chore: simplify README example + example.py

Simplify README example and example.py to use `resample` and data dict input

README.md

@@ -29,12 +29,11 @@ pip install -U git+https://github.com/alecksphillips/retrospectr.git
 ## Example
 ```python
 import cmdstanpy
-import numpy as np
 import matplotlib.pyplot as plt
 import seaborn
 import pandas as pd
-import json
 from retrospectr.importance_weights import calculate_log_weights, extract_samples
+from retrospectr.resampling import resample
 
 
 model_file = "test/test_models/bernoulli/bernoulli.stan"
@@ -44,35 +43,22 @@ original_data = {
     "N": 10,
     "y": [0, 1, 0, 0, 0, 0, 0, 0, 0, 1]
 }
-
-original_data_file_path = "original_data.json"
-with open(original_data_file_path, "w") as f:
-    json.dump(original_data, f)
-
 original_fit = stan_model.sample(data=original_data, chains=1)
 
 new_data = {
     "N": 20,
     "y": [0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1]
 }
 
-new_data_file_path = "new_data.json"
-with open(new_data_file_path, "w") as f:
-    json.dump(new_data, f)
-
-
 new_fit = stan_model.sample(data=new_data, chains=1)
 
 original_samples = extract_samples(original_fit)
 
 new_samples = extract_samples(new_fit)
 
-log_weights = calculate_log_weights(model_file, original_samples, original_data_file_path, new_data_file_path)
-
-resampled_iterations = np.random.choice(
-    len(log_weights), size=len(log_weights), p=np.exp(log_weights.reshape(len(log_weights))))
+log_weights = calculate_log_weights(model_file, original_samples, original_data, new_data)
 
-resampled_original_samples = original_samples[resampled_iterations, :]
+resampled_original_samples = resample(original_samples, log_weights)
 
 df_original = pd.DataFrame({
     "theta": original_samples.reshape(len(original_samples)),

example.py

@@ -1,10 +1,9 @@
 import cmdstanpy
-import numpy as np
 import matplotlib.pyplot as plt
 import seaborn
 import pandas as pd
-import json
 from retrospectr.importance_weights import calculate_log_weights, extract_samples
+from retrospectr.resampling import resample
 
 
 model_file = "test/test_models/bernoulli/bernoulli.stan"
@@ -14,35 +13,22 @@
     "N": 10,
     "y": [0, 1, 0, 0, 0, 0, 0, 0, 0, 1]
 }
-
-original_data_file_path = "original_data.json"
-with open(original_data_file_path, "w") as f:
-    json.dump(original_data, f)
-
 original_fit = stan_model.sample(data=original_data, chains=1)
 
 new_data = {
     "N": 20,
     "y": [0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1]
 }
 
-new_data_file_path = "new_data.json"
-with open(new_data_file_path, "w") as f:
-    json.dump(new_data, f)
-
-
 new_fit = stan_model.sample(data=new_data, chains=1)
 
 original_samples = extract_samples(original_fit)
 
 new_samples = extract_samples(new_fit)
 
-log_weights = calculate_log_weights(model_file, original_samples, original_data_file_path, new_data_file_path)
-
-resampled_iterations = np.random.choice(
-    len(log_weights), size=len(log_weights), p=np.exp(log_weights.reshape(len(log_weights))))
+log_weights = calculate_log_weights(model_file, original_samples, original_data, new_data)
 
-resampled_original_samples = original_samples[resampled_iterations, :]
+resampled_original_samples = resample(original_samples, log_weights)
 
 df_original = pd.DataFrame({
     "theta": original_samples.reshape(len(original_samples)),