auto-commit-docs

docs/vame-docs-app/docs/reference/sidebar.json

@@ -7,7 +7,7 @@
         "reference/vame/analysis/gif_creator",
         "reference/vame/analysis/pose_segmentation",
         "reference/vame/analysis/tree_hierarchy",
-        "reference/vame/analysis/umap_visualization",
+        "reference/vame/analysis/umap",
         "reference/vame/analysis/videowriter"
       ],
       "label": "vame.analysis",
@@ -50,7 +50,9 @@
         "reference/vame/util/align_egocentrical",
         "reference/vame/util/auxiliary",
         "reference/vame/util/csv_to_npy",
-        "reference/vame/util/gif_pose_helper"
+        "reference/vame/util/data_manipulation",
+        "reference/vame/util/gif_pose_helper",
+        "reference/vame/util/model_util"
       ],
       "label": "vame.util",
       "type": "category"

docs/vame-docs-app/docs/reference/vame/analysis/community_analysis.md

@@ -51,24 +51,6 @@ Compute the transition matrix from the adjacency matrix.
 
 - `np.ndarray` - Transition matrix.
 
-#### consecutive
-
-```python
-def consecutive(data: np.ndarray, stepsize: int = 1) -> List[np.ndarray]
-```
-
-Identifies location of missing motif finding consecutive elements in an array and returns array(s) at the split.
-
-**Arguments**:
-
-- `data` _np.ndarray_ - Input array.
-- `stepsize` _int, optional_ - Step size. Defaults to 1.
-
-
-**Returns**:
-
-- `List[np.ndarray]` - List of arrays containing consecutive elements.
-
 #### find\_zero\_labels
 
 ```python
@@ -197,7 +179,7 @@ Create community bag for given files and labels (Markov chain to tree -> comm
 #### create\_cohort\_community\_bag
 
 ```python
-def create_cohort_community_bag(files: List[str], labels: List[np.ndarray],
+def create_cohort_community_bag(labels: List[np.ndarray],
                                 trans_mat_full: np.ndarray, cut_tree: int,
                                 n_cluster: int) -> Tuple
 ```
@@ -207,7 +189,6 @@ Create cohort community bag for given labels, transition matrix, cut tree, and n
 
 **Arguments**:
 
-- `files` _List[str]_ - List of files paths (deprecated).
 - `labels` _List[np.ndarray]_ - List of label arrays.
 - `trans_mat_full` _np.ndarray_ - Full transition matrix.
 - `cut_tree` _int_ - Cut line for tree.
@@ -260,59 +241,13 @@ Transform kmeans parameterized latent vector into communities. Get cohort commun
 
 - `List[np.ndarray]` - List of cohort community labels for each file.
 
-#### umap\_embedding
-
-```python
-def umap_embedding(cfg: dict, file: str, model_name: str, n_cluster: int,
-                   parametrization: str) -> np.ndarray
-```
-
-Perform UMAP embedding for given file and parameters.
-
-**Arguments**:
-
-- `cfg` _dict_ - Configuration parameters.
-- `file` _str_ - File path.
-- `model_name` _str_ - Model name.
-- `n_cluster` _int_ - Number of clusters.
-- `parametrization` _str_ - parametrization.
-
-
-**Returns**:
-
-- `np.ndarray` - UMAP embedding.
-
-#### umap\_vis
-
-```python
-def umap_vis(cfg: dict, file: str, embed: np.ndarray,
-             community_labels_all: np.ndarray, save_path: str | None) -> None
-```
-
-Create plotly visualizaton of UMAP embedding.
-
-**Arguments**:
-
-- `cfg` _dict_ - Configuration parameters.
-- `file` _str_ - File path.
-- `embed` _np.ndarray_ - UMAP embedding.
-- `community_labels_all` _np.ndarray_ - Community labels.
-- `save_path` - Path to save the plot. If None it will not save the plot.
-
-
-**Returns**:
-
-  None
-
 #### community
 
