diff --git a/.gitignore b/.gitignore index 756f9b9..c8d35f0 100644 --- a/.gitignore +++ b/.gitignore @@ -2,6 +2,28 @@ **/*.egg-info/ **/*~ src/pysonata/sonata/tests/tmp +src/pysonata/build +src/pysonata/dist +src/pysonata/.pytest_cache +**/mechanisms/x86_64 +examples/**/output/log.txt +examples/**/output/config.json .idea .cache **/mechanisms/x86_64 +/examples/sim_tests/biophysical/one_cell/soma_cm2_hh/.pytest_cache +/examples/sim_tests/intfire/**/.pytest_cache +.ipynb_checkpoints/ +/examples/checkouth5.sh +/examples/300_pointneurons/output/*.dat +/examples/sim_tests/intfire/*/test/x86_64 +/examples/sim_tests/intfire/*/test/LEMS*.xml +/examples/sim_tests/intfire/*/test/*.dat +/examples/sim_tests/intfire/*/test/report*txt +/examples/sim_tests/intfire/*/test/*spikes +/examples/sim_tests/intfire/*/test/*.net.nml +/examples/sim_tests/intfire/*/test/*_nrn.py +/examples/sim_tests/intfire/*/test/*.mod +/examples/sim_tests/intfire/*/test/output/*h5 +/examples/sim_tests/intfire/*/test/output/log.txt +/examples/sim_tests/intfire/*/test/output/*dat diff --git a/docs/SONATA_DEVELOPER_GUIDE.md b/docs/SONATA_DEVELOPER_GUIDE.md index 8037e08..7ae428e 100644 --- a/docs/SONATA_DEVELOPER_GUIDE.md +++ b/docs/SONATA_DEVELOPER_GUIDE.md @@ -113,10 +113,7 @@ The format used is SWC ( [http://www.neuronland.org/NLMorphologyConverter/Morpho - -* Point soma, interpreted as a sphere with radius from column 6 (**Cylindrical somas are not used**) - - Point soma will be interpreted in NEURON as cylinder with L=radius +* Single sample point soma, interpreted as a sphere with radius from column 6 (**Cylindrical somas are not used**). * Types: The following types are currently used. @@ -153,11 +150,16 @@ The format used is SWC ( [http://www.neuronland.org/NLMorphologyConverter/Morpho It is not required that the soma is located at 0,0,0 in the SWC file, but in cases where the morphology has a soma, the soma will be re-centered to 0,0,0 upon loading into the circuit. Node translations will then be applied to this recentered morphology. This behavior can be overridden by the optional reserved attribute "recenter" for nodes and node_types. See "Representing networks of neurons" for more details. -It is recommended, but not required, that morphologies in a network have a standardized orientation, so that their orientation vectors in the network can be inferred from node rotation angles. For circuits for which this is true, the circuit producer can declare it with the "standardized_morphology_orientation" entry in the circuit_config.json, so downstream users can safely assume it. See "Tying it all together - the circuit_config file" for more details. +It is recommended, but not required, that morphologies in a network have a standardized orientation, so that their orientation vectors in the network can be inferred from node rotation angles. ### Representing ion channel, point neuron and synapse models -To represent point neuron models, synapses and ion channels NEURON MOD files are used. Models provided as standard by NEURON are also valid, such as ExpSyn, IntFire1. +Representation of point neuron models, synapses and ion channels depends on +the target simulator. + +For NEURON, NEURON MOD files are used. Models provided as standard by NEURON +are also valid, such as ExpSyn, IntFire1. For NEST and PyNN, the names of +built-in/standard point neuron and synapse models are used. To support reproducible random numbers in NEURON, it is required to define conventions for the configuration of random number generators, and how they are assigned to channel models, synapses random number generators. To this end, NEURON mechanisms should follow idioms in the MOD files, so that a uniform and automated approach to random number configuration can be employed. Such an approach was out of scope of the current format specification, and will be the subject of future versions. @@ -401,18 +403,17 @@ The details of how node and edge populations are defined and represented are des In general, populations of neurons are heterogeneous in the types of cell models describing each node, implying heterogeneousequations and sets of parameters. We define a node group as a set of nodes with a homogeneous parameter namespace implying a uniform tabular layout. A population is defined as the union of one or more groups, which need not have uniform tabular layout among them, and further defines some indexing datasets. A population provides then a uniform view on a collection of nodes which have heterogeneous parameterization namespaces. -A model_type attribute allows nodes to be configured as "biophysical", “point_soma”, etc. and also “virtual” one may be provided to specify external (or “virtual”) nodes that are not explicitly simulated but provide inputs to the network. +A model_type attribute allows nodes to be configured as `biophysical`, `point_neuron`, etc. and also `virtual` one may be provided to specify external (or `virtual`) nodes that are not explicitly simulated but provide inputs to the network. A typical network may include multiple simulated populations as well as multiple populations of external input nodes. -Each node in a population has a node type. Attributes can be assigned to nodes and node types, whereby a node assumes attributes of itsnode type, and can override them at the individual node level. Below, attributes which have specified interpretation and expected units (where applicable) are defined, and are either "required" or “optional reserved”. +Each node in a population has a node type. Attributes can be assigned to nodes and node types, whereby a node assumes attributes of its node type, and can override them at the individual node level. Below, attributes which have specified interpretation and expected units (where applicable) are defined, and are either "required" or “optional reserved”. -Node types are defined in node types CSV files containing named columns, one for each attribute of a node type. First, the node_type_id column is required, and defines the node_type_id of each row. To allowmultiple populations define their own node_type_id’s independently, a population column is also required to resolve collisions between node_type_id’s among different populations. Other "required" attributes must either be defined by the population (see below), or be defined in a column in the associated node types CSV. The node types CSV may also include “optional reserved” columns names. Apart from these reserved names, the user is free to define any number of additional named columns to suit their needs. Node type columns will be assigned to node attributes with the column name as the key and the value coming from the row with a node’s assigned node_type_id. +Node types are defined in node types CSV files containing named columns, one for each attribute of a node type. First, the node_type_id column is required, and defines the node_type_id of each row. To allow multiple populations define their own node_type_id’s independently, a population column is also required to resolve collisions between node_type_id’s among different populations. Other "required" attributes must either be defined by the population (see below), or be defined in a column in the associated node types CSV. The node types CSV may also include “optional reserved” columns names. Apart from these reserved names, the user is free to define any number of additional named columns to suit their needs. Node type columns will be assigned to node attributes with the column name as the key and the value coming from the row with a node’s assigned node_type_id. +Non scalar attributes may be included in the node types CSV file provided the attribute values are quoted and their components are separated by spaces. Populations are serialized in nodes HDF5 files, and have a single associated node types CSV file to define the valid node_type_ids for the populations in the HDF5 file, and assign attributes applying to all nodes in with a given node_type_id. A node_types CSV file may be shared by multiple population HDF5 files. Node groups are represented as HDF5 groups (with population as parent) containing a dataset for each parameter of length equal to the number of nodes in the group. In the case a node attribute is defined in both the node types CSV and the nodes HDF5, the value in the nodes HDF5 overrides the node types CSV value. - - The HDF5 nodes file layout is designed to store multiple named populations that each may have multiple node groups, but each population with all of its node groups must be self-contained within one HDF5 file.. For each population, the node_id and node_type_id datasets are required because they uniquely identify nodes within a population irrespective of a model_type used. The node_group and node_group_index are required because they identify the location of the group specific data for each node.The model_type attribute is required, but may be defined only in the node_types CSV. The layout of the nodes HDF5 is as shown in Table 1. @@ -486,7 +487,6 @@ The HDF5 nodes file layout is designed to store multiple named populations that
- Table 1: The Layout of the HDF5 file format for describing nodes. ##### Nodes - Required Attributes @@ -497,31 +497,40 @@ Table 1: The Layout of the HDF5 file format for describing nodes. **node_type_id** - This is a unique integer for every node type used to associate a node to a node type. A node type has associated attributes, and a node inherits attributes from its node type. Attributes associated with a node override attributes inherited from the node type CSV. node_type_id is a unique integer associated with each node type and is used to index all the node type properties associated with a given node with known node_type_id. These need not be ordered or contiguous, but must be unique. -**model_type** - Has four valid values: , "biophysical"”,“virtual”, “single_compartment”, “point_process”. In the future, more model_types may be defined. The meaning of each of these model types is as follows. +**model_type** - Has 4 valid values: `biophysical`, `virtual`, `single_compartment`, and `point_neuron`. In the future, more `model_types` may be defined. The meaning of each of these model types is as follows. + +The `model_type`=`single_compartment` is a single-compartment model of a neuron. A cylindrical compartment is created with length equal to diameter, and the diameter being defined by an additional expected dynamics_param “D”, which if not specified defaults to 1 micron. The voltage of the neuron is defined by the voltage of the compartment. Further, the passive mechanism is inserted, and the additional mechanism named in the “model_template” required attribute. Note that a single compartment of length = diameter has the same effective area as that of a sphere of the same diameter (see [NEURON documentation](https://www.neuron.yale.edu/neuron/static/docs/help/neuron/neuron/geometry.html)). -The model_type=*"single_compartment"”* is a single-compartment model of a neuron. A cylindrical compartment is created with length equal to diameter, and the diameter being defined by an additional expected dynamics_param “D”, which if not specified defaults to 1 micron. The voltage of the neuron is defined by the voltage of the compartment. Further, the passive mechanism is inserted, and the additional mechanism named in the “model_template” required attribute. Note that a single compartment of length = diameter has the same effective area as that of a sphere of the same diameter (see [NEURON documentation](https://www.neuron.yale.edu/neuron/static/docs/help/neuron/neuron/geometry.html)). +The `model_type`=`point_neuron` results in a point process neuron. The actual +model type is defined by the `model_template` required attribute. In NEURON the +the `model_template` will point to an NMOD file. For the NEST and PyNN, +`model_template` will provide the name of a built-in neuron model. See below +for details. -The model_type=*"point_process"* results in a point process neuron, i.e. a NEURON artificial cell. The point process model is named in the “model_template” required attribute. +The `model_type`=`virtual` results in a placeholder neuron, which is not otherwise simulated, but can be the source of spikes which result in post-synaptic events. -The model_type=*"virtual"* results in a placeholder neuron, which is not otherwise simulated, but can be the source of spikes which result in post-synaptic events. +The `model_type`=`biophysical` results in a compartmental neuron. The attribute **morphology** must be defined, either via the node or node_type. -The model_type=*"biophysical"* results in a compartmental neuron. The attribute **morphology** must be defined, either via the node or node_type. **model_template** - Used to reference a template or class describing the electrophysical properties and mechanisms of the node(s). Its value and interpretation is context-dependent on the corresponding ‘model_type’. When there is no applicable model template for a given model type (i.e. model_type=virtual) it is assigned a value of NULL. Otherwise it uses a colon-separated string-pair with the following syntax: -**:** +*`schema`*:*`resource`* -** is a keyword used to identify the type of template being specified. Reserved options include: +*`schema`* is a keyword used to identify the type of template being specified. Reserved options include: -nml: Template is described by a NeuroML file. Valid for biophysical model types. +- **nml**: Template is described by a NeuroML file. Valid for biophysical model types. -actdb: Template is described using a pre-generated hoc template specifically designed to run AIBS cell type models. Valid for both biophysical and single_compartment model types +- **ctdb**: Template is described using a pre-generated hoc template specifically designed to run AIBS cell type models. Valid for both biophysical and single_compartment model types. -hoc: Template is described using a customized hoc file. Valid for both biophysical and single_compartmentmodel types +- **hoc**: Template is described using a customized hoc file. Valid for both biophysical and single_compartment model types. -nrn: Valid for both point_process and single_compartment model types. For a point_process model type, should specify the name of NEURON simulator point_process (i.e. IntFire1, IntFire2). For a single_compartment type, should specify the name of the mechanism to insert. +- **nrn**: Valid for both `point_neuron` and `single_compartment` model types. For a `point_neuron` model type, should specify the name of NEURON simulator `point_neuron` (i.e. IntFire1, IntFire2). For a `single_compartment` type, should specify the name of the mechanism to insert. -** is a reference to the template file-name or class. For file names if a full-path or url is not specified the interpreter is expected to use the "components" in the config file to find the full path (see below). +- **nest**: Indicates that the model is a built-in NEST model. + +- **pynn**: Indicates that the model is a standard PyNN model. + +*`resource`* is a reference to the template file-name or class. For file names if a full-path or url is not specified the interpreter is expected to use the "components" in the config file to find the full path (see below). **node_group_id** - Assigns each node to a specific group of nodes. @@ -539,86 +548,49 @@ For model_processing=*"fullaxon"*, the biophysical neuron will construct and sim **x, y, z** - position of the soma in world coordinates. -**rotation_angle_zaxis [FLOAT], rotation_angle_yaxis[FLOAT], rotation_angle_xaxis[FLOAT]** - rotation of the morphology around the soma. +**orientation** [4 FLOAT] - (w, x, y, z) quaternion with the local to world rotation of the morphology around the soma center. + +To place morphologies in world coordinates, a translation is applied first to move each morphology's soma center to (0, 0, 0) in its local coordinates system. +Then, the rotation implied by this quaternion is applied to the morphology. +Finally, each morphology is translated to move the soma center to the (x, y, z) location specified in the node/node_type files. + +**rotation_angle_zaxis [FLOAT], rotation_angle_yaxis[FLOAT], rotation_angle_xaxis[FLOAT]** - alternative representation of the rotation of the morphology around the soma. -Each morphology is first moved from its original coordinates in SWC to such a location that the soma is at (0, 0, 0). Then, three rotations are applied to each morphology, in exactly the following sequence: (1) rotation around the z-axis; (2) rotation around the y-axis; (3) rotation around the x-axis; all rotations are around the axes of the global coordinate system. Then, each morphology is shifted to such a location that its soma is at (x, y, z) coordinates specified in the node/node_type files. The angles for the three rotations are also provided in the nodes/node_types files, in radians. If a column is not provided, it is assumed that the rotation angle around that axis is 0 (that is, no rotation around that axis is applied). +This is an alternative representation to quaternions for cell orientation. +Implementations are required to support both representations, but users are strongly encouraged to only use quaternions due to its non ambiguity. +Files should not contain both representations, being implementation defined which one takes precedence otherwise. +The local to world coordinate system rotation specified by these three angles is formed in exactly the following sequence: (1) rotation around the z-axis; (2) rotation around the y-axis; (3) rotation around the x-axis; all rotations are around the axes of the world coordinate system. +All angles are in radians. +If a column is not provided, it is assumed that the rotation angle around that axis is 0 (that is, no rotation around that axis is applied). **morphology** [TEXT] - Name of the detailed morphology for a given node or node type. For name `foo`, the corresponding SWC file would be found at `$morphologies_dir/foo.swc`, where `$morphologies_dir` is specified in the [network config](#network_config). -**recenter** [INT8] - Optional reserved attribute, if the value is set to 0, morphology would _not_ be moved to (0, 0, 0) prior to rotation / translation. +**recenter** [INT8] - Optional reserved attribute, if the value is set to 0, morphologies would _not_ be moved to (0, 0, 0) prior to rotation / translation. -**dynamics_params** - Define parameter overrides for nodes. This attribute can exist in the node_types.csv file in which case a .json file is referenced, which should contain a dictionary of keys and values. A key should be a valid name in the namespace of parameters of the model, and the value specifies the assigned parameter override. Alternatively, dynamics_params overrides can be specified for each individual node in a group, in the corresponding nodes HDF5. In this case, a dynamics_params HDF5 group contains datasets named according to the parameter of the model to override in the namespace of parameters of the model (see Table 1). The length of such datasets is the number of nodes in the group. +**dynamics_params** - Define parameter overrides for nodes. This attribute can exist in the node_types.csv file in which case a .json file is referenced, which should contain a dictionary of keys and values. A key should be a valid name in the namespace of parameters of the model, and the value specifies the assigned parameter override. Alternatively, dynamics_params overrides can be specified for each individual node in a group in the corresponding H5 dataset. In this case, a dynamics_params HDF5 group contains datasets named according to the parameter of the model to override in the namespace of parameters of the model (see Table 1). The length of such datasets is the number of nodes in the group. Note that if an override is defined for a given name in both the nodes HDF5 file and the nodes_types CSV file, then the HDF5 file will override the latter. The namespace of parameters depends on model_type, and are defined as follows. -**For point_soma models**, it is the namespace of the NEURON Section containing the "pas" and user requested soma mechanism. +**For `single_compartment` models**, it is the namespace of the NEURON Section containing the "pas" and user requested soma mechanism. -**For point_process models**, it is the namespace of the point_process/artificial cell mechanism. +**For `point_neuron` models**, it is the namespace of the point neuron model. -**For biophysical models** defined according to the *nml *schema (see above), names take the form ".", where is the id of an element and an attribute of said element in the nml file defining the biophysical model. For example “g_pas_apic.erev” refers to the “erev” attribute of the “g_pas_apic” element of the nml biophysics block defining the channel composition of the model. It is worth noting that namespaces defined in this way apply equally to dynamics_params overrides at the node_types and node levels for all model types. +**For `biophysical` models** defined according to the *nml* schema (see above), names take the form **id**.**attribute**, where **id** is the id of an element and **attribute** an attribute of said element in the nml file defining the biophysical model. For example “g_pas_apic.erev” refers to the “erev” attribute of the “g_pas_apic” element of the nml biophysics block defining the channel composition of the model. It is worth noting that namespaces defined in this way apply equally to dynamics_params overrides at the node_types and node levels for all model types. -**For biophysical models** defined according to the *bmtk *(see above), the namespace definition is to be filled in by the Allen folks. +For `biophysical` models defined according to the *bmtk* (see above), the namespace definition is to be filled in by the Allen folks. -**For biophysical models** defined according to the *hoc *(see above), the namespace definition is to be filled in by the Allen folks. - -For a conceptual schematic of the architecture relating node attributes *model_type*,* model*, and *dynamics_params* overrides and their namespaces at the node_type and nodes level, see Table 2. - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Model Type
point_processpoint_somabiophysical
Parameter -OverrideNode -leveldynamics_params HDF5 group
Node_type -leveldynamics_params .json
Model -ObjectParameter -Namespacepoint processSection ∪ pas ∪ -user mechanism. -∈ .nml file
Modelpoint process namemechanism name.nml file
