=group_excludes,
--> 187 spikes_file=spikes_file, nodes_file=nodes_file, node_types_file=node_types_file
188 )
189

~/anaconda3/envs/newenv/lib/python3.6/site-packages/bmtk/analyzer/spike_trains.py in _plot_helper(plot_fnc, config_file, population, times, title, show, group_by, group_excludes, spikes_file, nodes_file, node_types_file)
99 spikes_file=None, nodes_file=None, node_types_file=None):
100 sonata_config = SonataConfig.from_json(config_file) if config_file else None
--> 101 pop, spike_trains = _find_spikes(config_file=config_file, spikes_file=spikes_file, population=population)
102
103 # Create the title

~/anaconda3/envs/newenv/lib/python3.6/site-packages/bmtk/analyzer/spike_trains.py in _find_spikes(spikes_file, config_file, population)
76 raise ValueError('Spikes file {} contains more than one node population.'.format(spikes_f))
77 else:
---> 78 return spikes_obj.populations[0], spikes_obj
79
80

IndexError: list index out of range

build_env_bionet fails when config_file is not explicitly provided

The default value of None for the kwarg config_file to bmtk.utils.sim_setup.build_env_filternet is an invalid one and causes an error, since a string or Path is expected. I suggest only passing config_file to env_builder.build if it is not None, so that env_builder can specify its own correct default.

Bionet nrn.py loading hoc files in Windows not working

In Windows Neuron doesn't like the use of double slashes (\\) and we get the following error.

(clean) C:\Users\Tyler\Desktop\BMTK Morpho Work\hippocampus-bmtk\ca3>python run_bionet.py simulation_config.json
2018-12-29 00:38:09,774 [INFO] Created log file
loading stuff
C:\Users\Tyler\Anaconda3\envs\clean\lib\site-packages\bmtk-0.0.7-py3.7.egg\bmtk\simulator\bionet
NEURON: Can't open  C:UsersTylerAnaconda3envscleanlibsite-packagemtk-0.0.7-py3.7.egmtksimulatoionetimport3d.hoc
 near line 1
character \010 at position 54 is not printable
 {xopen("C:UsersTylerAnaconda3envscleanlibsite-packagemtk-0.0.7-py3.7.egmtksimulatoionetimport3d.hoc")}
                                                                                                             ^
        xopen("C:UsersTyl...")
      execute1("{xopen("C:...")
    load_file("C:\Users\T...")
C:\Users\Tyler\Anaconda3\envs\clean\lib\site-packages\bmtk-0.0.7-py3.7.egg\bmtk\simulator\bionet\import3d.hoc
NEURON: Can't open  C:UsersTylerAnaconda3envscleanlibsite-packagemtk-0.0.7-py3.7.egmtksimulatoionetdefault_templatesadvance.hoc
 near line 1
character \010 at position 54 is not printable
 {xopen("C:UsersTylerAnaconda3envscleanlibsite-packagemtk-0.0.7-py3.7.egmtksimulatoionetdefault_templatesadvance.hoc")}
                                                                                                                             ^
        xopen("C:UsersTyl...")
      execute1("{xopen("C:...")
    load_file("C:\Users\T...")
NEURON: Import3d_SWC_read is not a template
 in BioAxonStub.hoc near line 25
        nl = new Import3d_SWC_read()
                             ^
        xopen("BioAxonStub.hoc")
      execute1("{xopen("Bi...")
    load_file("BioAxonStub.hoc")
NEURON: Import3d_SWC_read is not a template
 in Biophys1.hoc near line 24
        nl = new Import3d_SWC_read()
                             ^
        xopen("Biophys1.hoc")
      execute1("{xopen("Bi...")
    load_file("Biophys1.hoc")

I will follow up with a pull request for a possible solution.

sonata model processing ignored when using nml e-models

Is there already a solution to specify the model_processing method when using nml files ? As mentioned in the comment here:
https://github.com/AllenInstitute/bmtk/blob/develop/bmtk/simulator/bionet/default_setters/cell_models.py#L399

The problem is that when one uses a single compartmental cell, the aibs perisomatic model processing crashes becauses there is no axon. When specifying 'fullaxon' in the biophysical_node_types, it seems to be ignored (which is logical if one looks at the comment above).

