changing import statments to exclude src

pyproject.toml

@@ -1,8 +1,9 @@
 [tool.poetry]
-name = "deeptemplate-science"
+name = "DeepDiagnostics" 
+packages = [{include = "*", from="src"}]
 version = "0.1.0"
-description = "a template for a science motivated project, focus on ease of reproducablity"
-authors = ["voetberg <[email protected]>"]
+description = "a package for diagnosing posterior quality from inference methods"
+authors = ["Becky Nevin <[email protected]>"]
 license = "MIT"
 
 [tool.poetry.dependencies]

src/scripts/evaluate.py

@@ -5,7 +5,7 @@
 Includes utilities for posterior diagnostics as well as some
 inference functions.
 """
-from src.scripts.io import ModelLoader
+from scripts.io import ModelLoader
 
 import argparse
 from sbi.analysis import run_sbc, sbc_rank_plot, check_sbc, pairplot
@@ -62,7 +62,7 @@ def improved_corner_plot(self, posterior):
         """
 
 
-class Diagnose_on_the_fly_data_generation:
+class Diagnose_generative:
     def posterior_predictive(self,
                              theta_true,
                              x_true,

tests/test_evaluate.py

@@ -7,7 +7,9 @@
 
 # flake8: noqa
 #sys.path.append("..")
-from src.scripts.evaluate import Diagnose, InferenceModel
+print(sys.path)
+from scripts.evaluate import Diagnose_static, Diagnose_generative
+from scripts.io import ModelLoader
 #from src.scripts import evaluate
 
 
@@ -21,16 +23,20 @@
 
 
 @pytest.fixture
-def diagnose_instance():
-    return Diagnose()
+def diagnose_static_instance():
+    return Diagnose_static()
+
+@pytest.fixture
+def diagnose_generative_instance():
+    return Diagnose_generative()
 
 
 @pytest.fixture
 def inference_instance():
-    inference_model = InferenceModel()
+    modelloader = ModelLoader()
     path = "savedmodels/sbi/"
-    model_name = "sbi_linear"
-    posterior = inference_model.load_model_pkl(path, model_name)
+    model_name = "sbi_linear_from_data"
+    posterior = modelloader.load_model_pkl(path, model_name)
     return posterior
 
 
@@ -68,7 +74,7 @@ def simulator(thetas):  # , percent_errors):
     return torch.Tensor(y.T)
 
 
-def test_generate_sbc_samples(diagnose_instance, inference_instance):
+def test_generate_sbc_samples(diagnose_generative_instance, inference_instance):
     # Mock data
     low_bounds = torch.tensor([0, -10])
     high_bounds = torch.tensor([10, 10])
@@ -80,14 +86,14 @@ def test_generate_sbc_samples(diagnose_instance, inference_instance):
     num_posterior_samples = 1000
 
     # Generate SBC samples
-    thetas, ys, ranks, dap_samples = diagnose_instance.generate_sbc_samples(
+    thetas, ys, ranks, dap_samples = diagnose_generative_instance.generate_sbc_samples(
         prior, posterior, simulator_test, num_sbc_runs, num_posterior_samples
     )
 
     # Add assertions based on the expected behavior of the method
 
 
-def test_run_all_sbc(diagnose_instance, inference_instance):
+def test_run_all_sbc(diagnose_generative_instance, inference_instance):
     labels_list = ["$m$", "$b$"]
     colorlist = ["#9C92A3", "#0F5257"]
     low_bounds = torch.tensor([0, -10])
@@ -99,7 +105,7 @@ def test_run_all_sbc(diagnose_instance, inference_instance):
 
     save_path = "plots/"
 
-    diagnose_instance.run_all_sbc(
+    diagnose_generative_instance.run_all_sbc(
         prior,
         posterior,
         simulator_test,