+For `biophysical` models defined according to the *hoc* (see above), the namespace definition is to be filled in by the Allen folks. +#### Nodes - Enum datatypes -Table 2: A conceptual schematic of the architecture relating node attributes* model*, and *dynamics_params* overrides at the node_type and nodes level for each model_type. The namespace of both node_type and node level parameter overrides are given uniquely by the model object parameter namespace. Node level parameter overrides take precedence over node_type level parameter overrides. +It is often the case that string attributes have a limited number of possible values (for instance, `model_type`, or cell morphological type). +For space efficiency, it is better to represent these attributes with *enumerations*, i.e. data types consisting of a limited set of named integer values. +However, Enum Datatypes offered by HDF5 impose a limit on the total number of possible values due to [64K limit on object header](https://support.hdfgroup.org/HDF5/hdf5-quest.html). +To work around this limitation, SONATA nodes/edges HDF5 files may use *explicit enumerations*. +Each attribute `///X` with integer datatype may have a corresponding attribute `///@library/X` with a limited set of string values. +The group `@library` is reserved for this purpose. #### Representing Edges @@ -758,7 +730,7 @@ The edge populations as defined above only allow fast enumeration of the edges f The indexing has been designed for networks where if two nodes are connected, they tend to have multiple edges connecting them (multi-synapse connections in detailed morphology networks). For single edge networks this index has excessive overhead. For maximum space and time efficiency, edges connecting two nodes should be contiguous in the edge population. In any case, the index allows edges between different nodes to be placed in any order in the population datasets. -The indexing is rooted at a single group called indices which is a subgroup of an edge population group. For convenience, the prefix /edges/**/indices/ is stripped from the following layout description: +The indexing is rooted at a single group called indices which is a subgroup of an edge population group. For convenience, the prefix /edges/**population_name**/indices/ is stripped from the following layout description: @@ -794,7 +766,7 @@ The source_to_target/range_to_edge_id dataset defines ranges of edges in the edg The datasets from the target_to_source group are defined symmetrically. From this symmetry is easy to infer that edges should be grouped by source, target pairs, otherwise an important overhead will be incurred in one or both of the indices. -### Tying it all together - the network/circuit config file +### Tying it all together - the network/circuit config file The config file is a .json file that defines the relative location of each part of the network: @@ -831,10 +803,10 @@ Where to find the .nml for biophysical neuron model types: Where to find the hoc templates for the edges: - "templates": "$COMPONENT_DIR/hoc_templates", + "templates_dir": "$COMPONENT_DIR/hoc_templates", }, -The network is defined by nodes and edges. In the example below, a V1 model is being simulated (with recurrent connections) that receives input from virtual LGN source nodes. Each population of nodes should contain "nodes" and “node_types” while each population of edges should “edges”, “edge_types”. Gids are assigned to nodes in advance (using another tool) or during the simulation, depending on implementation, and are global across all populations in the network (see the (gid mapping)[#gid_mapping] section for details) +The network is defined by nodes and edges. In the example below, a V1 model is being simulated (with recurrent connections) that receives input from virtual LGN source nodes. Each population of nodes should contain "nodes" and “node_types” while each population of edges should “edges”, “edge_types”. "networks": { "nodes": [ @@ -910,10 +882,10 @@ to simulators to let them know if already existing output files must be overwritten. The default behaviour is for simulators to produce spike data (a series of -gid, timestamp pairs). By default the name of the file is "spikes.h5" and it -is written to . The name of the output file for spikes can be -configured with the optional attribute "spikes_file" (using a relative or -absolute path in spikes_file has undefined behaviour) +population, node id, timestamp tuples). By default the name of the file is +"spikes.h5" and it is written to "output_dir". The name of the output file for +spikes can be configured with the optional attribute "spikes_file" (using a +relative or absolute path in spikes_file has undefined behaviour) Example "output": { @@ -1097,7 +1069,7 @@ There can be one or more reports in the block, each one identified by a unique n } Simulators are expected to create a file for each specified report under the -output directory using the file name ., where ext is the +output directory using the file name `.`, where `ext` is the file extension specific to the report configuration. The file name can be overriden with the attribute "file_name". @@ -1113,7 +1085,7 @@ Some reserved attributes are the following: - + @@ -1222,17 +1194,17 @@ Some reserved attributes are the following: #### **Node Sets** File -A Node Sets json file contains subsets of cells that act as targets for +A *node sets* json file contains subsets of cells that act as targets for different reports or stimulations, or can also be used to name and define the target subpopulation to simulate. The top level element in the json schema is a dictionary with one entry per node set. The keys are the node set names -and the values and depends on whether a node set is basic or compound. +and the values depend on whether a node set is basic or compound. The general schema is as follows. { "": { - "": ["", "", ...], + "": "" "": ["", "", ...], }, ... @@ -1256,7 +1228,8 @@ Each entry specifies a rule. For scalar attributes a node matches the rule if the value of its attribute matches the value in the entry. For arrays, a node matches if its value matches any of the values in the array. A node is part of a node set if it matches all the rules in the node set definition (logical -AND). +AND). Valid node attributes can be either the mandatory and reserved attributes +or user defined ones. Compound node sets are declared as an array of node sets names, where each name may refer to another compound node set or a basic node set. The final node set @@ -1267,12 +1240,6 @@ The first one is "population", this attribute refers to the node populations to be considered. Node populations and their names are implicitly defined in the Node Set namespace, and needn’t be declared explicitly. -At time of interpretation of the node set file, gids must also be defined for -each node in the network to be simulated. For that purpose, "gid" is also a -valid node attribute to appear in key-value pairs. The "gid" to population and -node_id mapping is specified according to [description below](#gid_mapping). - - ##### An Example of a Node Set File { @@ -1285,68 +1252,77 @@ node_id mapping is specified according to [description below](#gid_mapping). "model_type": "point", "node_id": [1, 2, 3, 5, 7, 9, ...] } - "layer4": { - "gids": [1, 2, 3, 4, 5, ...] - }, + "combined": ["bio_layer45", "V1_point_prime"] } ### **Output file formats** Each report name in the "reports" block results in a separate HDF5 file with -the filename .h5 written to the output directory (unless the user -provides a different file name). +the filename `.h5` written to the output directory (unless the +user provides a different file name). #### Spike file -Spikes from all cells will be stored in a single HDF5 file that contains (gid, spike time) pairs in separate datasets. These datasets may be unsorted, sorted by gid or sorted by spike time. The gids are not to be confused with node_ids from populations, see below for details about gid to node_id and population mapping. +Spikes from all cells will be stored in a single HDF5 file that contains +per population (node id, spike time) pairs. +Each pair element is stored in a separate dataset. +These datasets may be unsorted, sorted by node id or sorted by spike time. The layout of a spike file is as follows: -* **/spikes** (group), attributes: - - **sorting** (dtype: enum) Optional. It can take one of these - values: `none`, `by_gid`, `by_time`. Both datasets below are sorted using - as sorting key the dataset specified by the attribute. When sorting by gid, - spikes of the same gid are expected to be also sorted by timestamp as - secondary key. When sorting by timestamp, spikes with the same timestamp - can be in any order. If missing, no sorting can be assumed. -* **/spikes/timestamps** (dytpe: double, shape: N spikes), attributes: +* **/spikes** (group): contains one or more groups of per population spikes. +* **/spikes/** (group), attributes: + - **sorting** (dtype: enum, optional): It can take one of these + values: `none`, `by_id`, `by_time`. All the datasets below are sorted using + the datasets specified by the attribute. When sorting `by_id`, spikes are + sorted by `node_id`; spikes from the same node are expected to be also + sorted by timestamp as secondary key. When sorting `by_time`, spikes with + the same timestamp may be in any order. If missing, no sorting can be + assumed. +* **/spikes//timestamps** (dtype: double, shape: N spikes), + attributes: - **units** (dytpe: str) -* **/spikes/gids** (dytpe: uint64, shape: N spikes), attributes: +* **/spikes//node_ids** (dtype: uint64, shape: N spikes) -#### Frame oriented, cell element recordings +#### Frame oriented, node element recordings -Used when recording simulation data from elements from one or more cells. -The reported elements are usually the electrical compartments, but other -elements such as synapses could also be reported. The only requisite is that -the cell elements can be identified by an element identifier composed by an -integer and an optional float value. +Used when recording simulation data from elements from one or more nodes. +The reported elements are usually the electrical compartments from cells, but +other elements such as synapses could also be reported. The only requisite is +that the node elements can be identified by an element identifier composed by +an integer and an optional float value. -* **/data** (dtype:float, shape: N_time x N_values). Writers are - encouraged to use chunking for efficient read access. Attributes: +* **/report** (group): contains one or more groups of per population reports. +* **/report//data** (dtype:float, shape: N_time x N_values): + Writers are encouraged to use chunking for efficient read access. Attributes: - **units** (dtype: str) -* **/mapping/gids** (dtype: uint64, shape: N_cells). Attributes: - - **sorted** (dtype: bool) Optional. Indicates whether the GID list is - sorted or not. The list is considered unsorted if not present. -* **/mapping/index_pointer** (dtype: uint64, shape: N_cells) -* **/mapping/element_id** (dtype: uint32, shape: N_values). All - values referring to the same element must appear together. -* **/mapping/element_pos** (dtype: float, shape: N_values). Optional -* **/mapping/time** (dtype: double, shape: 3), +* **/report//mapping** (group) +* **/report//mapping/node_ids** (dtype: uint64, shape: N_nodes) + , attributes: + - **sorted** (dtype: bool, optional): Indicates whether the ids are sorted + or not. Defaults to false if not present. +* **/report//mapping/index_pointers** (dtype: uint64, + shape: N_nodes): Per node frame offsets. +* **/report//mapping/element_ids** (dtype: uint32, + shape: N_values): All values referring to the same element must appear + together. +* **/report//mapping/element_pos** (dtype: float, + shape: N_values, optional) +* **/report//mapping/time** (dtype: double, shape: 3): the values of the data set are start time, stop time and time step. The interval is open on the right (i.e. no data frame for t=stop). Attributes: - **units** (dtype: str) -For a particular gid[ix], the data for all the recorded elements is -determined by `data[index_pointer[ix], index_pointer[ix+1]]`. +For a node `node_ids[i]`, the data for all the recorded elements is determined +by `data[index_pointer[i], index_pointer[i + 1]]`. -For compartment reports, the values in `element_id[index_pointer[ix], -index_pointer[ix+1]]` and `element_pos[index_pointer[ix], index_pointer[ix+1]]` +For compartment reports, the values in `element_id[index_pointer[i], +index_pointer[x + 1]]` and `element_pos[index_pointer[i], index_pointer[i + 1]]` are used to specify the compartment’s section id and the relative position, -respectively, for each gid[ix]’s data column. Note that for single compartment -reports `element_id` and `element_pos` are just arrays of 1s. If the -`element_pos` dataset is not present, for every recorded section all its -compartments will be reported and they will appear in the dataset in -morphological order. +respectively, for the node `i`. Note that for single compartment reports +`element_id` and `element_pos` are just arrays of 1s. If the `element_pos` +dataset is not present, for every recorded section all its compartments will be +reported and they will appear in the dataset in morphological order. #### Extracellular report @@ -1364,29 +1340,6 @@ Used when reporting variables that are not associated with the individual cells. The data for a particular electrode channel_id[i] found in data[i,:] -### Mapping between gids and cells in the network - -In the model description, the cells are uniquely defined by their population name and node_id, whereas in the simulation output they are uniquely defined by the gids. To relate the two, we need to have a mapping: (population,node_id) <-> gid - -The mapping could be created by the simulator or prior to simulation (implementation specific) and stored as an HDF5 file having 3 datasets: - -**GlobalReferencing** - -* **gid** (dtype: uint64, shape: N_gid) -* **population** (dtype: uint32, shape: N_gid) -* **node_id** (dtype: uint32, shape: N_gid) -* **population_names**(dtype: str, shape: N_population) - -One can search these datasets to find gid corresponding to a particular (population, node_id) pair or vice versa. The population dataset contains indices in the population_names dataset. - -The location of the mapping file is specified in the simulation_config.json as follows: - - { - "gid_mapping_file": ”” - } - -For implementations that generate the mapping at runtime, this location should be used to write the file. - ## Appendix ### Background material @@ -1401,7 +1354,7 @@ See CodeJam talks on this topic: model_processing is a string attribute of nodes allowing the specification of alternative -processing approaches in the model construction behaviour of biophysical neurons. The following values are currently defined (more will be defined in the future, as required). It is currently not valid to specify this attribute for model_type != "biophysical”. +processing approaches in the model construction behaviour of biophysical neurons. The following values are currently defined (more will be defined in the future, as required). It is currently not valid to specify this attribute for model_type != `biophysical`. For model_processing=*"fullaxon",* the biophysical neuron will construct and simulate the full axon. This is the default behaviour if model_processing is undefined for a given node. @@ -1426,4 +1379,3 @@ Allen folks to fill in For the case that the model_template follows the *bmtk* schema, the following is the expected structure of the hoc template. Allen folks to fill in - diff --git a/examples/300_cells/analyze_output.py b/examples/300_cells/analyze_output.py new file mode 100644 index 0000000..9e14e0a --- /dev/null +++ b/examples/300_cells/analyze_output.py @@ -0,0 +1,12 @@ +import sys +from bmtk.analyzer.visualization.spikes import plot_spikes + + +sys.path.append("../sim_tests/shared_components/scripts") + +from analyze_output_utils import plot_data + +if __name__ == '__main__': + + plot_data('output/membrane_potential.h5', 'mV', 'Membrane Potential', show_already=True, max_num_traces=12) + plot_spikes('network/internal_nodes.h5', 'network/internal_node_types.csv', 'output/spikes.h5', group_key='model_name') \ No newline at end of file diff --git a/examples/300_cells/simulation_config.json b/examples/300_cells/simulation_config.json index 1400f71..6de34e2 100755 --- a/examples/300_cells/simulation_config.json +++ b/examples/300_cells/simulation_config.json @@ -22,7 +22,7 @@ "v_init": -80 }, - "node_sets_file": "node_sets.json", + "node_sets_file": "$BASE_DIR/node_sets.json", "inputs": { "external_spike_trains": { diff --git a/examples/300_intfire/build_network.py b/examples/300_intfire/build_network.py index dd6341f..30692a3 100644 --- a/examples/300_intfire/build_network.py +++ b/examples/300_intfire/build_network.py @@ -7,12 +7,12 @@ # Step 1: Create a v1 mock network of 14 cells (nodes) with across 7 different cell "types" net = NetworkBuilder("v1") -net.add_nodes(N=240, pop_name='LIF_exc', location='VisL4', ei='e', +net.add_nodes(N=240, model_name='LIF_exc', location='VisL4', ei='e', model_type='point_process', # use point_process to indicate were are using point model cells model_template='nrn:IntFire1', # Tell the simulator to use the NEURON built-in IntFire1 type cell dynamics_params='IntFire1_exc_1.json') -net.add_nodes(N=60, pop_name='LIF_inh', location='VisL4', ei='i', +net.add_nodes(N=60, model_name='LIF_inh', location='VisL4', ei='i', model_type='point_process', model_template='nrn:IntFire1', dynamics_params='IntFire1_inh_1.json') @@ -46,7 +46,7 @@ def recurrent_connections(src_cells, trg_cell, n_syns): dynamics_params='instanteneousInh.json') -net.add_edges(source={'ei': 'e'}, target={'pop_name': 'LIF_inh'}, +net.add_edges(source={'ei': 'e'}, target={'model_name': 'LIF_inh'}, iterator='all_to_one', connection_rule=recurrent_connections, connection_params={'n_syns': 10}, @@ -56,7 +56,7 @@ def recurrent_connections(src_cells, trg_cell, n_syns): dynamics_params='instanteneousExc.json') -net.add_edges(source={'ei': 'e'}, target={'pop_name': 'LIF_exc'}, +net.add_edges(source={'ei': 'e'}, target={'model_name': 'LIF_exc'}, iterator='all_to_one', connection_rule=recurrent_connections, connection_params={'n_syns': 10}, @@ -78,33 +78,33 @@ def generate_positions(N, x0=0.0, x1=300.0, y0=0.0, y1=100.0): def select_source_cells(src_cells, trg_cell, n_syns): if np.random.random() > 0.1: - synapses = [n_syns if src['pop_name'] == 'tON' or src['pop_name'] == 'tOFF' else 0 for src in src_cells] + synapses = [n_syns if src['model_name'] == 'tON' or src['model_name'] == 'tOFF' else 0 for src in src_cells] else: - synapses = [n_syns if src['pop_name'] == 'tONOFF' else 0 for src in src_cells] + synapses = [n_syns if src['model_name'] == 'tONOFF' else 0 for src in src_cells] return synapses lgn = NetworkBuilder("lgn") pos_x, pos_y = generate_positions(30) -lgn.add_nodes(N=30, pop_name='tON', ei='e', location='LGN', +lgn.add_nodes(N=30, model_name='tON', ei='e', location='LGN', x=pos_x, y=pos_y, model_type='virtual') pos_x, pos_y = generate_positions(30) -lgn.add_nodes(N=30, pop_name='tOFF', ei='e', location='LGN', +lgn.add_nodes(N=30, model_name='tOFF', ei='e', location='LGN', x=pos_x, y=pos_y, model_type='virtual') pos_x, pos_y = generate_positions(30) -lgn.add_nodes(N=30, pop_name='tONOFF', ei='e', location='LGN', +lgn.add_nodes(N=30, model_name='tONOFF', ei='e', location='LGN', x=pos_x, y=pos_y, model_type='virtual') -lgn.add_edges(source=lgn.nodes(), target=net.nodes(pop_name='LIF_exc'), +lgn.add_edges(source=lgn.nodes(), target=net.nodes(model_name='LIF_exc'), iterator='all_to_one', connection_rule=select_source_cells, connection_params={'n_syns': 10}, @@ -113,7 +113,7 @@ def select_source_cells(src_cells, trg_cell, n_syns): delay=2.0, dynamics_params='instanteneousExc.json') -lgn.add_edges(source=lgn.nodes(), target=net.nodes(pop_name='LIF_inh'), +lgn.add_edges(source=lgn.nodes(), target=net.nodes(model_name='LIF_inh'), iterator='all_to_one', connection_rule=select_source_cells, connection_params={'n_syns': 10}, @@ -127,16 +127,16 @@ def select_source_cells(src_cells, trg_cell, n_syns): tw = NetworkBuilder("tw") -tw.add_nodes(N=30, pop_name='TW', ei='e', location='TW', model_type='virtual') +tw.add_nodes(N=30, model_name='TW', ei='e', location='TW', model_type='virtual') -tw.add_edges(source=tw.nodes(), target=net.nodes(pop_name='LIF_exc'), +tw.add_edges(source=tw.nodes(), target=net.nodes(model_name='LIF_exc'), connection_rule=5, syn_weight=0.01, weight_function='wmax', delay=2.0, dynamics_params='instanteneousExc.json') -tw.add_edges(source=tw.nodes(), target=net.nodes(pop_name='LIF_inh'), +tw.add_edges(source=tw.nodes(), target=net.nodes(model_name='LIF_inh'), connection_rule=5, syn_weight=0.02, weight_function='wmax', diff --git a/examples/300_intfire/network/lgn_node_types.csv b/examples/300_intfire/network/lgn_node_types.csv index 2ead28a..bf27cc6 100644 --- a/examples/300_intfire/network/lgn_node_types.csv +++ b/examples/300_intfire/network/lgn_node_types.csv @@ -1,4 +1,4 @@ -node_type_id model_type ei location pop_name +node_type_id model_type ei location model_name 100 virtual e LGN tON 101 virtual e LGN tOFF 102 virtual e LGN tONOFF diff --git a/examples/300_intfire/network/lgn_v1_edge_types.csv b/examples/300_intfire/network/lgn_v1_edge_types.csv index 21d9f24..7bce56b 100644 --- a/examples/300_intfire/network/lgn_v1_edge_types.csv +++ b/examples/300_intfire/network/lgn_v1_edge_types.csv @@ -1,3 +1,3 @@ edge_type_id target_query source_query delay weight_function syn_weight dynamics_params -100 pop_name=='LIF_exc' * 2.0 wmax 0.0045 instanteneousExc.json -101 pop_name=='LIF_inh' * 2.0 wmax 0.0015 instanteneousExc.json +100 model_name=='LIF_exc' * 2.0 wmax 0.0045 instanteneousExc.json +101 model_name=='LIF_inh' * 2.0 wmax 0.0015 instanteneousExc.json diff --git a/examples/300_intfire/network/tw_node_types.csv b/examples/300_intfire/network/tw_node_types.csv index e9ad70a..a76afaf 100644 --- a/examples/300_intfire/network/tw_node_types.csv +++ b/examples/300_intfire/network/tw_node_types.csv @@ -1,2 +1,2 @@ -node_type_id model_type ei location pop_name +node_type_id model_type ei location model_name 100 virtual e TW TW diff --git a/examples/300_intfire/network/tw_v1_edge_types.csv b/examples/300_intfire/network/tw_v1_edge_types.csv index b3dbdd4..14068d3 100644 --- a/examples/300_intfire/network/tw_v1_edge_types.csv +++ b/examples/300_intfire/network/tw_v1_edge_types.csv @@ -1,3 +1,3 @@ edge_type_id target_query source_query delay weight_function syn_weight dynamics_params -100 pop_name=='LIF_exc' * 2.0 wmax 0.01 instanteneousExc.json -101 pop_name=='LIF_inh' * 2.0 wmax 0.02 instanteneousExc.json +100 model_name=='LIF_exc' * 2.0 wmax 0.01 instanteneousExc.json +101 model_name=='LIF_inh' * 2.0 wmax 0.02 instanteneousExc.json diff --git a/examples/300_intfire/network/v1_node_types.csv b/examples/300_intfire/network/v1_node_types.csv index ad51c61..900fe10 100644 --- a/examples/300_intfire/network/v1_node_types.csv +++ b/examples/300_intfire/network/v1_node_types.csv @@ -1,3 +1,3 @@ -node_type_id ei pop_name location model_template model_type dynamics_params +node_type_id ei model_name location model_template model_type dynamics_params 100 e LIF_exc VisL4 nrn:IntFire1 point_process IntFire1_exc_1.json 101 i LIF_inh VisL4 nrn:IntFire1 point_process IntFire1_inh_1.json diff --git a/examples/300_intfire/network/v1_v1_edge_types.csv b/examples/300_intfire/network/v1_v1_edge_types.csv index 269e0f1..4e83484 100644 --- a/examples/300_intfire/network/v1_v1_edge_types.csv +++ b/examples/300_intfire/network/v1_v1_edge_types.csv @@ -1,5 +1,5 @@ edge_type_id target_query source_query delay weight_function syn_weight dynamics_params 100 model_type=='point_process'&ei=='i' ei=='i' 2.0 wmax 0.01 instanteneousInh.json 101 model_type=='point_process'&ei=='e' ei=='i' 2.0 wmax 0.15 instanteneousInh.json -102 pop_name=='LIF_inh' ei=='e' 2.0 wmax 0.3 instanteneousExc.json -103 pop_name=='LIF_exc' ei=='e' 2.0 wmax 0.002 instanteneousExc.json +102 model_name=='LIF_inh' ei=='e' 2.0 wmax 0.3 instanteneousExc.json +103 model_name=='LIF_exc' ei=='e' 2.0 wmax 0.002 instanteneousExc.json diff --git a/examples/300_intfire/plot_spikes.py b/examples/300_intfire/plot_spikes.py index 785cbfe..23557d5 100644 --- a/examples/300_intfire/plot_spikes.py +++ b/examples/300_intfire/plot_spikes.py @@ -1,3 +1,3 @@ from bmtk.analyzer.visualization.spikes import plot_spikes -plot_spikes('network/v1_nodes.h5', 'network/v1_node_types.csv', 'output/spikes.h5', group_key='pop_name') +plot_spikes('network/v1_nodes.h5', 'network/v1_node_types.csv', 'output/spikes.h5', group_key='model_name') diff --git a/examples/300_intfire/simulation_config.json b/examples/300_intfire/simulation_config.json index 3454de9..99839b3 100644 --- a/examples/300_intfire/simulation_config.json +++ b/examples/300_intfire/simulation_config.json @@ -1,7 +1,8 @@ { "manifest": { - "$OUTPUT_DIR": "./output", - "$INPUT_DIR": "./inputs", + "$BASE_DIR": ".", + "$OUTPUT_DIR": "$BASE_DIR/output", + "$INPUT_DIR": "$BASE_DIR/inputs", "$COMPONENT_DIR": "../shared_components" }, @@ -16,7 +17,7 @@ "target_simulator":"NEURON", - "node_sets_file": "node_sets.json", + "node_sets_file": "$BASE_DIR/node_sets.json", "conditions": { "celsius": 34.0, diff --git a/examples/300_intfire/test_pynn.py b/examples/300_intfire/test_pynn.py new file mode 100644 index 0000000..de6a856 --- /dev/null +++ b/examples/300_intfire/test_pynn.py @@ -0,0 +1,7 @@ +from pyNN.serialization import import_from_sonata, load_sonata_simulation_plan +import pyNN.neuron as sim + +simulation_plan = load_sonata_simulation_plan("simulation_config.json") +simulation_plan.setup(sim) +net = import_from_sonata("circuit_config.json", sim) +simulation_plan.execute(net) diff --git a/examples/300_pointneurons/build_network.py b/examples/300_pointneurons/build_network.py new file mode 100644 index 0000000..ee8a562 --- /dev/null +++ b/examples/300_pointneurons/build_network.py @@ -0,0 +1,157 @@ +import os +import numpy as np + +from bmtk.builder import NetworkBuilder +from bmtk.builder.bionet import SWCReader +from bmtk.utils.io.spike_trains import PoissonSpikesGenerator +from bmtk.builder.aux.node_params import positions_columinar, xiter_random + +build_recurrent_edges = True + +print('Building internal network') +# List of non-virtual cell models +cell_models = [ + { + 'model_name': 'Scnn1a', 'ei': 'e', + 'model_template': 'nest:iaf_psc_alpha', + 'dynamics_params': '472363762_point.json' + }, + { + 'model_name': 'Rorb', 'ei': 'e', + 'model_template': 'nest:iaf_psc_alpha', + 'dynamics_params': '473863510_point.json' + }, + { + 'model_name': 'Nr5a1', 'ei': 'e', + 'model_template': 'nest:iaf_psc_alpha', + 'dynamics_params': '473863035_point.json' + }, + { + 'model_name': 'PV1', 'ei': 'i', + 'model_template': 'nest:iaf_psc_alpha', + 'dynamics_params': '472912177_point.json' + }, + { + 'model_name': 'PV2', 'ei': 'i', + 'model_template': 'nest:iaf_psc_alpha', + 'dynamics_params': '473862421_point.json' + } +] + +''' +morphologies = {p['model_name']: SWCReader(os.path.join('../shared_components/morphologies', + '{}.swc'.format(p['morphology']))) + for p in cell_models} +def build_edges(src, trg, sections=['basal', 'apical'], dist_range=[50.0, 150.0]): + """Function used to randomly assign a synaptic location based on the section (soma, basal, apical) and an + arc-length dist_range from the soma. This function should be passed into the network and called during the build + process. + + :param src: source cell (dict) + :param trg: target cell (dict) + :param sections: list of target cell sections to synapse onto + :param dist_range: range (distance from soma center) to place + :return: + """ + # Get morphology and soma center for the target cell + swc_reader = morphologies[trg['model_name']] + target_coords = [trg['x'], trg['y'], trg['z']] + + sec_ids, sec_xs = swc_reader.choose_sections(sections, dist_range) # randomly choose sec_ids + coords = swc_reader.get_coord(sec_ids, sec_xs, soma_center=target_coords) # get coords of sec_ids + dist = swc_reader.get_dist(sec_ids) + swctype = swc_reader.get_type(sec_ids) + return sec_ids, sec_xs, coords[0][0], coords[0][1], coords[0][2], dist[0], swctype[0] +''' + +# Build a network of 300 biophysical cells to simulate +internal = NetworkBuilder("internal") +for i, model_props in enumerate(cell_models): + n_cells = 80 if model_props['ei'] == 'e' else 30 # 80% excitatory, 20% inhib + + # Randomly get positions uniformly distributed in a column + positions = positions_columinar(N=n_cells, center=[0, 10.0, 0], max_radius=50.0, height=200.0) + + internal.add_nodes(N=n_cells, + x=positions[:, 0], y=positions[:, 1], z=positions[:, 2], + rotation_angle_yaxis=xiter_random(N=n_cells, min_x=0.0, max_x=2 * np.pi), # randomly rotate y axis + model_type='point_process', + **model_props) + + + +if build_recurrent_edges: + def n_connections(src, trg, prob=0.5, min_syns=2, max_syns=7): + return 0 if np.random.uniform() > prob else np.random.randint(min_syns, max_syns) + + # exc --> exc connections + cm = internal.add_edges(source={'ei': 'e'}, target={'ei': 'e'}, + connection_rule=lambda s, t: np.random.binomial(1, 0.2), + dynamics_params='ExcToExc.json', + model_template='static_synapse', + # syn_weight=2.5, + delay=2.0) + cm.add_properties('syn_weight', rule=2.5, dtypes=np.float) + + # exc --> inh connections + cm = internal.add_edges(source={'ei': 'e'}, target={'ei': 'i'}, + connection_rule=lambda s, t: np.random.binomial(1, 0.5), + dynamics_params='ExcToInh.json', + model_template='static_synapse', + # syn_weight=7.0, + delay=2.0) + cm.add_properties('syn_weight', rule=7.0, dtypes=np.float) + + # inh --> exc connections + cm = internal.add_edges(source={'ei': 'i'}, target={'ei': 'e'}, + connection_rule=lambda s, t: np.random.binomial(1, 0.5), + #connection_rule=lambda *_: np.random.randint(0, 4), + dynamics_params='InhToExc.json', + model_template='static_synapse', + # syn_weight=-7.5, + delay=2.0) + cm.add_properties('syn_weight', rule=-7.5, dtypes=np.float) + + # inh --> inh connections + cm = internal.add_edges(source={'ei': 'i'}, target={'ei': 'i'}, + connection_rule=lambda s, t: np.random.binomial(1, 0.5), + dynamics_params='InhToInh.json', + model_template='static_synapse', + # syn_weight=-3.0, + delay=2.0) + cm.add_properties('syn_weight', rule=-3.0, dtypes=np.float) + + +internal.build() + +print('Saving internal') +internal.save(output_dir='network') + + +print('Building external connections') +external = NetworkBuilder("external") +external.add_nodes(N=100, model_type='virtual', ei='e') +cm = external.add_edges(target=internal.nodes(ei='e'), source=external.nodes(), + connection_rule=lambda *_: np.random.binomial(1, .7), + dynamics_params='ExcToExc.json', + model_template='static_synapse', + # syn_weight=50.0, + delay=2.0) +cm.add_properties('syn_weight', rule=50.0, dtypes=np.float) + + +cm = external.add_edges(target=internal.nodes(ei='i'), source=external.nodes(), + connection_rule=lambda *_: np.random.binomial(1, .7), + dynamics_params='ExcToInh.json', + model_template='static_synapse', + # syn_weight=75.0, + delay=2.0) +cm.add_properties('syn_weight', rule=65.0, dtypes=np.float) + + +external.build() + +print('Saving external') +external.save(output_dir='network') + + diff --git a/examples/300_pointneurons/circuit_config.json b/examples/300_pointneurons/circuit_config.json new file mode 100755 index 0000000..7090290 --- /dev/null +++ b/examples/300_pointneurons/circuit_config.json @@ -0,0 +1,38 @@ +{ + "manifest": { + "$NETWORK_DIR": "./network", + "$COMPONENT_DIR": "../shared_components/nest_models" + }, + + "components": { + "point_neuron_models_dir": "$COMPONENT_DIR/cell_models", + "synaptic_models_dir": "$COMPONENT_DIR/synaptic_models" + }, + + "networks": { + "nodes": [ + { + "nodes_file": "$NETWORK_DIR/internal_nodes.h5", + "node_types_file": "$NETWORK_DIR/internal_node_types.csv" + }, + { + "nodes_file": "$NETWORK_DIR/external_nodes.h5", + "node_types_file": "$NETWORK_DIR/external_node_types.csv" + } + ], + + "edges": [ + { + "edges_file": "$NETWORK_DIR/internal_internal_edges.h5", + "edge_types_file": "$NETWORK_DIR/internal_internal_edge_types.csv", + "enabled": true + }, + { + "edges_file": "$NETWORK_DIR/external_internal_edges.h5", + "edge_types_file": "$NETWORK_DIR/external_internal_edge_types.csv" + } + ] + }, + + "target_simulator": "NEST" +} diff --git a/examples/300_pointneurons/config.json b/examples/300_pointneurons/config.json new file mode 100755 index 0000000..890caa3 --- /dev/null +++ b/examples/300_pointneurons/config.json @@ -0,0 +1,4 @@ +{ + "network": "./circuit_config.json", + "simulation": "./simulation_config.json" +} diff --git a/examples/300_pointneurons/generate_spikes.py b/examples/300_pointneurons/generate_spikes.py new file mode 100644 index 0000000..aaf1739 --- /dev/null +++ b/examples/300_pointneurons/generate_spikes.py @@ -0,0 +1,7 @@ +import os +from bmtk.utils.io.spike_trains import PoissonSpikesGenerator + +if not os.path.exists('inputs'): + os.mkdir('inputs') +psg = PoissonSpikesGenerator(range(100), 14.0, tstop=3000.0, precision=3) +psg.to_hdf5('inputs/external_spike_trains.h5') \ No newline at end of file diff --git a/examples/300_pointneurons/inputs/external_spike_trains.h5 b/examples/300_pointneurons/inputs/external_spike_trains.h5 new file mode 100644 index 0000000..106a377 Binary files /dev/null and b/examples/300_pointneurons/inputs/external_spike_trains.h5 differ diff --git a/examples/300_pointneurons/network/external_internal_edge_types.csv b/examples/300_pointneurons/network/external_internal_edge_types.csv new file mode 100644 index 0000000..c3fa238 --- /dev/null +++ b/examples/300_pointneurons/network/external_internal_edge_types.csv @@ -0,0 +1,3 @@ +edge_type_id target_query source_query delay dynamics_params model_template +100 ei=='e' * 2.0 ExcToExc.json nest:static_synapse +101 ei=='i' * 2.0 ExcToInh.json nest:static_synapse diff --git a/examples/300_pointneurons/network/external_internal_edges.h5 b/examples/300_pointneurons/network/external_internal_edges.h5 new file mode 100644 index 0000000..344d118 Binary files /dev/null and b/examples/300_pointneurons/network/external_internal_edges.h5 differ diff --git a/examples/300_pointneurons/network/external_node_types.csv b/examples/300_pointneurons/network/external_node_types.csv new file mode 100644 index 0000000..07b4593 --- /dev/null +++ b/examples/300_pointneurons/network/external_node_types.csv @@ -0,0 +1,2 @@ +node_type_id model_type ei +100 virtual e diff --git a/examples/300_pointneurons/network/external_nodes.h5 b/examples/300_pointneurons/network/external_nodes.h5 new file mode 100644 index 0000000..d3375c6 Binary files /dev/null and b/examples/300_pointneurons/network/external_nodes.h5 differ diff --git a/examples/300_pointneurons/network/internal_internal_edge_types.csv b/examples/300_pointneurons/network/internal_internal_edge_types.csv new file mode 100644 index 0000000..6ea7f24 --- /dev/null +++ b/examples/300_pointneurons/network/internal_internal_edge_types.csv @@ -0,0 +1,5 @@ +edge_type_id target_query source_query delay dynamics_params model_template +100 ei=='e' ei=='e' 2.0 ExcToExc.json nest:static_synapse +101 ei=='i' ei=='e' 2.0 ExcToInh.json nest:static_synapse +102 ei=='e' ei=='i' 2.0 InhToExc.json nest:static_synapse +103 ei=='i' ei=='i' 2.0 InhToInh.json nest:static_synapse diff --git a/examples/300_pointneurons/network/internal_internal_edges.h5 b/examples/300_pointneurons/network/internal_internal_edges.h5 new file mode 100644 index 0000000..3c6390b Binary files /dev/null and b/examples/300_pointneurons/network/internal_internal_edges.h5 differ diff --git a/examples/300_pointneurons/network/internal_node_types.csv b/examples/300_pointneurons/network/internal_node_types.csv new file mode 100644 index 0000000..2209b58 --- /dev/null +++ b/examples/300_pointneurons/network/internal_node_types.csv @@ -0,0 +1,6 @@ +node_type_id ei model_template model_type dynamics_params model_name +104 i nest:iaf_psc_alpha point_process 473862421_point.json PV2 +100 e nest:iaf_psc_alpha point_process 472363762_point.json Scnn1a +101 e nest:iaf_psc_alpha point_process 473863510_point.json Rorb +102 e nest:iaf_psc_alpha point_process 473863035_point.json Nr5a1 +103 i nest:iaf_psc_alpha point_process 472912177_point.json PV1 diff --git a/examples/300_pointneurons/network/internal_nodes.h5 b/examples/300_pointneurons/network/internal_nodes.h5 new file mode 100644 index 0000000..d039746 Binary files /dev/null and b/examples/300_pointneurons/network/internal_nodes.h5 differ diff --git a/examples/300_pointneurons/node_sets.json b/examples/300_pointneurons/node_sets.json new file mode 100644 index 0000000..07ea987 --- /dev/null +++ b/examples/300_pointneurons/node_sets.json @@ -0,0 +1,10 @@ +{ + "external": { + "population": "external" + }, + + "recorded_cells": { + "population": "internal", + "node_id": [0, 80, 160, 240, 270] + } +} \ No newline at end of file diff --git a/examples/300_pointneurons/output/membrane_potential.h5 b/examples/300_pointneurons/output/membrane_potential.h5 new file mode 100644 index 0000000..8d26757 Binary files /dev/null and b/examples/300_pointneurons/output/membrane_potential.h5 differ diff --git a/examples/300_pointneurons/output/spikes.h5 b/examples/300_pointneurons/output/spikes.h5 new file mode 100644 index 0000000..06da4dc Binary files /dev/null and b/examples/300_pointneurons/output/spikes.h5 differ diff --git a/examples/300_pointneurons/plot_potential.py b/examples/300_pointneurons/plot_potential.py new file mode 100644 index 0000000..b60fae1 --- /dev/null +++ b/examples/300_pointneurons/plot_potential.py @@ -0,0 +1,87 @@ +""" +A script for plotting cell variable (eg voltage, calcium) traces from a SONATA hdf5 file. + +To plot a single ouput: + $ python plot_potential.py path/to/cell_var.h5 + +If you want to perform a side-by-side comparison of multiple output files: + $ python plot_potential.py cell_var_0.h5 cell_var_1.h5 ... cell_var_n.h5 +cell_var_0.h5 is used as the base-line for plotting number of gid/subplots and time-traces. + +""" + +import os +import matplotlib.pyplot as plt +from optparse import OptionParser +from bmtk.utils.cell_vars import CellVarsFile + + +def plot_vars(file_names, cell_var='v', gid_list=[], t_min=None, t_max=None): + """Plots variable traces for a SONATA h5 file. If multiple spike files are specified will do a side-by-side + comparsion for each gid. + + :param file_names: list of cell_var file names + :param cell_var: cell variable to plot trace + :param gid_list: used to set what gid/subplots to show (if empty list just plot all possible gids) + """ + # convert to list if single spike file passed in + file_names = [file_names] if not isinstance(file_names, (tuple, list)) else file_names + assert(len(file_names) > 0) + + # Use bmtk to parse the cell-var files + cell_var_files = [] + for fn in file_names: + try: + cell_var_files.append(CellVarsFile(fn, h5_root="/report/internal")) + except KeyError: + cell_var_files.append(CellVarsFile(fn)) + + # get first spike file and properties + cvf_base = cell_var_files[0] + xlim = [t_min or cvf_base.time_trace[0], t_max or cvf_base.time_trace[-1]] # Use the same x-axis across all subplots + gid_list = cvf_base.gids if not gid_list else gid_list # if gid_list is None just get all gids in first file + n_cells = len(cvf_base.gids) + + fig, ax = plt.subplots(n_cells, 1, figsize=(10, 10)) + for subplot, gid in enumerate(gid_list): + for i, cvf in enumerate(cell_var_files): + # plot all traces + ax[subplot].plot(cvf.time_trace, cvf.data(gid, cell_var), label=file_names[i]) + + ax[subplot].yaxis.set_label_position("right") + ax[subplot].set_ylabel('gid {}'.format(gid), fontsize='xx-small') + ax[subplot].set_xlim(xlim) + if subplot + 1 < n_cells: + # remove x-axis labels on all but the last plot + ax[subplot].set_xticklabels([]) + else: + # Use the last plot to get the legend + handles, labels = ax[subplot].get_legend_handles_labels() + fig.legend(handles, labels, loc='upper right') + + plt.show() + + +if __name__ == '__main__': + parser = OptionParser(usage="Usage: python %prog [options] .h5 [.h5 ...]") + parser.add_option('--variable', type=str, dest='cell_var', default='V_m', help='Cell variable to compare (v, cai, etc)') + parser.add_option('--gids', type='string', dest='gids', default=[], + action='callback', + callback=lambda option, opt, value, parser: setattr(parser.values, option.dest, [int(v) for v in value.split(',')]), + help='comma seperated list of gids to plot') + parser.add_option('--tmin', type=float, dest='t_min', default=None) + parser.add_option('--tmax', type=float, dest='t_max', default=None) + options, args = parser.parse_args() + + if len(args) == 0: + # If no file is specified see if there is a output/membrane_potential.h5 file to plot membrane voltage + if not os.path.exists('output/membrane_potential.h5'): + raise Exception('Please specifiy hdf5 file to read in arguments. Exiting!') + else: + plot_vars('output/membrane_potential.h5', cell_var=options.cell_var, t_min=options.t_min, t_max=options.t_max) + else: + plot_vars(file_names=args, cell_var=options.cell_var, gid_list=options.gids, t_min=options.t_min, t_max=options.t_max) + if len(args) == 1: + plt.savefig(args[0].replace(".h5", ".png")) + else: + plt.savefig("comparison_{}.png".format(options.cell_var)) diff --git a/examples/300_pointneurons/plot_spikes.py b/examples/300_pointneurons/plot_spikes.py new file mode 100644 index 0000000..999c3a9 --- /dev/null +++ b/examples/300_pointneurons/plot_spikes.py @@ -0,0 +1,3 @@ +from bmtk.analyzer.visualization.spikes import plot_spikes + +plot_spikes('network/internal_nodes.h5', 'network/internal_node_types.csv', 'output/spikes.h5', group_key='model_name') diff --git a/examples/300_pointneurons/run_PyNN.py b/examples/300_pointneurons/run_PyNN.py new file mode 100644 index 0000000..0b1acdd --- /dev/null +++ b/examples/300_pointneurons/run_PyNN.py @@ -0,0 +1,7 @@ +from pyNN.serialization import import_from_sonata, load_sonata_simulation_plan +import pyNN.nest as sim + +simulation_plan = load_sonata_simulation_plan("simulation_config.json") +simulation_plan.setup(sim) +net = import_from_sonata("circuit_config.json", sim) +simulation_plan.execute(net) diff --git a/examples/300_pointneurons/run_pointnet.py b/examples/300_pointneurons/run_pointnet.py new file mode 100755 index 0000000..49a015f --- /dev/null +++ b/examples/300_pointneurons/run_pointnet.py @@ -0,0 +1,18 @@ +import os, sys +from bmtk.simulator import pointnet +from bmtk.analyzer.visualization.spikes import plot_spikes + + +def main(config_file): + configure = pointnet.Config.from_json(config_file) + configure.build_env() + + graph = pointnet.PointNetwork.from_config(configure) + sim = pointnet.PointSimulator.from_config(configure, graph) + sim.run() + + # plot_spikes('network/internal_nodes.h5', 'network/internal_node_types.csv', 'output/spikes.h5', group_key='model_name') + + +if __name__ == '__main__': + main('config.json') diff --git a/examples/300_pointneurons/simulation_config.json b/examples/300_pointneurons/simulation_config.json new file mode 100755 index 0000000..0f7e15f --- /dev/null +++ b/examples/300_pointneurons/simulation_config.json @@ -0,0 +1,49 @@ +{ + "manifest": { + "$BASE_DIR": ".", + "$OUTPUT_DIR": "$BASE_DIR/output", + "$INPUT_DIR": "$BASE_DIR/inputs" + }, + + "run": { + "tstop": 1500.0, + "dt": 0.001, + "nsteps_block": 5000 + }, + + "target_simulator":"NEST", + + "network": "$BASE_DIR/circuit_config.json", + + "conditions": { + "celsius": 34.0, + "v_init": -80 + }, + + "node_sets_file": "node_sets.json", + + "inputs": { + "external_spike_trains": { + "input_type": "spikes", + "module": "h5", + "input_file": "$INPUT_DIR/external_spike_trains.h5", + "node_set": "external" + } + }, + + "output": { + "log_file": "log.txt", + "output_dir": "$OUTPUT_DIR", + "spikes_file": "spikes.h5", + "spikes_sort_order": "time" + }, + + "reports": { + "membrane_potential": { + "cells": "recorded_cells", + "variable_name": "V_m", + "module": "membrane_report", + "sections": "multimeter_report" + } + } +} diff --git a/examples/5_cells_iclamp/build_network.py b/examples/5_cells_iclamp/build_network.py index 5dd4747..13a916e 100755 --- a/examples/5_cells_iclamp/build_network.py +++ b/examples/5_cells_iclamp/build_network.py @@ -5,28 +5,23 @@ cell_models = [ { 'model_name': 'Scnn1a', 'x': [0.0], 'y': [0.0], 'z': [0.0], 'ei': 'e', 'morphology': 'Scnn1a_473845048_m', - 'model_template': 'ctdb:Biophys1.hoc', - 'dynamics_params': '472363762_fit.json', + 'model_template': 'nml:Cell_472363762.cell.nml' }, { 'model_name': 'Rorb', 'x': [200.0], 'y': [0.0], 'z': [0.0], 'ei': 'e', 'morphology': 'Rorb_325404214_m', - 'model_template': 'ctdb:Biophys1.hoc', - 'dynamics_params': '473863510_fit.json', + 'model_template': 'nml:Cell_473863510.cell.nml' }, { 'model_name': 'Nr5a1', 'x': [-200.0], 'y': [0.0], 'z': [0.0], 'ei': 'e', 'morphology': 'Nr5a1_471087815_m', - 'model_template': 'ctdb:Biophys1.hoc', - 'dynamics_params': '473863035_fit.json', + 'model_template': 'nml:Cell_473863035.cell.nml' }, { 'model_name': 'PV1', 'x': [0.0], 'y': [200.0], 'z': [0.0], 'ei': 'i', 'morphology': 'Pvalb_470522102_m', - 'model_template': 'ctdb:Biophys1.hoc', - 'dynamics_params': '472912177_fit.json', + 'model_template': 'nml:Cell_472912177.cell.nml' }, { 'model_name': 'PV2', 'x': [0.0], 'y': [-200.0], 'z': [0.0], 'ei': 'i', 'morphology': 'Pvalb_469628681_m', - 'model_template': 'ctdb:Biophys1.hoc', - 'dynamics_params': '473862421_fit.json', + 'model_template': 'nml:Cell_473862421.cell.nml' } ] diff --git a/examples/5_cells_iclamp/circuit_config.json b/examples/5_cells_iclamp/circuit_config.json index d7cd5b3..32e5054 100644 --- a/examples/5_cells_iclamp/circuit_config.json +++ b/examples/5_cells_iclamp/circuit_config.json @@ -8,7 +8,7 @@ "components": { "morphologies_dir": "$COMPONENT_DIR/morphologies", "mechanisms_dir":"$COMPONENT_DIR/mechanisms", - "biophysical_neuron_models_dir": "$COMPONENT_DIR/biophysical_neuron_templates/json" + "biophysical_neuron_models_dir": "$COMPONENT_DIR/biophysical_neuron_templates/nml" }, "networks": { diff --git a/examples/5_cells_iclamp/network/biophysical_node_types.csv b/examples/5_cells_iclamp/network/biophysical_node_types.csv index df7ee1e..d348ebd 100755 --- a/examples/5_cells_iclamp/network/biophysical_node_types.csv +++ b/examples/5_cells_iclamp/network/biophysical_node_types.csv @@ -1,6 +1,6 @@ -node_type_id dynamics_params model_processing model_type model_template morphology ei model_name -104 473862421_fit.json aibs_perisomatic biophysical ctdb:Biophys1.hoc Pvalb_469628681_m i PV2 -100 472363762_fit.json aibs_perisomatic biophysical ctdb:Biophys1.hoc Scnn1a_473845048_m e Scnn1a -101 473863510_fit.json aibs_perisomatic biophysical ctdb:Biophys1.hoc Rorb_325404214_m e Rorb -102 473863035_fit.json aibs_perisomatic biophysical ctdb:Biophys1.hoc Nr5a1_471087815_m e Nr5a1 -103 472912177_fit.json aibs_perisomatic biophysical ctdb:Biophys1.hoc Pvalb_470522102_m i PV1 +node_type_id model_processing model_type model_template morphology ei model_name +104 aibs_perisomatic biophysical nml:Cell_473862421.cell.nml Pvalb_469628681_m i PV2 +100 aibs_perisomatic biophysical nml:Cell_472363762.cell.nml Scnn1a_473845048_m e Scnn1a +101 aibs_perisomatic biophysical nml:Cell_473863510.cell.nml Rorb_325404214_m e Rorb +102 aibs_perisomatic biophysical nml:Cell_473863035.cell.nml Nr5a1_471087815_m e Nr5a1 +103 aibs_perisomatic biophysical nml:Cell_472912177.cell.nml Pvalb_470522102_m i PV1 diff --git a/examples/5_cells_iclamp/simulation_config.json b/examples/5_cells_iclamp/simulation_config.json index ad58620..2f5b39c 100644 --- a/examples/5_cells_iclamp/simulation_config.json +++ b/examples/5_cells_iclamp/simulation_config.json @@ -23,7 +23,7 @@ "v_init": -80 }, - "node_sets_file": "node_sets.json", + "node_sets_file": "$BASE_DIR/node_sets.json", "inputs": { "current_clamp_1": { diff --git a/examples/layer4_sample/README.md b/examples/layer4_sample/README.md new file mode 100644 index 0000000..3331669 --- /dev/null +++ b/examples/layer4_sample/README.md @@ -0,0 +1,31 @@ +# Layer 4 (subsampled) network + +Based on the mouse V1 layer4 network as described in [Arkhipov 2018](https://www.biorxiv.org/content/10.1101/292839v1). +Includes a ~450 cells (1% of the original 45,000) recurrently connected V1 network with mixed compartmental and point-process +models. Plus a network of feedforward LGN virtual cells that is used to drive network activity + +## Directory structure +* ./network/ - V1/L4 and LGN SONATA network files +* [simulation|circuit]_config.json - SONATA configuration files that specifies simulation parameters +* run_bionet.py - BMTK script for running a simulation +* generate_spikes.py - A script to generate LGN spike trains to drive the network. +* build_l4.py - BMTK script to generate SONATA V1/L4 network files +* build_lgn.py - BMTK script to generate SONATA LGN network files + + +### To run a simulation +Install BMTK with the NEURON and run the following +```python +$ python run_bionet.py config.json +``` + +### To rebuild the network +The easiest way to regenerate or rebuild the network is to change and rerun the ```build_l4.py``` and ``build_lgn.py``` +scripts. Running them will overwrite the _network_ directory so be careful. Also the rules use a bit or randomness +when determining the number/placement of synapses so don't expect the same results every time you rebuild and rerun +the network. + +To make the simulation capable easier to run the network was built with only 1% of the actual 45,000 cells. If you want +to try the full network you can open up ```build_l4.py``` and change the *PERCENTAGE_CELLS* variable. However be warned, +expect at least 4+ hours to build and save the network. And simulating the full network will usually not work without +access to a HPC cluster. diff --git a/examples/layer4_sample/build_helpers.py b/examples/layer4_sample/build_helpers.py new file mode 100644 index 0000000..9ba9bba --- /dev/null +++ b/examples/layer4_sample/build_helpers.py @@ -0,0 +1,287 @@ +import math +import random +import csv +import numpy as np + +""""Functions used by build_l4.py""" + + +def lerp(v0, v1, t): + return v0 * (1.0 - t) + v1 * t + + +def distance_weight(delta_p, w_min, w_max, r_max): + r = np.linalg.norm(delta_p) + if r >= r_max: + return 0.0 + else: + return lerp(w_max, w_min, r / r_max) + + +def orientation_tuning_weight(tuning1, tuning2, w_min, w_max): + + # 0-180 is the same as 180-360, so just modulo by 180 + delta_tuning = math.fmod(abs(tuning1 - tuning2), 180.0) + + # 90-180 needs to be flipped, then normalize to 0-1 + delta_tuning = delta_tuning if delta_tuning < 90.0 else 180.0 - delta_tuning + + # t = delta_tuning / 90.0 + return lerp(w_max, w_min, delta_tuning / 90.0) + + +def distance_tuning_connection_handler(source, target, d_weight_min, d_weight_max, d_max, t_weight_min, + t_weight_max, nsyn_min, nsyn_max): + # Avoid self-connections.n_nodes + sid = source.node_id + tid = target.node_id + if sid == tid: + if sid % 100 == 0: + print "processing connections for node", sid + return None + + # first create weights by euclidean distance between cells + # DO NOT use PERIODIC boundary conditions in x and y! + dw = distance_weight(np.array([source['x'], source['y']]) - np.array([target['x'], target['y']]), d_weight_min, + d_weight_max, d_max) + + # drop the connection if the weight is too low + if dw <= 0: + return None + + # next create weights by orientation tuning [ aligned, misaligned ] --> [ 1, 0 ] + # Check that the orientation tuning property exists for both cells; otherwise, + # ignore the orientation tuning. + if source['tuning_angle'] > 0 and target['tuning_angle'] > 0: + tw = dw * orientation_tuning_weight(source['tuning_angle'], + target['tuning_angle'], + t_weight_min, t_weight_max) + else: + tw = dw + + # filter out nodes by treating the weight as a probability of connection + if random.random() > tw: + return None + + # Add the number of synapses for every connection. + # It is probably very useful to take this out into a separate function. + + tmp_nsyn = random.randint(nsyn_min, nsyn_max) + return tmp_nsyn + + +def distance_connection_handler(source, target, d_weight_min, d_weight_max, d_max, nsyn_min, nsyn_max): + # Avoid self-connections. + sid = source.node_id + tid = target.node_id + + if sid == tid: + if sid % 100 == 0: + print "processing connections for node", sid + return None + + # first create weights by euclidean distance between cells + # DO NOT use PERIODIC boundary conditions in x and y! + dw = distance_weight(np.array([source['x'], source['y']]) - np.array([target['x'], target['y']]), d_weight_min, + d_weight_max, d_max) + + # drop the connection if the weight is too low + if dw <= 0: + return None + + # filter out nodes by treating the weight as a probability of connection + if random.random() > dw: + return None + + # Add the number of synapses for every connection. + # It is probably very useful to take this out into a separate function. + tmp_nsyn = random.randint(nsyn_min, nsyn_max) + return tmp_nsyn + + +def generate_random_positions(N, center, height, radius_outer, radius_inner): + """ + + :param N: umber of positions to generate + :param center: center of the cylinder (numpy array) + :param height: cylinder height + :param radius_outer: outer radius, within which all positions are generated + :param radius_inner: inner radius, within which no positions are generated + :return: A generated list of poisitons with in the given bounds + """ + # Generate N random x and y values using polar coordinates; + # for phi, use uniform distribution; + # for r, the probability density is p(r)dr = r dr, so use inverse transform sampling: + # integral_R0_R p(r) dr = R^2/2 - R0^2/2; draw x = R^2/2 - R0^2/2 from a uniform distribution with values of x + # between 0 and R1^2/2 - R0^2/2. + phi = 2.0 * math.pi * np.random.random([N]) + r = np.sqrt((radius_outer**2 - radius_inner**2) * np.random.random([N]) + radius_inner**2) + x = center[0] + r * np.cos(phi) + z = center[2] + r * np.sin(phi) + + # Generate N random z values. + y = center[1] + height * (np.random.random([N]) - 0.5) + return np.column_stack((x, y, z)) + + +def cylinder_from_density(N, density, height, center=None): + """ + Build a cylinder for given point density, center and height. + N: number of points + density: density of points + height: height of the cylinder + center: desired center of the cylinder + """ + if center is None: + center = np.array([0.0, 0.0, 0.0]) + + height = float(height) + radius = math.sqrt((N / density) / (height * math.pi) ) + return center, height, radius + + +def gaussianLL(src, trg, weight, weight_sigma=50.0): + src_tuning = src['tuning_angle'] + tar_tuning = trg['tuning_angle'] + delta_tuning = abs(abs(abs(180.0 - abs(float(tar_tuning) - float(src_tuning)) % 360.0) - 90.0) - 90.0) + return weight * math.exp(-(delta_tuning / weight_sigma) ** 2) + + +"""Functions used by build_lgn.py""" +def read_dat_file(filename, type_mapping={'transient_ON': 'tON_001', 'transient_OFF': 'tOFF_001', 'transient_ON_OFF': 'tONOFF_001'}): + positions_table = {val: [] for val in type_mapping.values()} + offset_table = {val: [] for val in type_mapping.values()} + with open(filename, 'rb') as csvfile: + csvreader = csv.reader(csvfile, delimiter=' ') + for row in csvreader: + model_type = type_mapping.get(row[0], None) + if model_type: + positions_table[model_type].append([float(row[1]), float(row[2])]) + offset_table[model_type].append([float(row[3]), float(row[4])]) + + return positions_table, offset_table + + +def calc_tuning_angle(offset_vect): + offset_sum = sum(offset_vect) + if offset_sum == 0: + return -1.0 + else: + tmp_vec = offset_vect / np.sqrt(offset_vect[0]**2 + offset_vect[1]**2) + return (360.0 + 180.0 * np.arctan2(tmp_vec[1], tmp_vec[0]) / np.pi) % 360.0 + + +def select_source_cells(sources, target, lgn_mean, lgn_dim, l4_mean, l4_dim, N_syn): + + target_id = target.node_id + source_ids = [s.node_id for s in sources] + + if target_id%1000 == 0: + print "connection LGN cells to L4 cell #", target_id + + subfields_centers_distance_min = 10.0 # 10.0 + subfields_centers_distance_max = 11.0 # 10.0 + subfields_centers_distance_L = subfields_centers_distance_max - subfields_centers_distance_min + + subfields_ON_OFF_width_min = 6.0 # 8.0 #10.0 #8.0 #8.0 #14.0 #15.0 + subfields_ON_OFF_width_max = 8.0 # 10.0 #12.0 #10.0 #15.0 #20.0 #15.0 + subfields_ON_OFF_width_L = subfields_ON_OFF_width_max - subfields_ON_OFF_width_min + + subfields_width_aspect_ratio_min = 2.8 # 1.9 #1.4 #0.9 #1.0 + subfields_width_aspect_ratio_max = 3.0 # 2.0 #1.5 #1.1 #1.0 + subfields_width_aspect_ratio_L = subfields_width_aspect_ratio_max - subfields_width_aspect_ratio_min + + vis_x = lgn_mean[0] + ((target['x'] - l4_mean[0]) / l4_dim[0]) * lgn_dim[0] + vis_y = lgn_mean[1] + ((target['y'] - l4_mean[2]) / l4_dim[2]) * lgn_dim[1] + + ellipse_center_x0 = vis_x #tar_cells[tar_gid]['vis_x'] + ellipse_center_y0 = vis_y #tar_cells[tar_gid]['vis_y'] + + try: + tuning_angle = float(target['tuning_angle']) + tuning_angle = None if math.isnan(tuning_angle) or tuning_angle < 0 else tuning_angle + except Exception: + tuning_angle = None + + if tuning_angle is None: + ellipse_b0 = (subfields_ON_OFF_width_min + random() * subfields_ON_OFF_width_L) / 2.0 # Divide by 2 to convert from width to radius. + ellipse_b0 = 2.5 * ellipse_b0 # 1.5 * ellipse_b0 + ellipse_a0 = ellipse_b0 # ellipse_b0 + top_N_src_cells_subfield = 15 # 20 + ellipses_centers_halfdistance = 0.0 + else: + tuning_angle_value = float(tuning_angle) + ellipses_centers_halfdistance = (subfields_centers_distance_min + random() * subfields_centers_distance_L) / 2.0 + ellipse_b0 = (subfields_ON_OFF_width_min + random() * subfields_ON_OFF_width_L) / 2.0 # Divide by 2 to convert from width to radius. + ellipse_a0 = ellipse_b0 * (subfields_width_aspect_ratio_min + random() * subfields_width_aspect_ratio_L) + ellipse_phi = tuning_angle_value + 180.0 + 90.0 # Angle, in degrees, describing the rotation of the canonical ellipse away from the x-axis. + ellipse_cos_mphi = math.cos(-math.radians(ellipse_phi)) + ellipse_sin_mphi = math.sin(-math.radians(ellipse_phi)) + top_N_src_cells_subfield = 8 # 10 #9 + + # to match previous algorithm reorganize source cells by type + cell_type_dict = { + 'tON_001': [(src_id, src_dict) for src_id, src_dict in zip(source_ids, sources) if src_dict['pop_id'] == 'tON_001'], + 'tOFF_001': [(src_id, src_dict) for src_id, src_dict in zip(source_ids, sources) if src_dict['pop_id'] == 'tOFF_001'], + 'tONOFF_001': [(src_id, src_dict) for src_id, src_dict in zip(source_ids, sources) if src_dict['pop_id'] == 'tONOFF_001'] + } + + src_cells_selected = {} + for src_type in cell_type_dict.keys(): + src_cells_selected[src_type] = [] + + if tuning_angle is None: + ellipse_center_x = ellipse_center_x0 + ellipse_center_y = ellipse_center_y0 + ellipse_a = ellipse_a0 + ellipse_b = ellipse_b0 + else: + if src_type == 'tON_001': + ellipse_center_x = ellipse_center_x0 + ellipses_centers_halfdistance * ellipse_sin_mphi + ellipse_center_y = ellipse_center_y0 + ellipses_centers_halfdistance * ellipse_cos_mphi + ellipse_a = ellipse_a0 + ellipse_b = ellipse_b0 + elif src_type == 'tOFF_001': + ellipse_center_x = ellipse_center_x0 - ellipses_centers_halfdistance * ellipse_sin_mphi + ellipse_center_y = ellipse_center_y0 - ellipses_centers_halfdistance * ellipse_cos_mphi + ellipse_a = ellipse_a0 + ellipse_b = ellipse_b0 + else: + # Make this a simple circle. + ellipse_center_x = ellipse_center_x0 + ellipse_center_y = ellipse_center_y0 + # Make the region from which source cells are selected a bit smaller for the transient_ON_OFF cells, + # since each source cell in this case produces both ON and OFF responses. + ellipse_b = ellipses_centers_halfdistance/2.0 #0.01 #ellipses_centers_halfdistance + 1.0*ellipse_b0 #0.01 #0.5 * ellipse_b0 # 0.8 * ellipse_b0 + ellipse_a = ellipse_b0 #0.01 #ellipse_b0 + + # Find those source cells of the appropriate type that have their visual space coordinates within the ellipse. + for src_id, src_dict in cell_type_dict[src_type]: + x, y = (src_dict['x'], src_dict['y']) + x = x - ellipse_center_x + y = y - ellipse_center_y + + x_new = x + y_new = y + if tuning_angle is not None: + x_new = x * ellipse_cos_mphi - y * ellipse_sin_mphi + y_new = x * ellipse_sin_mphi + y * ellipse_cos_mphi + + if ((x_new/ellipse_a)**2 + (y_new/ellipse_b) ** 2) <= 1.0: + if (tuning_angle is not None) and (src_type == 'tONOFF_001'): + src_tuning_angle = float(src_dict['tuning_angle']) + delta_tuning = abs(abs(abs(180.0-abs(tuning_angle_value-src_tuning_angle)%360.0)-90.0)-90.0) + if delta_tuning < 15.0: + src_cells_selected[src_type].append(src_id) + else: + src_cells_selected[src_type].append(src_id) + + while len(src_cells_selected[src_type]) > top_N_src_cells_subfield: + src_cells_selected[src_type].remove(random.choice(src_cells_selected[src_type])) + + + select_cell_ids = [id for _, selected in src_cells_selected.items() for id in selected] + nsyns_ret = [N_syn if id in select_cell_ids else None for id in source_ids] + return nsyns_ret + + diff --git a/examples/layer4_sample/build_l4.py b/examples/layer4_sample/build_l4.py new file mode 100644 index 0000000..39cad98 --- /dev/null +++ b/examples/layer4_sample/build_l4.py @@ -0,0 +1,283 @@ +import os +import numpy as np +from build_helpers import * + +from bmtk.builder.bionet import SWCReader +from bmtk.builder.networks import NetworkBuilder + + +# Percentage of 45,000 network to build, default to 1 percent (~ 450 cells) +PERCENTAGE_CELLS = 0.01 +WEIGHT_ADJ = np.sqrt(1.0/PERCENTAGE_CELLS) # A general rule for adjust weight based on change in cell pop + +# Build the layer as a cylinder, the radius will be calculated based on cylinder height, density and Num of cells +# NOTE: Due to the way recurrent and lgn --> l4 synaptic connections are made it's a good idea to adjust the height +# based on the number of cells to ensure we have a flat disk of about the same radius +CYL_CENTER = np.array([0, -370.0, 0]) +CYL_HEIGHT = 100.0*PERCENTAGE_CELLS +CYL_DENSITY = 0.0002 + + +cell_models = [ + { + 'model_name': 'Scnn1a', + 'N': int(PERCENTAGE_CELLS*3700), + 'ei': 'e', + 'model_type': 'biophysical', + 'morphology': 'Scnn1a_473845048_m', + 'electrophysiology': '472363762_fit.json', + 'rotation_angle_zaxis': -3.646878266, + 'model_template': 'nml:Cell_472363762.cell.nml' + }, + { + 'model_name': 'Rorb', + 'N': int(PERCENTAGE_CELLS*3300), + 'ei': 'e', + 'model_type': 'biophysical', + 'morphology': 'Rorb_325404214_m', + 'electrophysiology': '473863510_fit.json', + 'rotation_angle_zaxis': -4.159763785, + 'model_template': 'nml:Cell_473863510.cell.nml' + }, + { + 'model_name': 'Nr5a1', + 'N': int(PERCENTAGE_CELLS*1500), + 'ei': 'e', + 'model_type': 'biophysical', + 'morphology': 'Nr5a1_471087815_m', + 'electrophysiology': '473863035_fit.json', + 'rotation_angle_zaxis': -2.639275277, + 'model_template': 'nml:Cell_473863035.cell.nml' + }, + { + 'model_name': 'PV1', + 'N': int(PERCENTAGE_CELLS*800), + 'ei': 'i', + 'model_type': 'biophysical', + 'morphology': 'Pvalb_470522102_m', + 'electrophysiology': '472912177_fit.json', + 'rotation_angle_zaxis': -2.539551891, + 'model_template': 'nml:Cell_472912177.cell.nml' + }, + { + 'model_name': 'PV2', + 'N': int(PERCENTAGE_CELLS*700), + 'ei': 'i', + 'model_type': 'biophysical', + 'morphology': 'Pvalb_469628681_m', + 'electrophysiology': '473862421_fit.json', + 'rotation_angle_zaxis': -3.684439949, + 'model_template': 'nml:Cell_473862421.cell.nml' + }, + { + 'model_name': 'LIF_exc', + 'N': int(PERCENTAGE_CELLS*29750), + 'ei': 'e', + 'model_type': 'point_process', + 'model_template': 'nrn:IntFire1', + 'dynamics_params': 'IntFire1_exc_1.json' + }, + { + 'model_name': 'LIF_inh', + 'N': int(PERCENTAGE_CELLS*5250), + 'ei': 'i', + 'model_type': 'point_process', + 'model_template': 'nrn:IntFire1', + 'dynamics_params': 'IntFire1_inh_1.json' + } +] + +# Cacluated dimensions of column +n_nodes = sum(v['N'] for v in cell_models) +n_point_nodes = sum(v['N'] for v in cell_models if v['model_type'] == 'point_process') +cyl_center, cyl_height, cyl_radius = cylinder_from_density(n_nodes, density=CYL_DENSITY, height=CYL_HEIGHT, + center=CYL_CENTER) +radius_biophys = ((n_nodes - n_point_nodes) / (n_nodes * 1.0))**0.5 * cyl_radius + +net = NetworkBuilder('l4') +for model_params in cell_models: + N = model_params['N'] + + # get positions + r_outer = radius_biophys + r_inner = 0.0 + if model_params['model_type'] == 'point_process': + # place point-neurons on the outer ring + r_outer = cyl_radius + r_inner = radius_biophys + positions = generate_random_positions(N, center=cyl_center, height=cyl_height, radius_outer=r_outer, + radius_inner=r_inner) + rotation_angle_yaxis = np.random.uniform(0.0, 2*np.pi, (N,)) + tuning_angle = np.linspace(0, 360.0, N, endpoint=False) if model_params['ei'] == 'e' else [np.NaN]*N + net.add_nodes(x=positions[:, 0], + y=positions[:, 1], + z=positions[:, 2], + rotation_angle_yaxis=rotation_angle_yaxis, + tuning_angle=tuning_angle, + **model_params) + + +morphologies = {p['model_name']: SWCReader(os.path.join('../shared_components/morphologies', p['morphology'])) + for p in cell_models if 'morphology' in p} +def build_edges(src, trg, sections=['basal', 'apical'], dist_range=[50.0, 150.0]): + """Function used to randomly assign a synaptic location based on the section (soma, basal, apical) and an + arc-length dist_range from the soma. This function should be passed into the network and called during the build + process. + + :param src: source cell (dict) + :param trg: target cell (dict) + :param sections: list of target cell sections to synapse onto + :param dist_range: range (distance from soma center) to place + :return: + """ + # Get morphology and soma center for the target cell + swc_reader = morphologies[trg['model_name']] + target_coords = [trg['x'], trg['y'], trg['z']] + + sec_ids, sec_xs = swc_reader.choose_sections(sections, dist_range) # randomly choose sec_ids + coords = swc_reader.get_coord(sec_ids, sec_xs, soma_center=target_coords) # get coords of sec_ids + dist = swc_reader.get_dist(sec_ids) + swctype = swc_reader.get_type(sec_ids) + return sec_ids, sec_xs, coords[0][0], coords[0][1], coords[0][2], dist[0], swctype[0] + + +cparameters = {'d_weight_min': 0.0, 'd_weight_max': 1.0, 'd_max': 160.0, 'nsyn_min': 3, 'nsyn_max': 7} + +# inh --> bio inh +cm = net.add_edges(source={'ei': 'i'}, target={'ei': 'i', 'model_type': 'biophysical'}, + connection_rule=distance_connection_handler, + connection_params=cparameters, + delay=2.0, + dynamics_params='GABA_InhToInh.json', + model_template='exp2syn') +cm.add_properties('syn_weight', rule=0.0002*WEIGHT_ADJ, dtypes=np.float) +cm.add_properties(['sec_id', 'sec_x', 'pos_x', 'pos_y', 'pos_z', 'dist', 'type'], + rule=build_edges, + rule_params={'sections': ['somatic', 'basal'], 'dist_range': [0.0, 1e+20]}, + dtypes=[np.int32, np.float, np.float, np.float, np.float, np.float, np.uint8]) + +# inh --> point inh +cm = net.add_edges(source={'ei': 'i'}, target={'ei': 'i', 'model_type': 'point_process'}, + connection_rule=distance_connection_handler, + connection_params=cparameters, + delay=2.0, + dynamics_params='instanteneousInh.json', + model_template='exp2syn') +cm.add_properties('syn_weight', rule=0.00225, dtypes=np.float) + +# inh --> bio exc +cparameters = {'d_weight_min': 0.0, 'd_weight_max': 1.0, 'd_max': 160.0, 'nsyn_min': 3, 'nsyn_max': 7} +cm = net.add_edges(source={'ei': 'i'}, target={'ei': 'e', 'model_type': 'biophysical'}, + connection_rule=distance_connection_handler, + connection_params=cparameters, + delay=2.0, + dynamics_params='GABA_InhToExc.json', + model_template='exp2syn') +cm.add_properties('syn_weight', rule=0.00018*WEIGHT_ADJ, dtypes=np.float) +cm.add_properties(['sec_id', 'sec_x', 'pos_x', 'pos_y', 'pos_z', 'dist', 'type'], + rule=build_edges, + rule_params={'sections': ['somatic', 'basal', 'apical'], 'dist_range': [0.0, 50.0]}, + dtypes=[np.int32, np.float, np.float, np.float, np.float, np.float, np.uint8]) + +# inh --> point exc +cm = net.add_edges(source={'ei': 'i'}, target={'ei': 'e', 'model_type': 'intfire'}, + connection_rule=distance_connection_handler, + connection_params=cparameters, + delay=2.0, + dynamics_params='instanteneousInh.json', + model_template='exp2syn') +cm.add_properties('syn_weight', rule=0.009*WEIGHT_ADJ, dtypes=np.float) + +# exc --> PV1 +cparameters = {'d_weight_min': 0.0, 'd_weight_max': 0.26, 'd_max': 300.0, 'nsyn_min': 3, 'nsyn_max': 7} +cm = net.add_edges(source={'ei': 'e'}, target={'model_name': 'PV1'}, + connection_rule=distance_connection_handler, + connection_params=cparameters, + delay=2.0, + dynamics_params='AMPA_ExcToInh.json', + model_template='exp2syn') +cm.add_properties('syn_weight', rule=0.00035*WEIGHT_ADJ, dtypes=np.float) +cm.add_properties(['sec_id', 'sec_x', 'pos_x', 'pos_y', 'pos_z', 'dist', 'type'], + rule=build_edges, + rule_params={'sections': ['somatic', 'basal'], 'dist_range': [0.0, 1e+20]}, + dtypes=[np.int32, np.float, np.float, np.float, np.float, np.float, np.uint8]) + +# exc --> PV2 +cm = net.add_edges(source={'ei': 'e'}, target={'model_name': 'PV2'}, + connection_rule=distance_connection_handler, + connection_params=cparameters, + delay=2.0, + dynamics_params='AMPA_ExcToInh.json', + model_template='exp2syn') +cm.add_properties('syn_weight', rule=0.00027*WEIGHT_ADJ, dtypes=np.float) +cm.add_properties(['sec_id', 'sec_x', 'pos_x', 'pos_y', 'pos_z', 'dist', 'type'], + rule=build_edges, + rule_params={'sections': ['somatic', 'basal'], 'dist_range': [0.0, 1e+20]}, + dtypes=[np.int32, np.float, np.float, np.float, np.float, np.float, np.uint8]) + +# exc --> LIF_inh +cm = net.add_edges(source={'ei': 'e'}, target={'model_name': 'LIF_inh'}, + connection_rule=distance_connection_handler, + connection_params=cparameters, + delay=2.0, + dynamics_params='instanteneousExc.json', + model_template='exp2syn') +cm.add_properties('syn_weight', rule=0.0043*WEIGHT_ADJ, dtypes=np.float) + + +cparameters = {'d_weight_min': 0.0, 'd_weight_max': 0.34, 'd_max': 300.0, 't_weight_min': 0.5, + 't_weight_max': 1.0, 'nsyn_min': 3, 'nsyn_max': 7} + +# exc --> Scnn1a +cm = net.add_edges(source={'ei': 'e'}, target={'model_name': 'Scnn1a'}, + connection_rule=distance_tuning_connection_handler, + connection_params=cparameters, + delay=2.0, + dynamics_params='AMPA_ExcToExc.json', + model_template='exp2syn') +cm.add_properties('syn_weight', rule=gaussianLL, rule_params={'weight': 6.4e-05*WEIGHT_ADJ}, dtypes=np.float) +cm.add_properties(['sec_id', 'sec_x', 'pos_x', 'pos_y', 'pos_z', 'dist', 'type'], + rule=build_edges, + rule_params={'sections': ['basal', 'apical'], 'dist_range': [30.0, 150.0]}, + dtypes=[np.int32, np.float, np.float, np.float, np.float, np.float, np.uint8]) + +# exc --> Rorb +cm = net.add_edges(source={'ei': 'e'}, target={'model_name': 'Rorb'}, + connection_rule=distance_tuning_connection_handler, + connection_params=cparameters, + delay=2.0, + dynamics_params='AMPA_ExcToExc.json', + model_template='exp2syn') +cm.add_properties('syn_weight', rule=gaussianLL, rule_params={'weight': 5.5e-05*WEIGHT_ADJ}, dtypes=np.float) +cm.add_properties(['sec_id', 'sec_x', 'pos_x', 'pos_y', 'pos_z', 'dist', 'type'], + rule=build_edges, + rule_params={'sections': ['basal', 'apical'], 'dist_range': [30.0, 150.0]}, + dtypes=[np.int32, np.float, np.float, np.float, np.float, np.float, np.uint8]) + +# exc --> Nr5a1 +cm = net.add_edges(source={'ei': 'e'}, target={'model_name': 'Nr5a1'}, + connection_rule=distance_tuning_connection_handler, + connection_params=cparameters, + delay=2.0, + dynamics_params='AMPA_ExcToExc.json', + model_template='exp2syn') +cm.add_properties('syn_weight', rule=gaussianLL, rule_params={'weight': 7.2e-05*WEIGHT_ADJ}, dtypes=np.float) +cm.add_properties(['sec_id', 'sec_x', 'pos_x', 'pos_y', 'pos_z', 'dist', 'type'], + rule=build_edges, + rule_params={'sections': ['basal', 'apical'], 'dist_range': [30.0, 150.0]}, + dtypes=[np.int32, np.float, np.float, np.float, np.float, np.float, np.uint8]) + +# exc --> LIF_exc +cm = net.add_edges(source={'ei': 'e'}, target={'model_name': 'LIF_exc'}, + connection_rule=distance_tuning_connection_handler, + connection_params=cparameters, + delay=2.0, + dynamics_params='instanteneousExc.json', + model_template='exp2syn') +cm.add_properties('syn_weight', rule=0.0019*WEIGHT_ADJ, dtypes=np.float) + +print('Building Network') +net.build() + +print('Saving Network') +net.save(output_dir='network') diff --git a/examples/layer4_sample/build_lgn.py b/examples/layer4_sample/build_lgn.py new file mode 100644 index 0000000..9a5d49d --- /dev/null +++ b/examples/layer4_sample/build_lgn.py @@ -0,0 +1,173 @@ +import os +import csv +import math +import numpy as np +from random import * +from build_helpers import * + +from bmtk.builder.networks import NetworkBuilder +from bmtk.builder.bionet import SWCReader + + +positions_table, offset_table = read_dat_file('lgn_positions.csv') # file containing cell positions +cell_models = { + 'tON_001': { + 'N': len(positions_table['tON_001']), 'ei': 'e', 'location': 'LGN', + 'model_type': 'virtual', 'pop_name': 'tON', 'pop_id': 'tON_001' + }, + 'tOFF_001': { + 'N': len(positions_table['tOFF_001']),'ei': 'e', 'location': 'LGN', + 'model_type': 'virtual', 'pop_name': 'tOFF', 'pop_id': 'tOFF_001' + }, + 'tONOFF_001': { + 'N': len(positions_table['tONOFF_001']), 'ei': 'e', 'location': 'LGN', + 'model_type': 'virtual', 'pop_name': 'tONOFF', 'pop_id': 'tONOFF_001' + } +} + +lgn_net = NetworkBuilder('lgn') +xcoords = [] +ycoords = [] +for model_name, model_params in cell_models.items(): + positions = positions_table[model_name] + xcoords += [p[0] for p in positions] + ycoords += [p[1] for p in positions] + tuning_angles = [calc_tuning_angle(o) for o in offset_table[model_name]] + + lgn_net.add_nodes(model_params['N'], + # position=positions, + x=[p[0] for p in positions], + y=[p[1] for p in positions], + tuning_angle=tuning_angles, + ei=model_params['ei'], + location=model_params['location'], + model_type=model_params['model_type'], + pop_name=model_params['pop_name'], + pop_id=model_params['pop_id']) + +lgn_net.build() +lgn_net.save_nodes(output_dir='network') + + +v1_net = NetworkBuilder('l4') +v1_net.import_nodes('network/l4_nodes.h5', 'network/l4_node_types.csv') + +morphologies = {n['model_name']: SWCReader(os.path.join('../shared_components/morphologies', n['morphology'])) + for n in v1_net.nodes() if 'morphology' in n and n['morphology']} +def build_edges(src, trg, sections=['basal', 'apical'], dist_range=[50.0, 150.0]): + """Function used to randomly assign a synaptic location based on the section (soma, basal, apical) and an + arc-length dist_range from the soma. This function should be passed into the network and called during the build + process. + + :param src: source cell (dict) + :param trg: target cell (dict) + :param sections: list of target cell sections to synapse onto + :param dist_range: range (distance from soma center) to place + :return: + """ + # Get morphology and soma center for the target cell + swc_reader = morphologies[trg['model_name']] + target_coords = [trg['x'], trg['y'], trg['z']] + + sec_ids, sec_xs = swc_reader.choose_sections(sections, dist_range) # randomly choose sec_ids + coords = swc_reader.get_coord(sec_ids, sec_xs, soma_center=target_coords) # get coords of sec_ids + dist = swc_reader.get_dist(sec_ids) + swctype = swc_reader.get_type(sec_ids) + return sec_ids, sec_xs, coords[0][0], coords[0][1], coords[0][2], dist[0], swctype[0] + + +lgn_mean = (np.mean(xcoords), np.mean(ycoords)) +lgn_dim = (140.0, 70.0) + +# Determine the mean center of the CC cells +xcoords = [n['x'] for n in v1_net.nodes()] +ycoords = [n['y'] for n in v1_net.nodes()] +zcoords = [n['z'] for n in v1_net.nodes()] +l4_mean = (np.mean(xcoords), np.mean(ycoords), np.mean(zcoords)) +l4_dim = (max(xcoords) - min(xcoords), max(ycoords) - min(ycoords), max(zcoords) - min(zcoords)) + + +cparams = {'lgn_mean': lgn_mean, 'lgn_dim': lgn_dim, 'l4_mean': l4_mean, 'l4_dim': l4_dim, 'N_syn': 30} +cm = v1_net.add_edges(source=lgn_net.nodes(), target={'model_name': 'Rorb'}, + iterator='all_to_one', + connection_rule=select_source_cells, + connection_params=cparams, + delay=2.0, + dynamics_params='AMPA_ExcToExc.json', + model_template='exp2syn') +cm.add_properties('syn_weight', rule=5e-05, dtypes=np.float) +cm.add_properties(['sec_id', 'sec_x', 'pos_x', 'pos_y', 'pos_z', 'dist', 'type'], + rule=build_edges, + rule_params={'sections': ['basal', 'apical'], 'dist_range': [0.0, 150.0]}, + dtypes=[np.int32, np.float, np.float, np.float, np.float, np.float, np.uint8]) + +cm = v1_net.add_edges(source=lgn_net.nodes(), target={'model_name': 'Nr5a1'}, + iterator='all_to_one', + connection_rule=select_source_cells, + connection_params=cparams, + delay=2.0, + dynamics_params='AMPA_ExcToExc.json', + model_template='exp2syn') +cm.add_properties('syn_weight', rule=5e-05, dtypes=np.float) +cm.add_properties(['sec_id', 'sec_x', 'pos_x', 'pos_y', 'pos_z', 'dist', 'type'], + rule=build_edges, + rule_params={'sections': ['basal', 'apical'], 'dist_range': [0.0, 150.0]}, + dtypes=[np.int32, np.float, np.float, np.float, np.float, np.float, np.uint8]) + +cm = v1_net.add_edges(source=lgn_net.nodes(), target={'model_name': 'Scnn1a'}, + iterator='all_to_one', + connection_rule=select_source_cells, + connection_params=cparams, + delay=2.0, + dynamics_params='AMPA_ExcToExc.json', + model_template='exp2syn') +cm.add_properties('syn_weight', rule=4e-05, dtypes=np.float) +cm.add_properties(['sec_id', 'sec_x', 'pos_x', 'pos_y', 'pos_z', 'dist', 'type'], + rule=build_edges, + rule_params={'sections': ['basal', 'apical'], 'dist_range': [0.0, 