Allow BMTK to load spike input times with higher temporal resolution than dt

e.g. if this value for dt:
https://github.com/AllenInstitute/sonata/blob/ad1d46a97029749aea835f9f016bfa1ccba1f258/examples/300_pointneurons/simulation_config.json#L10

is changed to a more realistic 0.01 ms, the following error occurs:

...
2019-04-29 19:27:47,332 [INFO] Setting up output directory
2019-04-29 19:27:47,332 [INFO] Building cells.
2019-04-29 19:27:47,343 [INFO] Building recurrent connections
2019-04-29 19:27:47,468 [INFO] Build virtual cell stimulations for external_spike_trains
Traceback (most recent call last):
  File "run_pointnet.py", line 18, in <module>
    main('config.json')
  File "run_pointnet.py", line 11, in main
    sim = pointnet.PointSimulator.from_config(configure, graph)
  File "/home/padraig/anaconda2/lib/python2.7/site-packages/bmtk-0.0.7-py2.7.egg/bmtk/simulator/pointnet/pointsimulator.py", line 230, in from_config
    graph.add_spike_trains(spikes, node_set)
  File "/home/padraig/anaconda2/lib/python2.7/site-packages/bmtk-0.0.7-py2.7.egg/bmtk/simulator/pointnet/pointnetwork.py", line 155, in add_spike_trains
    nest.SetStatus([nest_id], {'spike_times': np.array(spike_trains.get_spikes(node_id))})
  File "/home/padraig/opt/nest/lib/python2.7/site-packages/nest/lib/hl_api_helper.py", line 230, in stack_checker_func
    return f(*args, **kwargs)
  File "/home/padraig/opt/nest/lib/python2.7/site-packages/nest/lib/hl_api_info.py", line 199, in SetStatus
    sr('Transpose { arrayload pop SetStatus } forall')
  File "/home/padraig/opt/nest/lib/python2.7/site-packages/nest/__init__.py", line 117, in catching_sli_run
    raise _kernel.NESTError(encode(errorstring))
pynestkernel.NESTError: BadProperty in SetStatus_id: Setting status of a 'spike_generator' with GID 301: spike_generator: Time point 0.384322 is not representable in current resolution.

Error recording synapse properties for cells with multiple synapse types

The most recent feature merge for recording synapse properties works great. Initial testing went fine until I had multiple synapse types on the post synaptic cell. I started running into this issue:

(base) C:\Users\Tyler\Desktop\git_stage\hippocampus-bmtk\HummosBanks-bmtk>python run_bionet.py
C:\Users\Tyler\Anaconda3\lib\site-packages\h5py\__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
2019-03-19 17:57:25,900 [INFO] Created log file
2019-03-19 17:57:26,291 [INFO] Building cells.
2019-03-19 17:57:27,072 [INFO] Building recurrent connections
2019-03-19 17:57:36,431 [INFO] Building virtual cell stimulations for exc_spikes
2019-03-19 17:57:36,778 [INFO] Running simulation for 100.000 ms with the time step 0.100 ms
2019-03-19 17:57:36,778 [INFO] Starting timestep: 0 at t_sim: 0.000 ms
2019-03-19 17:57:36,778 [INFO] Block save every 1000 steps
Traceback (most recent call last):
  File "C:\Users\Tyler\Anaconda3\lib\site-packages\bmtk-0.0.7-py3.6.egg\bmtk\simulator\bionet\biosimulator.py", line 253, in post_fadvance
    mod.step(self, self.tstep)
  File "C:\Users\Tyler\Anaconda3\lib\site-packages\bmtk-0.0.7-py3.6.egg\bmtk\simulator\bionet\modules\record_netcons.py", line 129, in step
    self._var_recorder.record_cell(gid, var_name, syn_values, tstep)
  File "C:\Users\Tyler\Anaconda3\lib\site-packages\bmtk-0.0.7-py3.6.egg\bmtk\utils\io\cell_vars.py", line 205, in record_cell
    buffer_block[update_index, gid_beg:gid_end] = seg_vals
ValueError: cannot copy sequence with size 22 to array axis with dimension 25
NEURON: PyObject method call failed: post_fadvance
 near line 0
 {DA_IZH = 2}
             ^
        advance()
      step()
    continuerun(100)
  run(100)
Traceback (most recent call last):
  File "run_bionet.py", line 94, in <module>
    run('simulation_config.json')
  File "run_bionet.py", line 86, in run
    sim.run()
  File "C:\Users\Tyler\Anaconda3\lib\site-packages\bmtk-0.0.7-py3.6.egg\bmtk\simulator\bionet\biosimulator.py", line 224, in run
    h.run(h.tstop)        # <- runs simuation: works in parallel
RuntimeError: hoc error

It appears there's an array mismatch in cell_vars.py brought on when registering cells in record_netcons.py.

