fixing links in autodocs

Author: kaeldai

docs/autodocs/source/analyzer.rst

@@ -6,13 +6,14 @@ Analyzing the Results
 
 After a simulation has completed BMTK will automatically save the results to the output folder. It is possible to have
 BMTK read and analyze the results before the simulation has exited but usually not required. The type of results saved
-during the simulation is determined by the “reports” section of the simulation config [LINK]. But most commonly it is a
-spikes-trains file, a cell-variable report, an extracellular potential recording, and in the case of PopNet a report of
-the firing rate dynamics.
+during the simulation is determined by the “reports” section of the `simulation config <./simulators.html#configuration-files>`_.
+But most commonly it is a spikes-trains file, a cell-variable report, an extracellular potential recording, and in the
+case of PopNet a report of the firing rate dynamics.
 
-The output files follow the SONATA Data format [LINK], and tools like pySONATA [LINK] or libSONATA [link] can be used
-to read them. The following consists of a high-level overview of these different formats and how you can use BMTK to
-find the results.
+The output files follow the `SONATA Data format <https://github.com/AllenInstitute/sonata>`_, and tools like
+`pySONATA <https://github.com/AllenInstitute/sonata/tree/master/src/pysonata>`_  or
+`libSONATA <https://github.com/BlueBrain/libsonata>`_ can be used to read them. The following consists of a high-level
+overview of these different formats and how you can use BMTK to find the results.
 
 
 Spike-Trains
@@ -32,7 +33,8 @@ sorted (default none). There is also an optional attribute “units” for the t
 
 Reading
 +++++++
-You can use the SpikeTrains [LINK] class to load a sonata spikes files (or csv or nwb) into memory:
+You can use the :py:class:`SpikeTrains <bmtk.utils.reports.spike_trains.spike_trains_api.SpikeTrainsAPI>` class to load
+a sonata spikes files (or csv or nwb) into memory:
 
 .. code:: python
 
@@ -44,14 +46,17 @@ You can use the SpikeTrains [LINK] class to load a sonata spikes files (or csv o
    print(spikes.n_spikes())
    print(spikes.node_ids())
 
-If you know what node you want use the ``get_times`` method to return an array of all spikes for a single node
+If you know what node you want use the :py:meth:`get_times() <bmtk.utils.reports.spike_trains.spike_trains_api.SpikeTrainsAPI.get_times>`
+method to return an array of all spikes for a single node
 
 .. code:: python
 
    node0_times = spikes.get_times(node_id=0)
    print(node0_times)
 
-Otherwise you can use the ``to_dataframe`` or ``spikes`` method to get a list of all node_ids + timestamps
+Otherwise you can use the :py:meth:`to_dataframe() <bmtk.utils.reports.spike_trains.spike_trains_api.SpikeTrainsAPI.to_dataframe>`
+or :py:meth:`spikes() <bmtk.utils.reports.spike_trains.spike_trains_api.SpikeTrainsAPI.spikes>`  method to get a list of
+all node_ids plus timestamps
 
 .. code:: python
 
@@ -77,7 +82,9 @@ Creating Spike Trains
 Commonly it’s necessary to generate spike-trains to use as inputs for a simulation. One option is to use FilterNet to
 generate inputs from external stimuli. You can also use the output of one simulation as the input to the next. But if
 you need to generate your own spike files, in property SONATA format, BMTK provides two ways of readily doing so. One
-is to user the ``SpikeTrains`` [LINK] class’s ``add_spikes`` or ``add_spike`` method:
+is to use the :py:class:`SpikeTrains <bmtk.utils.reports.spike_trains.spike_trains_api.SpikeTrainsAPI>` class
+:py:meth:`add_spikes() <bmtk.utils.reports.spike_trains.spike_trains_api.SpikeTrainsAPI.add_spikes>` or
+:py:meth:`add_spike() <bmtk.utils.reports.spike_trains.spike_trains_api.SpikeTrainsAPI.add_spike>` method:
 
 .. code:: python
 
@@ -89,7 +96,7 @@ is to user the ``SpikeTrains`` [LINK] class’s ``add_spikes`` or ``add_spike``
                     timestamps=[0.5, 0.9, 1.0, 1.0])
 