150.0]}, + dtypes=[np.int32, np.float, np.float, np.float, np.float, np.float, np.uint8]) + + +cm = v1_net.add_edges(source=lgn_net.nodes(), target={'model_name': 'PV1'}, + iterator='all_to_one', + connection_rule=select_source_cells, + connection_params=cparams, + delay=2.0, + dynamics_params='AMPA_ExcToInh.json', + model_template='exp2syn') +cm.add_properties('syn_weight', rule=0.0001, dtypes=np.float) +cm.add_properties(['sec_id', 'sec_x', 'pos_x', 'pos_y', 'pos_z', 'dist', 'type'], + rule=build_edges, + rule_params={'sections': ['somatic', 'basal'], 'dist_range': [0.0, 1.0e+20]}, + dtypes=[np.int32, np.float, np.float, np.float, np.float, np.float, np.uint8]) + +cm = v1_net.add_edges(source=lgn_net.nodes(), target={'model_name': 'PV2'}, + iterator='all_to_one', + connection_rule=select_source_cells, + connection_params=cparams, + delay=2.0, + dynamics_params='AMPA_ExcToInh.json', + model_template='exp2syn') +cm.add_properties('syn_weight', rule=9e-05, dtypes=np.float) +cm.add_properties(['sec_id', 'sec_x', 'pos_x', 'pos_y', 'pos_z', 'dist', 'type'], + rule=build_edges, + rule_params={'sections': ['somatic', 'basal'], 'dist_range': [0.0, 1.0e+20]}, + dtypes=[np.int32, np.float, np.float, np.float, np.float, np.float, np.uint8]) + +cm = v1_net.add_edges(source=lgn_net.nodes(), target={'model_name': 'LIF_exc'}, + iterator='all_to_one', + connection_rule=select_source_cells, + connection_params=cparams, + delay=2.0, + dynamics_params='instanteneousExc.json') +cm.add_properties('syn_weight', rule=0.0045, dtypes=np.float) + +cm = v1_net.add_edges(source=lgn_net.nodes(), target={'model_name': 'LIF_inh'}, + iterator='all_to_one', + connection_rule=select_source_cells,connection_params=cparams, + delay=2.0, + dynamics_params='instanteneousExc.json') +cm.add_properties('syn_weight', rule=0.002, dtypes=np.float) + +v1_net.build() +v1_net.save_edges(output_dir='network') diff --git a/examples/layer4_sample/circuit_config.json b/examples/layer4_sample/circuit_config.json new file mode 100755 index 0000000..0407f6e --- /dev/null +++ b/examples/layer4_sample/circuit_config.json @@ -0,0 +1,39 @@ +{ + "manifest": { + "$NETWORK_DIR": "./network", + "$COMPONENT_DIR": "../shared_components" + }, + + "components": { + "morphologies_dir": "$COMPONENT_DIR/morphologies", + "synaptic_models_dir": "$COMPONENT_DIR/synaptic_models", + "mechanisms_dir":"$COMPONENT_DIR/mechanisms", + "biophysical_neuron_models_dir": "$COMPONENT_DIR/biophysical_neuron_templates/nml", + "point_neuron_models_dir": "$COMPONENT_DIR/point_neuron_models_dir" + }, + + "networks": { + "nodes": [ + { + "nodes_file": "$NETWORK_DIR/l4_nodes.h5", + "node_types_file": "$NETWORK_DIR/l4_node_types.csv" + }, + { + "nodes_file": "$NETWORK_DIR/lgn_nodes.h5", + "node_types_file": "$NETWORK_DIR/lgn_node_types.csv" + } + ], + + "edges": [ + { + "edges_file": "$NETWORK_DIR/l4_l4_edges.h5", + "edge_types_file": "$NETWORK_DIR/l4_l4_edge_types.csv" + }, + { + "edges_file": "$NETWORK_DIR/lgn_l4_edges.h5", + "edge_types_file": "$NETWORK_DIR/lgn_l4_edge_types.csv" + } + ] + + } +} \ No newline at end of file diff --git a/examples/layer4_sample/config.json b/examples/layer4_sample/config.json new file mode 100755 index 0000000..890caa3 --- /dev/null +++ b/examples/layer4_sample/config.json @@ -0,0 +1,4 @@ +{ + "network": "./circuit_config.json", + "simulation": "./simulation_config.json" +} diff --git a/examples/layer4_sample/generate_spikes.py b/examples/layer4_sample/generate_spikes.py new file mode 100644 index 0000000..ecf0630 --- /dev/null +++ b/examples/layer4_sample/generate_spikes.py @@ -0,0 +1,7 @@ +import os +from bmtk.utils.io.spike_trains import PoissonSpikesGenerator + +if not os.path.exists('inputs'): + os.mkdir('inputs') +psg = PoissonSpikesGenerator(range(9000), 15.0, tstop=3000.0) +psg.to_hdf5('inputs/lgn_spike_trains.h5') \ No newline at end of file diff --git a/examples/layer4_sample/inputs/lgn_spike_trains.h5 b/examples/layer4_sample/inputs/lgn_spike_trains.h5 new file mode 100644 index 0000000..4176413 Binary files /dev/null and b/examples/layer4_sample/inputs/lgn_spike_trains.h5 differ diff --git a/examples/layer4_sample/lgn_positions.csv b/examples/layer4_sample/lgn_positions.csv new file mode 100755 index 0000000..235e109 --- /dev/null +++ b/examples/layer4_sample/lgn_positions.csv @@ -0,0 +1,9001 @@ +model_type x y x_offset y_offset +transient_ON 149.496406774 89.0144387113 0.0 0.0 +transient_ON 6.96125478807 55.8747185254 0.0 0.0 +transient_ON 112.576571468 29.5887399144 0.0 0.0 +transient_ON 67.0757678427 109.961444617 0.0 0.0 +transient_ON 148.188604912 15.203907906 0.0 0.0 +transient_ON 235.781066118 104.688931852 0.0 0.0 +transient_ON 49.1470834881 112.917120131 0.0 0.0 +transient_ON 86.6855843334 19.9147268556 0.0 0.0 +transient_ON 0.811948649846 81.3521099457 0.0 0.0 +transient_ON 75.7903454031 57.7462063484 0.0 0.0 +transient_ON 179.95080535 101.385706727 0.0 0.0 +transient_ON 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53.9426934083 99.2684520138 -2.0394060066 -3.25365349874 +transient_ON_OFF 79.7819686225 36.0237882843 3.67609747826 1.58588518633 +transient_ON_OFF 21.3144140817 115.649428758 0.225930770346 -4.08790281468 +transient_ON_OFF 31.2734087231 91.4228863958 0.650406930622 4.80174507004 diff --git a/examples/layer4_sample/network/l4_l4_edge_types.csv b/examples/layer4_sample/network/l4_l4_edge_types.csv new file mode 100644 index 0000000..e087cae --- /dev/null +++ b/examples/layer4_sample/network/l4_l4_edge_types.csv @@ -0,0 +1,12 @@ +edge_type_id target_query source_query delay dynamics_params model_template +100 model_type=='biophysical'&ei=='i' ei=='i' 2.0 GABA_InhToInh.json exp2syn +101 model_type=='point_process'&ei=='i' ei=='i' 2.0 instanteneousInh.json exp2syn +102 model_type=='biophysical'&ei=='e' ei=='i' 2.0 GABA_InhToExc.json exp2syn +103 model_type=='intfire'&ei=='e' ei=='i' 2.0 instanteneousInh.json exp2syn +104 model_name=='PV1' ei=='e' 2.0 AMPA_ExcToInh.json exp2syn +105 model_name=='PV2' ei=='e' 2.0 AMPA_ExcToInh.json exp2syn +106 model_name=='LIF_inh' ei=='e' 2.0 instanteneousExc.json exp2syn +107 model_name=='Scnn1a' ei=='e' 2.0 AMPA_ExcToExc.json exp2syn +108 model_name=='Rorb' ei=='e' 2.0 AMPA_ExcToExc.json exp2syn +109 model_name=='Nr5a1' ei=='e' 2.0 AMPA_ExcToExc.json exp2syn +110 model_name=='LIF_exc' ei=='e' 2.0 instanteneousExc.json exp2syn diff --git a/examples/layer4_sample/network/l4_l4_edges.h5 b/examples/layer4_sample/network/l4_l4_edges.h5 new file mode 100644 index 0000000..12893c1 Binary files /dev/null and b/examples/layer4_sample/network/l4_l4_edges.h5 differ diff --git a/examples/layer4_sample/network/l4_node_types.csv b/examples/layer4_sample/network/l4_node_types.csv new file mode 100644 index 0000000..2f482d3 --- /dev/null +++ b/examples/layer4_sample/network/l4_node_types.csv @@ -0,0 +1,8 @@ +node_type_id ei model_type electrophysiology model_template morphology dynamics_params rotation_angle_zaxis model_name +100 e biophysical 472363762_fit.json nml:Cell_472363762.cell.nml Scnn1a_473845048_m NULL -3.646878266 Scnn1a +101 e biophysical 473863510_fit.json nml:Cell_473863510.cell.nml Rorb_325404214_m NULL -4.159763785 Rorb +102 e biophysical 473863035_fit.json nml:Cell_473863035.cell.nml Nr5a1_471087815_m NULL -2.639275277 Nr5a1 +103 i biophysical 472912177_fit.json nml:Cell_472912177.cell.nml Pvalb_470522102_m NULL -2.539551891 PV1 +104 i biophysical 473862421_fit.json nml:Cell_473862421.cell.nml Pvalb_469628681_m NULL -3.684439949 PV2 +105 e point_process NULL nrn:IntFire1 NULL IntFire1_exc_1.json NULL LIF_exc +106 i point_process NULL nrn:IntFire1 NULL IntFire1_inh_1.json NULL LIF_inh diff --git a/examples/layer4_sample/network/l4_nodes.h5 b/examples/layer4_sample/network/l4_nodes.h5 new file mode 100644 index 0000000..e6425de Binary files /dev/null and b/examples/layer4_sample/network/l4_nodes.h5 differ diff --git a/examples/layer4_sample/network/lgn_l4_edge_types.csv b/examples/layer4_sample/network/lgn_l4_edge_types.csv new file mode 100644 index 0000000..c206586 --- /dev/null +++ b/examples/layer4_sample/network/lgn_l4_edge_types.csv @@ -0,0 +1,8 @@ +edge_type_id target_query source_query delay dynamics_params model_template +100 model_name=='Rorb' * 2.0 AMPA_ExcToExc.json exp2syn +101 model_name=='Nr5a1' * 2.0 AMPA_ExcToExc.json exp2syn +102 model_name=='Scnn1a' * 2.0 AMPA_ExcToExc.json exp2syn +103 model_name=='PV1' * 2.0 AMPA_ExcToInh.json exp2syn +104 model_name=='PV2' * 2.0 AMPA_ExcToInh.json exp2syn +105 model_name=='LIF_exc' * 2.0 instanteneousExc.json NULL +106 model_name=='LIF_inh' * 2.0 instanteneousExc.json NULL diff --git a/examples/layer4_sample/network/lgn_l4_edges.h5 b/examples/layer4_sample/network/lgn_l4_edges.h5 new file mode 100644 index 0000000..f1b305e Binary files /dev/null and b/examples/layer4_sample/network/lgn_l4_edges.h5 differ diff --git a/examples/layer4_sample/network/lgn_node_types.csv b/examples/layer4_sample/network/lgn_node_types.csv new file mode 100644 index 0000000..279028b --- /dev/null +++ b/examples/layer4_sample/network/lgn_node_types.csv @@ -0,0 +1,4 @@ +node_type_id ei pop_id pop_name location model_type +100 e tON_001 tON LGN virtual +101 e tOFF_001 tOFF LGN virtual +102 e tONOFF_001 tONOFF LGN virtual diff --git a/examples/layer4_sample/network/lgn_nodes.h5 b/examples/layer4_sample/network/lgn_nodes.h5 new file mode 100644 index 0000000..3508997 Binary files /dev/null and b/examples/layer4_sample/network/lgn_nodes.h5 differ diff --git a/examples/layer4_sample/run_bionet.py b/examples/layer4_sample/run_bionet.py new file mode 100755 index 0000000..1a967cc --- /dev/null +++ b/examples/layer4_sample/run_bionet.py @@ -0,0 +1,17 @@ +import sys, os +from bmtk.simulator import bionet + + +def run(config_file): + conf = bionet.Config.from_json(config_file, validate=True) + conf.build_env() + net = bionet.BioNetwork.from_config(conf) + sim = bionet.BioSimulator.from_config(conf, network=net) + sim.run() + + +if __name__ == '__main__': + if __file__ != sys.argv[-1]: + run(sys.argv[-1]) + else: + run('config.json') diff --git a/examples/layer4_sample/simulation_config.json b/examples/layer4_sample/simulation_config.json new file mode 100755 index 0000000..675cb45 --- /dev/null +++ b/examples/layer4_sample/simulation_config.json @@ -0,0 +1,38 @@ +{ + "manifest": { + "$BASE_DIR": ".", + "$OUTPUT_DIR": "$BASE_DIR/output", + "$INPUT_DIR": "$BASE_DIR/inputs" + }, + + "run": { + "tstop": 3000.0, + "dt": 0.1, + "dL": 20.0, + "spike_threshold": -15, + "nsteps_block": 5000 + }, + + "target_simulator":"NEURON", + + "conditions": { + "celsius": 34.0, + "v_init": -80 + }, + + "inputs": { + "external_spike_trains": { + "input_type": "spikes", + "module": "h5", + "input_file": "$INPUT_DIR/lgn_spike_trains.h5", + "node_set": "lgn" + } + }, + + "output": { + "log_file": "log.txt", + "output_dir": "$OUTPUT_DIR", + "spikes_file": "spikes.h5", + "spikes_sort_order": "time" + } +} diff --git a/examples/shared_components/nest_models/cell_models/472363762_point.json b/examples/shared_components/nest_models/cell_models/472363762_point.json new file mode 100644 index 0000000..e6154b1 --- /dev/null +++ b/examples/shared_components/nest_models/cell_models/472363762_point.json @@ -0,0 +1,9 @@ +{ + "I_e": 0.0, + "tau_m": 44.9, + "C_m": 239.0, + "t_ref": 3.0, + "E_L": -78.0, + "V_th": -43.0, + "V_reset": -55.0 +} diff --git a/examples/shared_components/nest_models/cell_models/472912177_point.json b/examples/shared_components/nest_models/cell_models/472912177_point.json new file mode 100644 index 0000000..30b9822 --- /dev/null +++ b/examples/shared_components/nest_models/cell_models/472912177_point.json @@ -0,0 +1,9 @@ +{ + "I_e": 0.0, + "tau_m": 22.2, + "C_m": 180.0, + "t_ref": 3.0, + "E_L": -82.0, + "V_th": -35.0, + "V_reset": -50.0 +} diff --git a/examples/shared_components/nest_models/cell_models/473862421_point.json b/examples/shared_components/nest_models/cell_models/473862421_point.json new file mode 100644 index 0000000..6d7e76a --- /dev/null +++ b/examples/shared_components/nest_models/cell_models/473862421_point.json @@ -0,0 +1,9 @@ +{ + "I_e": 0.0, + "tau_m": 12.5, + "C_m": 78.0, + "t_ref": 3.0, + "E_L": -73.0, + "V_th": -37.0, + "V_reset": -55.0 +} diff --git a/examples/shared_components/nest_models/cell_models/473863035_point.json b/examples/shared_components/nest_models/cell_models/473863035_point.json new file mode 100644 index 0000000..db8e5e4 --- /dev/null +++ b/examples/shared_components/nest_models/cell_models/473863035_point.json @@ -0,0 +1,9 @@ +{ + "I_e": 0.0, + "tau_m": 22.1, + "C_m": 117.0, + "t_ref": 3.0, + "E_L": -78.0, + "V_th": -47.0, + "V_reset": -50.0 +} diff --git a/examples/shared_components/nest_models/cell_models/473863510_point.json b/examples/shared_components/nest_models/cell_models/473863510_point.json new file mode 100644 index 0000000..348c569 --- /dev/null +++ b/examples/shared_components/nest_models/cell_models/473863510_point.json @@ -0,0 +1,9 @@ +{ + "I_e": 0.0, + "tau_m": 11.5, + "C_m": 53.0, + "t_ref": 3.0, + "E_L": -72.0, + "V_th": -25.0, + "V_reset": -50.0 +} diff --git a/examples/shared_components/nest_models/synaptic_models/ExcToExc.json b/examples/shared_components/nest_models/synaptic_models/ExcToExc.json new file mode 100644 index 0000000..2c63c08 --- /dev/null +++ b/examples/shared_components/nest_models/synaptic_models/ExcToExc.json @@ -0,0 +1,2 @@ +{ +} diff --git a/examples/shared_components/nest_models/synaptic_models/ExcToInh.json b/examples/shared_components/nest_models/synaptic_models/ExcToInh.json new file mode 100644 index 0000000..2c63c08 --- /dev/null +++ b/examples/shared_components/nest_models/synaptic_models/ExcToInh.json @@ -0,0 +1,2 @@ +{ +} diff --git a/examples/shared_components/nest_models/synaptic_models/InhToExc.json b/examples/shared_components/nest_models/synaptic_models/InhToExc.json new file mode 100644 index 0000000..bfd870e --- /dev/null +++ b/examples/shared_components/nest_models/synaptic_models/InhToExc.json @@ -0,0 +1,3 @@ +{ +} + diff --git a/examples/shared_components/nest_models/synaptic_models/InhToInh.json b/examples/shared_components/nest_models/synaptic_models/InhToInh.json new file mode 100644 index 0000000..bfd870e --- /dev/null +++ b/examples/shared_components/nest_models/synaptic_models/InhToInh.json @@ -0,0 +1,3 @@ +{ +} + diff --git a/examples/sim_tests/intfire/one_cell_iclamp_nest/bmtk_build/build_network.py b/examples/sim_tests/intfire/one_cell_iclamp_nest/bmtk_build/build_network.py new file mode 100644 index 0000000..935b1c7 --- /dev/null +++ b/examples/sim_tests/intfire/one_cell_iclamp_nest/bmtk_build/build_network.py @@ -0,0 +1,26 @@ +#!/bin/env python + +# Sonata Simulator Test Suite +# 1-cell circuit, integrate and fire cell, current clamp input + +# Authors: Padraig Gleeson @ UCL + +from bmtk.builder import NetworkBuilder + +net = NetworkBuilder("one_cell_iclamp") + +pos_x, pos_y = [0,0] + +template = 'nest:iaf_psc_alpha' + +net.add_nodes(N=1, pop_name='LIF_exc', location='VisL4', ei='e', + model_type='point_process', # use point_process to indicate we are using point model cells + model_template=template, + x=pos_x, y=pos_y, + dynamics_params='473863035_point.json') + + +net.build() +net.save(output_dir='../input/network') + +print 'Built: %s'%net.name diff --git a/examples/sim_tests/intfire/one_cell_iclamp_nest/input/circuit_config.json b/examples/sim_tests/intfire/one_cell_iclamp_nest/input/circuit_config.json new file mode 100644 index 0000000..7983bda --- /dev/null +++ b/examples/sim_tests/intfire/one_cell_iclamp_nest/input/circuit_config.json @@ -0,0 +1,24 @@ +{ + "manifest": { + "$NETWORK_DIR": "../input/network", + "$COMPONENT_DIR": "../../../shared_components" + }, + + "components": { + "synaptic_models_dir": "$COMPONENT_DIR/synaptic_models", + "mechanisms_dir":"$COMPONENT_DIR/mechanisms", + "point_neuron_models_dir": "$COMPONENT_DIR/point_neuron_models_dir" + }, + + "networks": { + "nodes": [ + { + "nodes_file": "$NETWORK_DIR/one_cell_iclamp_nodes.h5", + "node_types_file": "$NETWORK_DIR/one_cell_iclamp_node_types.csv" + } + ] + }, + + + "target_simulator": "NEST" +} diff --git a/examples/sim_tests/intfire/one_cell_iclamp_nest/input/config.json b/examples/sim_tests/intfire/one_cell_iclamp_nest/input/config.json new file mode 100644 index 0000000..890caa3 --- /dev/null +++ b/examples/sim_tests/intfire/one_cell_iclamp_nest/input/config.json @@ -0,0 +1,4 @@ +{ + "network": "./circuit_config.json", + "simulation": "./simulation_config.json" +} diff --git a/examples/sim_tests/intfire/one_cell_iclamp_nest/input/network/one_cell_iclamp_node_types.csv b/examples/sim_tests/intfire/one_cell_iclamp_nest/input/network/one_cell_iclamp_node_types.csv new file mode 100644 index 0000000..8783e2a --- /dev/null +++ b/examples/sim_tests/intfire/one_cell_iclamp_nest/input/network/one_cell_iclamp_node_types.csv @@ -0,0 +1,2 @@ +node_type_id ei model_type pop_name location model_template x y dynamics_params +100 e point_process LIF_exc VisL4 nest:iaf_psc_alpha 0 0 473863035_point.json diff --git a/examples/sim_tests/intfire/one_cell_iclamp_nest/input/network/one_cell_iclamp_nodes.h5 b/examples/sim_tests/intfire/one_cell_iclamp_nest/input/network/one_cell_iclamp_nodes.h5 new file mode 100644 index 0000000..ac9bfb1 Binary files /dev/null and b/examples/sim_tests/intfire/one_cell_iclamp_nest/input/network/one_cell_iclamp_nodes.h5 differ diff --git a/examples/sim_tests/intfire/one_cell_iclamp_nest/input/node_sets.json b/examples/sim_tests/intfire/one_cell_iclamp_nest/input/node_sets.json new file mode 100644 index 0000000..baa3154 --- /dev/null +++ b/examples/sim_tests/intfire/one_cell_iclamp_nest/input/node_sets.json @@ -0,0 +1,5 @@ +{ + "point_nodes": { + "model_type": "point_process" + } +} diff --git a/examples/sim_tests/intfire/one_cell_iclamp_nest/input/simulation_config.json b/examples/sim_tests/intfire/one_cell_iclamp_nest/input/simulation_config.json new file mode 100644 index 0000000..387269f --- /dev/null +++ b/examples/sim_tests/intfire/one_cell_iclamp_nest/input/simulation_config.json @@ -0,0 +1,57 @@ +{ + "manifest": { + "$OUTPUT_DIR": "../test/output", + "$INPUT_DIR": "../input" + }, + + "run": { + "tstop": 1000.0, + "dt": 0.01, + "spike_threshold": -15, + "nsteps_block": 10000 + }, + + "target_simulator":"NEST", + + "network": "$INPUT_DIR/circuit_config.json", + + "conditions": { + "celsius": 34.0, + "v_init": -80 + }, + + "node_sets_file": "$INPUT_DIR/node_sets.json", + + "mechanisms_dir": "../shared_components_mechanisms", + + + "inputs": { + "current_clamp_1": { + "input_type": "current_clamp", + "module": "IClamp", + "node_set": "point_nodes", + "amp": 190.0, + "delay": 100.0, + "duration": 800.0 + } + }, + + "output":{ + "output_dir": "$OUTPUT_DIR", + "log_file": "log.txt", + "spikes_file": "spikes.h5", + "spikes_sort_order": "time" + }, + + "reports": { + + "membrane_potential": { + "cells": "point_nodes", + "variable_name": "V_m", + "module": "multimeter_report", + "sections": "soma", + "enabled": true + } + + } +} diff --git a/examples/sim_tests/intfire/one_cell_iclamp_nest/test/analyze_output.py b/examples/sim_tests/intfire/one_cell_iclamp_nest/test/analyze_output.py new file mode 100644 index 0000000..508c1c2 --- /dev/null +++ b/examples/sim_tests/intfire/one_cell_iclamp_nest/test/analyze_output.py @@ -0,0 +1,9 @@ +import sys + +sys.path.append("../../../shared_components/scripts") + +from analyze_output_utils import plot_data + +if __name__ == '__main__': + + plot_data('output/membrane_potential.h5', 'mV', 'Membrane Potential') \ No newline at end of file diff --git a/examples/sim_tests/intfire/one_cell_iclamp_nest/test/run_bmtk.sh b/examples/sim_tests/intfire/one_cell_iclamp_nest/test/run_bmtk.sh new file mode 100755 index 0000000..bcb2854 --- /dev/null +++ b/examples/sim_tests/intfire/one_cell_iclamp_nest/test/run_bmtk.sh @@ -0,0 +1,10 @@ +#!/bin/bash + +set -e + +rm -rf output +rm -rf __pycache__ +# python -m trace --trace ../shared_components/scripts/run_bionet.py ../input/config.json +python ../../../shared_components/scripts/run_bionet.py NEST ../input/config.json + +pytest -vvs diff --git a/examples/sim_tests/intfire/one_cell_iclamp_nest/test/run_pynml.jnml.sh b/examples/sim_tests/intfire/one_cell_iclamp_nest/test/run_pynml.jnml.sh new file mode 100755 index 0000000..8e4d810 --- /dev/null +++ b/examples/sim_tests/intfire/one_cell_iclamp_nest/test/run_pynml.jnml.sh @@ -0,0 +1,5 @@ +#!/bin/bash + +set -e + +pynml-sonata sim ../input/config.json -jnml diff --git a/examples/sim_tests/intfire/one_cell_iclamp_nest/test/run_pynml.neuron.sh b/examples/sim_tests/intfire/one_cell_iclamp_nest/test/run_pynml.neuron.sh new file mode 100755 index 0000000..54c7337 --- /dev/null +++ b/examples/sim_tests/intfire/one_cell_iclamp_nest/test/run_pynml.neuron.sh @@ -0,0 +1,5 @@ +#!/bin/bash + +set -e + +pynml-sonata sim ../input/config.json -neuron diff --git a/examples/sim_tests/intfire/one_cell_iclamp_nest/test/run_pynn.py b/examples/sim_tests/intfire/one_cell_iclamp_nest/test/run_pynn.py new file mode 100644 index 0000000..51ecfe1 --- /dev/null +++ b/examples/sim_tests/intfire/one_cell_iclamp_nest/test/run_pynn.py @@ -0,0 +1,8 @@ +from pyNN.serialization import import_from_sonata, load_sonata_simulation_plan +import pyNN.nest as sim + +simulation_plan = load_sonata_simulation_plan("../input/simulation_config.json") +simulation_plan.setup(sim) +net = import_from_sonata("../input/circuit_config.json", sim) + +simulation_plan.execute(net) \ No newline at end of file diff --git a/examples/sim_tests/intfire/ten_cells_iclamp_nest/bmtk_build/build_network.py b/examples/sim_tests/intfire/ten_cells_iclamp_nest/bmtk_build/build_network.py new file mode 100644 index 0000000..b0e699b --- /dev/null +++ b/examples/sim_tests/intfire/ten_cells_iclamp_nest/bmtk_build/build_network.py @@ -0,0 +1,65 @@ +#!