From 333afe791ecc2edb8f7e8eed6f9039bd4289fd9d Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Fri, 22 Nov 2024 07:03:48 +0000 Subject: [PATCH] Deployed ba42d64 to dev with MkDocs 1.6.1 and mike 2.1.3 --- dev/reference/utils/index.html | 120 ++++++++++++++++----------------- dev/sitemap.xml | 50 +++++++------- dev/sitemap.xml.gz | Bin 452 -> 452 bytes 3 files changed, 85 insertions(+), 85 deletions(-) diff --git a/dev/reference/utils/index.html b/dev/reference/utils/index.html index bbf74c32..ac088366 100644 --- a/dev/reference/utils/index.html +++ b/dev/reference/utils/index.html @@ -1595,8 +1595,7 @@

Source code in jaxley/utils/cell_utils.py - 
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def build_radiuses_from_xyzr(
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def build_radiuses_from_xyzr(
     radius_fns: List[Callable],
     branch_indices: List[int],
     min_radius: Optional[float],
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               Source code in jaxley/utils/cell_utils.py
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def compute_axial_conductances(
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def compute_axial_conductances(
     comp_edges: pd.DataFrame, params: Dict[str, jnp.ndarray]
 ) -> jnp.ndarray:
     """Given `comp_edges`, radius, length, r_a, cm, compute the axial conductances.
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               Source code in jaxley/utils/cell_utils.py
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def compute_children_and_parents(
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def compute_children_and_parents(
     branch_edges: pd.DataFrame,
 ) -> Tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray, int]:
     """Build indices used during `._init_morph_custom_spsolve()."""
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               Source code in jaxley/utils/cell_utils.py
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def compute_children_indices(parents) -> List[jnp.ndarray]:
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def compute_children_indices(parents) -> List[jnp.ndarray]:
     """Return all children indices of every branch.
 
     Example:
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               Source code in jaxley/utils/cell_utils.py
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def compute_coupling_cond(rad1, rad2, r_a1, r_a2, l1, l2):
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def compute_coupling_cond(rad1, rad2, r_a1, r_a2, l1, l2):
     """Return the coupling conductance between two compartments.
 
     Equations taken from `https://en.wikipedia.org/wiki/Compartmental_neuron_models`.
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               Source code in jaxley/utils/cell_utils.py
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def compute_coupling_cond_branchpoint(rad, r_a, l):
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def compute_coupling_cond_branchpoint(rad, r_a, l):
     r"""Return the coupling conductance between one compartment and a comp with l=0.
 
     From https://en.wikipedia.org/wiki/Compartmental_neuron_models
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               Source code in jaxley/utils/cell_utils.py
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def compute_impact_on_node(rad, r_a, l):
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def compute_impact_on_node(rad, r_a, l):
     r"""Compute the weight with which a compartment influences its node.
 
     In order to satisfy Kirchhoffs current law, the current at a branch point must be
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               Source code in jaxley/utils/cell_utils.py
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def compute_morphology_indices_in_levels(
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def compute_morphology_indices_in_levels(
     num_branchpoints,
     child_belongs_to_branchpoint,
     par_inds,
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               Source code in jaxley/utils/cell_utils.py
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def convert_point_process_to_distributed(
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def convert_point_process_to_distributed(
     current: jnp.ndarray, radius: jnp.ndarray, length: jnp.ndarray
 ) -> jnp.ndarray:
     """Convert current point process (nA) to distributed current (uA/cm2).
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               Source code in jaxley/utils/cell_utils.py
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def equal_segments(branch_property: list, ncomp_per_branch: int):
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def equal_segments(branch_property: list, ncomp_per_branch: int):
     """Generates segments where some property is the same in each segment.
 
     Args:
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               Source code in jaxley/utils/cell_utils.py
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def get_num_neighbours(
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def get_num_neighbours(
     num_children: jnp.ndarray,
     ncomp_per_branch: int,
     num_branches: int,
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def group_and_sum(
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def group_and_sum(
     values_to_sum: jnp.ndarray, inds_to_group_by: jnp.ndarray, num_branchpoints: int
 ) -> jnp.ndarray:
     """Group values by whether they have the same integer and sum values within group.
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def interpolate_xyzr(loc: float, coords: np.ndarray):
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def interpolate_xyzr(loc: float, coords: np.ndarray):
     """Perform a linear interpolation between xyz-coordinates.
 