 
-Or use the ``PoissonSpikeGenerator`` class
+Or use the :py:class:`PoissonSpikeGenerator <bmtk.utils.reports.spike_trains.spike_trains.PoissonSpikeGenerator>` class
 
 .. code:: python
 
@@ -128,11 +135,12 @@ used:
 
 Reading Cell Variables
 ++++++++++++++++++++++
-The ``CompartmentReport`` class should be used to pull data from a cell report.
+The :py:class:`CompartmentReport <bmtk.utils.reports.compartment.core.CompartmentReaderABC>` class should be used to
+pull data from a cell report.
 
 .. code:: python
 
-   bmtk.utils.reports.compartments import CompartmentReport
+   bmtk.utils.reports.compartment import CompartmentReport
 
    pop_name = ...
    report = CompartmentReport('output/membrane_vm.h5',

docs/autodocs/source/bionet.rst

@@ -9,7 +9,8 @@ large-scale networks of multicompartmental neurons. Some of its main features in
 
 * Automatically integrates MPI for parallel simulations without the need of extra coding.
 
-* Supports models and morphologies from the Allen `Cell-Types Database <http://celltypes.brain-map.org/data>`_, as well as custom hoc and NeuroML2 cell and synapse models.
+* Supports models and morphologies from the Allen `Cell-Types Database <http://celltypes.brain-map.org/data>`_, as well
+  as custom hoc and NeuroML2 cell and synapse models.
 
 * Use spike-trains, synaptic connections, current clamps or even extracellular stimulation to drive network firing.
 
@@ -18,15 +19,16 @@ large-scale networks of multicompartmental neurons. Some of its main features in
 
 Inputs
 --------
-Inputs can be specified in the “inputs” sections of the simulation config [LINK], following the rules specified in the
-SONATA Data format [LINK].
+Inputs can be specified in the “inputs” sections of the `simulation config <./simulators.html#configuration-files>`_,
+following the rules specified in the `SONATA Data format <https://github.com/AllenInstitute/sonata>`_.
 
 Spike-Trains
 +++++++++
 The modeler may wish to have certain cells in the circuit generate a pre-arranged series of spikes to drive the network.
 These cells must have ``model_type`` value ``virtual`` and are not actual cell objects (you can’t record from them). You
-may use either a SONATA Spike-Train file [LINK], an NWB file, or a space-separated csv file with columns “node_id”,
-“population”, and “timestamps”. The following shows some examples of how to generate spike-train files using bmtk [LINK].
+may use either a `SONATA spike file <https://github.com/AllenInstitute/sonata/blob/master/docs/SONATA_DEVELOPER_GUIDE.md#spike-file>`_,
+an NWB file, or a space-separated csv file with columns **node_id**, **population**, and **timestamps**. The following
+shows some examples of how to generate `spike-train files using bmtk <./analyzer.html#creating-spike-trains>`_.
 
 .. code:: json
 
@@ -41,7 +43,7 @@ may use either a SONATA Spike-Train file [LINK], an NWB file, or a space-separat
 
 * module:  either sonata, hdf5, csv, or nwb: depending on the format of the spikes file
 
-* node_set [LINK]: used to filter which cells will receive the inputs
+* `node_set <./simulators.html#node-sets>`_: used to filter which cells will receive the inputs
 
 * input_file: path to file contain spike-trains for one or mode node
 
@@ -66,7 +68,7 @@ only support injections at the soma.
     }
 
 * module:  Always IClamp
-* node_set [LINK]: used to filter which cells will receive the inputs
+* `node_set <./simulators.html#node-sets>`_: used to filter which cells will receive the inputs
 * amp: injection in pA
 * delay: onset of current injection in ms
 * duration: duration of current injection in ms
@@ -105,7 +107,7 @@ And in the configuration file
     }
 