/bin/env python + +# Sonata Simulator Test Suite +# 10-cell circuit, integrate and fire cells, 2 populations, current clamp input to one + +# Authors: Padraig Gleeson @ UCL + +import numpy as np +from neuron import h +from bmtk.builder import NetworkBuilder + +net = NetworkBuilder("ten_cells_iclamp") # ten aot 10, as NeuroML doesn't like ids that start with a digit... + +def generate_positions(N, x0=0.0, x1=300.0, y0=0.0, y1=100.0): + X = np.random.uniform(x0, x1, N) + Y = np.random.uniform(y0, y1, N) + return X, Y + +per_pop = 5 + +pos_x, pos_y = generate_positions(per_pop) + +template = 'nest:iaf_psc_alpha' + +net.add_nodes(N=per_pop, pop_name='LIF_exc', location='VisL4', ei='e', + model_type='point_process', # use point_process to indicate were are using point model cells + model_template=template, + x=pos_x, y=pos_y, + dynamics_params='472363762_point.json') + +pos_x, pos_y = generate_positions(per_pop) + +net.add_nodes(N=per_pop, pop_name='LIF_inh', location='VisL4', ei='i', + model_type='point_process', # use point_process to indicate were are using point model cells + model_template=template, + x=pos_x, y=pos_y, + dynamics_params='472363762_point.json') + + +def recurrent_connections(src_cells, trg_cell, n_syns): + + # Increasing numbers of synapses per target cell... + #print(trg_cell) + #print(dir(trg_cell)) + #print('--- %s'%trg_cell._node_params) + synapses = [20 * n_syns* (trg_cell.node_id-per_pop)] *len(src_cells) + return synapses + + +net.add_edges(source={'ei': 'e'}, target={'ei': 'i', 'model_type': 'point_process'}, + iterator='all_to_one', + connection_rule=recurrent_connections, + connection_params={'n_syns': 1}, + syn_weight=10, + weight_function='wmax', + delay=2.0, + dynamics_params='ExcToInh.json', + model_template='static_synapse') + + +net.build() +net.save(output_dir='../input/network') + +print 'Built: %s'%net.name + diff --git a/examples/sim_tests/intfire/ten_cells_iclamp_nest/input/circuit_config.json b/examples/sim_tests/intfire/ten_cells_iclamp_nest/input/circuit_config.json new file mode 100644 index 0000000..b73ec46 --- /dev/null +++ b/examples/sim_tests/intfire/ten_cells_iclamp_nest/input/circuit_config.json @@ -0,0 +1,29 @@ +{ + "manifest": { + "$NETWORK_DIR": "../input/network", + "$COMPONENT_DIR": "../../../shared_components" + }, + + "components": { + "morphologies_dir": "$COMPONENT_DIR/morphologies", + "synaptic_models_dir": "$COMPONENT_DIR/synaptic_models", + "mechanisms_dir":"$COMPONENT_DIR/mechanisms", + "point_neuron_models_dir": "$COMPONENT_DIR/point_neuron_models_dir" + }, + + "networks": { + "nodes": [ + { + "nodes_file": "$NETWORK_DIR/ten_cells_iclamp_nodes.h5", + "node_types_file": "$NETWORK_DIR/ten_cells_iclamp_node_types.csv" + } + ], + + "edges": [ + { + "edges_file": "$NETWORK_DIR/ten_cells_iclamp_ten_cells_iclamp_edges.h5", + "edge_types_file": "$NETWORK_DIR/ten_cells_iclamp_ten_cells_iclamp_edge_types.csv" + } + ] + } +} \ No newline at end of file diff --git a/examples/sim_tests/intfire/ten_cells_iclamp_nest/input/config.json b/examples/sim_tests/intfire/ten_cells_iclamp_nest/input/config.json new file mode 100644 index 0000000..890caa3 --- /dev/null +++ b/examples/sim_tests/intfire/ten_cells_iclamp_nest/input/config.json @@ -0,0 +1,4 @@ +{ + "network": "./circuit_config.json", + "simulation": "./simulation_config.json" +} diff --git a/examples/sim_tests/intfire/ten_cells_iclamp_nest/input/network/ten_cells_iclamp_node_types.csv b/examples/sim_tests/intfire/ten_cells_iclamp_nest/input/network/ten_cells_iclamp_node_types.csv new file mode 100644 index 0000000..4f0a45f --- /dev/null +++ b/examples/sim_tests/intfire/ten_cells_iclamp_nest/input/network/ten_cells_iclamp_node_types.csv @@ -0,0 +1,3 @@ +node_type_id ei pop_name location model_template model_type dynamics_params +100 e LIF_exc VisL4 nest:iaf_psc_alpha point_process 472363762_point.json +101 i LIF_inh VisL4 nest:iaf_psc_alpha point_process 472363762_point.json diff --git a/examples/sim_tests/intfire/ten_cells_iclamp_nest/input/network/ten_cells_iclamp_nodes.h5 b/examples/sim_tests/intfire/ten_cells_iclamp_nest/input/network/ten_cells_iclamp_nodes.h5 new file mode 100644 index 0000000..094ea26 Binary files /dev/null and b/examples/sim_tests/intfire/ten_cells_iclamp_nest/input/network/ten_cells_iclamp_nodes.h5 differ diff --git a/examples/sim_tests/intfire/ten_cells_iclamp_nest/input/network/ten_cells_iclamp_ten_cells_iclamp_edge_types.csv b/examples/sim_tests/intfire/ten_cells_iclamp_nest/input/network/ten_cells_iclamp_ten_cells_iclamp_edge_types.csv new file mode 100644 index 0000000..1f10453 --- /dev/null +++ b/examples/sim_tests/intfire/ten_cells_iclamp_nest/input/network/ten_cells_iclamp_ten_cells_iclamp_edge_types.csv @@ -0,0 +1,2 @@ +edge_type_id target_query source_query delay weight_function syn_weight dynamics_params model_template +100 model_type=='point_process'&ei=='i' ei=='e' 2.0 wmax 10 ExcToInh.json static_synapse diff --git a/examples/sim_tests/intfire/ten_cells_iclamp_nest/input/network/ten_cells_iclamp_ten_cells_iclamp_edges.h5 b/examples/sim_tests/intfire/ten_cells_iclamp_nest/input/network/ten_cells_iclamp_ten_cells_iclamp_edges.h5 new file mode 100644 index 0000000..18953c3 Binary files /dev/null and b/examples/sim_tests/intfire/ten_cells_iclamp_nest/input/network/ten_cells_iclamp_ten_cells_iclamp_edges.h5 differ diff --git a/examples/sim_tests/intfire/ten_cells_iclamp_nest/input/node_sets.json b/examples/sim_tests/intfire/ten_cells_iclamp_nest/input/node_sets.json new file mode 100644 index 0000000..48b4c5d --- /dev/null +++ b/examples/sim_tests/intfire/ten_cells_iclamp_nest/input/node_sets.json @@ -0,0 +1,9 @@ +{ + "point_nodes": { + "model_type": "point_process" + }, + "pre_nodes": { + "model_type": "point_process", + "ei": "e" + } +} diff --git a/examples/sim_tests/intfire/ten_cells_iclamp_nest/input/simulation_config.json b/examples/sim_tests/intfire/ten_cells_iclamp_nest/input/simulation_config.json new file mode 100644 index 0000000..0f1313e --- /dev/null +++ b/examples/sim_tests/intfire/ten_cells_iclamp_nest/input/simulation_config.json @@ -0,0 +1,53 @@ +{ + "manifest": { + "$OUTPUT_DIR": "../test/output", + "$INPUT_DIR": "../input" + }, + + "run": { + "tstop": 1000.0, + "dt": 0.01, + "spike_threshold": -15, + "nsteps_block": 10000 + }, + + "target_simulator":"NEST", + + "conditions": { + "celsius": 34.4, + "v_init": -80 + }, + + "node_sets_file": "$INPUT_DIR/node_sets.json", + + "inputs": { + "current_clamp_1": { + "input_type": "current_clamp", + "module": "IClamp", + "node_set": "pre_nodes", + "amp": 190.0, + "delay": 100.0, + "duration": 800.0 + } + }, + + "output":{ + "output_dir": "$OUTPUT_DIR", + "log_file": "log.txt", + "spikes_file": "spikes.h5", + "spikes_sort_order": "time" + }, + + "reports": { + + "membrane_potential": { + "cells": "point_nodes", + "variable_name": "V_m", + "module": "multimeter_report", + "sections": "soma", + "enabled": true + } + + } + +} diff --git a/examples/sim_tests/intfire/ten_cells_iclamp_nest/test/analyze_output.py b/examples/sim_tests/intfire/ten_cells_iclamp_nest/test/analyze_output.py new file mode 100644 index 0000000..a4eec1b --- /dev/null +++ b/examples/sim_tests/intfire/ten_cells_iclamp_nest/test/analyze_output.py @@ -0,0 +1,12 @@ +import sys +from bmtk.analyzer.visualization.spikes import plot_spikes + + +sys.path.append("../../../shared_components/scripts") + +from analyze_output_utils import plot_data + +if __name__ == '__main__': + + plot_data('output/membrane_potential.h5', 'mV', 'Membrane Potential', show_already=True) + plot_spikes('../input/network/ten_cells_iclamp_nodes.h5', '../input/network/ten_cells_iclamp_node_types.csv', 'output/spikes.h5') \ No newline at end of file diff --git a/examples/sim_tests/intfire/ten_cells_iclamp_nest/test/run_bmtk.sh b/examples/sim_tests/intfire/ten_cells_iclamp_nest/test/run_bmtk.sh new file mode 100755 index 0000000..bcb2854 --- /dev/null +++ b/examples/sim_tests/intfire/ten_cells_iclamp_nest/test/run_bmtk.sh @@ -0,0 +1,10 @@ +#!/bin/bash + +set -e + +rm -rf output +rm -rf __pycache__ +# python -m trace --trace ../shared_components/scripts/run_bionet.py ../input/config.json +python ../../../shared_components/scripts/run_bionet.py NEST ../input/config.json + +pytest -vvs diff --git a/examples/sim_tests/intfire/ten_cells_iclamp_nest/test/run_pynml.jnml.sh b/examples/sim_tests/intfire/ten_cells_iclamp_nest/test/run_pynml.jnml.sh new file mode 100755 index 0000000..8e4d810 --- /dev/null +++ b/examples/sim_tests/intfire/ten_cells_iclamp_nest/test/run_pynml.jnml.sh @@ -0,0 +1,5 @@ +#!/bin/bash + +set -e + +pynml-sonata sim ../input/config.json -jnml diff --git a/examples/sim_tests/intfire/ten_cells_iclamp_nest/test/run_pynml.neuron.sh b/examples/sim_tests/intfire/ten_cells_iclamp_nest/test/run_pynml.neuron.sh new file mode 100755 index 0000000..54c7337 --- /dev/null +++ b/examples/sim_tests/intfire/ten_cells_iclamp_nest/test/run_pynml.neuron.sh @@ -0,0 +1,5 @@ +#!/bin/bash + +set -e + +pynml-sonata sim ../input/config.json -neuron diff --git a/examples/sim_tests/intfire/ten_cells_iclamp_nest/test/run_pynn.py b/examples/sim_tests/intfire/ten_cells_iclamp_nest/test/run_pynn.py new file mode 100644 index 0000000..51ecfe1 --- /dev/null +++ b/examples/sim_tests/intfire/ten_cells_iclamp_nest/test/run_pynn.py @@ -0,0 +1,8 @@ +from pyNN.serialization import import_from_sonata, load_sonata_simulation_plan +import pyNN.nest as sim + +simulation_plan = load_sonata_simulation_plan("../input/simulation_config.json") +simulation_plan.setup(sim) +net = import_from_sonata("../input/circuit_config.json", sim) + +simulation_plan.execute(net) \ No newline at end of file diff --git a/examples/sim_tests/intfire/ten_cells_spikes_nest/bmtk_build/build_network.py b/examples/sim_tests/intfire/ten_cells_spikes_nest/bmtk_build/build_network.py new file mode 100644 index 0000000..5ea68b0 --- /dev/null +++ b/examples/sim_tests/intfire/ten_cells_spikes_nest/bmtk_build/build_network.py @@ -0,0 +1,55 @@ +import os +import numpy as np + +from bmtk.builder.networks import NetworkBuilder + + +net_pre = NetworkBuilder("pre") +net_post = NetworkBuilder("post") + +num_pre_post = 5 + + +def generate_positions(N, x0=0.0, x1=300.0, y0=0.0, y1=100.0): + X = np.random.uniform(x0, x1, N) + Y = np.random.uniform(y0, y1, N) + return X, Y + +net_pre.add_nodes(N=num_pre_post, pop_name='VirtualCells', ei='e', location='TW', model_type='virtual') + +pos_x, pos_y = generate_positions(num_pre_post) +net_post.add_nodes(N=num_pre_post, pop_name='Exc', location='VisL4', ei='e', + model_type='point_process', # use point_process to indicate were are using point model cells + model_template='nest:iaf_psc_alpha', # Tell the simulator to use the NEURON built-in IntFire1 type cell + x=pos_x, y=pos_y, + dynamics_params='472363762_point.json') + + + +def recurrent_connections(src_cells, trg_cell, n_syns): + + synapses = [n_syns*(np.random.random() > 0.5) for i in range(len(src_cells))] + + return synapses + + + +pre_post = net_pre.add_edges(source=net_pre.nodes(), target=net_post.nodes(pop_name='Exc'), + iterator='all_to_one', + connection_rule=recurrent_connections, + connection_params={'n_syns': 1}, + model_template='static_synapse', + weight_function='wmax', + delay=0.1, + dynamics_params='ExcToExc.json') + +pre_post.add_properties('syn_weight', rule=25, dtypes=np.float) + +net_pre.build() +net_pre.save(output_dir='../input/network') + +net_post.build() +net_post.save(output_dir='../input/network') + + +print 'done' \ No newline at end of file diff --git a/examples/sim_tests/intfire/ten_cells_spikes_nest/bmtk_build/generate_spikes.py b/examples/sim_tests/intfire/ten_cells_spikes_nest/bmtk_build/generate_spikes.py new file mode 100644 index 0000000..401e7e1 --- /dev/null +++ b/examples/sim_tests/intfire/ten_cells_spikes_nest/bmtk_build/generate_spikes.py @@ -0,0 +1,5 @@ +import os +from bmtk.utils.io.spike_trains import PoissonSpikesGenerator + +psg = PoissonSpikesGenerator(range(5), 10.0, tstop=200.0) +psg.to_hdf5('../input/external_spike_trains.h5') diff --git a/examples/sim_tests/intfire/ten_cells_spikes_nest/input/circuit_config.json b/examples/sim_tests/intfire/ten_cells_spikes_nest/input/circuit_config.json new file mode 100644 index 0000000..db5f0c5 --- /dev/null +++ b/examples/sim_tests/intfire/ten_cells_spikes_nest/input/circuit_config.json @@ -0,0 +1,36 @@ +{ + "manifest": { + "$NETWORK_DIR": "../input/network", + "$COMPONENT_DIR": "../../../shared_components/nest_models" + }, + + "components": { + "morphologies_dir": "$COMPONENT_DIR/morphologies", + "synaptic_models_dir": "$COMPONENT_DIR/synaptic_models", + "mechanisms_dir":"$COMPONENT_DIR/mechanisms", + "point_neuron_models_dir": "$COMPONENT_DIR/cell_models" + }, + + "networks": { + "nodes": [ + { + "nodes_file": "$NETWORK_DIR/pre_nodes.h5", + "node_types_file": "$NETWORK_DIR/pre_node_types.csv" + }, + { + "nodes_file": "$NETWORK_DIR/post_nodes.h5", + "node_types_file": "$NETWORK_DIR/post_node_types.csv" + } + ], + + "edges": [ + { + "edges_file": "$NETWORK_DIR/pre_post_edges.h5", + "edge_types_file": "$NETWORK_DIR/pre_post_edge_types.csv" + } + ] + }, + + "target_simulator": "NEST" +} + diff --git a/examples/sim_tests/intfire/ten_cells_spikes_nest/input/config.json b/examples/sim_tests/intfire/ten_cells_spikes_nest/input/config.json new file mode 100644 index 0000000..890caa3 --- /dev/null +++ b/examples/sim_tests/intfire/ten_cells_spikes_nest/input/config.json @@ -0,0 +1,4 @@ +{ + "network": "./circuit_config.json", + "simulation": "./simulation_config.json" +} diff --git a/examples/sim_tests/intfire/ten_cells_spikes_nest/input/external_spike_trains.h5 b/examples/sim_tests/intfire/ten_cells_spikes_nest/input/external_spike_trains.h5 new file mode 100644 index 0000000..d6a09f7 Binary files /dev/null and b/examples/sim_tests/intfire/ten_cells_spikes_nest/input/external_spike_trains.h5 differ diff --git a/examples/sim_tests/intfire/ten_cells_spikes_nest/input/network/post_node_types.csv b/examples/sim_tests/intfire/ten_cells_spikes_nest/input/network/post_node_types.csv new file mode 100644 index 0000000..ce7494e --- /dev/null +++ b/examples/sim_tests/intfire/ten_cells_spikes_nest/input/network/post_node_types.csv @@ -0,0 +1,2 @@ +node_type_id ei pop_name location model_template model_type dynamics_params +100 e Exc VisL4 nest:iaf_psc_alpha point_process 472363762_point.json diff --git a/examples/sim_tests/intfire/ten_cells_spikes_nest/input/network/post_nodes.h5 b/examples/sim_tests/intfire/ten_cells_spikes_nest/input/network/post_nodes.h5 new file mode 100644 index 0000000..a459ce1 Binary files /dev/null and b/examples/sim_tests/intfire/ten_cells_spikes_nest/input/network/post_nodes.h5 differ diff --git a/examples/sim_tests/intfire/ten_cells_spikes_nest/input/network/pre_node_types.csv b/examples/sim_tests/intfire/ten_cells_spikes_nest/input/network/pre_node_types.csv new file mode 100644 index 0000000..b4a880c --- /dev/null +++ b/examples/sim_tests/intfire/ten_cells_spikes_nest/input/network/pre_node_types.csv @@ -0,0 +1,2 @@ +node_type_id model_type ei location pop_name +100 virtual e TW VirtualCells diff --git a/examples/sim_tests/intfire/ten_cells_spikes_nest/input/network/pre_nodes.h5 b/examples/sim_tests/intfire/ten_cells_spikes_nest/input/network/pre_nodes.h5 new file mode 100644 index 0000000..a0268dc Binary files /dev/null and b/examples/sim_tests/intfire/ten_cells_spikes_nest/input/network/pre_nodes.h5 differ diff --git a/examples/sim_tests/intfire/ten_cells_spikes_nest/input/network/pre_post_edge_types.csv b/examples/sim_tests/intfire/ten_cells_spikes_nest/input/network/pre_post_edge_types.csv new file mode 100644 index 0000000..ec56c67 --- /dev/null +++ b/examples/sim_tests/intfire/ten_cells_spikes_nest/input/network/pre_post_edge_types.csv @@ -0,0 +1,2 @@ +edge_type_id target_query source_query delay weight_function dynamics_params model_template +100 pop_name=='Exc' * 0.1 wmax ExcToExc.json static_synapse diff --git a/examples/sim_tests/intfire/ten_cells_spikes_nest/input/network/pre_post_edges.h5 b/examples/sim_tests/intfire/ten_cells_spikes_nest/input/network/pre_post_edges.h5 new file mode 100644 index 0000000..c4031f5 Binary files /dev/null and b/examples/sim_tests/intfire/ten_cells_spikes_nest/input/network/pre_post_edges.h5 differ diff --git a/examples/sim_tests/intfire/ten_cells_spikes_nest/input/node_sets.json b/examples/sim_tests/intfire/ten_cells_spikes_nest/input/node_sets.json new file mode 100644 index 0000000..e2293e4 --- /dev/null +++ b/examples/sim_tests/intfire/ten_cells_spikes_nest/input/node_sets.json @@ -0,0 +1,20 @@ +{ + "point_nodes": { + "model_type": "point_process" + }, + "e_nodes": { + "model_type": "point_process", + "ei": "e" + }, + "pre": { + "population": "pre" + }, + "post": { + "population": "post" + }, + "recorded_cells": { + "population": "post", + "node_id": [0, 1, 2, 3, 4] + } + +} \ No newline at end of file diff --git a/examples/sim_tests/intfire/ten_cells_spikes_nest/input/simulation_config.json b/examples/sim_tests/intfire/ten_cells_spikes_nest/input/simulation_config.json new file mode 100644 index 0000000..dd03aa1 --- /dev/null +++ b/examples/sim_tests/intfire/ten_cells_spikes_nest/input/simulation_config.json @@ -0,0 +1,50 @@ +{ + "manifest": { + "$OUTPUT_DIR": "../test/output", + "$INPUT_DIR": "../input" + }, + + "run": { + "tstop": 400.0, + "dt": 0.001, + "nsteps_block": 5000 + }, + + "target_simulator":"NEST", + + "conditions": { + "celsius": 34.0, + "v_init": -80 + }, + + "node_sets_file": "$INPUT_DIR/node_sets.json", + + "inputs": { + "external_spike_trains": { + "input_type": "spikes", + "module": "h5", + "input_file": "$INPUT_DIR/external_spike_trains.h5", + "node_set": "pre" + } + }, + + + "output":{ + "output_dir": "$OUTPUT_DIR", + "log_file": "log.txt", + "spikes_file": "spikes.h5", + "spikes_sort_order": "time" + }, + + + "reports": { + "membrane_potential": { + "cells": "recorded_cells", + "variable_name": "V_m", + "module": "multimeter_report", + "sections": "soma", + "enabled": true + } + } + +} \ No newline at end of file diff --git a/examples/sim_tests/intfire/ten_cells_spikes_nest/test/analyze_output.py b/examples/sim_tests/intfire/ten_cells_spikes_nest/test/analyze_output.py new file mode 100644 index 0000000..2f81b17 --- /dev/null +++ b/examples/sim_tests/intfire/ten_cells_spikes_nest/test/analyze_output.py @@ -0,0 +1,13 @@ +import sys +from bmtk.analyzer.visualization.spikes import plot_spikes + + +sys.path.append("../../../shared_components/scripts") + +from analyze_output_utils import plot_data + +if __name__ == '__main__': + + plot_data('output/membrane_potential.h5', 'mV', 'Membrane Potential', show_already=True) + #plot_spikes('../input/network/pre_nodes.h5', '../input/network/pre_node_types.csv', 'output/spikes.h5') + #plot_spikes('../input/network/post_nodes.h5', '../input/network/post_node_types.csv', 'output/spikes.h5') \ No newline at end of file diff --git a/examples/sim_tests/intfire/ten_cells_spikes_nest/test/run_bmtk.sh b/examples/sim_tests/intfire/ten_cells_spikes_nest/test/run_bmtk.sh new file mode 100755 index 0000000..bcb2854 --- /dev/null +++ b/examples/sim_tests/intfire/ten_cells_spikes_nest/test/run_bmtk.sh @@ -0,0 +1,10 @@ +#!/bin/bash + +set -e + +rm -rf output +rm -rf __pycache__ +# python -m trace --trace ../shared_components/scripts/run_bionet.py ../input/config.json +python ../../../shared_components/scripts/run_bionet.py NEST ../input/config.json + +pytest -vvs diff --git a/examples/sim_tests/intfire/ten_cells_spikes_nest/test/run_pynml.jnml.sh b/examples/sim_tests/intfire/ten_cells_spikes_nest/test/run_pynml.jnml.sh new file mode 100755 index 0000000..8e4d810 --- /dev/null +++ b/examples/sim_tests/intfire/ten_cells_spikes_nest/test/run_pynml.jnml.sh @@ -0,0 +1,5 @@ +#!/bin/bash + +set -e + +pynml-sonata sim ../input/config.json -jnml diff --git a/examples/sim_tests/intfire/ten_cells_spikes_nest/test/run_pynml.neuron.sh b/examples/sim_tests/intfire/ten_cells_spikes_nest/test/run_pynml.neuron.sh new file mode 100755 index 0000000..54c7337 --- /dev/null +++ b/examples/sim_tests/intfire/ten_cells_spikes_nest/test/run_pynml.neuron.sh @@ -0,0 +1,5 @@ +#!/bin/bash + +set -e + +pynml-sonata sim ../input/config.json -neuron diff --git a/examples/sim_tests/intfire/ten_cells_spikes_nest/test/run_pynn.py b/examples/sim_tests/intfire/ten_cells_spikes_nest/test/run_pynn.py new file mode 100644 index 0000000..51ecfe1 --- /dev/null +++ b/examples/sim_tests/intfire/ten_cells_spikes_nest/test/run_pynn.py @@ -0,0 +1,8 @@ +from pyNN.serialization import import_from_sonata, load_sonata_simulation_plan +import pyNN.nest as sim + +simulation_plan = load_sonata_simulation_plan("../input/simulation_config.json") +simulation_plan.setup(sim) +net = import_from_sonata("../input/circuit_config.json", sim) + +simulation_plan.execute(net) \ No newline at end of file diff --git a/examples/sim_tests/intfire/ten_cells_spikes_nrn/bmtk_build/build_network.py b/examples/sim_tests/intfire/ten_cells_spikes_nrn/bmtk_build/build_network.py new file mode 100644 index 0000000..9d36180 --- /dev/null +++ b/examples/sim_tests/intfire/ten_cells_spikes_nrn/bmtk_build/build_network.py @@ -0,0 +1,136 @@ +import os +import numpy as np + +from bmtk.builder.networks import NetworkBuilder + + +net_pre = NetworkBuilder("pre") +net_post = NetworkBuilder("post") + + +net_pre.add_nodes(N=5, pop_name='VirtualCells', ei='e', location='TW', model_type='virtual') + +net_post.add_nodes(N=5, pop_name='Exc', location='VisL4', ei='e', + model_type='point_process', # use point_process to indicate were are using point model cells + model_template='nrn:IntFire1', # Tell the simulator to use the NEURON built-in IntFire1 type cell + dynamics_params='IntFire1_exc_1.json') + + + + +def recurrent_connections(src_cells, trg_cell, n_syns): + if np.random.random() > .5: + synapses = [n_syns]*len(src_cells) + else: + synapses = [None]*len(src_cells) + return synapses + +def recurrent_connections_low(src_cells, trg_cell, n_syns): + + synapses = [n_syns*(np.random.random() > .1) for i in range(len(src_cells))] + + return synapses + +def recurrent_connections_low2(src_cells, trg_cell, n_syns): + + synapses = [n_syns*(np.random.random() > .1) for i in range(len(src_cells))] + + return synapses + + +net_pre.add_edges(source=net_pre.nodes(), target=net_post.nodes(pop_name='Exc'), + iterator='all_to_one', + connection_rule=recurrent_connections_low, + connection_params={'n_syns': 1}, + syn_weight=5, + weight_function='wmax', + delay=0.0, + dynamics_params='instanteneousExc.json') +''' + ''' + + +''' +net.add_edges(source={'ei': 'i'}, target={'ei': 'i', 'model_type': 'point_process'}, + iterator='all_to_one', + connection_rule=recurrent_connections, + connection_params={'n_syns': 10}, + syn_weight=0.01, + weight_function='wmax', + delay=0.0, + dynamics_params='instanteneousInh.json') + + + +net.add_edges(source={'ei': 'i'}, target={'ei': 'e', 'model_type': 'point_process'}, + iterator='all_to_one', + connection_rule=recurrent_connections, + connection_params={'n_syns': 10}, + syn_weight=0.15, + weight_function='wmax', + delay=0.0, + dynamics_params='instanteneousInh.json') + + +net.add_edges(source={'ei': 'e'}, target={'pop_name': 'LIF_inh'}, + iterator='all_to_one', + connection_rule=recurrent_connections, + connection_params={'n_syns': 10}, + syn_weight=0.3, + weight_function='wmax', + delay=0.0, + dynamics_params='instanteneousExc.json') + + +net.add_edges(source={'ei': 'e'}, target={'pop_name': 'LIF_exc'}, + iterator='all_to_one', + connection_rule=recurrent_connections_low2, + connection_params={'n_syns': 1}, + syn_weight=0.002, + weight_function='wmax', + delay=2.0, + dynamics_params='instanteneousExc.json') +''' + + + + +def generate_positions(N, x0=0.0, x1=300.0, y0=0.0, y1=100.0): + X = np.random.uniform(x0, x1, N) + Y = np.random.uniform(y0, y1, N) + return X, Y + + +''' +tw = NetworkBuilder("tw") +tw.add_nodes(N=30, pop_name='TW', ei='e', location='TW', model_type='virtual') + +tw.add_edges(source=tw.nodes(), target=net.nodes(pop_name='LIF_exc'), + iterator='all_to_one', + connection_rule=recurrent_connections_low, + connection_params={'n_syns': 1}, + syn_weight=0.5, + weight_function='wmax', + delay=0.0, + dynamics_params='instanteneousExc.json') + + +tw.add_edges(source=tw.nodes(), target=net.nodes(pop_name='LIF_inh'), + connection_rule=1, + syn_weight=0.2, + weight_function='wmax', + delay=0.0, + dynamics_params='instanteneousExc.json') + +tw.build() +tw.save(output_dir='../input/network')''' + + +net_pre.build() +net_pre.save(output_dir='../input/network') + +net_post.build() +net_post.save(output_dir='../input/network') + + +print 'done' \ No newline at end of file diff --git a/examples/sim_tests/intfire/ten_cells_spikes_nrn/input/circuit_config.json b/examples/sim_tests/intfire/ten_cells_spikes_nrn/input/circuit_config.json new file mode 100644 index 0000000..370c55c --- /dev/null +++ b/examples/sim_tests/intfire/ten_cells_spikes_nrn/input/circuit_config.json @@ -0,0 +1,34 @@ +{ + "manifest": { + "$NETWORK_DIR": "../input/network", + "$COMPONENT_DIR": "../../../shared_components" + }, + + "components": { + "morphologies_dir": "$COMPONENT_DIR/morphologies", + "synaptic_models_dir": "$COMPONENT_DIR/synaptic_models", + "mechanisms_dir":"$COMPONENT_DIR/mechanisms", + "point_neuron_models_dir": "$COMPONENT_DIR/point_neuron_models_dir" + }, + + "networks": { + "nodes": [ + { + "nodes_file": "$NETWORK_DIR/pre_nodes.h5", + "node_types_file": "$NETWORK_DIR/pre_node_types.csv" + }, + { + "nodes_file": "$NETWORK_DIR/post_nodes.h5", + "node_types_file": "$NETWORK_DIR/post_node_types.csv" + } + ], + + "edges": [ + { + "edges_file": "$NETWORK_DIR/pre_post_edges.h5", + "edge_types_file": "$NETWORK_DIR/pre_post_edge_types.csv" + } + ] + } +} + diff --git a/examples/sim_tests/intfire/ten_cells_spikes_nrn/input/config.json b/examples/sim_tests/intfire/ten_cells_spikes_nrn/input/config.json new file mode 100644 index 0000000..890caa3 --- /dev/null +++ b/examples/sim_tests/intfire/ten_cells_spikes_nrn/input/config.json @@ -0,0 +1,4 @@ +{ + "network": "./circuit_config.json", + "simulation": "./simulation_config.json" +} diff --git a/examples/sim_tests/intfire/ten_cells_spikes_nrn/input/network/post_node_types.csv b/examples/sim_tests/intfire/ten_cells_spikes_nrn/input/network/post_node_types.csv new file mode 100644 index 0000000..78d1f83 --- /dev/null +++ b/examples/sim_tests/intfire/ten_cells_spikes_nrn/input/network/post_node_types.csv @@ -0,0 +1,2 @@ +node_type_id ei pop_name location model_template model_type dynamics_params +100 e Exc VisL4 nrn:IntFire1 point_process IntFire1_exc_1.json diff --git a/examples/sim_tests/intfire/ten_cells_spikes_nrn/input/network/post_nodes.h5 b/examples/sim_tests/intfire/ten_cells_spikes_nrn/input/network/post_nodes.h5 new file mode 100644 index 0000000..936b7ce Binary files /dev/null and b/examples/sim_tests/intfire/ten_cells_spikes_nrn/input/network/post_nodes.h5 differ diff --git a/examples/sim_tests/intfire/ten_cells_spikes_nrn/input/network/pre_node_types.csv b/examples/sim_tests/intfire/ten_cells_spikes_nrn/input/network/pre_node_types.csv new file mode 100644 index 0000000..b4a880c --- /dev/null +++ b/examples/sim_tests/intfire/ten_cells_spikes_nrn/input/network/pre_node_types.csv @@ -0,0 +1,2 @@ +node_type_id model_type ei location pop_name +100 virtual e TW VirtualCells diff --git a/examples/sim_tests/intfire/ten_cells_spikes_nrn/input/network/pre_nodes.h5 b/examples/sim_tests/intfire/ten_cells_spikes_nrn/input/network/pre_nodes.h5 new file mode 100644 index 0000000..2a24af3 Binary files /dev/null and b/examples/sim_tests/intfire/ten_cells_spikes_nrn/input/network/pre_nodes.h5 differ diff --git a/examples/sim_tests/intfire/ten_cells_spikes_nrn/input/network/pre_post_edge_types.csv b/examples/sim_tests/intfire/ten_cells_spikes_nrn/input/network/pre_post_edge_types.csv new file mode 100644 index 0000000..52ca28a --- /dev/null +++ b/examples/sim_tests/intfire/ten_cells_spikes_nrn/input/network/pre_post_edge_types.csv @@ -0,0 +1,2 @@ +edge_type_id target_query source_query delay weight_function syn_weight dynamics_params +100 pop_name=='Exc' * 0.0 wmax 5 instanteneousExc.json diff --git a/examples/sim_tests/intfire/ten_cells_spikes_nrn/input/network/pre_post_edges.h5 b/examples/sim_tests/intfire/ten_cells_spikes_nrn/input/network/pre_post_edges.h5 new file mode 100644 index 0000000..74d78c7 Binary files /dev/null and b/examples/sim_tests/intfire/ten_cells_spikes_nrn/input/network/pre_post_edges.h5 differ diff --git a/examples/sim_tests/intfire/ten_cells_spikes_nrn/input/node_sets.json b/examples/sim_tests/intfire/ten_cells_spikes_nrn/input/node_sets.json new file mode 100644 index 0000000..753820e --- /dev/null +++ b/examples/sim_tests/intfire/ten_cells_spikes_nrn/input/node_sets.json @@ -0,0 +1,9 @@ +{ + "point_nodes": { + "model_type": "point_process" + }, + "e_nodes": { + "model_type": "point_process", + "ei": "e" + } +} \ No newline at end of file diff --git a/examples/sim_tests/intfire/ten_cells_spikes_nrn/input/simulation_config.json b/examples/sim_tests/intfire/ten_cells_spikes_nrn/input/simulation_config.json new file mode 100644 index 0000000..26c7800 --- /dev/null +++ b/examples/sim_tests/intfire/ten_cells_spikes_nrn/input/simulation_config.json @@ -0,0 +1,52 @@ +{ + "manifest": { + "$OUTPUT_DIR": "./output", + "$INPUT_DIR": "../input" + }, + + "run": { + "tstop": 3000.0, + "dt": 0.1, + "dL": 20.0, + "spike_threshold": -15, + "nsteps_block": 5000, + "overwrite_output_dir": true + }, + + "target_simulator":"NEURON", + + "conditions": { + "celsius": 34.0, + "v_init": -80 + }, + + "node_sets_file": "$INPUT_DIR/node_sets.json", + + "inputs": { + "virtual_nodes_spikes": { + "input_type": "spikes", + "module": "h5", + "input_file": "$INPUT_DIR/tw_spikes.h5", + "node_set": "pre" + } + }, + + + "output":{ + "output_dir": "$OUTPUT_DIR", + "log_file": "log.txt", + "spikes_file": "spikes.h5", + "spikes_sort_order": "time" + }, + + + "reports": { + "membrane_potential": { + "cells": "point_nodes", + "variable_name": "m", + "module": "membrane_report", + "sections": "soma" + } + } + +} \ No newline at end of file diff --git a/examples/sim_tests/intfire/ten_cells_spikes_nrn/input/tw_spikes.h5 b/examples/sim_tests/intfire/ten_cells_spikes_nrn/input/tw_spikes.h5 new file mode 100644 index 0000000..82a029d Binary files /dev/null and b/examples/sim_tests/intfire/ten_cells_spikes_nrn/input/tw_spikes.h5 differ diff --git a/examples/sim_tests/intfire/ten_cells_spikes_nrn/test/analyze_output.py b/examples/sim_tests/intfire/ten_cells_spikes_nrn/test/analyze_output.py new file mode 100644 index 0000000..efd4fbe --- /dev/null +++ b/examples/sim_tests/intfire/ten_cells_spikes_nrn/test/analyze_output.py @@ -0,0 +1,13 @@ +import sys +from bmtk.analyzer.visualization.spikes import plot_spikes + + +sys.path.append("../../../shared_components/scripts") + +from analyze_output_utils import plot_data + +if __name__ == '__main__': + + #plot_data('output/membrane_potential.h5', 'mV', 'Membrane Potential', show_already=True) + plot_spikes('../input/network/pre_nodes.h5', '../input/network/pre_node_types.csv', 'output/spikes.h5') + plot_spikes('../input/network/post_nodes.h5', '../input/network/post_node_types.csv', 'output/spikes.h5') \ No newline at end of file diff --git a/examples/sim_tests/intfire/ten_cells_spikes_nrn/test/run_bmtk.sh b/examples/sim_tests/intfire/ten_cells_spikes_nrn/test/run_bmtk.sh new file mode 100755 index 0000000..f6f175e --- /dev/null +++ b/examples/sim_tests/intfire/ten_cells_spikes_nrn/test/run_bmtk.sh @@ -0,0 +1,10 @@ +#!