I've got a followup pull request to fix it with your approval. (#69)

Python3 support

Update bmtk to support python3.

Using external function as iterator

Hi,
I'm a new user of bmtk and I want to know if it's possible to use an external function as iterator when we create a new edge. So we can have: net.add_edge(N=1, iterator=external_function, ...). If it's possible what are the steps to follow.

Thank you

Option to disable log file

Could the ability to disable the log file be added btmk ?
When specifying the 'output' block
Sonata doesn't require to generate such a log file, the only 'default' behavior that is defined in sonata is the a spike output file is written.

Broken links in Tutorial ipynb

The tutorial ipynb has some tutorials for PointNet and PopNet (Ch. 4 and Ch. 5) I was interested in reading about but the links are broken

Broken Links:

Part 1: Complete workflow tutorials

...
Chapter 5: Point-neuron simulation (with PointNet)
Chapter 6: Population-level simulation (with PopNet)

The tests and pycache are packaged

The tests and pycaches are unconventionally placed inside the source directory and as such are packaged and installed with every pip install bmtk:

PS C:\Users\robin> pip show -f bmtk
Name: bmtk
Version: 0.0.7
Summary: Brain Modeling Toolkit
Home-page: https://github.com/AllenInstitute/bmtk
Author: Kael Dai
Author-email: [email protected]
License: UNKNOWN
Location: c:\users\robin\appdata\local\programs\python\python39\lib\site-packages
Requires: matplotlib, h5py, jsonschema, six, numpy, pandas
Required-by:
Files:
  bmtk-0.0.7.dist-info\INSTALLER
  bmtk-0.0.7.dist-info\LICENSE.txt
  bmtk-0.0.7.dist-info\METADATA
  bmtk-0.0.7.dist-info\RECORD
  bmtk-0.0.7.dist-info\REQUESTED
  bmtk-0.0.7.dist-info\WHEEL
  bmtk-0.0.7.dist-info\top_level.txt
  bmtk\__init__.py
  bmtk\__pycache__\__init__.cpython-36.pyc
  bmtk\__pycache__\__init__.cpython-39.pyc
  bmtk\analyzer\__init__.py
  bmtk\analyzer\__pycache__\__init__.cpython-39.pyc
  bmtk\analyzer\__pycache__\cell_vars.cpython-39.pyc
  bmtk\analyzer\__pycache__\firing_rates.cpython-39.pyc
  bmtk\analyzer\__pycache__\io_tools.cpython-39.pyc
  bmtk\analyzer\__pycache__\spike_trains.cpython-39.pyc
  bmtk\analyzer\__pycache__\spikes_analyzer.cpython-39.pyc
  bmtk\analyzer\__pycache__\spikes_loader.cpython-39.pyc
  bmtk\analyzer\__pycache__\utils.cpython-39.pyc
  bmtk\analyzer\cell_vars.py
  bmtk\analyzer\firing_rates.py
  bmtk\analyzer\io_tools.py
  bmtk\analyzer\spike_trains.py
  bmtk\analyzer\spikes_analyzer.py
  bmtk\analyzer\spikes_loader.py
  bmtk\analyzer\utils.py
  bmtk\analyzer\visualization\__init__.py
  bmtk\analyzer\visualization\__pycache__\__init__.cpython-39.pyc
  bmtk\analyzer\visualization\__pycache__\rasters.cpython-39.pyc
  bmtk\analyzer\visualization\__pycache__\spikes.cpython-39.pyc
  bmtk\analyzer\visualization\__pycache__\widgets.cpython-39.pyc
  bmtk\analyzer\visualization\rasters.py
  bmtk\analyzer\visualization\spikes.py
  bmtk\analyzer\visualization\widgets.py
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  bmtk\builder\__pycache__\node_set.cpython-39.pyc