 ```python
 @save_state(model=CommunityFunctionSchema)
 def community(config: str,
               cohort: bool = True,
-              show_umap: bool = False,
               cut_tree: int | None = None,
-              save_umap_figure: bool = True,
               save_logs: bool = False) -> None
 ```
 
@@ -322,7 +257,6 @@ Perform community analysis.
 
 - `config` _str_ - Path to the configuration file.
 - `cohort` _bool, optional_ - Flag indicating cohort analysis. Defaults to True.
-- `show_umap` _bool, optional_ - Flag indicating weather to show UMAP visualization. Defaults to False.
 - `cut_tree` _int, optional_ - Cut line for tree. Defaults to None.
 
 

docs/vame-docs-app/docs/reference/vame/analysis/generative_functions.md

@@ -94,24 +94,6 @@ Visualize cluster centers.
 
   None
 
-#### load\_model
-
-```python
-def load_model(cfg: dict, model_name: str) -> torch.nn.Module
-```
-
-Load PyTorch model.
-
-**Arguments**:
-
-- `cfg` _dict_ - Configuration dictionary.
-- `model_name` _str_ - Name of the model.
-
-
-**Returns**:
-
-- `torch.nn.Module` - Loaded PyTorch model.
-
 #### generative\_model
 
 ```python

docs/vame-docs-app/docs/reference/vame/analysis/pose_segmentation.md

@@ -10,25 +10,6 @@ Leibniz Institute for Neurobiology, Magdeburg, Germany
 https://github.com/LINCellularNeuroscience/VAME
 Licensed under GNU General Public License v3.0
 
-#### load\_model
-
-```python
-def load_model(cfg: dict, model_name: str, fixed: bool) -> RNN_VAE
-```
-
-Load the VAME model.
-
-**Arguments**:
-
-- `cfg` _dict_ - Configuration dictionary.
-- `model_name` _str_ - Name of the model.
-- `fixed` _bool_ - Fixed or variable length sequences.
-
-
-**Returns**:
-
-- `RNN_VAE` - Loaded VAME model.
-
 #### embedd\_latent\_vectors
 
 ```python
@@ -52,24 +33,6 @@ Embed latent vectors for the given files using the VAME model.
 
 - `List[np.ndarray]` - List of latent vectors for each file.
 
-#### consecutive
-
-```python
-def consecutive(data: np.ndarray, stepsize: int = 1) -> List[np.ndarray]
-```
-
-Find consecutive sequences in the data array.
-
-**Arguments**:
-
-- `data` _np.ndarray_ - Input array.
-- `stepsize` _int, optional_ - Step size. Defaults to 1.
-
-
-**Returns**:
-
-- `List[np.ndarray]` - List of consecutive sequences.
-
 #### get\_motif\_usage
 
 ```python

docs/vame-docs-app/docs/reference/vame/analysis/tree_hierarchy.md

@@ -109,42 +109,6 @@ Draw a tree.
 
