Update README.rst

Added simple functionality into the readme

README.rst

@@ -42,4 +42,79 @@ Navigate to the cloned repository and install the module
 
 .. code:: bash
 
-    pip install .
+    pip install .
+
+Simulating cryo-EM particles
+-----------------------------
+To simulate cryo-EM particles, you can use the CryoEmSimulator class. The class takes in a simulation config file and simulates cryo-EM particles based on the parameters specified in the config file.
+
+.. code:: python
+
+    from cryo_sbi import CryoEmSimulator
+    simulator = CryoEmSimulator("path_to_simulation_config_file.json")
+    images, parameters = simulator.simulate(num_sim=10, return_parameters=True)
+
+The simulation config file should be a json file with the following structure:
+
+.. code:: json
+
+    {   
+        "N_PIXELS": 128,
+        "PIXEL_SIZE": 1.5,
+        "SIGMA": [0.5, 5.0],
+        "MODEL_FILE": "path_to_models/models.pt",
+        "SHIFT": 25.0,
+        "DEFOCUS": [0.5, 2.0],
+        "SNR": [0.001, 0.5],
+        "AMP": 0.1,
+        "B_FACTOR": [1.0, 100.0] 
+    }
+
+Training an amortized posterior model
+--------------------------------------
+Training of an amortized posterior can be done using the train_npe_model command line utility. The utility takes in an image config file, a train config file, and other training parameters. The utility trains a neural network to approximate the posterior distribution of the parameters given the images.
+
+.. code:: bash
+
+    train_npe_model \
+        --image_config_file path_to_simulation_config_file.json \
+        --train_config_file path_to_train_config_file.json\
+        --epochs 150 \
+        --estimator_file posterior.estimator \
+        --loss_file posterior.loss \
+        --n_workers 4 \
+        --simulation_batch_size 5120 \
+        --train_device cuda
+
+The training config file should be a json file with the following structure:
+
+.. code:: json
+
+    {
+        "EMBEDDING": "RESNET18",
+        "OUT_DIM": 256,
+        "NUM_TRANSFORM": 5,
+        "NUM_HIDDEN_FLOW": 10,
+        "HIDDEN_DIM_FLOW": 256,
+        "MODEL": "NSF",
+        "LEARNING_RATE": 0.0003,
+        "CLIP_GRADIENT": 5.0,
+        "THETA_SHIFT": 25,
+        "THETA_SCALE": 25,
+        "BATCH_SIZE": 256
+    }   
+
+Inference
+---------
+Sampling from the posterior distribution can be done using the sample_posterior function in the estimator_utils module. The function takes in an estimator, images, and other parameters and returns samples from the posterior distribution.
+
+.. code:: python
+
+    import cryo_sbi.utils.estimator_utils as est_utils
+    samples = est_utils.sample_posterior(
+        estimator=posterior,
+        images=images,
+        num_samples=20000,
+        batch_size=100,
+        device="cuda",
+    )