  bmtk\builder\auxi\__init__.py
  bmtk\builder\auxi\__pycache__\__init__.cpython-39.pyc
  bmtk\builder\auxi\__pycache__\edge_connectors.cpython-39.pyc
  bmtk\builder\auxi\__pycache__\node_params.cpython-39.pyc
  bmtk\builder\auxi\edge_connectors.py
  bmtk\builder\auxi\node_params.py
  bmtk\builder\bionet\__init__.py
  bmtk\builder\bionet\__pycache__\__init__.cpython-39.pyc
  bmtk\builder\bionet\__pycache__\swc_reader.cpython-39.pyc
  bmtk\builder\bionet\swc_reader.py
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  bmtk\builder\connector.py
  bmtk\builder\edge.py
  bmtk\builder\formats\__init__.py
  bmtk\builder\formats\__pycache__\__init__.cpython-39.pyc
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  bmtk\builder\formats\__pycache__\iformats.cpython-39.pyc
  bmtk\builder\formats\hdf5_format.py
  bmtk\builder\formats\iformats.py
  bmtk\builder\functor_cache.py
  bmtk\builder\id_generator.py
  bmtk\builder\io\__init__.py
  bmtk\builder\io\__pycache__\__init__.cpython-39.pyc
  bmtk\builder\iterator.py
  bmtk\builder\network.py
  bmtk\builder\networks\__init__.py
  bmtk\builder\networks\__pycache__\__init__.cpython-39.pyc
  bmtk\builder\networks\__pycache__\dm_network.cpython-39.pyc
  bmtk\builder\networks\__pycache__\input_network.cpython-39.pyc
  bmtk\builder\networks\__pycache__\mpi_network.cpython-39.pyc
  bmtk\builder\networks\__pycache__\nxnetwork.cpython-39.pyc
  bmtk\builder\networks\__pycache__\sparse_network.cpython-39.pyc
  bmtk\builder\networks\dm_network.py
  bmtk\builder\networks\input_network.py
  bmtk\builder\networks\mpi_network.py
  bmtk\builder\networks\nxnetwork.py
  bmtk\builder\networks\sparse_network.py
  bmtk\builder\node.py
  bmtk\builder\node_pool.py
  bmtk\builder\node_set.py
  bmtk\simulator\__init__.py
  bmtk\simulator\__pycache__\__init__.cpython-39.pyc
  bmtk\simulator\bionet\README.md
  bmtk\simulator\bionet\__init__.py
  bmtk\simulator\bionet\__pycache__\__init__.cpython-39.pyc
  bmtk\simulator\bionet\__pycache__\biocell.cpython-39.pyc
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  bmtk\simulator\bionet\__pycache__\biosimulator.cpython-39.pyc
  bmtk\simulator\bionet\__pycache__\cell.cpython-39.pyc
  bmtk\simulator\bionet\__pycache__\config.cpython-39.pyc
  bmtk\simulator\bionet\__pycache__\iclamp.cpython-39.pyc
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  bmtk\simulator\bionet\__pycache__\morphology.cpython-39.pyc
  bmtk\simulator\bionet\__pycache__\nml_reader.cpython-39.pyc
  bmtk\simulator\bionet\__pycache__\nrn.cpython-39.pyc
  bmtk\simulator\bionet\__pycache__\pointprocesscell.cpython-39.pyc
  bmtk\simulator\bionet\__pycache__\pointsomacell.cpython-39.pyc
  bmtk\simulator\bionet\__pycache__\pyfunction_cache.cpython-39.pyc
  bmtk\simulator\bionet\__pycache__\sonata_adaptors.cpython-39.pyc
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  bmtk\simulator\bionet\__pycache__\virtualcell.cpython-39.pyc
  bmtk\simulator\bionet\biocell.py
  bmtk\simulator\bionet\bionetwork.py
