Update README.rst

added latent analysis and loading posterior

README.rst

@@ -1,6 +1,6 @@
-===========
+================================================
 cryoSBI - Simulation-based Inference for Cryo-EM
-===========
+================================================
 
 .. start-badges
 
@@ -134,7 +134,20 @@ The training config file should be a json file with the following structure:
         "THETA_SHIFT": 25,
         "THETA_SCALE": 25,
         "BATCH_SIZE": 256
-    }   
+    }
+
+Loading the posterior after training
+------------------------------------
+After training the estimator, loading it in Python can be done with the load_estimator in the estimator_utils module.
+
+.. code:: python
+    
+    import cryo_sbi.utils.estimator_utils as est_utils
+    posterior = est_utils.load_estimator(
+        config_file_path="path_to_config_file",
+        estimator_path="path_to_estimator_file", 
+        device="cuda"
+    )
 
 Inference
 ---------
@@ -161,3 +174,47 @@ We can quickly generate a histogram with 50 bins with the following piece of cod
     plt.hist(samples[:, idx_image].flatten(), np.linspace(0, simulator.max_index, 50))
 
 In this case the x-axis is just the index of the structures in increasing order.
+
+Latent space
+------------
+
+Computing the latent features for simulated or experimental particles can be done using the compute_latent_repr function in the estimator_utils module. The function needs a trained posterior estimator and images and computes the latent representation for each image. 
+
+.. code:: python
+
+    import cryo_sbi.utils.estimator_utils as est_utils
+    latent_vecs = est_utils.compute_latent_repr(
+        compute_latent_repr(
+        estimator=posterior,
+        images=images,
+        batch_size=100,
+        device="cuda",
+    )
+
+After we computed the latent representation for the images, one possible way to visualize the latent space is to use `UMAP <https://umap-learn.readthedocs.io/en/latest/>`_ . UMAP generates a two-dimensional representation of the latent space, which should allow us to analyze its important features.
+
+.. code:: python
+
+    import umap
+    reducer = umap.UMAP(metric="euclidian", n_components=2, n_neighbors=50)
+    embedding = reducer.fit_transform(latent_vecs.numpy())
+
+We can quickly visualize the 2d latent space with matplotlib.
+
+.. code:: python
+
+    import matplotlib.pyplot as plt
+    plt.scatter(
+        embedding[:, 0],
+        embedding[:, 1],
+    )
+
+
+
+
+
+
+
+
+    
+