   None
 
-#### traverse\_tree
-
-```python
-def traverse_tree(T: nx.Graph, root_node: str | None = None) -> str
-```
-
-Traverse a tree and return the traversal sequence.
-
-**Arguments**:
-
-- `T` _nx.Graph_ - The tree to be traversed.
-- `root_node` _str, optional_ - The root node of the tree. If None, traversal starts from the root.
-
-
-**Returns**:
-
-- `str` - The traversal sequence.
-
-#### traverse\_tree
-
-```python
-def traverse_tree(T: nx.Graph, root_node: str | None = None) -> str
-```
-
-Traverse a tree and return the traversal sequence.
-
-**Arguments**:
-
-- `T` _nx.Graph_ - The tree to be traversed.
-- `root_node` _str, optional_ - The root node of the tree. If None, traversal starts from the root.
-
-
-**Returns**:
-
-- `str` - The traversal sequence.
-
 #### traverse\_tree\_cutline
 
 ```python

docs/vame-docs-app/docs/reference/vame/analysis/umap.md

@@ -0,0 +1,136 @@
+---
+sidebar_label: umap
+title: vame.analysis.umap
+---
+
+Variational Animal Motion Embedding 1.0-alpha Toolbox
+© K. Luxem & P. Bauer, Department of Cellular Neuroscience
+Leibniz Institute for Neurobiology, Magdeburg, Germany
+
+https://github.com/LINCellularNeuroscience/VAME
+Licensed under GNU General Public License v3.0
+
+#### umap\_embedding
+
+```python
+def umap_embedding(cfg: dict, file: str, model_name: str, n_cluster: int,
+                   parametrization: str) -> np.ndarray
+```
+
+Perform UMAP embedding for given file and parameters.
+
+**Arguments**:
+
+- `cfg` _dict_ - Configuration parameters.
+- `file` _str_ - File path.
+- `model_name` _str_ - Model name.
+- `n_cluster` _int_ - Number of clusters.
+- `parametrization` _str_ - parametrization.
+
+
+**Returns**:
+
+- `np.ndarray` - UMAP embedding.
+
+#### umap\_vis\_community\_labels
+
+```python
+def umap_vis_community_labels(cfg: dict, embed: np.ndarray,
+                              community_labels_all: np.ndarray,
+                              save_path: str | None) -> None
+```
+
+Create plotly visualizaton of UMAP embedding with community labels.
+
+**Arguments**:
+
+- `cfg` _dict_ - Configuration parameters.
+- `embed` _np.ndarray_ - UMAP embedding.
+- `community_labels_all` _np.ndarray_ - Community labels.
+- `save_path` - Path to save the plot. If None it will not save the plot.
+
+
+**Returns**:
+
+  None
+
+#### umap\_vis
+
+```python
+def umap_vis(embed: np.ndarray, num_points: int) -> None
+```
+
+Visualize UMAP embedding without labels.
+
+**Arguments**:
+
+- `embed` _np.ndarray_ - UMAP embedding.
+- `num_points` _int_ - Number of data points to visualize.
+
+
+**Returns**:
+
+  None - Plot Visualization of UMAP embedding.
+
+#### umap\_label\_vis
+
+```python
+def umap_label_vis(embed: np.ndarray, label: np.ndarray, n_cluster: int,
+                   num_points: int) -> None
+```
+
+Visualize UMAP embedding with motif labels.
+
+**Arguments**:
+
+- `embed` _np.ndarray_ - UMAP embedding.
+- `label` _np.ndarray_ - Motif labels.
+- `n_cluster` _int_ - Number of clusters.
+- `num_points` _int_ - Number of data points to visualize.
+
+
+**Returns**:
+
+  fig - Plot figure of UMAP visualization embedding with motif labels.
+
+#### umap\_vis\_comm
+
+```python
+def umap_vis_comm(embed: np.ndarray, community_label: np.ndarray,
+                  num_points: int) -> None
+```
+
+Visualize UMAP embedding with community labels.
+
+**Arguments**:
+
+- `embed` _np.ndarray_ - UMAP embedding.
+- `community_label` _np.ndarray_ - Community labels.
+- `num_points` _int_ - Number of data points to visualize.
+
+
+**Returns**:
+
+  fig - Plot figure of UMAP visualization embedding with community labels.
+
+#### visualization
+
+```python
+@save_state(model=VisualizationFunctionSchema)
+def visualization(config: Union[str, Path],
+                  label: Optional[str] = None,
+                  save_logs: bool = False) -> None
+```
+
+Visualize UMAP embeddings based on configuration settings.
+
+**Arguments**:
+
+- `config` _Union[str, Path]_ - Path to the configuration file.
+- `label` _str, optional_ - Type of labels to visualize. Default is None.
+
+
+**Returns**:
+
+  None - Plot Visualization of UMAP embeddings.
+