  bmtk\simulator\bionet\biosimulator.py
  bmtk\simulator\bionet\cell.py
  bmtk\simulator\bionet\config.py
  bmtk\simulator\bionet\default_setters\__init__.py
  bmtk\simulator\bionet\default_setters\__pycache__\__init__.cpython-39.pyc
  bmtk\simulator\bionet\default_setters\__pycache__\cell_models.cpython-39.pyc
  bmtk\simulator\bionet\default_setters\__pycache__\synapse_models.cpython-39.pyc
  bmtk\simulator\bionet\default_setters\__pycache__\synaptic_weights.cpython-39.pyc
  bmtk\simulator\bionet\default_setters\cell_models.py
  bmtk\simulator\bionet\default_setters\synapse_models.py
  bmtk\simulator\bionet\default_setters\synaptic_weights.py
  bmtk\simulator\bionet\default_templates\BioAxonStub.hoc
  bmtk\simulator\bionet\default_templates\Biophys1.hoc
  bmtk\simulator\bionet\default_templates\advance.hoc
  bmtk\simulator\bionet\iclamp.py
  bmtk\simulator\bionet\import3d.hoc
  bmtk\simulator\bionet\import3d\import3d_gui.hoc
  bmtk\simulator\bionet\import3d\import3d_sec.hoc
  bmtk\simulator\bionet\import3d\read_morphml.hoc
  bmtk\simulator\bionet\import3d\read_nlcda.hoc
  bmtk\simulator\bionet\import3d\read_nlcda3.hoc
  bmtk\simulator\bionet\import3d\read_nts.hoc
  bmtk\simulator\bionet\import3d\read_swc.hoc
  bmtk\simulator\bionet\io_tools.py
  bmtk\simulator\bionet\modules\__init__.py
  bmtk\simulator\bionet\modules\__pycache__\__init__.cpython-39.pyc
  bmtk\simulator\bionet\modules\__pycache__\ecp.cpython-39.pyc
  bmtk\simulator\bionet\modules\__pycache__\record_cellvars.cpython-39.pyc
  bmtk\simulator\bionet\modules\__pycache__\record_netcons.cpython-39.pyc
  bmtk\simulator\bionet\modules\__pycache__\record_spikes.cpython-39.pyc
  bmtk\simulator\bionet\modules\__pycache__\save_synapses.cpython-39.pyc
  bmtk\simulator\bionet\modules\__pycache__\sim_module.cpython-39.pyc
  bmtk\simulator\bionet\modules\__pycache__\xstim.cpython-39.pyc
  bmtk\simulator\bionet\modules\__pycache__\xstim_waveforms.cpython-39.pyc
  bmtk\simulator\bionet\modules\ecp.py
  bmtk\simulator\bionet\modules\record_cellvars.py
  bmtk\simulator\bionet\modules\record_netcons.py
  bmtk\simulator\bionet\modules\record_spikes.py
  bmtk\simulator\bionet\modules\save_synapses.py
  bmtk\simulator\bionet\modules\sim_module.py
  bmtk\simulator\bionet\modules\xstim.py
  bmtk\simulator\bionet\modules\xstim_waveforms.py
  bmtk\simulator\bionet\morphology.py
  bmtk\simulator\bionet\nml_reader.py
  bmtk\simulator\bionet\nrn.py
  bmtk\simulator\bionet\pointprocesscell.py
  bmtk\simulator\bionet\pointsomacell.py
  bmtk\simulator\bionet\pyfunction_cache.py
  bmtk\simulator\bionet\schemas\config_schema.json
  bmtk\simulator\bionet\schemas\csv_edge_types.json
  bmtk\simulator\bionet\schemas\csv_node_types_external.json
  bmtk\simulator\bionet\schemas\csv_node_types_internal.json
  bmtk\simulator\bionet\schemas\csv_nodes_external.json
  bmtk\simulator\bionet\schemas\csv_nodes_internal.json
  bmtk\simulator\bionet\sonata_adaptors.py
  bmtk\simulator\bionet\utils.py
  bmtk\simulator\bionet\virtualcell.py
  bmtk\simulator\core\__init__.py
  bmtk\simulator\core\__pycache__\__init__.cpython-39.pyc