/bin/bash + +set -e + +rm -rf output +rm -rf __pycache__ +# python -m trace --trace ../shared_components/scripts/run_bionet.py ../input/config.json +python ../../../shared_components/scripts/run_bionet.py NEURON ../input/config.json + +pytest -vvs diff --git a/examples/sim_tests/shared_components/mechanisms/modfiles/vecevent.mod b/examples/sim_tests/shared_components/mechanisms/modfiles/vecevent.mod new file mode 100644 index 0000000..503dfd2 --- /dev/null +++ b/examples/sim_tests/shared_components/mechanisms/modfiles/vecevent.mod @@ -0,0 +1,71 @@ +: Vector stream of events + +NEURON { + ARTIFICIAL_CELL VecStim +} + +ASSIGNED { + index + etime (ms) + space +} + +INITIAL { + index = 0 + element() + if (index > 0) { + net_send(etime - t, 1) + } +} + +NET_RECEIVE (w) { + if (flag == 1) { + net_event(t) + element() + if (index > 0) { + net_send(etime - t, 1) + } + } +} + +VERBATIM +extern double* vector_vec(); +extern int vector_capacity(); +extern void* vector_arg(); +ENDVERBATIM + +PROCEDURE element() { +VERBATIM + { void* vv; int i, size; double* px; + i = (int)index; + if (i >= 0) { + vv = *((void**)(&space)); + if (vv) { + size = vector_capacity(vv); + px = vector_vec(vv); + if (i < size) { + etime = px[i]; + index += 1.; + }else{ + index = -1.; + } + }else{ + index = -1.; + } + } + } +ENDVERBATIM +} + +PROCEDURE play() { +VERBATIM + void** vv; + vv = (void**)(&space); + *vv = (void*)0; + if (ifarg(1)) { + *vv = vector_arg(1); + } +ENDVERBATIM +} + + diff --git a/examples/sim_tests/shared_components/nest_models/cell_models/472363762_point.json b/examples/sim_tests/shared_components/nest_models/cell_models/472363762_point.json new file mode 100644 index 0000000..e6154b1 --- /dev/null +++ b/examples/sim_tests/shared_components/nest_models/cell_models/472363762_point.json @@ -0,0 +1,9 @@ +{ + "I_e": 0.0, + "tau_m": 44.9, + "C_m": 239.0, + "t_ref": 3.0, + "E_L": -78.0, + "V_th": -43.0, + "V_reset": -55.0 +} diff --git a/examples/sim_tests/shared_components/nest_models/cell_models/472912177_point.json b/examples/sim_tests/shared_components/nest_models/cell_models/472912177_point.json new file mode 100644 index 0000000..30b9822 --- /dev/null +++ b/examples/sim_tests/shared_components/nest_models/cell_models/472912177_point.json @@ -0,0 +1,9 @@ +{ + "I_e": 0.0, + "tau_m": 22.2, + "C_m": 180.0, + "t_ref": 3.0, + "E_L": -82.0, + "V_th": -35.0, + "V_reset": -50.0 +} diff --git a/examples/sim_tests/shared_components/nest_models/cell_models/473862421_point.json b/examples/sim_tests/shared_components/nest_models/cell_models/473862421_point.json new file mode 100644 index 0000000..6d7e76a --- /dev/null +++ b/examples/sim_tests/shared_components/nest_models/cell_models/473862421_point.json @@ -0,0 +1,9 @@ +{ + "I_e": 0.0, + "tau_m": 12.5, + "C_m": 78.0, + "t_ref": 3.0, + "E_L": -73.0, + "V_th": -37.0, + "V_reset": -55.0 +} diff --git a/examples/sim_tests/shared_components/nest_models/cell_models/473863035_point.json b/examples/sim_tests/shared_components/nest_models/cell_models/473863035_point.json new file mode 100644 index 0000000..db8e5e4 --- /dev/null +++ b/examples/sim_tests/shared_components/nest_models/cell_models/473863035_point.json @@ -0,0 +1,9 @@ +{ + "I_e": 0.0, + "tau_m": 22.1, + "C_m": 117.0, + "t_ref": 3.0, + "E_L": -78.0, + "V_th": -47.0, + "V_reset": -50.0 +} diff --git a/examples/sim_tests/shared_components/nest_models/cell_models/473863510_point.json b/examples/sim_tests/shared_components/nest_models/cell_models/473863510_point.json new file mode 100644 index 0000000..348c569 --- /dev/null +++ b/examples/sim_tests/shared_components/nest_models/cell_models/473863510_point.json @@ -0,0 +1,9 @@ +{ + "I_e": 0.0, + "tau_m": 11.5, + "C_m": 53.0, + "t_ref": 3.0, + "E_L": -72.0, + "V_th": -25.0, + "V_reset": -50.0 +} diff --git a/examples/sim_tests/shared_components/nest_models/synaptic_models/ExcToExc.json b/examples/sim_tests/shared_components/nest_models/synaptic_models/ExcToExc.json new file mode 100644 index 0000000..2c63c08 --- /dev/null +++ b/examples/sim_tests/shared_components/nest_models/synaptic_models/ExcToExc.json @@ -0,0 +1,2 @@ +{ +} diff --git a/examples/sim_tests/shared_components/nest_models/synaptic_models/ExcToInh.json b/examples/sim_tests/shared_components/nest_models/synaptic_models/ExcToInh.json new file mode 100644 index 0000000..2c63c08 --- /dev/null +++ b/examples/sim_tests/shared_components/nest_models/synaptic_models/ExcToInh.json @@ -0,0 +1,2 @@ +{ +} diff --git a/examples/sim_tests/shared_components/nest_models/synaptic_models/InhToExc.json b/examples/sim_tests/shared_components/nest_models/synaptic_models/InhToExc.json new file mode 100644 index 0000000..bfd870e --- /dev/null +++ b/examples/sim_tests/shared_components/nest_models/synaptic_models/InhToExc.json @@ -0,0 +1,3 @@ +{ +} + diff --git a/examples/sim_tests/shared_components/nest_models/synaptic_models/InhToInh.json b/examples/sim_tests/shared_components/nest_models/synaptic_models/InhToInh.json new file mode 100644 index 0000000..bfd870e --- /dev/null +++ b/examples/sim_tests/shared_components/nest_models/synaptic_models/InhToInh.json @@ -0,0 +1,3 @@ +{ +} + diff --git a/examples/sim_tests/shared_components/point_neuron_models_dir/472363762_point.json b/examples/sim_tests/shared_components/point_neuron_models_dir/472363762_point.json new file mode 100644 index 0000000..e6154b1 --- /dev/null +++ b/examples/sim_tests/shared_components/point_neuron_models_dir/472363762_point.json @@ -0,0 +1,9 @@ +{ + "I_e": 0.0, + "tau_m": 44.9, + "C_m": 239.0, + "t_ref": 3.0, + "E_L": -78.0, + "V_th": -43.0, + "V_reset": -55.0 +} diff --git a/examples/sim_tests/shared_components/point_neuron_models_dir/472912177_point.json b/examples/sim_tests/shared_components/point_neuron_models_dir/472912177_point.json new file mode 100644 index 0000000..30b9822 --- /dev/null +++ b/examples/sim_tests/shared_components/point_neuron_models_dir/472912177_point.json @@ -0,0 +1,9 @@ +{ + "I_e": 0.0, + "tau_m": 22.2, + "C_m": 180.0, + "t_ref": 3.0, + "E_L": -82.0, + "V_th": -35.0, + "V_reset": -50.0 +} diff --git a/examples/sim_tests/shared_components/point_neuron_models_dir/473862421_point.json b/examples/sim_tests/shared_components/point_neuron_models_dir/473862421_point.json new file mode 100644 index 0000000..6d7e76a --- /dev/null +++ b/examples/sim_tests/shared_components/point_neuron_models_dir/473862421_point.json @@ -0,0 +1,9 @@ +{ + "I_e": 0.0, + "tau_m": 12.5, + "C_m": 78.0, + "t_ref": 3.0, + "E_L": -73.0, + "V_th": -37.0, + "V_reset": -55.0 +} diff --git a/examples/sim_tests/shared_components/point_neuron_models_dir/473863035_point.json b/examples/sim_tests/shared_components/point_neuron_models_dir/473863035_point.json new file mode 100644 index 0000000..db8e5e4 --- /dev/null +++ b/examples/sim_tests/shared_components/point_neuron_models_dir/473863035_point.json @@ -0,0 +1,9 @@ +{ + "I_e": 0.0, + "tau_m": 22.1, + "C_m": 117.0, + "t_ref": 3.0, + "E_L": -78.0, + "V_th": -47.0, + "V_reset": -50.0 +} diff --git a/examples/sim_tests/shared_components/point_neuron_models_dir/473863510_point.json b/examples/sim_tests/shared_components/point_neuron_models_dir/473863510_point.json new file mode 100644 index 0000000..348c569 --- /dev/null +++ b/examples/sim_tests/shared_components/point_neuron_models_dir/473863510_point.json @@ -0,0 +1,9 @@ +{ + "I_e": 0.0, + "tau_m": 11.5, + "C_m": 53.0, + "t_ref": 3.0, + "E_L": -72.0, + "V_th": -25.0, + "V_reset": -50.0 +} diff --git a/examples/sim_tests/shared_components/point_neuron_models_dir/IntFire1_exc_1.json b/examples/sim_tests/shared_components/point_neuron_models_dir/IntFire1_exc_1.json new file mode 100644 index 0000000..6a58d3b --- /dev/null +++ b/examples/sim_tests/shared_components/point_neuron_models_dir/IntFire1_exc_1.json @@ -0,0 +1,5 @@ +{ + "tau": 0.024, + "type": "NEURON_IntFire1", + "refrac": 0.003 +} diff --git a/examples/sim_tests/shared_components/point_neuron_models_dir/IntFire1_inh_1.json b/examples/sim_tests/shared_components/point_neuron_models_dir/IntFire1_inh_1.json new file mode 100644 index 0000000..0da2f1f --- /dev/null +++ b/examples/sim_tests/shared_components/point_neuron_models_dir/IntFire1_inh_1.json @@ -0,0 +1,5 @@ +{ + "tau": 0.007, + "type": "NEURON_IntFire1", + "refrac": 0.003 +} diff --git a/examples/sim_tests/shared_components/scripts/analyze_output_utils.py b/examples/sim_tests/shared_components/scripts/analyze_output_utils.py new file mode 100644 index 0000000..99437df --- /dev/null +++ b/examples/sim_tests/shared_components/scripts/analyze_output_utils.py @@ -0,0 +1,66 @@ + +import h5py +import numpy as np +import matplotlib.pyplot as plt + + +def plot_data(reports_file, y_axis, title, show_already=True, max_num_traces=None): + print('Plotting data on %s (%s) from file: %s'%(title, y_axis, reports_file)) + cellvar_h5 = h5py.File(reports_file, 'r') + + + try: + top_level = cellvar_h5['/mapping'] + pop_prefixes = {'GLOBAL':'/'} + except: + pop_prefixes = {} + for pop in cellvar_h5['/report'].keys(): + pop_prefixes = {pop:'/report/%s/'%pop} + + for pop in pop_prefixes: + + gids = np.array(cellvar_h5['%smapping/gids'%pop_prefixes[pop]]) + + soma_locs = np.array(cellvar_h5['%smapping/index_pointer'%pop_prefixes[pop]]) # location of soma + t_start = cellvar_h5['%smapping/time'%pop_prefixes[pop]][0] + t_stop = cellvar_h5['%smapping/time'%pop_prefixes[pop]][1] + dt = cellvar_h5['%smapping/time'%pop_prefixes[pop]][2] + time_steps = np.linspace(t_start, t_stop, 1+(t_stop-t_start)/float(dt)) + print('Time steps %s -> %s, dt: %s (%s points)'%(t_start,t_stop,dt, len(time_steps))) + + n_plots = len(gids) + if max_num_traces: + n_plots = min(max_num_traces, n_plots) + data_table = np.array(cellvar_h5['%sdata'%pop_prefixes[pop]]) + f, axarr = plt.subplots(n_plots, 1) + f.suptitle(title) + for i, (gid, soma_index) in enumerate(zip(gids, soma_locs)): + if i\n", + "\n", + "
cellsDefines what cells will be reported. The value is a reference to a cell-group found in the cell-groups json file, which is used to resolve which subset of gids will be included in the report. Defines what cells will be reported. The value is a reference to a cell-group found in the cell-groups json file, which is used to resolve which subset of nodes will be included in the report. string (cell-group) True
\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
node_type_idnode_idrotation_angle_yaxisxyzeimodel_processingmorphologymodel_templatemodel_typemodel_name
010000.98654222.343432-79.627003-1.534002eaibs_perisomaticScnn1a_473845048_mnml:Cell_472363762.cell.nmlbiophysicalScnn1a
110012.221220-2.594341-43.37611824.234707eaibs_perisomaticScnn1a_473845048_mnml:Cell_472363762.cell.nmlbiophysicalScnn1a
210023.90694224.31001248.65292211.593498eaibs_perisomaticScnn1a_473845048_mnml:Cell_472363762.cell.nmlbiophysicalScnn1a
310030.2697377.004998-85.896637-4.738925eaibs_perisomaticScnn1a_473845048_mnml:Cell_472363762.cell.nmlbiophysicalScnn1a
410042.95010126.71627515.561911-23.239033eaibs_perisomaticScnn1a_473845048_mnml:Cell_472363762.cell.nmlbiophysicalScnn1a
\n", + "" + ], + "text/plain": [ + " node_type_id node_id rotation_angle_yaxis x y \\\n", + "0 100 0 0.986542 22.343432 -79.627003 \n", + "1 100 1 2.221220 -2.594341 -43.376118 \n", + "2 100 2 3.906942 24.310012 48.652922 \n", + "3 100 3 0.269737 7.004998 -85.896637 \n", + "4 100 4 2.950101 26.716275 15.561911 \n", + "\n", + " z ei model_processing morphology \\\n", + "0 -1.534002 e aibs_perisomatic Scnn1a_473845048_m \n", + "1 24.234707 e aibs_perisomatic Scnn1a_473845048_m \n", + "2 11.593498 e aibs_perisomatic Scnn1a_473845048_m \n", + "3 -4.738925 e aibs_perisomatic Scnn1a_473845048_m \n", + "4 -23.239033 e aibs_perisomatic Scnn1a_473845048_m \n", + "\n", + " model_template model_type model_name \n", + "0 nml:Cell_472363762.cell.nml biophysical Scnn1a \n", + "1 nml:Cell_472363762.cell.nml biophysical Scnn1a \n", + "2 nml:Cell_472363762.cell.nml biophysical Scnn1a \n", + "3 nml:Cell_472363762.cell.nml biophysical Scnn1a \n", + "4 nml:Cell_472363762.cell.nml biophysical Scnn1a " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "internal_nodes.to_dataframe().head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can use the *get_node_id* method to fetch information about a single node. Each node will be returned as a *sonata.Node* object with attributes and properties that can be accessed like a dictionary" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "class: \n", + "node_id: 1\n", + "node props: {'node_type_id': 100, 'ei': 'e', y: -43.37611757606959, 'model_processing': 'aibs_perisomatic', 'model_type': 'biophysical', 'node_id': 1, 'model_template': 'nml:Cell_472363762.cell.nml', x: -2.5943407870091946, 'morphology': 'Scnn1a_473845048_m', rotation_angle_yaxis: 2.2212202206885547, z: 24.23470695483876, 'model_name': 'Scnn1a'}\n", + "(-2.5943407870091946, -43.37611757606959, 24.23470695483876)\n" + ] + } + ], + "source": [ + "node1 = internal_nodes.get_node_id(1) # Get node in population with node_id = 1\n", + "print('class: {}'.format(type(node1)))\n", + "print('node_id: {}'.format(node1.node_id))\n", + "print('node props: {}'.format(node1))\n", + "\n", + "coords = (node1['x'], node1['y'], node1['z'])\n", + "print(coords)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can also use the *get_row* method to fetch the i'th row in the table. But it's important to note that node_id's do not have to be ordered or contigous - so the i'th row may not always contain node with node_id i." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n" + ] + } + ], + "source": [ + "node_row1 = internal_nodes.get_row(1)\n", + "print(node1.node_id == node_row1.node_id)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Iterating through Nodes\n", + "To iterate through all nodes in the population:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Node #127 has maximum distance 119.942558428um from origin\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "max_dist = 0.0\n", + "max_dist_node = None\n", + "for node in internal_nodes:\n", + " assert(0 <= node.node_id < 300)\n", + " ndist = np.sqrt(node['x']**2 + node['y']**2 + node['z']**2)\n", + " if ndist > max_dist:\n", + " max_dist = ndist\n", + " max_dist_node = node\n", + "\n", + "print('Node #{} has maximum distance {}um from origin'.format(max_dist_node.node_id, max_dist)) \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using the get_rows method we can pass in a list or range of the rows we want to analyze" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "processed 150 nodes\n", + "processed 3 nodes\n" + ] + } + ], + "source": [ + "n_count = 0\n", + "for node in internal_nodes.get_rows(range(0, len(internal_nodes), 2)):\n", + " n_count += 1\n", + " assert(node.node_id % 2 == 0)\n", + "print('processed {} nodes'.format(n_count))\n", + "\n", + "n_count = 0\n", + "for node in internal_nodes.get_rows([0, 100, 200]):\n", + " assert(node.node_id % 100 == 0)\n", + " n_count += 1\n", + "print('processed {} nodes'.format(n_count))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Filtering by attributes\n", + "Use the filter method to fetch nodes with certain properties" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 80 nodes that use Nr5a1_471087815_m morphology\n" + ] + } + ], + "source": [ + "n_count = 0\n", + "for node in internal_nodes.filter(morphology='Nr5a1_471087815_m'):\n", + " assert(node['morphology'] == 'Nr5a1_471087815_m')\n", + " n_count += 1\n", + " \n", + "print('Found {} nodes that use Nr5a1_471087815_m morphology'.format(n_count))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Vectorized node attributes\n", + "\n", + "SONATA stores all data into one or more **node groups**. Every node belongs to one node group, with every node group containing the same attributes. Thus nodes with different models (and therefore different attributes) will be split into different groups. To show the available node_groups:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model groups in internal_nodes population: [0]\n", + "Group 0 nodes contains the following attributes \n", + "\t [rotation_angle_yaxis, x, y, z, ei, model_processing, morphology, model_template, model_type, model_name]\n" + ] + } + ], + "source": [ + "# In this example there is only one node-group, group #0\n", + "print('Model groups in internal_nodes population: {}'.format(internal_nodes.group_ids))\n", + "\n", + "grp0 = internal_nodes.get_group(internal_nodes.group_ids[0])\n", + "print('Group {} nodes contains the following attributes \\n\\t {}'.format(grp0.group_id, grp0.all_columns))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instead of looping through and finding attributes of every individual node, we can use the *get_values* method to get all attributes at once as a list or numpy array. This allows us to use vectorization to speed up our analysis:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Node #127 has maximum distance 119.942558428um from origin\n" + ] + } + ], + "source": [ + "distances = np.sqrt(grp0.get_values('x')**2 + grp0.get_values('y')**2 + grp0.get_values('z')**2)\n", + "max_indx = np.argmax(distances)\n", + "print('Node #{} has maximum distance {}um from origin'.format(grp0.node_ids[max_indx], distances[max_indx])) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Working with edges\n", + "\n", + "Edges share a similar data structure as our nodes. Within a file there may be zero, one, or more edge populations each identified by a unique user defined population name." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Edge populations in file: [u'internal_to_internal']\n" + ] + } + ], + "source": [ + "file_edges = net.edges\n", + "print('Edge populations in file: {}'.format(file_edges.population_names))\n", + "recurrent_edges = file_edges['internal_to_internal']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Every edge population contains directed connections between the nodes of one node-population to another. The attributes *target_population* and *source_population* will be important to determine what set of nodes will be used for pre-synaptic/post-synaptic connections:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Edge population internal_to_internal contains 88316 connections from \"internal\" --> \"internal\"\n" + ] + } + ], + "source": [ + "# In this case both the source and target node population are the same indicating these are recurrent connections\n", + "print('Edge population {} contains {} connections from \"{}\" --> \"{}\"'.format(recurrent_edges.name, \n", + " len(recurrent_edges),\n", + " recurrent_edges.source_population, \n", + " recurrent_edges.target_population))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Iterating over edges\n", + "\n", + "When we iterate over all the edges in the edge population we are returned Edge objection. Every edge must contain a source (pre-synaptic) and target (post-synaptic) node-id, but also contain edge attributes which we fetch like a dictionary:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 --> 0\n", + "{pos_z: 276.13055401859, pos_x: 60.45881079218742, pos_y: 3.356650750251845, 'dynamics_params': 'AMPA_ExcToExc.json', 'edge_type_id': 100, 'target_query': \"ei=='e'\", 'delay': 2.0, syn_weight: 6e-05, 'model_template': 'Exp2Syn', sec_id: 28, sec_x: 0.5, dist: 98.29584776371554, 'source_query': \"ei=='e'\", type: 3}\n" + ] + } + ], + "source": [ + "for edge in recurrent_edges:\n", + " print('{} --> {}'.format(edge.source_node_id, edge.target_node_id))\n", + " print edge\n", + " assert(isinstance(edge['syn_weight'], float))\n", + " \n", + " break # Iterating through all the edges can take a long time, do so at your own risk" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Often we may want to get only those edges to connection to/from a given node or set of nodes. SONATA uses special indexing, so selecting by target or source can be achieved signficantly faster than iterating through all possible edges. To find all edges with a given target (or list of targets) we use the *get_target* (*get_targets*) method. :" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 182 connections onto target node #100\n" + ] + } + ], + "source": [ + "con_count = 0\n", + "for edge in recurrent_edges.get_target(100): # we can also use get_targets([id0, id1, ...])\n", + " assert(edge.target_node_id == 100)\n", + " con_count += 1\n", + " \n", + "print('There are {} connections onto target node #{}'.format(con_count, 100))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Or we can search for edges by source_node_id" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The first 5 nodes have on average 190.6 outgoing connections\n" + ] + } + ], + "source": [ + "con_count = 0\n", + "for edge in recurrent_edges.get_targets(range(5)):\n", + " assert(edge.target_node_id < 5)\n", + " con_count += 1\n", + " \n", + "print('The first 5 nodes have on average {} outgoing connections'.format(con_count/5.0))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Filtering by attributes\n", + "\n", + "We can use the *filter* method to find only those edges with a given matching attribute value:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "45610 out of 88316 edges use AMPA_ExcToExc parameters\n" + ] + } + ], + "source": [ + "# may take some time to complete\n", + "n_ampa = sum(1 for _ in recurrent_edges.filter(dynamics_params='AMPA_ExcToExc.json'))\n", + "print('{} out of {} edges use AMPA_ExcToExc parameters'.format(n_ampa, len(recurrent_edges)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:bmtk-py27]", + "language": "python", + "name": "conda-env-bmtk-py27-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/pysonata/setup.py b/src/pysonata/setup.py index 70baf3c..d997fb5 100644 --- a/src/pysonata/setup.py +++ b/src/pysonata/setup.py @@ -15,11 +15,19 @@ def prepend_find_packages(*roots): return packages + +with open('README.md', 'r') as fhandle: + long_description = fhandle.read() + + setup( name='sonata', - version=0.1, + version='0.0.1', description='SONATA Data Format', - package_data={'': ['*.md', '*.txt', '*.cfg', '**/*.json', '**/*.hoc', '**/*.h5', '**/*.csv']}, + long_description=long_description, + long_description_content_type='text/markdown', + url='https://github.com/AllenInstitute/bmtk', + package_data={'': ['*.md', '*.txt', '*.cfg', '**/*.json', '**/*.hoc']}, tests_require=['pytest'], install_requires=[ 'jsonschema', diff --git a/src/pysonata/sonata/io/edge.py b/src/pysonata/sonata/io/edge.py index 0f91a7a..4b2de9e 100644 --- a/src/pysonata/sonata/io/edge.py +++ b/src/pysonata/sonata/io/edge.py @@ -41,12 +41,13 @@ def next(self): class Edge(object): - def __init__(self, src_node_id, trg_node_id, source_pop, target_pop, group_props, edge_types_props): + def __init__(self, src_node_id, trg_node_id, source_pop, target_pop, group_id, group_props, edge_types_props): self._src_node_id = src_node_id self._trg_node_id = trg_node_id self._source_population = source_pop self._target_population = target_pop self._group_props = group_props + self._group_id = group_id self._edge_type_props = edge_types_props @property @@ -65,6 +66,14 @@ def source_population(self): def target_population(self): return self._target_population + @property + def group_id(self): + return self._group_id + + @property + def edge_type_id(self): + return self._edge_type_props['edge_type_id'] + @property def dynamics_params(self): raise NotImplementedError @@ -75,4 +84,15 @@ def __getitem__(self, prop_key): elif prop_key in self._edge_type_props: return self._edge_type_props[prop_key] else: - raise KeyError \ No newline at end of file + raise KeyError('Property {} not found in edge.'