 * module:  Always xstim
-* node_set [LINK]: used to filter which cells will receive the inputs
+* `node_set <./simulators.html#node-sets>`_: used to filter which cells will receive the inputs
 * positions_file: onset of current injection in ms
 * waveform: form on the input, requires arguments “shape”, “amp” (in pA), “del” (delay in ms) and “dur” (duration in ms). Shape may either be “dc” or “sin” (with optional arguments “freq”, “phase” and “offset”)
 
@@ -126,7 +128,8 @@ cell.
 Membrane and Intracellular Variables
 ++++++++++++++++++++++++++++++++++++
 Used to record the time trace of specific cell variables, usually the membrane potential (v). For multi-compartmental
-cells the report can record from any segment that contains mechanics for the desired variable. See SONATA docs [LINK]
+cells the report can record from any segment that contains mechanics for the desired variable. See
+`SONATA docs <https://github.com/AllenInstitute/sonata/blob/master/docs/SONATA_DEVELOPER_GUIDE.md#simulation-output---reports>`_
 for more information about how multi-segment recordings are represented.
 
 .. code:: json
@@ -149,7 +152,7 @@ for more information about how multi-segment recordings are represented.
     }
 
 * variable_name: name of variable being recorded, will depend on the cell model
-* cells: a node_set [LINK] to filter out what cells to record.
+* cells: a `node_set <./simulators.html#node-sets>`_ to filter out what cells to record.
 * sections: either “all”, “soma”, “basal” or “apical”
 * file_name: name of file where traces will be recorded, under the “output_dir”. If not specified the the report title
   will be used, eg “calcium_concentration.h5” and “membrane_potential.h5”
@@ -161,7 +164,9 @@ for more information about how multi-segment recordings are represented.
 
 Extracellular Potential
 +++++++++++++++++++++++
-Will simulate recording from an extracellular electrode placed in the neuropil. See SONATA documentation [LINK]. Requires a space-separated csv file to specify the location of each recording channel:
+Will simulate recording from an extracellular electrode placed in the neuropil. See
+`SONATA documentation <https://github.com/AllenInstitute/sonata/blob/master/docs/SONATA_DEVELOPER_GUIDE.md#extracellular-report>`_.
+Requires a space-separated csv file to specify the location of each recording channel:
 
 .. code::
     :name: ./components/xelectrode/linear_probe.csv
@@ -188,7 +193,7 @@ And in the config
         }
     }
 
-* cells: a node_set [LINK] to filter out what cells will contribute to the ecp.
+* cells: a `node_set <./simulators.html#node-sets>`_ to filter out what cells will contribute to the ecp.
 * variable_name: name of contributing variable, v for membrane potential
 * electrode_positions: name of electrode placement file
 * contributions_dir: The output ecp file will contain the combined contributes from all cells and not possible to
@@ -199,8 +204,9 @@ And in the config
 Synaptic Variables
 ++++++++++++++++++
 Similar to recording from membrane potential, by setting ``module`` parameter to ``netcon_report`` you can record the
-variables from a synapse. The output is similar to a SONATA membrane report [LINK], but instead of each column being a
-segment of a neuron, each column represents a different synapses.
+variables from a synapse. The output is similar to a
+`SONATA membrane report <https://github.com/AllenInstitute/sonata/blob/master/docs/SONATA_DEVELOPER_GUIDE.md#frame-oriented-node-element-recordings>`_,
+but instead of each column being a segment of a neuron, each column represents a different synapses.
 
 .. code:: json
 
@@ -221,8 +227,9 @@ Advanced Options
 
 Specifying Synapse locations
 ++++++++++++++++++++++++++++
-In SONATA the location of each synapse is determined by the "section_id" and "section_x" values [LINK], which requires
-modelers to know how NEURON parses the morphology of each cell. If these parameters are specified in the edges file
+In SONATA the location of each synapse is determined by the
+`"afferent_section_id" and "afferent_section_pos"attributes <https://github.com/AllenInstitute/sonata/blob/master/docs/SONATA_DEVELOPER_GUIDE.md#edges---required-attributes>`_,
+which requires modelers to know how NEURON parses the morphology of each cell. If these parameters are specified in the edges file
 SONATA will use them to place a synapse on the target.
 
 Alternatively BMTK supports the option parameters “distance_range" and “target_sections”, which if present in the edges