  bmtk\simulator\core\__pycache__\config.cpython-39.pyc
  bmtk\simulator\core\__pycache__\edge_population.cpython-39.pyc
  bmtk\simulator\core\__pycache__\graph.cpython-39.pyc
  bmtk\simulator\core\__pycache__\io_tools.cpython-39.pyc
  bmtk\simulator\core\__pycache__\network_reader.cpython-39.pyc
  bmtk\simulator\core\__pycache__\node_population.cpython-39.pyc
  bmtk\simulator\core\__pycache__\node_sets.cpython-39.pyc
  bmtk\simulator\core\__pycache__\simulator.cpython-39.pyc
  bmtk\simulator\core\__pycache__\simulator_network.cpython-39.pyc
  bmtk\simulator\core\config.py
  bmtk\simulator\core\edge_population.py
  bmtk\simulator\core\graph.py
  bmtk\simulator\core\io_tools.py
  bmtk\simulator\core\network_reader.py
  bmtk\simulator\core\node_population.py
  bmtk\simulator\core\node_sets.py
  bmtk\simulator\core\simulator.py
  bmtk\simulator\core\simulator_network.py
  bmtk\simulator\core\sonata_reader\__init__.py
  bmtk\simulator\core\sonata_reader\__pycache__\__init__.cpython-39.pyc
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  bmtk\utils\scripts\popnet\__pycache__\run_popnet.cpython-39.pyc
  bmtk\utils\scripts\popnet\population_models\exc_model.json
  bmtk\utils\scripts\popnet\population_models\inh_model.json
  bmtk\utils\scripts\popnet\run_popnet.py
  bmtk\utils\scripts\popnet\synaptic_models\ExcToExc.json
  bmtk\utils\scripts\popnet\synaptic_models\ExcToInh.json
  bmtk\utils\scripts\popnet\synaptic_models\InhToExc.json
  bmtk\utils\scripts\popnet\synaptic_models\InhToInh.json
  bmtk\utils\scripts\popnet\synaptic_models\input_ExcToExc.json
  bmtk\utils\scripts\popnet\synaptic_models\input_ExcToInh.json
  bmtk\utils\scripts\sonata.circuit_config.json
  bmtk\utils\scripts\sonata.simulation_config.json
  bmtk\utils\sim_setup.py
  bmtk\utils\sonata\__init__.py
  bmtk\utils\sonata\__pycache__\__init__.cpython-39.pyc
  bmtk\utils\sonata\__pycache__\column_property.cpython-39.pyc
  bmtk\utils\sonata\__pycache__\config.cpython-39.pyc
  bmtk\utils\sonata\__pycache__\edge.cpython-39.pyc
  bmtk\utils\sonata\__pycache__\file.cpython-39.pyc
  bmtk\utils\sonata\__pycache__\file_root.cpython-39.pyc
  bmtk\utils\sonata\__pycache__\group.cpython-39.pyc
  bmtk\utils\sonata\__pycache__\node.cpython-39.pyc
  bmtk\utils\sonata\__pycache__\population.cpython-39.pyc
  bmtk\utils\sonata\__pycache__\types_table.cpython-39.pyc
  bmtk\utils\sonata\__pycache__\utils.cpython-39.pyc
  bmtk\utils\sonata\column_property.py
  bmtk\utils\sonata\config.py
  bmtk\utils\sonata\edge.py
  bmtk\utils\sonata\file.py
  bmtk\utils\sonata\file_root.py
  bmtk\utils\sonata\group.py
  bmtk\utils\sonata\node.py
  bmtk\utils\sonata\population.py
  bmtk\utils\sonata\types_table.py
  bmtk\utils\sonata\utils.py
  bmtk\utils\spike_trains\__init__.py
  bmtk\utils\spike_trains\__pycache__\__init__.cpython-39.pyc
  bmtk\utils\spike_trains\__pycache__\spikes_csv.cpython-39.pyc
  bmtk\utils\spike_trains\__pycache__\spikes_file.cpython-39.pyc
  bmtk\utils\spike_trains\spikes_csv.py
  bmtk\utils\spike_trains\spikes_file.py