.format(prop_key)) + + def __repr__(self): + return self.__str__() + + def __str__(self): + ret_dict = self._edge_type_props.copy() + ret_dict.update(self._group_props) + return str(ret_dict) + + def __contains__(self, prop_key): + return prop_key in self._group_props or prop_key in self._edge_type_props diff --git a/src/pysonata/sonata/io/file.py b/src/pysonata/sonata/io/file.py index de8c729..d70f66a 100644 --- a/src/pysonata/sonata/io/file.py +++ b/src/pysonata/sonata/io/file.py @@ -20,20 +20,34 @@ # WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # -from sonata.io import utils -from sonata.io.file_root import NodesRoot, EdgesRoot +from . import utils +from .file_root import NodesRoot, EdgesRoot class File(object): - def __init__(self, data_files, data_type_files, mode='r', gid_table=None): + def __init__(self, data_files, data_type_files, mode='r', gid_table=None, require_magic=True): if mode != 'r': raise Exception('Currently only read mode is supported.') self._data_files = utils.listify(data_files) self._data_type_files = utils.listify(data_type_files) - # file handles - self._h5_file_handles = [(f, utils.load_h5(f, mode)) for f in self._data_files] + # Open and check HDF5 file(s) + self._h5_file_handles = [utils.load_h5(f, mode) for f in self._data_files] + if require_magic: + map(utils.check_magic, self._h5_file_handles) # Check magic attribute in h5 files + + # Check version number + avail_versions = set(map(utils.get_version, self._h5_file_handles)) + if len(avail_versions) == 1: + self._version = list(avail_versions)[0] + elif len(avail_versions) > 1: + # TODO: log as warning + print('Warning: Passing in multiple hdf5 files of different version') + self._version = ','.join(avail_versions) + else: + self._version = utils.VERSION_NA + self._csv_file_handles = [(f, utils.load_csv(f)) for f in self._data_type_files] self._has_nodes = False @@ -75,6 +89,10 @@ def edges(self): def has_edges(self): return self._has_edges + @property + def version(self): + return self._version + def _sort_types_file(self): # TODO: node/edge type_id columnn names should not be hardcoded for filename, df in self._csv_file_handles: @@ -92,11 +110,11 @@ def _sort_types_file(self): print('Warning: Could not determine if file {} was an edge-types or node-types file. Ignoring'.format(filename)) def _sort_h5_files(self): - for filename, h5 in self._h5_file_handles: + for h5 in self._h5_file_handles: has_nodes = '/nodes' in h5 has_edges = '/edges' in h5 if not (has_nodes or has_edges): - print('File {} contains neither nodes nor edges. Ignoring'.format(filename)) + print('File {} contains neither nodes nor edges. Ignoring'.format(h5.filename)) else: if has_nodes: self._nodes_groups.append(h5) diff --git a/src/pysonata/sonata/io/group.py b/src/pysonata/sonata/io/group.py index 4966152..fbb784f 100644 --- a/src/pysonata/sonata/io/group.py +++ b/src/pysonata/sonata/io/group.py @@ -43,6 +43,8 @@ def __init__(self, group_id, h5_group, parent): self._types_index_col = self._types_table.index_column_name self._group_columns = ColumnProperty.from_h5(h5_group) + # TODO: combine group_columns, group_column_names and group_columns_map, doesn't need to be 3 structures + self._group_column_map = {col.name: col for col in self._group_columns} self._group_column_names = set(col.name for col in self._group_columns) self._group_table = {prop: h5_group[prop.name] for prop in self._group_columns} self._ncolumns = len(self._group_columns) @@ -74,10 +76,33 @@ def has_dynamics_params(self): def columns(self): return self._group_columns + @property + def group_columns(self): + return self._group_columns + @property def all_columns(self): return self._all_columns + @property + def has_gids(self): + return self._parent.has_gids + + @property + def parent(self): + return self._parent + + def get_dataset(self, column_name): + return self._group_table[column_name] + + def column(self, column_name, group_only=False): + if column_name in self._group_column_map: + return self._group_column_map[column_name] + elif not group_only and column_name in self._types_table.columns: + return self._types_table.column(column_name) + else: + return KeyError + def check_format(self): # Check that all the properties have the same number of rows col_counts = [col.nrows for col in self._group_columns + self._dynamics_params_columns] @@ -130,7 +155,6 @@ def __init__(self, group_id, h5_group, parent): @property def node_ids(self): self.build_indicies() - # print self._parent_indicies return self._parent.inode_ids(self._parent_indicies) @property @@ -266,6 +290,7 @@ def filter(self, **filter_props): if group_filter: # Filter by group property values + # TODO: Allow group properties to handle lists src_failed = True for k, v in group_prop_filter.items(): if node[k] != v: @@ -306,6 +331,27 @@ def build_indicies(self, force=False): def to_dataframe(self): raise NotImplementedError + def _get_parent_ds(self, parent_ds): + self.build_indicies() + ds_vals = np.zeros(self._indicies_count, dtype=parent_ds.dtype) + c_indx = 0 + for indx_range in self._parent_indicies: + indx_beg, indx_end = indx_range[0], indx_range[1] + n_indx = c_indx + (indx_end - indx_beg) + ds_vals[c_indx:n_indx] = parent_ds[indx_beg:indx_end] + c_indx = n_indx + + return ds_vals + + def src_node_ids(self): + return self._get_parent_ds(self.parent._source_node_id_ds) + + def trg_node_ids(self): + return self._get_parent_ds(self.parent._target_node_id_ds) + + def node_type_ids(self): + return self._get_parent_ds(self.parent._type_id_ds) + def get_values(self, property_name, all_rows=False): # TODO: Need to take into account if property_name is in the edge-types if property_name not in self.columns: diff --git a/src/pysonata/sonata/io/node.py b/src/pysonata/sonata/io/node.py index 419d9f0..50d9260 100644 --- a/src/pysonata/sonata/io/node.py +++ b/src/pysonata/sonata/io/node.py @@ -20,6 +20,8 @@ # WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # + + class NodeSet(object): # TODO: Merge NodeSet and NodePopulation def __init__(self, node_indicies, population, **parameters): @@ -71,13 +73,14 @@ def __next__(self): class Node(object): # TODO: include population name/reference - # TODO: make a dictionary - def __init__(self, node_id, node_type_id, node_types_props, group_props, dynamics_params, gid=None): + # TODO: make a dictionary (or preferably a collections.MutableMap + def __init__(self, node_id, node_type_id, node_types_props, group_id, group_props, dynamics_params, gid=None): self._node_id = node_id self._gid = gid self._node_type_id = node_type_id - self._group_props = group_props self._node_type_props = node_types_props + self._group_id = group_id + self._group_props = group_props @property def node_id(self): @@ -87,10 +90,22 @@ def node_id(self): def gid(self): return self._gid + @property + def group_id(self): + return self._group_id + @property def node_type_id(self): return self._node_type_id + @property + def group_props(self): + return self._group_props + + @property + def node_type_properties(self): + return self._node_type_props + @property def dynamics_params(self): raise NotImplementedError @@ -100,8 +115,22 @@ def __getitem__(self, prop_key): return self._group_props[prop_key] elif prop_key in self._node_type_props: return self._node_type_props[prop_key] + elif prop_key == 'node_id': + return self.node_id + elif property == 'node_type_id': + return self.node_type_id else: - raise KeyError + raise KeyError('Unknown property {}'.format(prop_key)) def __contains__(self, prop_key): return prop_key in self._group_props or prop_key in self._node_type_props + + def __repr__(self): + return self.__str__() + + def __str__(self): + ret_dict = self._node_type_props.copy() + ret_dict.update(self._group_props) + ret_dict['node_id'] = self.node_id + ret_dict['node_type_id'] = self.node_type_id + return str(ret_dict) diff --git a/src/pysonata/sonata/io/population.py b/src/pysonata/sonata/io/population.py index 039433a..d525262 100644 --- a/src/pysonata/sonata/io/population.py +++ b/src/pysonata/sonata/io/population.py @@ -69,6 +69,10 @@ def groups(self): def types_table(self): return self._types_table + @property + def type_ids(self): + return np.array(self._type_id_ds) + @property def group_id_ds(self): return self._group_id_ds @@ -233,7 +237,13 @@ def add_gids(self, gid_map_df, force=False): self._has_gids = True def to_dataframe(self): - raise NotImplementedError + if len(self.groups) == 1: + return self.get_group(self.group_ids[0]).to_dataframe() + else: + dataframes = [] + for grp_id in self.group_ids: + dataframes.append(self.get_group(grp_id)).to_datframe() + return dataframes def get_row(self, row_indx): # TODO: Use helper function so we don't have to lookup gid/node_id twice @@ -247,7 +257,7 @@ def get_row(self, row_indx): node_group_props = self.get_group(node_group_id)[node_group_index] node_gid = self._gid_lookup_fnc(row_indx) - return Node(node_id, node_type_id, node_type_props, node_group_props, None, gid=node_gid) + return Node(node_id, node_type_id, node_type_props, node_group_id, node_group_props, None, gid=node_gid) def get_rows(self, row_indicies): """Returns a set of all nodes based on list of row indicies. @@ -285,6 +295,11 @@ def get_gid(self, gid): row_indx = self._index_gid2row.iloc[gid]['row_id'] return self.get_row(row_indx) + def filter(self, **filter_props): + for grp in self.groups: + for node in grp.filter(**filter_props): + yield node + def _build_node_id_index(self, force=False): if self._node_id_index_built and not force: return @@ -310,6 +325,22 @@ def __next__(self): self.__itr_index += 1 return nxt_node + def __getitem__(self, item): + if isinstance(item, slice): + # TODO: Check + start = item.start if item.start is not None else 0 + stop = item.stop if item.stop is not None else self._nrows + row_indicies = range_itr(start, stop, item.step) + return NodeSet(row_indicies, self) + + elif isinstance(item, int): + return self.get_row(item) + + elif isinstance(item, list): + return NodeSet(item) + else: + print('Unable to get item using {}.'.format(type(item))) + class EdgePopulation(Population): class __IndexStruct(object): @@ -364,23 +395,33 @@ def target_population(self): @staticmethod def get_source_population(pop_group_h5): - return get_attribute_h5(pop_group_h5['source_node_id'], 'network', None) + return get_attribute_h5(pop_group_h5['source_node_id'], 'node_population', None) @staticmethod def get_target_population(pop_group_h5): - return get_attribute_h5(pop_group_h5['target_node_id'], 'network', None) + return get_attribute_h5(pop_group_h5['target_node_id'], 'node_population', None) @property def edge_types_table(self): return self._types_table + @property + def indices_group(self): + if 'indices' in self._pop_group: + return self._pop_group['indices'] + elif 'indicies' in self._pop_group: + # spelling mistake, keeping in for backwards compatibility + return self._pop_group['indicies'] + else: + return None + def to_dataframe(self): raise NotImplementedError def build_indicies(self): - if 'indicies' in self._pop_group: - indicies_grp = self._pop_group['indicies'] - for index_name, index_grp in indicies_grp.items(): + indices_grp = self.indices_group + if indices_grp is not None: + for index_name, index_grp in indices_grp.items(): # TODO: Let __IndexStruct build the indicies # Make sure subgroup has the correct datasets if not isinstance(index_grp, h5py.Group): @@ -392,7 +433,6 @@ def build_indicies(self): raise Exception('index {} in {} edges is missing column {}.'.format(index_name, self.name, 'node_id_to_range')) if 'range_to_edge_id' not in index_grp: - # TODO: make this more general, i.e 'id_to_range' thus we can index on gids, edge_types, etc raise Exception('index {} in {} edges is missing column {}.'.format(index_name, self.name, 'range_to_edge_id')) @@ -463,10 +503,10 @@ def get_row(self, index): edge_group_index = self._group_index_ds[index] edge_group_props = self.get_group(edge_group_id)[edge_group_index] return Edge(trg_node_id=trg_node, src_node_id=src_node, source_pop=self.source_population, - target_pop=self.target_population, group_props=edge_group_props, edge_types_props=edge_types_props) + target_pop=self.target_population, group_id = edge_group_id, + group_props=edge_group_props, edge_types_props=edge_types_props) def filter(self, **filter_props): - selected_edge_types = set(self.edge_types_table.edge_type_ids) types_filter = False # Do we need to filter results by edge_type_id if 'edge_type_id' in filter_props: @@ -557,8 +597,11 @@ def get_sources(self, source_node_ids): def _get_index(self, index_struct, lookup_id): # TODO: Use a EdgeSet instead - edges_table = index_struct.edge_table + if lookup_id >= len(index_struct.lookup_table): + # TODO: Store length in index + raise StopIteration + edges_table = index_struct.edge_table lookup_beg, lookup_end = index_struct.lookup_table[lookup_id] for i in range_itr(lookup_beg, lookup_end): edge_indx_beg, edge_indx_end = edges_table[i] diff --git a/src/pysonata/sonata/io/types_table.py b/src/pysonata/sonata/io/types_table.py index 531dbaf..375d332 100644 --- a/src/pysonata/sonata/io/types_table.py +++ b/src/pysonata/sonata/io/types_table.py @@ -21,6 +21,7 @@ # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # import numpy as np +import pandas as pd import numbers import math @@ -46,6 +47,9 @@ def __init__(self, parent=None): self._cached_node_types = {} self._df_cache = None + self._itr_indx = 0 + self._itr_end = 0 + @property def index_column_name(self): raise NotImplementedError @@ -93,13 +97,18 @@ def find(self, column_key, column_val, silent=False): if not silent and column_key not in self.columns: raise KeyError + is_list = isinstance(column_val, list) selected_ids = [] # running list of valid type-ids column_dtype = self.column(column_key).dtype for df in self._column_map[column_key]: # if a csv column has all NONE values, pandas will load the values as float(NaN)'s. Thus for str/object # columns we need to check dtype otherwise we'll get an invalid comparisson. if df[column_key].dtype == column_dtype: - indicies = df[df[column_key] == column_val].index + if is_list: + indicies = df[df[column_key].isin(column_val)].index + else: + indicies = df[df[column_key] == column_val].index + if len(indicies) > 0: selected_ids.extend(list(indicies)) @@ -117,7 +126,24 @@ def to_dataframe(self, cache=False): # merge all dataframes together merged_table = self._dataframes[0].reset_index() # TODO: just merge on the indicies rather than reset for df in self._dataframes[1:]: - merged_table = merged_table.merge(df.reset_index(), how='outer') + try: + merged_table = merged_table.merge(df.reset_index(), how='outer') + except ValueError as ve: + # There is a potential issue if merging where one dtype is different from another (ex, if all + # model_template's are NONE pandas will load column as float64). First solution is to find columns + # that differ and upcast columns as object's (TODO: look for better solution) + right_df = df.reset_index() + for col in set(merged_table.columns) & set(right_df.columns): + # find all shared columns whose dtype differs + if merged_table[col].dtype != right_df[col].dtype: + # change column(s) dtype to object + merged_table[col] = merged_table[col] if merged_table[col].dtype == object \ + else merged_table[col].astype(object) + right_df[col] = right_df[col] if right_df[col].dtype == object \ + else right_df[col].astype(object) + + merged_table = merged_table.merge(right_df, how='outer') + merged_table.set_index(self.index_column_name, inplace=True) if cache: @@ -125,6 +151,22 @@ def to_dataframe(self, cache=False): return merged_table + def __iter__(self): + self._itr_indx = 0 + self._itr_end = len(self.type_ids) + return self + + def next(self): + return self.__next__() + + def __next__(self): + if self._itr_indx >= self._itr_end: + raise StopIteration + + ntid = self.type_ids[self._itr_indx] + self._itr_indx += 1 + return self[ntid] + def __getitem__(self, type_id): if isinstance(type_id, tuple): return [self[ntid] for ntid in type_id] @@ -148,6 +190,9 @@ def __getitem__(self, type_id): def __contains__(self, type_id): return type_id in self._index_typeid2df + def __repr__(self): + return repr(self.to_dataframe()) + class NodeTypesTable(TypesTable): def __init__(self, parent=None): diff --git a/src/pysonata/sonata/io/utils.py b/src/pysonata/sonata/io/utils.py index 5c3743a..953572d 100644 --- a/src/pysonata/sonata/io/utils.py +++ b/src/pysonata/sonata/io/utils.py @@ -25,6 +25,21 @@ import h5py import pandas as pd +import numpy as np + +MAGIC_ATTR = 'magic' +MAGIC_VAL = 0x0A7A +VERSION_ATTR = 'version' +VERSION_NA = 'NA' +VERSION_CURRENT = '0.1' + +try: + ver_split = VERSION_CURRENT.split('.') + VERSION_MAJOR = ver_split[0] + VERSION_MINOR = ver_split[1] +except (IndexError, AttributeError) as err: + VERSION_MAJOR = 0 + VERSION_MINOR = 1 def listify(files): @@ -35,7 +50,7 @@ def listify(files): return files -def load_h5(h5file, mode): +def load_h5(h5file, mode='r'): # TODO: Allow for h5py.Group also if isinstance(h5file, h5py.File): return h5file @@ -53,7 +68,7 @@ def load_csv(csvfile): def get_attribute_h5(h5obj, attribut_name, default=None): - val = h5obj.attrs.get('network', default) + val = h5obj.attrs.get(attribut_name, default) if using_py3 and isinstance(val, bytes): # There is an but with h5py returning unicode/str based attributes as bytes val = val.decode() @@ -61,6 +76,38 @@ def get_attribute_h5(h5obj, attribut_name, default=None): return val +def check_magic(hdf5_file): + """Check the magic attribute exists according to the sonata format""" + h5_file_obj = load_h5(hdf5_file) + if MAGIC_ATTR not in h5_file_obj.attrs: + raise Exception('File {} missing top-level \"{}\" attribute.'.format(h5_file_obj.filename, MAGIC_ATTR)) + elif np.uint32(get_attribute_h5(hdf5_file, MAGIC_ATTR)) != MAGIC_VAL: + raise Exception('File {} has unexpected magic value (expected {})'.format(h5_file_obj.filename, MAGIC_VAL)) + + return True + + +def get_version(hdf5_file): + h5_file_obj = load_h5(hdf5_file) + if VERSION_ATTR not in h5_file_obj.attrs: + return VERSION_NA + + else: + version_val = get_attribute_h5(h5_file_obj, VERSION_ATTR) + version_str = str(version_val[0]) + for ver_sub in version_val[1:]: + version_str += '.{}'.format(ver_sub) + return version_str + + +def add_hdf5_magic(hdf5_handle): + hdf5_handle['/'].attrs['magic'] = np.uint32(0x0A7A) + + +def add_hdf5_version(hdf5_handle): + hdf5_handle['/'].attrs['version'] = [np.uint32(VERSION_MAJOR), np.uint32(VERSION_MINOR)] + + if sys.version_info[0] == 3: using_py3 = True range_itr = range diff --git a/src/pysonata/sonata/tests/examples/gid_table.h5 b/src/pysonata/sonata/tests/examples/gid_table.h5 index c59142b..2469b9f 100644 Binary files a/src/pysonata/sonata/tests/examples/gid_table.h5 and b/src/pysonata/sonata/tests/examples/gid_table.h5 differ diff --git a/src/pysonata/sonata/tests/examples/lgn_node_types.csv b/src/pysonata/sonata/tests/examples/lgn_node_types.csv index 63ff2cf..dc2dda5 100644 --- a/src/pysonata/sonata/tests/examples/lgn_node_types.csv +++ b/src/pysonata/sonata/tests/examples/lgn_node_types.csv @@ -1,4 +1,4 @@ -node_type_id model_type model_template model_processing ei pop_name location -0 virtual NONE NONE e tON_001 LGN -1 virtual NONE NONE e tOFF_001 LGN -2 virtual NONE NONE e tONOFF_001 LGN +node_type_id model_type ei pop_name location +0 virtual e tON_001 LGN +1 virtual e tOFF_001 LGN +2 virtual e tONOFF_001 LGN diff --git a/src/pysonata/sonata/tests/examples/lgn_nodes.h5 b/src/pysonata/sonata/tests/examples/lgn_nodes.h5 index 2541b46..498f1ad 100644 Binary files a/src/pysonata/sonata/tests/examples/lgn_nodes.h5 and b/src/pysonata/sonata/tests/examples/lgn_nodes.h5 differ diff --git a/src/pysonata/sonata/tests/examples/lgn_v1_edges.h5 b/src/pysonata/sonata/tests/examples/lgn_v1_edges.h5 index 97f5b6b..8902f1e 100644 Binary files a/src/pysonata/sonata/tests/examples/lgn_v1_edges.h5 and b/src/pysonata/sonata/tests/examples/lgn_v1_edges.h5 differ diff --git a/src/pysonata/sonata/tests/examples/v1_nodes.h5 b/src/pysonata/sonata/tests/examples/v1_nodes.h5 index bc21043..f3899b0 100644 Binary files a/src/pysonata/sonata/tests/examples/v1_nodes.h5 and b/src/pysonata/sonata/tests/examples/v1_nodes.h5 differ diff --git a/src/pysonata/sonata/tests/examples/v1_v1_edges.h5 b/src/pysonata/sonata/tests/examples/v1_v1_edges.h5 index dfbcadf..ad927bd 100644 Binary files a/src/pysonata/sonata/tests/examples/v1_v1_edges.h5 and b/src/pysonata/sonata/tests/examples/v1_v1_edges.h5 differ diff --git a/src/pysonata/sonata/tests/test_file.py b/src/pysonata/sonata/tests/test_file.py index da0b2bd..69774ea 100644 --- a/src/pysonata/sonata/tests/test_file.py +++ b/src/pysonata/sonata/tests/test_file.py @@ -1,4 +1,5 @@ import pytest +import tempfile from sonata.io import File @@ -26,6 +27,30 @@ def test_load_files(): assert(net.has_edges) +def test_version(): + net = File(data_files=['examples/v1_nodes.h5', 'examples/v1_v1_edges.h5'], + data_type_files=['examples/v1_node_types.csv', 'examples/v1_v1_edge_types.csv']) + assert(net.version == '0.1') + + +def test_bad_magic(): + import h5py + tmp_file, tmp_file_name = tempfile.mkstemp(suffix='.hdf5') + # no magic + with h5py.File(tmp_file_name, 'r+') as h5: + h5.create_group('nodes') + + with pytest.raises(Exception): + File(data_files=tmp_file_name, data_type_files='examples/v1_node_types.csv') + + # bad magic + with h5py.File(tmp_file_name, 'r+') as h5: + h5.attrs['magic'] = 0x0A7B + + with pytest.raises(Exception): + File(data_files=tmp_file_name, data_type_files='examples/v1_node_types.csv') + + def test_no_files(): with pytest.raises(Exception): File(data_files=[], data_type_files=[]) diff --git a/src/pysonata/sonata/tests/test_nodes.py b/src/pysonata/sonata/tests/test_nodes.py index f250d85..385f1e1 100644 --- a/src/pysonata/sonata/tests/test_nodes.py +++ b/src/pysonata/sonata/tests/test_nodes.py @@ -171,5 +171,3 @@ def test_group_search(net): test_group_df(net()) test_group_properties(net()) test_group_search(net()) - - diff --git a/src/pysonata/sonata/tests/test_types.py b/src/pysonata/sonata/tests/test_types.py index f107998..650b2ed 100644 --- a/src/pysonata/sonata/tests/test_types.py +++ b/src/pysonata/sonata/tests/test_types.py @@ -25,7 +25,6 @@ def test_node_types(net): assert(1 in node_types) node_type1 = node_types[1] assert(node_type1['ei'] == 'e') - assert(node_type1['model_template'] is None) assert(node_type1['node_type_id'] == 1) assert(node_type1['location'] == 'LGN') @@ -63,4 +62,4 @@ def test_edge_types(net): if __name__ == '__main__': test_node_types(net()) - test_edge_types(net()) + #test_edge_types(net()) diff --git a/src/pysonata/sonata/utils/__init__.py b/src/pysonata/sonata/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/pysonata/sonata/utils/add_magic.py b/src/pysonata/sonata/utils/add_magic.py new file mode 100644 index 0000000..453e2bb --- /dev/null +++ b/src/pysonata/sonata/utils/add_magic.py @@ -0,0 +1,29 @@ +import os +import sys +import h5py +import numpy as np +import glob + + +def add_magic(files): + # files = glob.glob('*.h5') #['v1_nodes.h5', 'v1_v1_edges.h5', 'lgn_nodes.h'] + for h5_file in files: + print('Updating {}.'.format(h5_file)) + with h5py.File(h5_file, 'r+') as h5: + h5['/'].attrs['magic'] = np.uint32(0x0A7A) + h5['/'].attrs['version'] = [np.uint32(0), np.uint32(1)] + + +if __name__ == '__main__': + if len(sys.argv) == 1: + add_magic(glob.glob('*.h5')) + else: + for pth in sys.argv[1:]: + abs_pth = os.path.abspath(pth) + if os.path.isdir(abs_pth): + lastwd = os.getcwd() + os.chdir(abs_pth) + add_magic(glob.glob("*.h5")) + os.chdir(lastwd) + if os.path.isfile(abs_pth): + add_magic(pth)