From 9e52fbb3cecb6b5c236eb423915f3d9043b2f98c Mon Sep 17 00:00:00 2001 From: jnsbck-uni Date: Fri, 22 Nov 2024 15:05:16 +0100 Subject: [PATCH] fix: fix rebase --- docs/index.rst | 4 -- jaxley/io/swc.py | 10 --- jaxley/utils/cell_utils.py | 15 ----- mkdocs/docs/index.md | 3 - tests/regression_test_baselines.json | 92 ---------------------------- 5 files changed, 124 deletions(-) delete mode 100644 tests/regression_test_baselines.json diff --git a/docs/index.rst b/docs/index.rst index 0264569b..4279682a 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -42,11 +42,7 @@ Getting started plt.plot(v.T) # Plot voltage trace. -<<<<<<< HEAD If you want to learn more, check out our [Tutorials](https://jaxley.readthedocs.io/en/latest/tutorials.html), [FAQ](https://jaxley.readthedocs.io/en/latest/faq.html), or [Advanced tutorials](https://jaxley.readthedocs.io/en/latest/advanced_tutorials.html). -======= -If you want to learn more, check out our `Tutorial on the basics of Jaxley `_. For more resources, see the `FAQ `_ or `Advanced tutorials `_. ->>>>>>> wip: new baselines Installation diff --git a/jaxley/io/swc.py b/jaxley/io/swc.py index f5819d81..ff4a61d3 100644 --- a/jaxley/io/swc.py +++ b/jaxley/io/swc.py @@ -22,11 +22,7 @@ def swc_to_jaxley( fname: str, -<<<<<<< HEAD max_branch_len: float = 100.0, -======= - max_branch_len: Optional[float] = None, ->>>>>>> wip: new baselines sort: bool = True, num_lines: Optional[int] = None, ) -> Tuple[List[int], List[float], List[Callable], List[float], List[np.ndarray]]: @@ -103,15 +99,9 @@ def read_swc( fname: str, ncomp: Optional[int] = None, nseg: Optional[int] = None, -<<<<<<< HEAD max_branch_len: float = 300.0, min_radius: Optional[float] = None, assign_groups: bool = False, -======= - max_branch_len: Optional[float] = None, - min_radius: Optional[float] = None, - assign_groups: bool = True, ->>>>>>> wip: new baselines ) -> Cell: """Reads SWC file into a `Cell`. diff --git a/jaxley/utils/cell_utils.py b/jaxley/utils/cell_utils.py index 7bbfc5b4..229e5789 100644 --- a/jaxley/utils/cell_utils.py +++ b/jaxley/utils/cell_utils.py @@ -16,16 +16,11 @@ def _split_into_branches_and_sort( content: np.ndarray, -<<<<<<< HEAD max_branch_len: float, -======= - max_branch_len: Optional[float], ->>>>>>> wip: new baselines is_single_point_soma: bool, sort: bool = True, ) -> Tuple[np.ndarray, np.ndarray]: branches, types = _split_into_branches(content, is_single_point_soma) -<<<<<<< HEAD branches, types = _split_long_branches( branches, types, @@ -33,16 +28,6 @@ def _split_into_branches_and_sort( max_branch_len, is_single_point_soma=is_single_point_soma, ) -======= - if max_branch_len is not None: - branches, types = _split_long_branches( - branches, - types, - content, - max_branch_len, - is_single_point_soma=is_single_point_soma, - ) ->>>>>>> wip: new baselines if sort: first_val = np.asarray([b[0] for b in branches]) diff --git a/mkdocs/docs/index.md b/mkdocs/docs/index.md index bf51aa72..ae59df38 100644 --- a/mkdocs/docs/index.md +++ b/mkdocs/docs/index.md @@ -3,9 +3,6 @@

-> :warning: **The official documentation for Jaxley has moved to [jaxley.readthedocs.io](https://jaxley.readthedocs.io/en/latest/)**. -> The website you are currently on will be taken down in the future. - `Jaxley` is a differentiable simulator for biophysical neuron models in [JAX](https://github.com/google/jax). Its key features are: - automatic differentiation, allowing gradient-based optimization of thousands of parameters diff --git a/tests/regression_test_baselines.json b/tests/regression_test_baselines.json deleted file mode 100644 index 03c10dee..00000000 --- a/tests/regression_test_baselines.json +++ /dev/null @@ -1,92 +0,0 @@ -{ - "ec3a4fad11d2bfb1bc5f8f10529cb06f2ff9919b377e9c0a3419c7f7f237f06e": { - "test_name": "test_runtime", - "input_kwargs": { - "num_cells": 1, - "artificial": false, - "connect": false, - "connection_prob": 0.0, - "voltage_solver": "jaxley.stone" - }, - "runtimes": { - "build_time": 0.5469210942586263, - "compile_time": 18.876636425654095, - "run_time": 2.8356381257375083 - } - }, - "128cfe30d4ffb9c1abd9dc0fa25b0e86940437b3eb1d46584e21f2c780ed78e8": { - "test_name": "test_runtime", - "input_kwargs": { - "num_cells": 1, - "artificial": false, - "connect": false, - "connection_prob": 0.0, - "voltage_solver": "jax.sparse" - }, - "runtimes": { - "build_time": 0.3215004603068034, - "compile_time": 3.136414368947347, - "run_time": 2.462473233540853 - } - }, - "45cb5fa937517154a8d7bd2ac6d4542ff66c7cd3f5199976706ae44134eec301": { - "test_name": "test_runtime", - "input_kwargs": { - "num_cells": 10, - "artificial": false, - "connect": true, - "connection_prob": 0.1, - "voltage_solver": "jaxley.stone" - }, - "runtimes": { - "build_time": 1.6072161197662354, - "compile_time": 29.36460558573405, - "run_time": 18.947569131851196 - } - }, - "872ba2d409d18daf5e0e947953385c3d0967087ed122f72ba01990397429318e": { - "test_name": "test_runtime", - "input_kwargs": { - "num_cells": 10, - "artificial": false, - "connect": true, - "connection_prob": 0.1, - "voltage_solver": "jax.sparse" - }, - "runtimes": { - "build_time": 1.1171472867329915, - "compile_time": 26.5324444770813, - "run_time": 25.092686971028645 - } - }, - "da2f14fe319cf40d2ec65fdde6f3e0c997ef803e637d1ae7d2f2846c2369dbb2": { - "test_name": "test_runtime", - "input_kwargs": { - "num_cells": 1000, - "artificial": true, - "connect": true, - "connection_prob": 0.001, - "voltage_solver": "jaxley.stone" - }, - "runtimes": { - "build_time": 108.81107568740845, - "compile_time": 45.42118573188782, - "run_time": 41.27101055781046 - } - }, - "4a250131b8b31132e19d9f82ececa4d2dc26b0678326089f8a2c9de3696418fc": { - "test_name": "test_runtime", - "input_kwargs": { - "num_cells": 1000, - "artificial": true, - "connect": true, - "connection_prob": 0.001, - "voltage_solver": "jax.sparse" - }, - "runtimes": { - "build_time": 105.97441498438518, - "compile_time": 60.26486460367838, - "run_time": 58.21769714355469 - } - } -} \ No newline at end of file