astype() got an unexpected keyword argument 'copy'

When I run the codes in the tutorial, I keep coming into the error: astype() got an unexpected keyword argument 'copy'. I have no idea how to solve it. I read some reports that the version of h5py and pandas should be downgraded to 2.8 and 0.23 respectively, but I tried and encountered another problem : "Error: Fail to build wheel for pandas"

Problem with spike trains in tutorials 2 and 3.

When the PoissonSpikeGenerator is used in both tutorials, the numbers passed in for times and firing_rate are in the wrong units. The problem is that when the spikes are eventually played into the vecstim in the virtual cells, the times are interpreted as ms instead of seconds. Therefore, the start and stop that is passed into times for psg should be in ms and the firing_rate passed into the psg should be in kHz instead of Hz.

For example, in tutorial 2, the spike train is supposed to be 3 seconds long with spikes firing at 10 Hz. In the tutorial they use firing_rate=10.0 and times=(0.0, 3.0), but what they really want is firing_rate=0.01 and times=(0.0, 3000.0).

raster_plot not working in Python3

Error received:

>>> raster_plot(cells_file='network/hippocampus_nodes.h5', cell_models_file='network/hippocampus_node_types.csv', spikes_file='output/spikes.h5')
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\Users\user\AppData\Local\Continuum\anaconda3\lib\site-packages\bmtk-0.0.7-py3.6.egg\bmtk\analyzer\visualization\spikes.py", line 148, in plot_spikes
    population = list(cells_h5['/nodes'].keys())
TypeError: 'KeysView' object does not support indexing

The root of the problem seems to be bmtk.analyzer.visualizer.spikes.plot_spikes is not using python3 h5py friendly syntax.

Changing line 148 from
population = cells_h5['/nodes'].keys()[0]
to
population = list(cells_h5['/nodes'])[0]

will prevent this error but I'm not sure this will hold in python2.

Fix add_step_currents in pointnet/pointsimulator.py

As mentioned here: #107 (comment) there is still an issue with pointsimulator.py with the new code for adding currents. Example error (running https://github.com/AllenInstitute/bmtk/blob/develop/docs/examples/point_iclamp/run_pointnet.py):

2019-12-12 16:20:21,171 [INFO] Building recurrent connections
Traceback (most recent call last):
  File "run_pointnet.py", line 14, in <module>
    main('config.json')
  File "run_pointnet.py", line 9, in main
    sim = pointnet.PointSimulator.from_config(configure, network)
  File "/home/padraig/git/bmtk_ai/bmtk/simulator/pointnet/pointsimulator.py", line 266, in from_config
    network.add_step_currents(amp_times, amp_values, node_set, sim_input.name)
  File "/home/padraig/git/bmtk_ai/bmtk/simulator/pointnet/pointsimulator.py", line 153, in add_step_currents
    nest_ids = [self.net._gid2nestid[gid] for gid in node_set.gids()]

Install run_bionet.py etc as commandline utils

It's just a suggestion, but wouldn't it make sense to install run_bionet.py, run_popnet.py etc as commandline tools when pip installing bmtk ? (Or maybe wrap everything in one commandline tool ('bmtk' ?), with 'popnet' / 'bionet' etc has argument.
As far as I can see these scripts get reused a lot without modification, and always have to be present in the project directories ? Being able to have them available immediately would make things easier.

Add dendrodendritic synapses

Hello, would be great to include the possibility to specify different sections of the "source" internal nodes like dendrites as opposed to axons, like you do for the target_sections to establish connectivity rules. This will allow for dendrodendritic synapses.

FilterNet tutorial

FilterNet tutorial is incomplete

Add voltage clamping

For BioNet implement voltage clamping mechanisms. See SEClamp input stimulus in SONATA documentation for examples of how it should be instantiated.

Don't delete all contents of output_dir...

Currently BMTK will delete all files & subfolders in the output_dir as specified of a Sonata file:

 "output":{ 
    "output_dir": "$OUTPUT_DIR",
   ...
  }

including presumably the user's home directory if they specify that...

https://github.com/AllenInstitute/bmtk/blob/develop/bmtk/simulator/core/io_tools.py#L73

It would be better to only delete the files BMTK expects to generate (as is suggested by the spec: "An optional attribute named "overwrite_output_dir" provides a hint to simulators to let them know if already existing output files must be overwritten."), or better still warn the user that that dir contains files BMTK didn't create.

Please consider generating a new release tarball (Debian-Med).

Hello,

I am currently packaging bmtk1 for Debian-Med2. It seems like the last release tarball was on the 5th of May 2019. We take preference on always packaging the release tarball and not straight from the git repository. Unfortunately, with the latest release tarball being close to a year old and a couple hundred commits on the most recent one, there is more potential in packaging the latest version as opposed to the release tarball generated a year ago.

Please could you consider prioritising for generating a release tarball?

Kind regards,
Shayan Doust

example "1cell_all_active" unrunnable

there is no file called "318331342_fit.json", and also even if I changed to some existing file, it would still give me error TypeError: init() got multiple values for keyword argument 'network'
at line 24: sim = Simulation(conf, network=net) # initialize a simulation

I asked this question as a followup from AllenInstitute/AllenSDK#107

Output full vectors of dynamical variables

I'm running the bmtk chapter 4 tutorial and using a data reporting method from the bmtk-howto from @tjbanks. However, what I need is an output of all the dynamic variables at each time step. I don't see how to get access to more than just a few variables or spike times. Is there a way to get a complete dump of the ODE output?