     Args:
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def linear_segments(
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def linear_segments(
     initial_val: float, endpoint_vals: list, parents: jnp.ndarray, ncomp_per_branch: int
 ):
     """Generates segments where some property is linearly interpolated.
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               Source code in jaxley/utils/cell_utils.py
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def loc_of_index(global_comp_index, global_branch_index, ncomp_per_branch):
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def loc_of_index(global_comp_index, global_branch_index, ncomp_per_branch):
     """Return location corresponding to global compartment index."""
     cumsum_ncomp = cumsum_leading_zero(ncomp_per_branch)
     index = global_comp_index - cumsum_ncomp[global_branch_index]
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               Source code in jaxley/utils/cell_utils.py
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def local_index_of_loc(
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def local_index_of_loc(
     loc: float, global_branch_ind: int, ncomp_per_branch: int
 ) -> int:
     """Returns the local index of a comp given a loc [0, 1] and the index of a branch.
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               Source code in jaxley/utils/cell_utils.py
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def merge_cells(
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def merge_cells(
     cumsum_num_branches: List[int],
     cumsum_num_branchpoints: List[int],
     arrs: List[List[np.ndarray]],
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def params_to_pstate(
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def params_to_pstate(
     params: List[Dict[str, jnp.ndarray]],
     indices_set_by_trainables: List[jnp.ndarray],
 ):
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               Source code in jaxley/utils/cell_utils.py
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def query_channel_states_and_params(d, keys, idcs):
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def query_channel_states_and_params(d, keys, idcs):
     """Get dict with subset of keys and values from d.
 
     This is used to restrict a dict where every item contains __all__ states to only
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               Source code in jaxley/utils/cell_utils.py
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def remap_to_consecutive(arr):
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def remap_to_consecutive(arr):
     """Maps an array of integers to an array of consecutive integers.
 
     E.g. `[0, 0, 1, 4, 4, 6, 6] -> [0, 0, 1, 2, 2, 3, 3]`
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          https://jaxleyverse.github.io/jaxley/dev/
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          https://jaxleyverse.github.io/jaxley/dev/code_of_conduct/
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          https://jaxleyverse.github.io/jaxley/dev/contribute/
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          https://jaxleyverse.github.io/jaxley/dev/credits/
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          https://jaxleyverse.github.io/jaxley/dev/faq/
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          https://jaxleyverse.github.io/jaxley/dev/install/
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          https://jaxleyverse.github.io/jaxley/dev/faq/question_01/
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          https://jaxleyverse.github.io/jaxley/dev/faq/question_02/
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          https://jaxleyverse.github.io/jaxley/dev/faq/question_03/
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          https://jaxleyverse.github.io/jaxley/dev/reference/connect/
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          https://jaxleyverse.github.io/jaxley/dev/reference/integration/
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          https://jaxleyverse.github.io/jaxley/dev/reference/mechanisms/
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          https://jaxleyverse.github.io/jaxley/dev/reference/modules/
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          https://jaxleyverse.github.io/jaxley/dev/reference/optimize/
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          https://jaxleyverse.github.io/jaxley/dev/reference/utils/
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          https://jaxleyverse.github.io/jaxley/dev/tutorial/00_jaxley_api/
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          https://jaxleyverse.github.io/jaxley/dev/tutorial/01_morph_neurons/
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          https://jaxleyverse.github.io/jaxley/dev/tutorial/02_small_network/
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          https://jaxleyverse.github.io/jaxley/dev/tutorial/04_jit_and_vmap/
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          https://jaxleyverse.github.io/jaxley/dev/tutorial/05_channel_and_synapse_models/
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          https://jaxleyverse.github.io/jaxley/dev/tutorial/06_groups/
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          https://jaxleyverse.github.io/jaxley/dev/tutorial/07_gradient_descent/
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          https://jaxleyverse.github.io/jaxley/dev/tutorial/08_importing_morphologies/
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          https://jaxleyverse.github.io/jaxley/dev/tutorial/09_advanced_indexing/
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          https://jaxleyverse.github.io/jaxley/dev/tutorial/10_advanced_parameter_sharing/
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