For reference, here is my configuration file:

{
  "target_simulator": "NEURON",
  "run": {
    "tstop": 3000.0,
    "dt": 0.1,
    "dL": 20.0,
    "spike_threshold": -15.0,
    "nsteps_block": 5000
  },
  "conditions": {
    "celsius": 34.0,
    "v_init": -80.0
  },
  "inputs": {
    "LGN_spikes": {
      "input_type": "spikes",
      "module": "nwb",
      "input_file": "./lgn_spikes.nwb",
      "node_set": "LGN",
      "trial": "trial_0"
    }
  },
  "output": {
    "log_file": "log.txt",
    "output_dir": "./output",
    "spikes_file": "spikes.h5",
    "spikes_file_csv": "spikes.csv",
    "overwrite_output_dir": true,
    "cell_vars_dir": "./output/cellvars"
  },
  "reports": {
    "membrane_report": {
      "cells": [
        10,80
      ],
      "sections": "soma",
      "module": "membrane_report",
      "variable_name": [
        "v",
        "cai"
      ]
    },
    "ecp": {
      "cells": [10,80],
      "variable_name": "v",
      "module": "extracellular",
      "electrode_positions": "../components/recXelectrodes/linear_electrode.csv",
      "file_name": "ecp.h5",
      "contributions_dir": "ecp_contributions"
    }
  },
  "recXelectrode": {
    "positions": "$COMPONENT_DIR/recXelectrodes/linear_electrode.csv"
    },
  "network": "./circuit_config.json",
  "config_path": "/Users/chris/Documents/Brown/Research/v1corticalcol/sim_ch04/simulation_config.json",
  "config_dir": "/Users/chris/Documents/Brown/Research/v1corticalcol/sim_ch04",
  "components": {
    "morphologies_dir": "./biophys_components/morphologies",
    "synaptic_models_dir": "./biophys_components/synaptic_models",
    "mechanisms_dir": "./biophys_components/mechanisms",
    "biophysical_neuron_models_dir": "./biophys_components/biophysical_neuron_templates",
    "point_neuron_models_dir": "./biophys_components/point_neuron_templates"
  },
  "networks": {
    "nodes": [
      {
        "node_types_file": "./network/LGN_node_types.csv",
        "nodes_file": "./network/LGN_nodes.h5"
      },
      {
        "node_types_file": "./network/V1_node_types.csv",
        "nodes_file": "./network/V1_nodes.h5"
      }
    ],
    "edges": [
      {
        "edge_types_file": "./network/V1_V1_edge_types.csv",
        "edges_file": "./network/V1_V1_edges.h5"
      },
      {
        "edge_types_file": "./network/LGN_V1_edge_types.csv",
        "edges_file": "./network/LGN_V1_edges.h5"
      }
    ]
  }
}

Bionet - compile vecevent.mod on installation?

The neuron VecStim definition, from vecevent.mod, is required to run a network with artificial cells created by bmtk - it would be great if it could be compiled and added to the path automatically, either on installation, or maybe when running build_env_bionet or something similar (ie. compile not only modfiles in the local network directory but also some core modfiles in a central directory).

	def make_morphologies(self):
	"""Creating a Morphology object for each biophysical model"""
	# TODO: Let Morphology take care of the cache
	# TODO: Let other types have morphologies
	# TODO: Get all available morphologies from TypesTable or group
	for gid, cell in self._rank_node_gids.items():
	if not isinstance(cell, BioCell):
	continue

	morphology_file = cell.morphology_file
	if morphology_file in self._morphologies_cache:
	# create a single morphology object for each model_group which share that morphology
	morph = self._morphologies_cache[morphology_file]

	# associate morphology with a cell
	cell.set_morphology(morph)
	self._morphology_lookup[morphology_file].append(cell.gid)

	else:
	hobj = cell.hobj # get hoc object (hobj) from the first cell with a new morphologys
	morph = Morphology(hobj)

	# associate morphology with a cell
	cell.set_morphology(morph)

	# create a single morphology object for each model_group which share that morphology
	self._morphologies_cache[morphology_file] = morph
	self._morphology_lookup[morphology_file] = [cell.gid]

	self.io.barrier()

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